2025-03-04T20:09:58.7184477Z Current runner version: '2.322.0' 2025-03-04T20:09:58.7190062Z Runner name: 'i-034d656ab88aab9e9' 2025-03-04T20:09:58.7190679Z Runner group name: 'Default' 2025-03-04T20:09:58.7191724Z Machine name: 'ip-10-0-52-54' 2025-03-04T20:09:58.7194968Z ##[group]GITHUB_TOKEN Permissions 2025-03-04T20:09:58.7197320Z Actions: read 2025-03-04T20:09:58.7197808Z Attestations: read 2025-03-04T20:09:58.7198267Z Checks: read 2025-03-04T20:09:58.7198695Z Contents: read 2025-03-04T20:09:58.7199320Z Deployments: read 2025-03-04T20:09:58.7199797Z Discussions: read 2025-03-04T20:09:58.7200268Z Issues: read 2025-03-04T20:09:58.7200718Z Metadata: read 2025-03-04T20:09:58.7201179Z Packages: read 2025-03-04T20:09:58.7201712Z Pages: read 2025-03-04T20:09:58.7202440Z PullRequests: read 2025-03-04T20:09:58.7202914Z RepositoryProjects: read 2025-03-04T20:09:58.7203446Z SecurityEvents: read 2025-03-04T20:09:58.7203920Z Statuses: read 2025-03-04T20:09:58.7204329Z ##[endgroup] 2025-03-04T20:09:58.7207089Z Secret source: Actions 2025-03-04T20:09:58.7207937Z Prepare workflow directory 2025-03-04T20:09:58.7568049Z Prepare all required actions 2025-03-04T20:09:58.7598179Z Getting action download info 2025-03-04T20:09:58.9717004Z Download action repository 'pytorch/test-infra@main' (SHA:79438512a0632583899938d3b0277da78f5569e0) 2025-03-04T20:10:00.2457235Z Download action repository 'pytorch/pytorch@main' (SHA:439395c0ae0234b529fd1b5ce30efca68be93f97) 2025-03-04T20:10:13.6724900Z Download action repository 'aws-actions/configure-aws-credentials@v3' (SHA:50ac8dd1e1b10d09dac7b8727528b91bed831ac0) 2025-03-04T20:10:13.8867246Z Download action repository 'seemethere/upload-artifact-s3@v5' (SHA:baba72d0712b404f646cebe0730933554ebce96a) 2025-03-04T20:10:14.1531221Z Getting action download info 2025-03-04T20:10:14.2666076Z Download action repository 'actions/checkout@v4' (SHA:11bd71901bbe5b1630ceea73d27597364c9af683) 2025-03-04T20:10:14.5011229Z Getting action download info 2025-03-04T20:10:14.6324031Z Download action repository 'nick-fields/retry@v3.0.0' (SHA:7152eba30c6575329ac0576536151aca5a72780e) 2025-03-04T20:10:14.7741277Z Getting action download info 2025-03-04T20:10:14.8694631Z Download action repository 'nick-fields/retry@3e91a01664abd3c5cd539100d10d33b9c5b68482' (SHA:3e91a01664abd3c5cd539100d10d33b9c5b68482) 2025-03-04T20:10:15.0550566Z Getting action download info 2025-03-04T20:10:15.1801196Z Uses: pytorch/pytorch/.github/workflows/_linux-test.yml@refs/tags/ciflow/inductor/148205 (1b7498080987913ecb3aff6253c5e88f3540d911) 2025-03-04T20:10:15.1803984Z ##[group] Inputs 2025-03-04T20:10:15.1804282Z build-environment: linux-jammy-py3.9-gcc11-build 2025-03-04T20:10:15.1805979Z test-matrix: {"include": [{"config": "cpu_inductor_torchbench", "shard": 1, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "cpu_inductor_torchbench", "shard": 2, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_huggingface", "shard": 1, "num_shards": 1, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_timm", "shard": 1, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_timm", "shard": 2, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_torchbench", "shard": 1, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_torchbench", "shard": 2, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "inductor_torchbench_cpu_smoketest_perf", "shard": 1, "num_shards": 1, "runner": "linux.24xl.spr-metal"}]} 2025-03-04T20:10:15.1807819Z docker-image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e 2025-03-04T20:10:15.1808326Z sync-tag: 2025-03-04T20:10:15.1809049Z timeout-minutes: 240 2025-03-04T20:10:15.1809238Z use-gha: 2025-03-04T20:10:15.1809410Z dashboard-tag: 2025-03-04T20:10:15.1809592Z s3-bucket: gha-artifacts 2025-03-04T20:10:15.1809787Z aws-role-to-assume: 2025-03-04T20:10:15.1810468Z disable-monitor: false 2025-03-04T20:10:15.1810755Z ##[endgroup] 2025-03-04T20:10:15.1811099Z Complete job name: linux-jammy-cpu-py3.9-gcc11-inductor / test (cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx) 2025-03-04T20:10:15.2222530Z A job started hook has been configured by the self-hosted runner administrator 2025-03-04T20:10:15.2293981Z ##[group]Run '/home/ec2-user/runner-scripts/before_job.sh' 2025-03-04T20:10:15.2300388Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T20:10:15.2300839Z ##[endgroup] 2025-03-04T20:10:16.0522332Z Runner Type: linux.8xlarge.amx 2025-03-04T20:10:16.0522806Z Instance Type: m7i-flex.8xlarge 2025-03-04T20:10:16.0523017Z AMI Name: unknown 2025-03-04T20:10:16.0550382Z AMI ID: ami-05b10e08d247fb927 2025-03-04T20:10:20.0685741Z ##[group]Run pytorch/test-infra/.github/actions/setup-ssh@main 2025-03-04T20:10:20.0686074Z with: 2025-03-04T20:10:20.0686684Z github-secret: *** 2025-03-04T20:10:20.0687141Z instructions: All testing is done inside the container, to start an interactive session run: docker exec -it $(docker container ps --format '{{.ID}}') bash 2025-03-04T20:10:20.0687636Z activate-with-label: false 2025-03-04T20:10:20.0687839Z label: with-ssh 2025-03-04T20:10:20.0688025Z remove-existing-keys: true 2025-03-04T20:10:20.0688221Z fail-silently: true 2025-03-04T20:10:20.0688398Z env: 2025-03-04T20:10:20.0688564Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:10:20.0688752Z ##[endgroup] 2025-03-04T20:10:20.1634155Z Please see https://github.com/pytorch/pytorch/wiki/Debugging-using-with-ssh-for-Github-Actions for more info. 2025-03-04T20:10:20.1634759Z ciflow reference detected, attempting to extract PR number 2025-03-04T20:10:20.4881553Z Grabbing public ssh keys from https://github.com/pytorch-bot[bot].keys 2025-03-04T20:10:20.5497362Z No SSH keys found for user pytorch-bot[bot] 2025-03-04T20:10:20.5498555Z Grabbing public ssh keys from https://github.com/williamwen42.keys 2025-03-04T20:10:20.6149532Z ~/.ssh/authorized_keys file found on node, removing ~/.ssh and starting fresh 2025-03-04T20:10:20.6161288Z Public keys pulled and installed to /home/ec2-user/.ssh/authorized_keys 2025-03-04T20:10:20.6195547Z Login using: ssh ec2-user@ec2-54-221-129-92.compute-1.amazonaws.com 2025-03-04T20:10:20.6195962Z All testing is done inside the container, to start an interactive session run: 2025-03-04T20:10:20.6196311Z docker exec -it $(docker container ps --format '{{.ID}}') bash 2025-03-04T20:10:20.6318237Z ##[group]Run pytorch/pytorch/.github/actions/checkout-pytorch@main 2025-03-04T20:10:20.6318978Z with: 2025-03-04T20:10:20.6319172Z no-sudo: true 2025-03-04T20:10:20.6319366Z submodules: recursive 2025-03-04T20:10:20.6319563Z fetch-depth: 0 2025-03-04T20:10:20.6319736Z env: 2025-03-04T20:10:20.6319915Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:10:20.6320113Z ##[endgroup] 2025-03-04T20:10:20.6383841Z ##[group]Run echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-03-04T20:10:20.6384460Z echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-03-04T20:10:20.6392158Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T20:10:20.6392408Z env: 2025-03-04T20:10:20.6392573Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:10:20.6392759Z ##[endgroup] 2025-03-04T20:10:20.6475215Z ##[group]Run retry () { 2025-03-04T20:10:20.6475506Z retry () { 2025-03-04T20:10:20.6475745Z  $* || (sleep 1 && $*) || (sleep 2 && $*) || (sleep 4 && $*) || (sleep 8 && $*) 2025-03-04T20:10:20.6475998Z } 2025-03-04T20:10:20.6476183Z echo "${GITHUB_WORKSPACE}" 2025-03-04T20:10:20.6476420Z if [ -z "${NO_SUDO}" ]; then 2025-03-04T20:10:20.6476660Z  retry sudo rm -rf "${GITHUB_WORKSPACE}" 2025-03-04T20:10:20.6476887Z else 2025-03-04T20:10:20.6477075Z  retry rm -rf "${GITHUB_WORKSPACE}" 2025-03-04T20:10:20.6477280Z fi 2025-03-04T20:10:20.6477589Z mkdir "${GITHUB_WORKSPACE}" 2025-03-04T20:10:20.6477908Z  2025-03-04T20:10:20.6478130Z # Use all available CPUs for fetching 2025-03-04T20:10:20.6478369Z cd "${GITHUB_WORKSPACE}" 2025-03-04T20:10:20.6478604Z git config --global fetch.parallel 0 2025-03-04T20:10:20.6478871Z git config --global submodule.fetchJobs 0 2025-03-04T20:10:20.6482794Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T20:10:20.6483028Z env: 2025-03-04T20:10:20.6483190Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:10:20.6483376Z NO_SUDO: true 2025-03-04T20:10:20.6483541Z ##[endgroup] 2025-03-04T20:10:20.6500133Z /home/ec2-user/actions-runner/_work/pytorch/pytorch 2025-03-04T20:10:20.6606052Z ##[group]Run actions/checkout@v4 2025-03-04T20:10:20.6606279Z with: 2025-03-04T20:10:20.6606470Z ref: 1b7498080987913ecb3aff6253c5e88f3540d911 2025-03-04T20:10:20.6606697Z fetch-depth: 0 2025-03-04T20:10:20.6606887Z submodules: recursive 2025-03-04T20:10:20.6607087Z show-progress: false 2025-03-04T20:10:20.6607279Z repository: pytorch/pytorch 2025-03-04T20:10:20.6607577Z token: *** 2025-03-04T20:10:20.6607739Z ssh-strict: true 2025-03-04T20:10:20.6607905Z ssh-user: git 2025-03-04T20:10:20.6608084Z persist-credentials: true 2025-03-04T20:10:20.6608279Z clean: true 2025-03-04T20:10:20.6608460Z sparse-checkout-cone-mode: true 2025-03-04T20:10:20.6608669Z fetch-tags: false 2025-03-04T20:10:20.6608835Z lfs: false 2025-03-04T20:10:20.6609004Z set-safe-directory: true 2025-03-04T20:10:20.6609186Z env: 2025-03-04T20:10:20.6609344Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:10:20.6609522Z ##[endgroup] 2025-03-04T20:10:20.7510829Z Syncing repository: pytorch/pytorch 2025-03-04T20:10:20.7511837Z ##[group]Getting Git version info 2025-03-04T20:10:20.7512201Z Working directory is '/home/ec2-user/actions-runner/_work/pytorch/pytorch' 2025-03-04T20:10:20.7512739Z [command]/usr/bin/git version 2025-03-04T20:10:20.7512971Z git version 2.47.1 2025-03-04T20:10:20.7514608Z ##[endgroup] 2025-03-04T20:10:20.7526716Z Copying '/home/ec2-user/.gitconfig' to '/home/ec2-user/actions-runner/_work/_temp/014edfc3-4aff-4517-aee7-cc2220e99f9d/.gitconfig' 2025-03-04T20:10:20.7540300Z Temporarily overriding HOME='/home/ec2-user/actions-runner/_work/_temp/014edfc3-4aff-4517-aee7-cc2220e99f9d' before making global git config changes 2025-03-04T20:10:20.7541110Z Adding repository directory to the temporary git global config as a safe directory 2025-03-04T20:10:20.7543832Z [command]/usr/bin/git config --global --add safe.directory /home/ec2-user/actions-runner/_work/pytorch/pytorch 2025-03-04T20:10:20.7573324Z Deleting the contents of '/home/ec2-user/actions-runner/_work/pytorch/pytorch' 2025-03-04T20:10:20.7579066Z ##[group]Initializing the repository 2025-03-04T20:10:20.7583668Z [command]/usr/bin/git init /home/ec2-user/actions-runner/_work/pytorch/pytorch 2025-03-04T20:10:20.7611233Z hint: Using 'master' as the name for the initial branch. This default branch name 2025-03-04T20:10:20.7611905Z hint: is subject to change. To configure the initial branch name to use in all 2025-03-04T20:10:20.7612415Z hint: of your new repositories, which will suppress this warning, call: 2025-03-04T20:10:20.7612716Z hint: 2025-03-04T20:10:20.7612975Z hint: git config --global init.defaultBranch 2025-03-04T20:10:20.7613205Z hint: 2025-03-04T20:10:20.7613443Z hint: Names commonly chosen instead of 'master' are 'main', 'trunk' and 2025-03-04T20:10:20.7613789Z hint: 'development'. The just-created branch can be renamed via this command: 2025-03-04T20:10:20.7614068Z hint: 2025-03-04T20:10:20.7614238Z hint: git branch -m 2025-03-04T20:10:20.7614565Z Initialized empty Git repository in /home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/ 2025-03-04T20:10:20.7619790Z [command]/usr/bin/git remote add origin https://github.com/pytorch/pytorch 2025-03-04T20:10:20.7647710Z ##[endgroup] 2025-03-04T20:10:20.7654695Z ##[group]Disabling automatic garbage collection 2025-03-04T20:10:20.7656851Z [command]/usr/bin/git config --local gc.auto 0 2025-03-04T20:10:20.7672348Z ##[endgroup] 2025-03-04T20:10:20.7672674Z ##[group]Setting up auth 2025-03-04T20:10:20.7674597Z [command]/usr/bin/git config --local --name-only --get-regexp core\.sshCommand 2025-03-04T20:10:20.7696616Z [command]/usr/bin/git submodule foreach --recursive sh -c "git config --local --name-only --get-regexp 'core\.sshCommand' && git config --local --unset-all 'core.sshCommand' || :" 2025-03-04T20:10:20.7994103Z [command]/usr/bin/git config --local --name-only --get-regexp http\.https\:\/\/github\.com\/\.extraheader 2025-03-04T20:10:20.8017269Z [command]/usr/bin/git submodule foreach --recursive sh -c "git config --local --name-only --get-regexp 'http\.https\:\/\/github\.com\/\.extraheader' && git config --local --unset-all 'http.https://github.com/.extraheader' || :" 2025-03-04T20:10:20.8284970Z [command]/usr/bin/git config --local http.https://github.com/.extraheader AUTHORIZATION: basic *** 2025-03-04T20:10:20.8346526Z ##[endgroup] 2025-03-04T20:10:20.8348668Z ##[group]Fetching the repository 2025-03-04T20:10:20.8349695Z [command]/usr/bin/git -c protocol.version=2 fetch --prune --no-recurse-submodules origin +refs/heads/*:refs/remotes/origin/* +refs/tags/*:refs/tags/* 2025-03-04T20:11:03.8474934Z From https://github.com/pytorch/pytorch 2025-03-04T20:11:03.8477454Z * [new branch] 2.1-dynamic-doc -> origin/2.1-dynamic-doc 2025-03-04T20:11:03.8485199Z * [new branch] 2.6.0.dev20241004+ -> origin/2.6.0.dev20241004+ 2025-03-04T20:11:03.8485635Z * [new branch] 20250219_e8m0_intermediate -> origin/20250219_e8m0_intermediate 2025-03-04T20:11:03.8485985Z * [new branch] 20250219_test -> origin/20250219_test 2025-03-04T20:11:03.8486748Z * [new branch] Adjust-Description-for-linux-binary-test-Workflow -> origin/Adjust-Description-for-linux-binary-test-Workflow 2025-03-04T20:11:03.8487314Z * [new branch] Chillee-patch-5 -> origin/Chillee-patch-5 2025-03-04T20:11:03.8487771Z * [new branch] Flamefire-patch-1 -> origin/Flamefire-patch-1 2025-03-04T20:11:03.8488551Z * [new branch] HDCharles-2.6.0-release-notes -> origin/HDCharles-2.6.0-release-notes 2025-03-04T20:11:03.8489186Z * [new branch] JackCaoG/add_new_lazy_counter_macro -> origin/JackCaoG/add_new_lazy_counter_macro 2025-03-04T20:11:03.8489910Z * [new branch] JackCaoG/dynamo_make_fx_non_core_aten_ops -> origin/JackCaoG/dynamo_make_fx_non_core_aten_ops 2025-03-04T20:11:03.8490334Z * [new branch] JackCaoG/fix_xla_torchbench -> origin/JackCaoG/fix_xla_torchbench 2025-03-04T20:11:03.8490699Z * [new branch] JackCaoG/update_dynamo_doc -> origin/JackCaoG/update_dynamo_doc 2025-03-04T20:11:03.8491097Z * [new branch] JackCaoG/update_xla_pin_to_skip_test -> origin/JackCaoG/update_xla_pin_to_skip_test 2025-03-04T20:11:03.8491518Z * [new branch] JackCaoG/update_xla_pin_to_skip_test2 -> origin/JackCaoG/update_xla_pin_to_skip_test2 2025-03-04T20:11:03.8491909Z * [new branch] NicolasHug-patch-2 -> origin/NicolasHug-patch-2 2025-03-04T20:11:03.8492261Z * [new branch] PR-AOTInductorNoneBug -> origin/PR-AOTInductorNoneBug 2025-03-04T20:11:03.8492631Z * [new branch] PR-AOTInductorNoneBugFix -> origin/PR-AOTInductorNoneBugFix 2025-03-04T20:11:03.8492992Z * [new branch] PR-FixConfigsIssue -> origin/PR-FixConfigsIssue 2025-03-04T20:11:03.8493319Z * [new branch] PR-NoneBugFix-viable -> origin/PR-NoneBugFix-viable 2025-03-04T20:11:03.8493639Z * [new branch] PR-ResetToZero -> origin/PR-ResetToZero 2025-03-04T20:11:03.8494086Z * [new branch] Remove-linux_t4g_2xlarge-Usage -> origin/Remove-linux_t4g_2xlarge-Usage 2025-03-04T20:11:03.8494432Z * [new branch] Revert-PR-110949 -> origin/Revert-PR-110949 2025-03-04T20:11:03.8494846Z * [new branch] Update-Flash-Packaging -> origin/Update-Flash-Packaging 2025-03-04T20:11:03.8495217Z * [new branch] Valentine/flash_attention_bf16 -> origin/Valentine/flash_attention_bf16 2025-03-04T20:11:03.8495579Z * [new branch] _tmp-orig/release/2.6 -> origin/_tmp-orig/release/2.6 2025-03-04T20:11:03.8495901Z * [new branch] _tmp-release/2.6 -> origin/_tmp-release/2.6 2025-03-04T20:11:03.8496236Z * [new branch] abock/onnx-1.15.0-validation -> origin/abock/onnx-1.15.0-validation 2025-03-04T20:11:03.8496635Z * [new branch] abock/ort-nightly==1.16.0.dev20230908001 -> origin/abock/ort-nightly==1.16.0.dev20230908001 2025-03-04T20:11:03.8497147Z * [new branch] add-android-build-workflow -> origin/add-android-build-workflow 2025-03-04T20:11:03.8497479Z * [new branch] add-assign -> origin/add-assign 2025-03-04T20:11:03.8497833Z * [new branch] add_broadcast_functional_collective -> origin/add_broadcast_functional_collective 2025-03-04T20:11:03.8498220Z * [new branch] add_from_group_doc_and_test -> origin/add_from_group_doc_and_test 2025-03-04T20:11:03.8498560Z * [new branch] add_mha_to_autocast_policy -> origin/add_mha_to_autocast_policy 2025-03-04T20:11:03.8498933Z * [new branch] add_non_parallel_model_comparison -> origin/add_non_parallel_model_comparison 2025-03-04T20:11:03.8499300Z * [new branch] add_test_to_show_view_gap -> origin/add_test_to_show_view_gap 2025-03-04T20:11:03.8499652Z * [new branch] add_windows_testing_back -> origin/add_windows_testing_back 2025-03-04T20:11:03.8499975Z * [new branch] addmm-heuristic -> origin/addmm-heuristic 2025-03-04T20:11:03.8500268Z * [new branch] addsimde -> origin/addsimde 2025-03-04T20:11:03.8500556Z * [new branch] adi/gemm_bf16f32 -> origin/adi/gemm_bf16f32 2025-03-04T20:11:03.8500876Z * [new branch] ah-globalfeedback-hook -> origin/ah-globalfeedback-hook 2025-03-04T20:11:03.8501208Z * [new branch] alanwaketan/pin2 -> origin/alanwaketan/pin2 2025-03-04T20:11:03.8501575Z * [new branch] albanD-patch-1 -> origin/albanD-patch-1 2025-03-04T20:11:03.8502166Z * [new branch] albanD-patch-2 -> origin/albanD-patch-2 2025-03-04T20:11:03.8502495Z * [new branch] alt-disable -> origin/alt-disable 2025-03-04T20:11:03.8502799Z * [new branch] angelayi/144772 -> origin/angelayi/144772 2025-03-04T20:11:03.8503179Z * [new branch] angelayi/aot_inductor_bench_comp_time -> origin/angelayi/aot_inductor_bench_comp_time 2025-03-04T20:11:03.8509676Z * [new branch] angelayi/aot_inductor_benchmark -> origin/angelayi/aot_inductor_benchmark 2025-03-04T20:11:03.8510126Z * [new branch] angelayi/aot_inductor_torch -> origin/angelayi/aot_inductor_torch 2025-03-04T20:11:03.8510540Z * [new branch] angelayi/aoti_additional_files -> origin/angelayi/aoti_additional_files 2025-03-04T20:11:03.8510927Z * [new 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ciflow/binaries/147607 -> ciflow/binaries/147607 2025-03-04T20:11:04.1461295Z * [new tag] ciflow/binaries/147664 -> ciflow/binaries/147664 2025-03-04T20:11:04.1461463Z * [new tag] ciflow/binaries/147817 -> ciflow/binaries/147817 2025-03-04T20:11:04.1461624Z * [new tag] ciflow/binaries/147917 -> ciflow/binaries/147917 2025-03-04T20:11:04.1461769Z * [new tag] ciflow/binaries/147945 -> ciflow/binaries/147945 2025-03-04T20:11:04.1462360Z * [new tag] ciflow/binaries/147964 -> ciflow/binaries/147964 2025-03-04T20:11:04.1462535Z * [new tag] ciflow/binaries/148163 -> ciflow/binaries/148163 2025-03-04T20:11:04.1462668Z * [new tag] ciflow/binaries/148173 -> ciflow/binaries/148173 2025-03-04T20:11:04.1462793Z * [new tag] ciflow/binaries/148343 -> ciflow/binaries/148343 2025-03-04T20:11:04.1462944Z * [new tag] ciflow/binaries_wheel/138834 -> ciflow/binaries_wheel/138834 2025-03-04T20:11:04.1463271Z * [new tag] ciflow/binaries_wheel/142279 -> ciflow/binaries_wheel/142279 2025-03-04T20:11:04.1463577Z * [new tag] ciflow/binaries_wheel/143388 -> ciflow/binaries_wheel/143388 2025-03-04T20:11:04.1463816Z * [new tag] ciflow/binaries_wheel/144049 -> ciflow/binaries_wheel/144049 2025-03-04T20:11:04.1464240Z * [new tag] ciflow/binaries_wheel/146055 -> ciflow/binaries_wheel/146055 2025-03-04T20:11:04.1464592Z * [new tag] ciflow/binaries_wheel/146573 -> ciflow/binaries_wheel/146573 2025-03-04T20:11:04.1465233Z * [new tag] ciflow/binaries_wheel/147074 -> ciflow/binaries_wheel/147074 2025-03-04T20:11:04.1465381Z * [new tag] ciflow/binaries_wheel/147448 -> ciflow/binaries_wheel/147448 2025-03-04T20:11:04.1468112Z * [new tag] ciflow/binaries_wheel/147455 -> ciflow/binaries_wheel/147455 2025-03-04T20:11:04.1468294Z * [new tag] ciflow/binaries_wheel/148313 -> ciflow/binaries_wheel/148313 2025-03-04T20:11:04.1468429Z * [new tag] ciflow/binaries_wheel/148319 -> ciflow/binaries_wheel/148319 2025-03-04T20:11:04.1468551Z * [new tag] ciflow/cuda/70978 -> ciflow/cuda/70978 2025-03-04T20:11:04.1468658Z * [new tag] ciflow/cuda/70979 -> ciflow/cuda/70979 2025-03-04T20:11:04.1468769Z * [new tag] ciflow/cuda/70989 -> ciflow/cuda/70989 2025-03-04T20:11:04.1468914Z * [new tag] ciflow/inductor-cu126/140793 -> ciflow/inductor-cu126/140793 2025-03-04T20:11:04.1469130Z * [new tag] ciflow/inductor-micro-benchmark/141910 -> ciflow/inductor-micro-benchmark/141910 2025-03-04T20:11:04.1469570Z * [new tag] ciflow/inductor-perf-compare/140195 -> ciflow/inductor-perf-compare/140195 2025-03-04T20:11:04.1470005Z * [new tag] ciflow/inductor-perf-test-nightly/140195 -> ciflow/inductor-perf-test-nightly/140195 2025-03-04T20:11:04.1470743Z * [new tag] ciflow/inductor-periodic/140793 -> ciflow/inductor-periodic/140793 2025-03-04T20:11:04.1471212Z * [new tag] ciflow/inductor-periodic/145612 -> ciflow/inductor-periodic/145612 2025-03-04T20:11:04.1471407Z * [new tag] ciflow/inductor-periodic/147315 -> ciflow/inductor-periodic/147315 2025-03-04T20:11:04.1473261Z * [new tag] ciflow/inductor-rocm/140989 -> ciflow/inductor-rocm/140989 2025-03-04T20:11:04.1473409Z * [new tag] ciflow/inductor-rocm/141309 -> ciflow/inductor-rocm/141309 2025-03-04T20:11:04.1473567Z * [new tag] ciflow/inductor-rocm/141355 -> ciflow/inductor-rocm/141355 2025-03-04T20:11:04.1473872Z * [new tag] ciflow/inductor-rocm/146264 -> ciflow/inductor-rocm/146264 2025-03-04T20:11:04.1474183Z * [new tag] ciflow/inductor-rocm/146903 -> ciflow/inductor-rocm/146903 2025-03-04T20:11:04.1474438Z * [new tag] ciflow/inductor-rocm/147315 -> ciflow/inductor-rocm/147315 2025-03-04T20:11:04.1474831Z * [new tag] ciflow/inductor-rocm/147320 -> ciflow/inductor-rocm/147320 2025-03-04T20:11:04.1477317Z * [new tag] ciflow/inductor-rocm/147452 -> ciflow/inductor-rocm/147452 2025-03-04T20:11:04.1477484Z * [new tag] ciflow/inductor-rocm/147583 -> ciflow/inductor-rocm/147583 2025-03-04T20:11:04.1477629Z * [new tag] ciflow/inductor-rocm/147619 -> ciflow/inductor-rocm/147619 2025-03-04T20:11:04.1477779Z * [new tag] ciflow/inductor-rocm/148305 -> ciflow/inductor-rocm/148305 2025-03-04T20:11:04.1477915Z * [new tag] ciflow/inductor-rocm/148437 -> ciflow/inductor-rocm/148437 2025-03-04T20:11:04.1478584Z * [new tag] ciflow/inductor/110155 -> ciflow/inductor/110155 2025-03-04T20:11:04.1478739Z * [new tag] ciflow/inductor/113257 -> ciflow/inductor/113257 2025-03-04T20:11:04.1478863Z * [new tag] ciflow/inductor/119496 -> ciflow/inductor/119496 2025-03-04T20:11:04.1479391Z * [new tag] ciflow/inductor/119977 -> ciflow/inductor/119977 2025-03-04T20:11:04.1479805Z * [new tag] ciflow/inductor/120076 -> ciflow/inductor/120076 2025-03-04T20:11:04.1480324Z * [new tag] ciflow/inductor/121445 -> ciflow/inductor/121445 2025-03-04T20:11:04.1480518Z * [new tag] ciflow/inductor/124490 -> ciflow/inductor/124490 2025-03-04T20:11:04.1480828Z * [new tag] ciflow/inductor/125270 -> ciflow/inductor/125270 2025-03-04T20:11:04.1481484Z * [new tag] ciflow/inductor/125326 -> ciflow/inductor/125326 2025-03-04T20:11:04.1481614Z * [new tag] ciflow/inductor/125428 -> ciflow/inductor/125428 2025-03-04T20:11:04.1481999Z * [new tag] ciflow/inductor/125469 -> ciflow/inductor/125469 2025-03-04T20:11:04.1483485Z * [new tag] ciflow/inductor/125806 -> ciflow/inductor/125806 2025-03-04T20:11:04.1483794Z * [new tag] ciflow/inductor/125888 -> ciflow/inductor/125888 2025-03-04T20:11:04.1483935Z * [new tag] ciflow/inductor/125995 -> ciflow/inductor/125995 2025-03-04T20:11:04.1484065Z * [new tag] ciflow/inductor/126348 -> ciflow/inductor/126348 2025-03-04T20:11:04.1484389Z * [new tag] ciflow/inductor/127011 -> ciflow/inductor/127011 2025-03-04T20:11:04.1485687Z * [new tag] ciflow/inductor/127171 -> ciflow/inductor/127171 2025-03-04T20:11:04.1485872Z * [new tag] ciflow/inductor/127293 -> ciflow/inductor/127293 2025-03-04T20:11:04.1486001Z * [new tag] ciflow/inductor/127294 -> ciflow/inductor/127294 2025-03-04T20:11:04.1486210Z * [new tag] ciflow/inductor/129352 -> ciflow/inductor/129352 2025-03-04T20:11:04.1486690Z * [new tag] ciflow/inductor/129420 -> ciflow/inductor/129420 2025-03-04T20:11:04.1486842Z * [new tag] ciflow/inductor/130141 -> ciflow/inductor/130141 2025-03-04T20:11:04.1487785Z * [new tag] ciflow/inductor/130499 -> ciflow/inductor/130499 2025-03-04T20:11:04.1488067Z * [new tag] ciflow/inductor/130887 -> ciflow/inductor/130887 2025-03-04T20:11:04.1488332Z * [new tag] ciflow/inductor/131354 -> ciflow/inductor/131354 2025-03-04T20:11:04.1488554Z * [new tag] ciflow/inductor/132021 -> ciflow/inductor/132021 2025-03-04T20:11:04.1489072Z * [new tag] ciflow/inductor/132414 -> ciflow/inductor/132414 2025-03-04T20:11:04.1489415Z * [new tag] ciflow/inductor/133044 -> ciflow/inductor/133044 2025-03-04T20:11:04.1489729Z * [new tag] ciflow/inductor/133121 -> ciflow/inductor/133121 2025-03-04T20:11:04.1490260Z * [new tag] ciflow/inductor/133287 -> ciflow/inductor/133287 2025-03-04T20:11:04.1490617Z * [new tag] ciflow/inductor/133289 -> ciflow/inductor/133289 2025-03-04T20:11:04.1490968Z * [new tag] ciflow/inductor/133296 -> ciflow/inductor/133296 2025-03-04T20:11:04.1491293Z * [new tag] ciflow/inductor/133297 -> ciflow/inductor/133297 2025-03-04T20:11:04.1491657Z * [new tag] ciflow/inductor/133315 -> ciflow/inductor/133315 2025-03-04T20:11:04.1493063Z * [new tag] ciflow/inductor/133392 -> ciflow/inductor/133392 2025-03-04T20:11:04.1493252Z * [new tag] ciflow/inductor/133419 -> ciflow/inductor/133419 2025-03-04T20:11:04.1493381Z * [new tag] ciflow/inductor/133423 -> ciflow/inductor/133423 2025-03-04T20:11:04.1493510Z * [new tag] ciflow/inductor/133667 -> ciflow/inductor/133667 2025-03-04T20:11:04.1493818Z * [new tag] ciflow/inductor/133753 -> ciflow/inductor/133753 2025-03-04T20:11:04.1494054Z * [new tag] ciflow/inductor/134681 -> ciflow/inductor/134681 2025-03-04T20:11:04.1494865Z * [new tag] ciflow/inductor/135708 -> ciflow/inductor/135708 2025-03-04T20:11:04.1495116Z * [new tag] ciflow/inductor/135792 -> ciflow/inductor/135792 2025-03-04T20:11:04.1495259Z * [new tag] ciflow/inductor/136355 -> ciflow/inductor/136355 2025-03-04T20:11:04.1495601Z * [new tag] ciflow/inductor/136702 -> ciflow/inductor/136702 2025-03-04T20:11:04.1496331Z * [new tag] ciflow/inductor/137400 -> ciflow/inductor/137400 2025-03-04T20:11:04.1496841Z * [new tag] ciflow/inductor/137568 -> ciflow/inductor/137568 2025-03-04T20:11:04.1497113Z * [new tag] ciflow/inductor/137583 -> ciflow/inductor/137583 2025-03-04T20:11:04.1497269Z * [new tag] ciflow/inductor/137846 -> ciflow/inductor/137846 2025-03-04T20:11:04.1497555Z * [new tag] ciflow/inductor/137884 -> ciflow/inductor/137884 2025-03-04T20:11:04.1499624Z * [new tag] ciflow/inductor/138185 -> ciflow/inductor/138185 2025-03-04T20:11:04.1499791Z * [new tag] ciflow/inductor/138202 -> ciflow/inductor/138202 2025-03-04T20:11:04.1499917Z * [new tag] ciflow/inductor/138213 -> ciflow/inductor/138213 2025-03-04T20:11:04.1500041Z * [new tag] ciflow/inductor/138214 -> ciflow/inductor/138214 2025-03-04T20:11:04.1500183Z * [new tag] ciflow/inductor/138388 -> ciflow/inductor/138388 2025-03-04T20:11:04.1500313Z * [new tag] ciflow/inductor/138513 -> ciflow/inductor/138513 2025-03-04T20:11:04.1500898Z * [new tag] ciflow/inductor/138519 -> ciflow/inductor/138519 2025-03-04T20:11:04.1501297Z * [new tag] ciflow/inductor/138555 -> ciflow/inductor/138555 2025-03-04T20:11:04.1501684Z * [new tag] ciflow/inductor/138626 -> ciflow/inductor/138626 2025-03-04T20:11:04.1502138Z * [new tag] ciflow/inductor/138889 -> ciflow/inductor/138889 2025-03-04T20:11:04.1502863Z * [new tag] ciflow/inductor/138930 -> ciflow/inductor/138930 2025-03-04T20:11:04.1503119Z * [new tag] ciflow/inductor/139094 -> ciflow/inductor/139094 2025-03-04T20:11:04.1503490Z * [new tag] ciflow/inductor/139271 -> ciflow/inductor/139271 2025-03-04T20:11:04.1503941Z * [new tag] ciflow/inductor/139561 -> ciflow/inductor/139561 2025-03-04T20:11:04.1504265Z * [new tag] ciflow/inductor/139975 -> ciflow/inductor/139975 2025-03-04T20:11:04.1504722Z * [new tag] ciflow/inductor/140032 -> ciflow/inductor/140032 2025-03-04T20:11:04.1505412Z * [new tag] ciflow/inductor/140084 -> ciflow/inductor/140084 2025-03-04T20:11:04.1505581Z * [new tag] ciflow/inductor/140159 -> ciflow/inductor/140159 2025-03-04T20:11:04.1505933Z * [new tag] ciflow/inductor/140195 -> ciflow/inductor/140195 2025-03-04T20:11:04.1506756Z * [new tag] ciflow/inductor/140746 -> ciflow/inductor/140746 2025-03-04T20:11:04.1507017Z * [new tag] ciflow/inductor/140756 -> ciflow/inductor/140756 2025-03-04T20:11:04.1507403Z * [new tag] ciflow/inductor/140979 -> ciflow/inductor/140979 2025-03-04T20:11:04.1508432Z * [new tag] ciflow/inductor/141082 -> ciflow/inductor/141082 2025-03-04T20:11:04.1508694Z * [new tag] ciflow/inductor/141096 -> ciflow/inductor/141096 2025-03-04T20:11:04.1509153Z * [new tag] ciflow/inductor/141097 -> ciflow/inductor/141097 2025-03-04T20:11:04.1509506Z * [new tag] ciflow/inductor/141213 -> ciflow/inductor/141213 2025-03-04T20:11:04.1509853Z * [new tag] ciflow/inductor/141309 -> ciflow/inductor/141309 2025-03-04T20:11:04.1510165Z * [new tag] ciflow/inductor/141393 -> ciflow/inductor/141393 2025-03-04T20:11:04.1510561Z * [new tag] ciflow/inductor/141641 -> ciflow/inductor/141641 2025-03-04T20:11:04.1511221Z * [new tag] ciflow/inductor/141684 -> ciflow/inductor/141684 2025-03-04T20:11:04.1511437Z * [new tag] ciflow/inductor/141700 -> ciflow/inductor/141700 2025-03-04T20:11:04.1512121Z * [new tag] ciflow/inductor/141730 -> ciflow/inductor/141730 2025-03-04T20:11:04.1512427Z * [new tag] ciflow/inductor/141842 -> ciflow/inductor/141842 2025-03-04T20:11:04.1512659Z * [new tag] ciflow/inductor/141889 -> ciflow/inductor/141889 2025-03-04T20:11:04.1513321Z * [new tag] ciflow/inductor/141940 -> ciflow/inductor/141940 2025-03-04T20:11:04.1513700Z * [new tag] ciflow/inductor/141944 -> ciflow/inductor/141944 2025-03-04T20:11:04.1513823Z * [new tag] ciflow/inductor/141961 -> ciflow/inductor/141961 2025-03-04T20:11:04.1514357Z * [new tag] ciflow/inductor/142091 -> ciflow/inductor/142091 2025-03-04T20:11:04.1514669Z * [new tag] ciflow/inductor/142092 -> ciflow/inductor/142092 2025-03-04T20:11:04.1515515Z * [new tag] ciflow/inductor/142163 -> ciflow/inductor/142163 2025-03-04T20:11:04.1515719Z * [new tag] ciflow/inductor/142272 -> ciflow/inductor/142272 2025-03-04T20:11:04.1517038Z * [new tag] ciflow/inductor/142273 -> ciflow/inductor/142273 2025-03-04T20:11:04.1517171Z * [new tag] ciflow/inductor/142295 -> ciflow/inductor/142295 2025-03-04T20:11:04.1517296Z * [new tag] ciflow/inductor/142296 -> ciflow/inductor/142296 2025-03-04T20:11:04.1517568Z * [new tag] ciflow/inductor/142309 -> ciflow/inductor/142309 2025-03-04T20:11:04.1519583Z * [new tag] ciflow/inductor/142350 -> ciflow/inductor/142350 2025-03-04T20:11:04.1519747Z * [new tag] ciflow/inductor/142372 -> ciflow/inductor/142372 2025-03-04T20:11:04.1519869Z * [new tag] ciflow/inductor/142483 -> ciflow/inductor/142483 2025-03-04T20:11:04.1519999Z * [new tag] ciflow/inductor/142851 -> ciflow/inductor/142851 2025-03-04T20:11:04.1520120Z * [new tag] ciflow/inductor/143044 -> ciflow/inductor/143044 2025-03-04T20:11:04.1520272Z * [new tag] ciflow/inductor/143103 -> ciflow/inductor/143103 2025-03-04T20:11:04.1520728Z * [new tag] ciflow/inductor/143220 -> ciflow/inductor/143220 2025-03-04T20:11:04.1521456Z * [new tag] ciflow/inductor/143256 -> ciflow/inductor/143256 2025-03-04T20:11:04.1521776Z * [new tag] ciflow/inductor/143275 -> ciflow/inductor/143275 2025-03-04T20:11:04.1521915Z * [new tag] ciflow/inductor/143313 -> ciflow/inductor/143313 2025-03-04T20:11:04.1524397Z * [new tag] ciflow/inductor/143411 -> ciflow/inductor/143411 2025-03-04T20:11:04.1524533Z * [new tag] ciflow/inductor/143457 -> ciflow/inductor/143457 2025-03-04T20:11:04.1524660Z * [new tag] ciflow/inductor/143464 -> ciflow/inductor/143464 2025-03-04T20:11:04.1524777Z * [new tag] ciflow/inductor/143475 -> ciflow/inductor/143475 2025-03-04T20:11:04.1525755Z * [new tag] ciflow/inductor/143525 -> ciflow/inductor/143525 2025-03-04T20:11:04.1525877Z * [new tag] ciflow/inductor/143527 -> ciflow/inductor/143527 2025-03-04T20:11:04.1526005Z * [new tag] ciflow/inductor/143533 -> ciflow/inductor/143533 2025-03-04T20:11:04.1526126Z * [new tag] ciflow/inductor/143534 -> ciflow/inductor/143534 2025-03-04T20:11:04.1526749Z * [new tag] ciflow/inductor/143544 -> ciflow/inductor/143544 2025-03-04T20:11:04.1526886Z * [new tag] ciflow/inductor/143666 -> ciflow/inductor/143666 2025-03-04T20:11:04.1527310Z * [new tag] ciflow/inductor/143671 -> ciflow/inductor/143671 2025-03-04T20:11:04.1527661Z * [new tag] ciflow/inductor/143712 -> ciflow/inductor/143712 2025-03-04T20:11:04.1527872Z * [new tag] ciflow/inductor/143812 -> ciflow/inductor/143812 2025-03-04T20:11:04.1528168Z * [new tag] ciflow/inductor/143833 -> ciflow/inductor/143833 2025-03-04T20:11:04.1528350Z * [new tag] ciflow/inductor/143961 -> ciflow/inductor/143961 2025-03-04T20:11:04.1531808Z * [new tag] ciflow/inductor/143987 -> ciflow/inductor/143987 2025-03-04T20:11:04.1531999Z * [new tag] ciflow/inductor/144008 -> ciflow/inductor/144008 2025-03-04T20:11:04.1532133Z * [new tag] ciflow/inductor/144017 -> ciflow/inductor/144017 2025-03-04T20:11:04.1532254Z * [new tag] ciflow/inductor/144073 -> ciflow/inductor/144073 2025-03-04T20:11:04.1532380Z * [new tag] ciflow/inductor/144097 -> ciflow/inductor/144097 2025-03-04T20:11:04.1532502Z * [new tag] ciflow/inductor/144120 -> ciflow/inductor/144120 2025-03-04T20:11:04.1532627Z * [new tag] ciflow/inductor/144172 -> ciflow/inductor/144172 2025-03-04T20:11:04.1532755Z * [new tag] ciflow/inductor/144234 -> ciflow/inductor/144234 2025-03-04T20:11:04.1532884Z * [new tag] ciflow/inductor/144272 -> ciflow/inductor/144272 2025-03-04T20:11:04.1533002Z * [new tag] ciflow/inductor/144288 -> ciflow/inductor/144288 2025-03-04T20:11:04.1533439Z * [new tag] ciflow/inductor/144293 -> ciflow/inductor/144293 2025-03-04T20:11:04.1534473Z * [new tag] ciflow/inductor/144294 -> ciflow/inductor/144294 2025-03-04T20:11:04.1535014Z * [new tag] ciflow/inductor/144332 -> ciflow/inductor/144332 2025-03-04T20:11:04.1535144Z * [new tag] ciflow/inductor/144333 -> ciflow/inductor/144333 2025-03-04T20:11:04.1535267Z * [new tag] ciflow/inductor/144349 -> ciflow/inductor/144349 2025-03-04T20:11:04.1535823Z * [new tag] ciflow/inductor/144353 -> ciflow/inductor/144353 2025-03-04T20:11:04.1536071Z * [new tag] ciflow/inductor/144365 -> ciflow/inductor/144365 2025-03-04T20:11:04.1536928Z * [new tag] ciflow/inductor/144366 -> ciflow/inductor/144366 2025-03-04T20:11:04.1537217Z * [new tag] ciflow/inductor/144405 -> ciflow/inductor/144405 2025-03-04T20:11:04.1537410Z * [new tag] ciflow/inductor/144413 -> ciflow/inductor/144413 2025-03-04T20:11:04.1537617Z * [new tag] ciflow/inductor/144414 -> ciflow/inductor/144414 2025-03-04T20:11:04.1540057Z * [new tag] ciflow/inductor/144438 -> ciflow/inductor/144438 2025-03-04T20:11:04.1540212Z * [new tag] ciflow/inductor/144452 -> ciflow/inductor/144452 2025-03-04T20:11:04.1541040Z * [new tag] ciflow/inductor/144458 -> ciflow/inductor/144458 2025-03-04T20:11:04.1541194Z * [new tag] ciflow/inductor/144501 -> ciflow/inductor/144501 2025-03-04T20:11:04.1541407Z * [new tag] ciflow/inductor/144505 -> ciflow/inductor/144505 2025-03-04T20:11:04.1541577Z * [new tag] ciflow/inductor/144507 -> ciflow/inductor/144507 2025-03-04T20:11:04.1541784Z * [new tag] ciflow/inductor/144516 -> ciflow/inductor/144516 2025-03-04T20:11:04.1541954Z * [new tag] ciflow/inductor/144542 -> ciflow/inductor/144542 2025-03-04T20:11:04.1542166Z * [new tag] ciflow/inductor/144548 -> ciflow/inductor/144548 2025-03-04T20:11:04.1542319Z * [new tag] ciflow/inductor/144551 -> ciflow/inductor/144551 2025-03-04T20:11:04.1542524Z * [new tag] ciflow/inductor/144553 -> ciflow/inductor/144553 2025-03-04T20:11:04.1542679Z * [new tag] ciflow/inductor/144555 -> ciflow/inductor/144555 2025-03-04T20:11:04.1542885Z * [new tag] ciflow/inductor/144556 -> ciflow/inductor/144556 2025-03-04T20:11:04.1543027Z * [new tag] ciflow/inductor/144579 -> ciflow/inductor/144579 2025-03-04T20:11:04.1543713Z * [new tag] ciflow/inductor/144598 -> ciflow/inductor/144598 2025-03-04T20:11:04.1543874Z * [new tag] ciflow/inductor/144712 -> ciflow/inductor/144712 2025-03-04T20:11:04.1544313Z * [new tag] ciflow/inductor/144721 -> ciflow/inductor/144721 2025-03-04T20:11:04.1545145Z * [new tag] ciflow/inductor/144724 -> ciflow/inductor/144724 2025-03-04T20:11:04.1545296Z * [new tag] ciflow/inductor/144733 -> ciflow/inductor/144733 2025-03-04T20:11:04.1545708Z * [new tag] ciflow/inductor/144741 -> ciflow/inductor/144741 2025-03-04T20:11:04.1546037Z * [new tag] ciflow/inductor/144765 -> ciflow/inductor/144765 2025-03-04T20:11:04.1547739Z * [new tag] ciflow/inductor/144771 -> ciflow/inductor/144771 2025-03-04T20:11:04.1548359Z * [new tag] ciflow/inductor/144880 -> ciflow/inductor/144880 2025-03-04T20:11:04.1548512Z * [new tag] ciflow/inductor/144905 -> ciflow/inductor/144905 2025-03-04T20:11:04.1549233Z * [new tag] ciflow/inductor/144925 -> ciflow/inductor/144925 2025-03-04T20:11:04.1549377Z * [new tag] ciflow/inductor/144943 -> ciflow/inductor/144943 2025-03-04T20:11:04.1550105Z * [new tag] ciflow/inductor/144953 -> ciflow/inductor/144953 2025-03-04T20:11:04.1550248Z * [new tag] ciflow/inductor/144975 -> ciflow/inductor/144975 2025-03-04T20:11:04.1550674Z * [new tag] ciflow/inductor/144979 -> ciflow/inductor/144979 2025-03-04T20:11:04.1551081Z * [new tag] ciflow/inductor/144986 -> ciflow/inductor/144986 2025-03-04T20:11:04.1551499Z * [new tag] ciflow/inductor/144992 -> ciflow/inductor/144992 2025-03-04T20:11:04.1551899Z * [new tag] ciflow/inductor/145024 -> ciflow/inductor/145024 2025-03-04T20:11:04.1552405Z * [new tag] ciflow/inductor/145061 -> ciflow/inductor/145061 2025-03-04T20:11:04.1552802Z * [new tag] ciflow/inductor/145117 -> ciflow/inductor/145117 2025-03-04T20:11:04.1552939Z * [new tag] ciflow/inductor/145119 -> ciflow/inductor/145119 2025-03-04T20:11:04.1553470Z * [new tag] ciflow/inductor/145150 -> ciflow/inductor/145150 2025-03-04T20:11:04.1553613Z * [new tag] ciflow/inductor/145153 -> ciflow/inductor/145153 2025-03-04T20:11:04.1555323Z * [new tag] ciflow/inductor/145254 -> ciflow/inductor/145254 2025-03-04T20:11:04.1555720Z * [new tag] ciflow/inductor/145331 -> ciflow/inductor/145331 2025-03-04T20:11:04.1556053Z * [new tag] ciflow/inductor/145353 -> ciflow/inductor/145353 2025-03-04T20:11:04.1556199Z * [new tag] ciflow/inductor/145475 -> ciflow/inductor/145475 2025-03-04T20:11:04.1556917Z * [new tag] ciflow/inductor/145523 -> ciflow/inductor/145523 2025-03-04T20:11:04.1557302Z * [new tag] ciflow/inductor/145540 -> ciflow/inductor/145540 2025-03-04T20:11:04.1557706Z * [new tag] ciflow/inductor/145559 -> ciflow/inductor/145559 2025-03-04T20:11:04.1558114Z * [new tag] ciflow/inductor/145562 -> ciflow/inductor/145562 2025-03-04T20:11:04.1558452Z * [new tag] ciflow/inductor/145594 -> ciflow/inductor/145594 2025-03-04T20:11:04.1558586Z * [new tag] ciflow/inductor/145595 -> ciflow/inductor/145595 2025-03-04T20:11:04.1558699Z * [new tag] ciflow/inductor/145605 -> ciflow/inductor/145605 2025-03-04T20:11:04.1559398Z * [new tag] ciflow/inductor/145612 -> ciflow/inductor/145612 2025-03-04T20:11:04.1560010Z * [new tag] ciflow/inductor/145636 -> ciflow/inductor/145636 2025-03-04T20:11:04.1560212Z * [new tag] ciflow/inductor/145647 -> ciflow/inductor/145647 2025-03-04T20:11:04.1560875Z * [new tag] ciflow/inductor/145681 -> ciflow/inductor/145681 2025-03-04T20:11:04.1561008Z * [new tag] ciflow/inductor/145865 -> ciflow/inductor/145865 2025-03-04T20:11:04.1561445Z * [new tag] ciflow/inductor/145885 -> ciflow/inductor/145885 2025-03-04T20:11:04.1561815Z * [new tag] ciflow/inductor/145911 -> ciflow/inductor/145911 2025-03-04T20:11:04.1562218Z * [new tag] ciflow/inductor/145922 -> ciflow/inductor/145922 2025-03-04T20:11:04.1562592Z * [new tag] ciflow/inductor/145936 -> ciflow/inductor/145936 2025-03-04T20:11:04.1563008Z * [new tag] ciflow/inductor/145966 -> ciflow/inductor/145966 2025-03-04T20:11:04.1563372Z * [new tag] ciflow/inductor/145969 -> ciflow/inductor/145969 2025-03-04T20:11:04.1563507Z * [new tag] ciflow/inductor/145979 -> ciflow/inductor/145979 2025-03-04T20:11:04.1564149Z * [new tag] ciflow/inductor/145992 -> ciflow/inductor/145992 2025-03-04T20:11:04.1564276Z * [new tag] ciflow/inductor/146051 -> ciflow/inductor/146051 2025-03-04T20:11:04.1564946Z * [new tag] ciflow/inductor/146063 -> ciflow/inductor/146063 2025-03-04T20:11:04.1565083Z * [new tag] ciflow/inductor/146101 -> ciflow/inductor/146101 2025-03-04T20:11:04.1565203Z * [new tag] ciflow/inductor/146115 -> ciflow/inductor/146115 2025-03-04T20:11:04.1565817Z * [new tag] ciflow/inductor/146135 -> ciflow/inductor/146135 2025-03-04T20:11:04.1566021Z * [new tag] ciflow/inductor/146171 -> ciflow/inductor/146171 2025-03-04T20:11:04.1566160Z * [new tag] ciflow/inductor/146172 -> ciflow/inductor/146172 2025-03-04T20:11:04.1567046Z * [new tag] ciflow/inductor/146176 -> ciflow/inductor/146176 2025-03-04T20:11:04.1567241Z * [new tag] ciflow/inductor/146180 -> ciflow/inductor/146180 2025-03-04T20:11:04.1567374Z * [new tag] ciflow/inductor/146218 -> ciflow/inductor/146218 2025-03-04T20:11:04.1568452Z * [new tag] ciflow/inductor/146228 -> ciflow/inductor/146228 2025-03-04T20:11:04.1568682Z * [new tag] ciflow/inductor/146264 -> ciflow/inductor/146264 2025-03-04T20:11:04.1568820Z * [new tag] ciflow/inductor/146267 -> ciflow/inductor/146267 2025-03-04T20:11:04.1570518Z * [new tag] ciflow/inductor/146275 -> ciflow/inductor/146275 2025-03-04T20:11:04.1570919Z * [new tag] ciflow/inductor/146280 -> ciflow/inductor/146280 2025-03-04T20:11:04.1571228Z * [new tag] ciflow/inductor/146288 -> ciflow/inductor/146288 2025-03-04T20:11:04.1571692Z * [new tag] ciflow/inductor/146319 -> ciflow/inductor/146319 2025-03-04T20:11:04.1572092Z * [new tag] ciflow/inductor/146335 -> ciflow/inductor/146335 2025-03-04T20:11:04.1572398Z * [new tag] ciflow/inductor/146341 -> ciflow/inductor/146341 2025-03-04T20:11:04.1573064Z * [new tag] ciflow/inductor/146393 -> ciflow/inductor/146393 2025-03-04T20:11:04.1573198Z * [new tag] ciflow/inductor/146395 -> ciflow/inductor/146395 2025-03-04T20:11:04.1573561Z * [new tag] ciflow/inductor/146415 -> ciflow/inductor/146415 2025-03-04T20:11:04.1573927Z * [new tag] ciflow/inductor/146421 -> ciflow/inductor/146421 2025-03-04T20:11:04.1574052Z * [new tag] ciflow/inductor/146436 -> ciflow/inductor/146436 2025-03-04T20:11:04.1574454Z * [new tag] ciflow/inductor/146455 -> ciflow/inductor/146455 2025-03-04T20:11:04.1574824Z * [new tag] ciflow/inductor/146499 -> ciflow/inductor/146499 2025-03-04T20:11:04.1575001Z * [new tag] ciflow/inductor/146500 -> ciflow/inductor/146500 2025-03-04T20:11:04.1575466Z * [new tag] ciflow/inductor/146501 -> ciflow/inductor/146501 2025-03-04T20:11:04.1576073Z * [new tag] ciflow/inductor/146502 -> ciflow/inductor/146502 2025-03-04T20:11:04.1576205Z * [new tag] ciflow/inductor/146504 -> ciflow/inductor/146504 2025-03-04T20:11:04.1576566Z * [new tag] ciflow/inductor/146505 -> ciflow/inductor/146505 2025-03-04T20:11:04.1577031Z * [new tag] ciflow/inductor/146506 -> ciflow/inductor/146506 2025-03-04T20:11:04.1577508Z * [new tag] ciflow/inductor/146526 -> ciflow/inductor/146526 2025-03-04T20:11:04.1578181Z * [new tag] ciflow/inductor/146530 -> ciflow/inductor/146530 2025-03-04T20:11:04.1578320Z * [new tag] ciflow/inductor/146535 -> ciflow/inductor/146535 2025-03-04T20:11:04.1578692Z * [new tag] ciflow/inductor/146558 -> ciflow/inductor/146558 2025-03-04T20:11:04.1579342Z * [new tag] ciflow/inductor/146561 -> ciflow/inductor/146561 2025-03-04T20:11:04.1579478Z * [new tag] ciflow/inductor/146562 -> ciflow/inductor/146562 2025-03-04T20:11:04.1580090Z * [new tag] ciflow/inductor/146636 -> ciflow/inductor/146636 2025-03-04T20:11:04.1580566Z * [new tag] ciflow/inductor/146661 -> ciflow/inductor/146661 2025-03-04T20:11:04.1580969Z * [new tag] ciflow/inductor/146678 -> ciflow/inductor/146678 2025-03-04T20:11:04.1581617Z * [new tag] ciflow/inductor/146706 -> ciflow/inductor/146706 2025-03-04T20:11:04.1581737Z * [new tag] ciflow/inductor/146718 -> ciflow/inductor/146718 2025-03-04T20:11:04.1581856Z * [new tag] ciflow/inductor/146779 -> ciflow/inductor/146779 2025-03-04T20:11:04.1583209Z * [new tag] ciflow/inductor/146781 -> ciflow/inductor/146781 2025-03-04T20:11:04.1583593Z * [new tag] ciflow/inductor/146823 -> ciflow/inductor/146823 2025-03-04T20:11:04.1583716Z * [new tag] ciflow/inductor/146826 -> ciflow/inductor/146826 2025-03-04T20:11:04.1584238Z * [new tag] ciflow/inductor/146827 -> ciflow/inductor/146827 2025-03-04T20:11:04.1585484Z * [new tag] ciflow/inductor/146844 -> ciflow/inductor/146844 2025-03-04T20:11:04.1585633Z * [new tag] ciflow/inductor/146845 -> ciflow/inductor/146845 2025-03-04T20:11:04.1586622Z * [new tag] ciflow/inductor/146850 -> ciflow/inductor/146850 2025-03-04T20:11:04.1587872Z * [new tag] ciflow/inductor/146864 -> ciflow/inductor/146864 2025-03-04T20:11:04.1588094Z * [new tag] ciflow/inductor/146870 -> ciflow/inductor/146870 2025-03-04T20:11:04.1588256Z * [new tag] ciflow/inductor/146873 -> ciflow/inductor/146873 2025-03-04T20:11:04.1588458Z * [new tag] ciflow/inductor/146874 -> ciflow/inductor/146874 2025-03-04T20:11:04.1588611Z * [new tag] ciflow/inductor/146894 -> ciflow/inductor/146894 2025-03-04T20:11:04.1588740Z * [new tag] ciflow/inductor/146895 -> ciflow/inductor/146895 2025-03-04T20:11:04.1588869Z * [new tag] ciflow/inductor/146919 -> ciflow/inductor/146919 2025-03-04T20:11:04.1589995Z * [new tag] ciflow/inductor/146921 -> ciflow/inductor/146921 2025-03-04T20:11:04.1590143Z * [new tag] ciflow/inductor/146928 -> ciflow/inductor/146928 2025-03-04T20:11:04.1590262Z * [new tag] ciflow/inductor/146935 -> ciflow/inductor/146935 2025-03-04T20:11:04.1590472Z * [new tag] ciflow/inductor/146942 -> ciflow/inductor/146942 2025-03-04T20:11:04.1590675Z * [new tag] ciflow/inductor/146962 -> ciflow/inductor/146962 2025-03-04T20:11:04.1591974Z * [new tag] ciflow/inductor/146983 -> ciflow/inductor/146983 2025-03-04T20:11:04.1592115Z * [new tag] ciflow/inductor/146989 -> ciflow/inductor/146989 2025-03-04T20:11:04.1592581Z * [new tag] ciflow/inductor/147007 -> ciflow/inductor/147007 2025-03-04T20:11:04.1592977Z * [new tag] ciflow/inductor/147014 -> ciflow/inductor/147014 2025-03-04T20:11:04.1593361Z * [new tag] ciflow/inductor/147021 -> ciflow/inductor/147021 2025-03-04T20:11:04.1593735Z * [new tag] ciflow/inductor/147036 -> ciflow/inductor/147036 2025-03-04T20:11:04.1594127Z * [new tag] ciflow/inductor/147049 -> ciflow/inductor/147049 2025-03-04T20:11:04.1594515Z * [new tag] ciflow/inductor/147105 -> ciflow/inductor/147105 2025-03-04T20:11:04.1594864Z * [new tag] ciflow/inductor/147146 -> ciflow/inductor/147146 2025-03-04T20:11:04.1595252Z * [new tag] ciflow/inductor/147149 -> ciflow/inductor/147149 2025-03-04T20:11:04.1595391Z * [new tag] ciflow/inductor/147155 -> ciflow/inductor/147155 2025-03-04T20:11:04.1595793Z * [new tag] ciflow/inductor/147178 -> ciflow/inductor/147178 2025-03-04T20:11:04.1596440Z * [new tag] ciflow/inductor/147205 -> ciflow/inductor/147205 2025-03-04T20:11:04.1596585Z * [new tag] ciflow/inductor/147225 -> ciflow/inductor/147225 2025-03-04T20:11:04.1597186Z * [new tag] ciflow/inductor/147229 -> ciflow/inductor/147229 2025-03-04T20:11:04.1597813Z * [new tag] ciflow/inductor/147269 -> ciflow/inductor/147269 2025-03-04T20:11:04.1598297Z * [new tag] ciflow/inductor/147272 -> ciflow/inductor/147272 2025-03-04T20:11:04.1598894Z * [new tag] ciflow/inductor/147314 -> ciflow/inductor/147314 2025-03-04T20:11:04.1599036Z * [new tag] ciflow/inductor/147315 -> ciflow/inductor/147315 2025-03-04T20:11:04.1599773Z * [new tag] ciflow/inductor/147320 -> ciflow/inductor/147320 2025-03-04T20:11:04.1599910Z * [new tag] ciflow/inductor/147341 -> ciflow/inductor/147341 2025-03-04T20:11:04.1600373Z * [new tag] ciflow/inductor/147360 -> ciflow/inductor/147360 2025-03-04T20:11:04.1600511Z * [new tag] ciflow/inductor/147368 -> ciflow/inductor/147368 2025-03-04T20:11:04.1601519Z * [new tag] ciflow/inductor/147403 -> ciflow/inductor/147403 2025-03-04T20:11:04.1601644Z * [new tag] ciflow/inductor/147410 -> ciflow/inductor/147410 2025-03-04T20:11:04.1601761Z * [new tag] ciflow/inductor/147414 -> ciflow/inductor/147414 2025-03-04T20:11:04.1602680Z * [new tag] ciflow/inductor/147415 -> ciflow/inductor/147415 2025-03-04T20:11:04.1602811Z * [new tag] ciflow/inductor/147422 -> ciflow/inductor/147422 2025-03-04T20:11:04.1603509Z * [new tag] ciflow/inductor/147445 -> ciflow/inductor/147445 2025-03-04T20:11:04.1603640Z * [new tag] ciflow/inductor/147452 -> ciflow/inductor/147452 2025-03-04T20:11:04.1604106Z * [new tag] ciflow/inductor/147481 -> ciflow/inductor/147481 2025-03-04T20:11:04.1604479Z * [new tag] ciflow/inductor/147485 -> ciflow/inductor/147485 2025-03-04T20:11:04.1605150Z * [new tag] ciflow/inductor/147498 -> ciflow/inductor/147498 2025-03-04T20:11:04.1605281Z * [new tag] ciflow/inductor/147514 -> ciflow/inductor/147514 2025-03-04T20:11:04.1606321Z * [new tag] ciflow/inductor/147528 -> ciflow/inductor/147528 2025-03-04T20:11:04.1606458Z * [new tag] ciflow/inductor/147552 -> ciflow/inductor/147552 2025-03-04T20:11:04.1607503Z * [new tag] ciflow/inductor/147557 -> ciflow/inductor/147557 2025-03-04T20:11:04.1607635Z * [new tag] ciflow/inductor/147561 -> ciflow/inductor/147561 2025-03-04T20:11:04.1607751Z * [new tag] ciflow/inductor/147562 -> ciflow/inductor/147562 2025-03-04T20:11:04.1607874Z * [new tag] ciflow/inductor/147574 -> ciflow/inductor/147574 2025-03-04T20:11:04.1608564Z * [new tag] ciflow/inductor/147583 -> ciflow/inductor/147583 2025-03-04T20:11:04.1608703Z * [new tag] ciflow/inductor/147592 -> ciflow/inductor/147592 2025-03-04T20:11:04.1608823Z * [new tag] ciflow/inductor/147603 -> ciflow/inductor/147603 2025-03-04T20:11:04.1610026Z * [new tag] ciflow/inductor/147619 -> ciflow/inductor/147619 2025-03-04T20:11:04.1610417Z * [new tag] ciflow/inductor/147648 -> ciflow/inductor/147648 2025-03-04T20:11:04.1611094Z * [new tag] ciflow/inductor/147660 -> ciflow/inductor/147660 2025-03-04T20:11:04.1611221Z * [new tag] ciflow/inductor/147727 -> ciflow/inductor/147727 2025-03-04T20:11:04.1612017Z * [new tag] ciflow/inductor/147741 -> ciflow/inductor/147741 2025-03-04T20:11:04.1612378Z * [new tag] ciflow/inductor/147745 -> ciflow/inductor/147745 2025-03-04T20:11:04.1612525Z * [new tag] ciflow/inductor/147768 -> ciflow/inductor/147768 2025-03-04T20:11:04.1612917Z * [new tag] ciflow/inductor/147790 -> ciflow/inductor/147790 2025-03-04T20:11:04.1613299Z * [new tag] ciflow/inductor/147797 -> ciflow/inductor/147797 2025-03-04T20:11:04.1613673Z * [new tag] ciflow/inductor/147798 -> ciflow/inductor/147798 2025-03-04T20:11:04.1613804Z * [new tag] ciflow/inductor/147800 -> ciflow/inductor/147800 2025-03-04T20:11:04.1614414Z * [new tag] ciflow/inductor/147817 -> ciflow/inductor/147817 2025-03-04T20:11:04.1614549Z * [new tag] ciflow/inductor/147821 -> ciflow/inductor/147821 2025-03-04T20:11:04.1614676Z * [new tag] ciflow/inductor/147836 -> ciflow/inductor/147836 2025-03-04T20:11:04.1615463Z * [new tag] ciflow/inductor/147863 -> ciflow/inductor/147863 2025-03-04T20:11:04.1615985Z * [new tag] ciflow/inductor/147870 -> ciflow/inductor/147870 2025-03-04T20:11:04.1616665Z * [new tag] ciflow/inductor/147881 -> ciflow/inductor/147881 2025-03-04T20:11:04.1616803Z * [new tag] ciflow/inductor/147899 -> ciflow/inductor/147899 2025-03-04T20:11:04.1617207Z * [new tag] ciflow/inductor/147902 -> ciflow/inductor/147902 2025-03-04T20:11:04.1617342Z * [new tag] ciflow/inductor/147903 -> ciflow/inductor/147903 2025-03-04T20:11:04.1619042Z * [new tag] ciflow/inductor/147908 -> ciflow/inductor/147908 2025-03-04T20:11:04.1619353Z * [new tag] ciflow/inductor/147910 -> ciflow/inductor/147910 2025-03-04T20:11:04.1619478Z * [new tag] ciflow/inductor/147915 -> ciflow/inductor/147915 2025-03-04T20:11:04.1619888Z * [new tag] ciflow/inductor/147917 -> ciflow/inductor/147917 2025-03-04T20:11:04.1620010Z * [new tag] ciflow/inductor/147927 -> ciflow/inductor/147927 2025-03-04T20:11:04.1621633Z * [new tag] ciflow/inductor/147945 -> ciflow/inductor/147945 2025-03-04T20:11:04.1621759Z * [new tag] ciflow/inductor/147955 -> ciflow/inductor/147955 2025-03-04T20:11:04.1622131Z * [new tag] ciflow/inductor/147956 -> ciflow/inductor/147956 2025-03-04T20:11:04.1622269Z * [new tag] ciflow/inductor/147957 -> ciflow/inductor/147957 2025-03-04T20:11:04.1622889Z * [new tag] ciflow/inductor/147958 -> ciflow/inductor/147958 2025-03-04T20:11:04.1623063Z * [new tag] ciflow/inductor/147959 -> ciflow/inductor/147959 2025-03-04T20:11:04.1623758Z * [new tag] ciflow/inductor/147960 -> ciflow/inductor/147960 2025-03-04T20:11:04.1623902Z * [new tag] ciflow/inductor/147962 -> ciflow/inductor/147962 2025-03-04T20:11:04.1624023Z * [new tag] ciflow/inductor/147990 -> ciflow/inductor/147990 2025-03-04T20:11:04.1624453Z * [new tag] ciflow/inductor/148002 -> ciflow/inductor/148002 2025-03-04T20:11:04.1625293Z * [new tag] ciflow/inductor/148007 -> ciflow/inductor/148007 2025-03-04T20:11:04.1625419Z * [new tag] ciflow/inductor/148008 -> ciflow/inductor/148008 2025-03-04T20:11:04.1625827Z * [new tag] ciflow/inductor/148010 -> ciflow/inductor/148010 2025-03-04T20:11:04.1626666Z * [new tag] ciflow/inductor/148042 -> ciflow/inductor/148042 2025-03-04T20:11:04.1626791Z * [new tag] ciflow/inductor/148046 -> ciflow/inductor/148046 2025-03-04T20:11:04.1627222Z * [new tag] ciflow/inductor/148063 -> ciflow/inductor/148063 2025-03-04T20:11:04.1627567Z * [new tag] ciflow/inductor/148083 -> ciflow/inductor/148083 2025-03-04T20:11:04.1627694Z * [new tag] ciflow/inductor/148091 -> ciflow/inductor/148091 2025-03-04T20:11:04.1628948Z * [new tag] ciflow/inductor/148092 -> ciflow/inductor/148092 2025-03-04T20:11:04.1629078Z * [new tag] ciflow/inductor/148104 -> ciflow/inductor/148104 2025-03-04T20:11:04.1629479Z * [new tag] ciflow/inductor/148130 -> ciflow/inductor/148130 2025-03-04T20:11:04.1630505Z * [new tag] ciflow/inductor/148131 -> ciflow/inductor/148131 2025-03-04T20:11:04.1630633Z * [new tag] ciflow/inductor/148132 -> ciflow/inductor/148132 2025-03-04T20:11:04.1631018Z * [new tag] ciflow/inductor/148138 -> ciflow/inductor/148138 2025-03-04T20:11:04.1631484Z * [new tag] ciflow/inductor/148139 -> ciflow/inductor/148139 2025-03-04T20:11:04.1632080Z * [new tag] ciflow/inductor/148160 -> ciflow/inductor/148160 2025-03-04T20:11:04.1632501Z * [new tag] ciflow/inductor/148163 -> ciflow/inductor/148163 2025-03-04T20:11:04.1633056Z * [new tag] ciflow/inductor/148173 -> ciflow/inductor/148173 2025-03-04T20:11:04.1633426Z * [new tag] ciflow/inductor/148174 -> ciflow/inductor/148174 2025-03-04T20:11:04.1633572Z * [new tag] ciflow/inductor/148176 -> ciflow/inductor/148176 2025-03-04T20:11:04.1633740Z * [new tag] ciflow/inductor/148186 -> ciflow/inductor/148186 2025-03-04T20:11:04.1635316Z * [new tag] ciflow/inductor/148190 -> ciflow/inductor/148190 2025-03-04T20:11:04.1635473Z * [new tag] ciflow/inductor/148202 -> ciflow/inductor/148202 2025-03-04T20:11:04.1635827Z * [new tag] ciflow/inductor/148205 -> ciflow/inductor/148205 2025-03-04T20:11:04.1636905Z * [new tag] ciflow/inductor/148206 -> ciflow/inductor/148206 2025-03-04T20:11:04.1637174Z * [new tag] ciflow/inductor/148209 -> ciflow/inductor/148209 2025-03-04T20:11:04.1637340Z * [new tag] ciflow/inductor/148210 -> ciflow/inductor/148210 2025-03-04T20:11:04.1637571Z * [new tag] ciflow/inductor/148212 -> ciflow/inductor/148212 2025-03-04T20:11:04.1637910Z * [new tag] ciflow/inductor/148220 -> ciflow/inductor/148220 2025-03-04T20:11:04.1638613Z * [new tag] ciflow/inductor/148223 -> ciflow/inductor/148223 2025-03-04T20:11:04.1639713Z * [new tag] ciflow/inductor/148231 -> ciflow/inductor/148231 2025-03-04T20:11:04.1640030Z * [new tag] ciflow/inductor/148233 -> ciflow/inductor/148233 2025-03-04T20:11:04.1640227Z * [new tag] ciflow/inductor/148234 -> ciflow/inductor/148234 2025-03-04T20:11:04.1640581Z * [new tag] ciflow/inductor/148235 -> ciflow/inductor/148235 2025-03-04T20:11:04.1641129Z * [new tag] ciflow/inductor/148236 -> ciflow/inductor/148236 2025-03-04T20:11:04.1641491Z * [new tag] ciflow/inductor/148243 -> ciflow/inductor/148243 2025-03-04T20:11:04.1642005Z * [new tag] ciflow/inductor/148260 -> ciflow/inductor/148260 2025-03-04T20:11:04.1642405Z * [new tag] ciflow/inductor/148261 -> ciflow/inductor/148261 2025-03-04T20:11:04.1643699Z * [new tag] ciflow/inductor/148279 -> ciflow/inductor/148279 2025-03-04T20:11:04.1643838Z * [new tag] ciflow/inductor/148288 -> ciflow/inductor/148288 2025-03-04T20:11:04.1644129Z * [new tag] ciflow/inductor/148290 -> ciflow/inductor/148290 2025-03-04T20:11:04.1644530Z * [new tag] ciflow/inductor/148292 -> ciflow/inductor/148292 2025-03-04T20:11:04.1648000Z * [new tag] ciflow/inductor/148294 -> ciflow/inductor/148294 2025-03-04T20:11:04.1648181Z * [new tag] ciflow/inductor/148303 -> ciflow/inductor/148303 2025-03-04T20:11:04.1648306Z * [new tag] ciflow/inductor/148305 -> ciflow/inductor/148305 2025-03-04T20:11:04.1648469Z * [new tag] ciflow/inductor/148323 -> ciflow/inductor/148323 2025-03-04T20:11:04.1648587Z * [new tag] ciflow/inductor/148328 -> ciflow/inductor/148328 2025-03-04T20:11:04.1648712Z * [new tag] ciflow/inductor/148357 -> ciflow/inductor/148357 2025-03-04T20:11:04.1648837Z * [new tag] ciflow/inductor/148358 -> ciflow/inductor/148358 2025-03-04T20:11:04.1648967Z * [new tag] ciflow/inductor/148359 -> ciflow/inductor/148359 2025-03-04T20:11:04.1652168Z * [new tag] ciflow/inductor/148363 -> ciflow/inductor/148363 2025-03-04T20:11:04.1652319Z * [new tag] ciflow/inductor/148364 -> ciflow/inductor/148364 2025-03-04T20:11:04.1653879Z * [new tag] ciflow/inductor/148366 -> ciflow/inductor/148366 2025-03-04T20:11:04.1654187Z * [new tag] ciflow/inductor/148367 -> ciflow/inductor/148367 2025-03-04T20:11:04.1654336Z * [new tag] ciflow/inductor/148376 -> ciflow/inductor/148376 2025-03-04T20:11:04.1656539Z * [new tag] ciflow/inductor/148377 -> ciflow/inductor/148377 2025-03-04T20:11:04.1656843Z * [new tag] ciflow/inductor/148380 -> ciflow/inductor/148380 2025-03-04T20:11:04.1656995Z * [new tag] ciflow/inductor/148381 -> ciflow/inductor/148381 2025-03-04T20:11:04.1657195Z * [new tag] ciflow/inductor/148385 -> ciflow/inductor/148385 2025-03-04T20:11:04.1657385Z * [new tag] ciflow/inductor/148386 -> ciflow/inductor/148386 2025-03-04T20:11:04.1657528Z * [new tag] ciflow/inductor/148401 -> ciflow/inductor/148401 2025-03-04T20:11:04.1657662Z * [new tag] ciflow/inductor/148407 -> ciflow/inductor/148407 2025-03-04T20:11:04.1657914Z * [new tag] ciflow/inductor/148413 -> ciflow/inductor/148413 2025-03-04T20:11:04.1658063Z * [new tag] ciflow/inductor/148414 -> ciflow/inductor/148414 2025-03-04T20:11:04.1658191Z * [new tag] ciflow/inductor/148415 -> ciflow/inductor/148415 2025-03-04T20:11:04.1658310Z * [new tag] ciflow/inductor/148418 -> ciflow/inductor/148418 2025-03-04T20:11:04.1658518Z * [new tag] ciflow/inductor/148423 -> ciflow/inductor/148423 2025-03-04T20:11:04.1658635Z * [new tag] ciflow/inductor/148424 -> ciflow/inductor/148424 2025-03-04T20:11:04.1658911Z * [new tag] ciflow/inductor/148430 -> ciflow/inductor/148430 2025-03-04T20:11:04.1659030Z * [new tag] ciflow/inductor/148432 -> ciflow/inductor/148432 2025-03-04T20:11:04.1659155Z * [new tag] ciflow/inductor/148445 -> ciflow/inductor/148445 2025-03-04T20:11:04.1659278Z * [new tag] ciflow/inductor/148450 -> ciflow/inductor/148450 2025-03-04T20:11:04.1659543Z * [new tag] ciflow/inductor/148454 -> ciflow/inductor/148454 2025-03-04T20:11:04.1659678Z * [new tag] ciflow/inductor/148459 -> ciflow/inductor/148459 2025-03-04T20:11:04.1660063Z * [new tag] ciflow/inductor/148470 -> ciflow/inductor/148470 2025-03-04T20:11:04.1662348Z * [new tag] ciflow/inductor/148473 -> ciflow/inductor/148473 2025-03-04T20:11:04.1662649Z * [new tag] ciflow/inductor/3b9a386 -> ciflow/inductor/3b9a386 2025-03-04T20:11:04.1662845Z * [new tag] ciflow/inductor/3d4b92b -> ciflow/inductor/3d4b92b 2025-03-04T20:11:04.1663002Z * [new tag] ciflow/inductor/88106 -> ciflow/inductor/88106 2025-03-04T20:11:04.1663146Z * [new tag] ciflow/inductor/88196 -> ciflow/inductor/88196 2025-03-04T20:11:04.1663482Z * [new tag] ciflow/inductor/88998 -> ciflow/inductor/88998 2025-03-04T20:11:04.1664447Z * [new tag] ciflow/inductor/d224ac7 -> ciflow/inductor/d224ac7 2025-03-04T20:11:04.1664692Z * [new tag] ciflow/linux-aarch64/125888 -> ciflow/linux-aarch64/125888 2025-03-04T20:11:04.1665019Z * [new tag] ciflow/linux-aarch64/126050 -> ciflow/linux-aarch64/126050 2025-03-04T20:11:04.1665366Z * [new tag] ciflow/linux-aarch64/126054 -> ciflow/linux-aarch64/126054 2025-03-04T20:11:04.1665741Z * [new tag] ciflow/linux-aarch64/133297 -> ciflow/linux-aarch64/133297 2025-03-04T20:11:04.1666078Z * [new tag] ciflow/linux-aarch64/133315 -> ciflow/linux-aarch64/133315 2025-03-04T20:11:04.1666881Z * [new tag] ciflow/linux-aarch64/133392 -> ciflow/linux-aarch64/133392 2025-03-04T20:11:04.1667579Z * [new tag] ciflow/linux-aarch64/133419 -> ciflow/linux-aarch64/133419 2025-03-04T20:11:04.1667907Z * [new tag] ciflow/linux-aarch64/133423 -> ciflow/linux-aarch64/133423 2025-03-04T20:11:04.1669148Z * [new tag] ciflow/linux-aarch64/133667 -> ciflow/linux-aarch64/133667 2025-03-04T20:11:04.1669681Z * [new tag] ciflow/linux-aarch64/133753 -> ciflow/linux-aarch64/133753 2025-03-04T20:11:04.1670036Z * [new tag] ciflow/linux-aarch64/135058 -> ciflow/linux-aarch64/135058 2025-03-04T20:11:04.1670455Z * [new tag] ciflow/linux-aarch64/135333 -> ciflow/linux-aarch64/135333 2025-03-04T20:11:04.1670873Z * [new tag] ciflow/linux-aarch64/135792 -> ciflow/linux-aarch64/135792 2025-03-04T20:11:04.1671228Z * [new tag] ciflow/linux-aarch64/136355 -> ciflow/linux-aarch64/136355 2025-03-04T20:11:04.1671581Z * [new tag] ciflow/linux-aarch64/137568 -> ciflow/linux-aarch64/137568 2025-03-04T20:11:04.1671751Z * [new tag] ciflow/linux-aarch64/138388 -> ciflow/linux-aarch64/138388 2025-03-04T20:11:04.1672785Z * [new tag] ciflow/linux-aarch64/138889 -> ciflow/linux-aarch64/138889 2025-03-04T20:11:04.1673129Z * [new tag] ciflow/linux-aarch64/140159 -> ciflow/linux-aarch64/140159 2025-03-04T20:11:04.1673956Z * [new tag] ciflow/linux-aarch64/143741 -> ciflow/linux-aarch64/143741 2025-03-04T20:11:04.1674310Z * [new tag] ciflow/linux-aarch64/145942 -> ciflow/linux-aarch64/145942 2025-03-04T20:11:04.1674441Z * [new tag] ciflow/linux-aarch64/146823 -> ciflow/linux-aarch64/146823 2025-03-04T20:11:04.1675209Z * [new tag] ciflow/linux-aarch64/146826 -> ciflow/linux-aarch64/146826 2025-03-04T20:11:04.1675624Z * [new tag] ciflow/linux-aarch64/146895 -> ciflow/linux-aarch64/146895 2025-03-04T20:11:04.1675954Z * [new tag] ciflow/linux-aarch64/147073 -> ciflow/linux-aarch64/147073 2025-03-04T20:11:04.1676105Z * [new tag] ciflow/linux-aarch64/147337 -> ciflow/linux-aarch64/147337 2025-03-04T20:11:04.1676623Z * [new tag] ciflow/linux-aarch64/147341 -> ciflow/linux-aarch64/147341 2025-03-04T20:11:04.1676756Z * [new tag] ciflow/linux-aarch64/147359 -> ciflow/linux-aarch64/147359 2025-03-04T20:11:04.1677105Z * [new tag] ciflow/linux-aarch64/147498 -> ciflow/linux-aarch64/147498 2025-03-04T20:11:04.1677251Z * [new tag] ciflow/linux-aarch64/147763 -> ciflow/linux-aarch64/147763 2025-03-04T20:11:04.1678036Z * [new tag] ciflow/linux-aarch64/147817 -> ciflow/linux-aarch64/147817 2025-03-04T20:11:04.1678379Z * [new tag] ciflow/linux-aarch64/147855 -> ciflow/linux-aarch64/147855 2025-03-04T20:11:04.1679088Z * [new tag] ciflow/linux-aarch64/147917 -> ciflow/linux-aarch64/147917 2025-03-04T20:11:04.1679441Z * [new tag] ciflow/linux-aarch64/147945 -> ciflow/linux-aarch64/147945 2025-03-04T20:11:04.1679590Z * [new tag] ciflow/linux-aarch64/147955 -> ciflow/linux-aarch64/147955 2025-03-04T20:11:04.1679915Z * [new tag] ciflow/linux-aarch64/147956 -> ciflow/linux-aarch64/147956 2025-03-04T20:11:04.1680620Z * [new tag] ciflow/linux-aarch64/147957 -> ciflow/linux-aarch64/147957 2025-03-04T20:11:04.1680765Z * [new tag] ciflow/linux-aarch64/147958 -> ciflow/linux-aarch64/147958 2025-03-04T20:11:04.1681148Z * [new tag] ciflow/linux-aarch64/147959 -> ciflow/linux-aarch64/147959 2025-03-04T20:11:04.1681291Z * [new tag] ciflow/linux-aarch64/147964 -> ciflow/linux-aarch64/147964 2025-03-04T20:11:04.1681424Z * [new tag] ciflow/linux-aarch64/148076 -> ciflow/linux-aarch64/148076 2025-03-04T20:11:04.1681949Z * [new tag] ciflow/linux-aarch64/148163 -> ciflow/linux-aarch64/148163 2025-03-04T20:11:04.1682288Z * [new tag] ciflow/linux-aarch64/148173 -> ciflow/linux-aarch64/148173 2025-03-04T20:11:04.1682484Z * [new tag] ciflow/linux-aarch64/148403 -> ciflow/linux-aarch64/148403 2025-03-04T20:11:04.1683856Z * [new tag] ciflow/mps/102148 -> ciflow/mps/102148 2025-03-04T20:11:04.1684245Z * [new tag] ciflow/mps/119496 -> ciflow/mps/119496 2025-03-04T20:11:04.1684367Z * [new tag] ciflow/mps/120076 -> ciflow/mps/120076 2025-03-04T20:11:04.1684752Z * [new tag] ciflow/mps/133423 -> ciflow/mps/133423 2025-03-04T20:11:04.1684883Z * [new tag] ciflow/mps/133667 -> ciflow/mps/133667 2025-03-04T20:11:04.1685419Z * [new tag] ciflow/mps/138640 -> ciflow/mps/138640 2025-03-04T20:11:04.1685544Z * [new tag] ciflow/mps/139469 -> ciflow/mps/139469 2025-03-04T20:11:04.1686250Z * [new tag] ciflow/mps/140159 -> ciflow/mps/140159 2025-03-04T20:11:04.1686691Z * [new tag] ciflow/mps/140211 -> ciflow/mps/140211 2025-03-04T20:11:04.1687863Z * [new tag] ciflow/mps/140725 -> ciflow/mps/140725 2025-03-04T20:11:04.1688257Z * [new tag] ciflow/mps/142097 -> ciflow/mps/142097 2025-03-04T20:11:04.1688366Z * [new tag] ciflow/mps/142202 -> ciflow/mps/142202 2025-03-04T20:11:04.1689250Z * [new tag] ciflow/mps/142477 -> ciflow/mps/142477 2025-03-04T20:11:04.1950749Z * [new tag] ciflow/mps/143630 -> ciflow/mps/143630 2025-03-04T20:11:04.1952713Z * [new tag] ciflow/mps/143666 -> ciflow/mps/143666 2025-03-04T20:11:04.1953118Z * [new tag] ciflow/mps/143911 -> ciflow/mps/143911 2025-03-04T20:11:04.1953240Z * [new tag] ciflow/mps/143966 -> ciflow/mps/143966 2025-03-04T20:11:04.1953350Z * [new tag] ciflow/mps/144405 -> ciflow/mps/144405 2025-03-04T20:11:04.1953487Z * [new tag] ciflow/mps/144664 -> ciflow/mps/144664 2025-03-04T20:11:04.1953594Z * [new tag] ciflow/mps/145955 -> ciflow/mps/145955 2025-03-04T20:11:04.1953712Z * [new tag] ciflow/mps/146098 -> ciflow/mps/146098 2025-03-04T20:11:04.1953822Z * [new tag] ciflow/mps/146436 -> ciflow/mps/146436 2025-03-04T20:11:04.1953938Z * [new tag] ciflow/mps/146754 -> ciflow/mps/146754 2025-03-04T20:11:04.1954046Z * [new tag] ciflow/mps/146989 -> ciflow/mps/146989 2025-03-04T20:11:04.1954165Z * [new tag] ciflow/mps/147205 -> ciflow/mps/147205 2025-03-04T20:11:04.1954276Z * [new tag] ciflow/mps/147583 -> ciflow/mps/147583 2025-03-04T20:11:04.1954462Z * [new tag] ciflow/mps/147644 -> ciflow/mps/147644 2025-03-04T20:11:04.1954816Z * [new tag] ciflow/mps/147893 -> ciflow/mps/147893 2025-03-04T20:11:04.1955225Z * [new tag] ciflow/mps/148305 -> ciflow/mps/148305 2025-03-04T20:11:04.1957328Z * [new tag] ciflow/mps/148350 -> ciflow/mps/148350 2025-03-04T20:11:04.1957609Z * [new tag] ciflow/mps/148415 -> ciflow/mps/148415 2025-03-04T20:11:04.1957752Z * [new tag] ciflow/mps/148449 -> ciflow/mps/148449 2025-03-04T20:11:04.1957861Z * [new tag] ciflow/mps/148468 -> ciflow/mps/148468 2025-03-04T20:11:04.1957978Z * [new tag] ciflow/mps/148471 -> ciflow/mps/148471 2025-03-04T20:11:04.1958230Z * [new tag] ciflow/op-benchmark/143733 -> ciflow/op-benchmark/143733 2025-03-04T20:11:04.1959242Z * [new tag] ciflow/periodic/054a2fd -> ciflow/periodic/054a2fd 2025-03-04T20:11:04.1959446Z * [new tag] ciflow/periodic/123020 -> ciflow/periodic/123020 2025-03-04T20:11:04.1960090Z * [new tag] ciflow/periodic/134817 -> ciflow/periodic/134817 2025-03-04T20:11:04.1960233Z * [new tag] ciflow/periodic/140989 -> ciflow/periodic/140989 2025-03-04T20:11:04.1960532Z * [new tag] ciflow/periodic/141309 -> ciflow/periodic/141309 2025-03-04T20:11:04.1960960Z * [new tag] ciflow/periodic/141355 -> ciflow/periodic/141355 2025-03-04T20:11:04.1961345Z * [new tag] ciflow/periodic/141730 -> ciflow/periodic/141730 2025-03-04T20:11:04.1961738Z * [new tag] ciflow/periodic/142179 -> ciflow/periodic/142179 2025-03-04T20:11:04.1962943Z * [new tag] ciflow/periodic/143959 -> ciflow/periodic/143959 2025-03-04T20:11:04.1963086Z * [new tag] ciflow/periodic/144953 -> ciflow/periodic/144953 2025-03-04T20:11:04.1967905Z * [new tag] ciflow/periodic/146264 -> ciflow/periodic/146264 2025-03-04T20:11:04.1968091Z * [new tag] ciflow/periodic/146403 -> ciflow/periodic/146403 2025-03-04T20:11:04.1968256Z * [new tag] ciflow/periodic/146823 -> ciflow/periodic/146823 2025-03-04T20:11:04.1968379Z * [new tag] ciflow/periodic/146903 -> ciflow/periodic/146903 2025-03-04T20:11:04.1968509Z * [new tag] ciflow/periodic/147459 -> ciflow/periodic/147459 2025-03-04T20:11:04.1968630Z * [new tag] ciflow/periodic/147870 -> ciflow/periodic/147870 2025-03-04T20:11:04.1968760Z * [new tag] ciflow/periodic/148351 -> ciflow/periodic/148351 2025-03-04T20:11:04.1969021Z * [new tag] ciflow/periodic/2a6d37d -> ciflow/periodic/2a6d37d 2025-03-04T20:11:04.1969161Z * [new tag] ciflow/periodic/317eeb8 -> ciflow/periodic/317eeb8 2025-03-04T20:11:04.1969285Z * [new tag] ciflow/periodic/3c32 -> ciflow/periodic/3c32 2025-03-04T20:11:04.1969427Z * [new tag] ciflow/periodic/3e98831 -> ciflow/periodic/3e98831 2025-03-04T20:11:04.1969574Z * [new tag] ciflow/periodic/94512-point -> ciflow/periodic/94512-point 2025-03-04T20:11:04.1969945Z * [new tag] ciflow/periodic/csl/test87519 -> ciflow/periodic/csl/test87519 2025-03-04T20:11:04.1970120Z * [new tag] ciflow/periodic/csltest88275 -> ciflow/periodic/csltest88275 2025-03-04T20:11:04.1970598Z * [new tag] ciflow/periodic/csltest88761 -> ciflow/periodic/csltest88761 2025-03-04T20:11:04.1974598Z * [new tag] ciflow/periodic/release_1.12 -> ciflow/periodic/release_1.12 2025-03-04T20:11:04.1974820Z * [new tag] ciflow/periodic/release_1.12.0 -> ciflow/periodic/release_1.12.0 2025-03-04T20:11:04.1974970Z * [new tag] ciflow/periodic/sha-ec5b83 -> ciflow/periodic/sha-ec5b83 2025-03-04T20:11:04.1975106Z * [new tag] ciflow/riscv64/143979 -> ciflow/riscv64/143979 2025-03-04T20:11:04.1975234Z * [new tag] ciflow/rocm/124424 -> ciflow/rocm/124424 2025-03-04T20:11:04.1975352Z * [new tag] ciflow/rocm/134817 -> ciflow/rocm/134817 2025-03-04T20:11:04.1975457Z * [new tag] ciflow/rocm/137136 -> ciflow/rocm/137136 2025-03-04T20:11:04.1975576Z * [new tag] ciflow/rocm/139469 -> ciflow/rocm/139469 2025-03-04T20:11:04.1975686Z * [new tag] ciflow/rocm/139975 -> ciflow/rocm/139975 2025-03-04T20:11:04.1977145Z * [new tag] ciflow/rocm/140989 -> ciflow/rocm/140989 2025-03-04T20:11:04.1977278Z * [new tag] ciflow/rocm/141309 -> ciflow/rocm/141309 2025-03-04T20:11:04.1977548Z * [new tag] ciflow/rocm/141355 -> ciflow/rocm/141355 2025-03-04T20:11:04.1977677Z * [new tag] ciflow/rocm/142097 -> ciflow/rocm/142097 2025-03-04T20:11:04.1978811Z * [new tag] ciflow/rocm/142859 -> ciflow/rocm/142859 2025-03-04T20:11:04.1979066Z * [new tag] ciflow/rocm/143416 -> ciflow/rocm/143416 2025-03-04T20:11:04.1979192Z * [new tag] ciflow/rocm/143971 -> ciflow/rocm/143971 2025-03-04T20:11:04.1979513Z * [new tag] ciflow/rocm/144120 -> ciflow/rocm/144120 2025-03-04T20:11:04.1982252Z * [new tag] ciflow/rocm/144572 -> ciflow/rocm/144572 2025-03-04T20:11:04.1982396Z * [new tag] ciflow/rocm/144664 -> ciflow/rocm/144664 2025-03-04T20:11:04.1982515Z * [new tag] ciflow/rocm/145475 -> ciflow/rocm/145475 2025-03-04T20:11:04.1982643Z * [new tag] ciflow/rocm/145584 -> ciflow/rocm/145584 2025-03-04T20:11:04.1982791Z * [new tag] ciflow/rocm/145685 -> ciflow/rocm/145685 2025-03-04T20:11:04.1983022Z * [new tag] ciflow/rocm/145946 -> ciflow/rocm/145946 2025-03-04T20:11:04.1983252Z * [new tag] ciflow/rocm/146227 -> ciflow/rocm/146227 2025-03-04T20:11:04.1983366Z * [new tag] ciflow/rocm/146264 -> ciflow/rocm/146264 2025-03-04T20:11:04.1983647Z * [new tag] ciflow/rocm/146448 -> ciflow/rocm/146448 2025-03-04T20:11:04.1984041Z * [new tag] ciflow/rocm/146903 -> ciflow/rocm/146903 2025-03-04T20:11:04.1984677Z * [new tag] ciflow/rocm/147034 -> ciflow/rocm/147034 2025-03-04T20:11:04.1985077Z * [new tag] ciflow/rocm/147243 -> ciflow/rocm/147243 2025-03-04T20:11:04.2001378Z * [new tag] ciflow/rocm/147315 -> ciflow/rocm/147315 2025-03-04T20:11:04.2001704Z * [new tag] ciflow/rocm/147320 -> ciflow/rocm/147320 2025-03-04T20:11:04.2002527Z * [new tag] ciflow/rocm/147382 -> ciflow/rocm/147382 2025-03-04T20:11:04.2002837Z * [new tag] ciflow/rocm/147403 -> ciflow/rocm/147403 2025-03-04T20:11:04.2003101Z * [new tag] ciflow/rocm/147452 -> ciflow/rocm/147452 2025-03-04T20:11:04.2003238Z * [new tag] ciflow/rocm/147459 -> ciflow/rocm/147459 2025-03-04T20:11:04.2003360Z * [new tag] ciflow/rocm/147527 -> ciflow/rocm/147527 2025-03-04T20:11:04.2003467Z * [new tag] ciflow/rocm/147619 -> ciflow/rocm/147619 2025-03-04T20:11:04.2003585Z * [new tag] ciflow/rocm/147630 -> ciflow/rocm/147630 2025-03-04T20:11:04.2003698Z * [new tag] ciflow/rocm/147821 -> ciflow/rocm/147821 2025-03-04T20:11:04.2006166Z * [new tag] ciflow/rocm/147904 -> ciflow/rocm/147904 2025-03-04T20:11:04.2006383Z * [new tag] ciflow/rocm/147993 -> ciflow/rocm/147993 2025-03-04T20:11:04.2006972Z * [new tag] ciflow/rocm/148223 -> ciflow/rocm/148223 2025-03-04T20:11:04.2007499Z * [new tag] ciflow/rocm/148228 -> ciflow/rocm/148228 2025-03-04T20:11:04.2007956Z * [new tag] ciflow/rocm/148371 -> ciflow/rocm/148371 2025-03-04T20:11:04.2008840Z * [new tag] ciflow/rocm/148394 -> ciflow/rocm/148394 2025-03-04T20:11:04.2009668Z * [new tag] ciflow/rocm/148432 -> ciflow/rocm/148432 2025-03-04T20:11:04.2010471Z * [new tag] ciflow/rocm/148437 -> ciflow/rocm/148437 2025-03-04T20:11:04.2014336Z * [new tag] ciflow/s390/142346 -> ciflow/s390/142346 2025-03-04T20:11:04.2014538Z * [new tag] ciflow/s390/143959 -> ciflow/s390/143959 2025-03-04T20:11:04.2014887Z * [new tag] ciflow/s390/148452 -> ciflow/s390/148452 2025-03-04T20:11:04.2015408Z * [new tag] ciflow/slow/01c7106 -> ciflow/slow/01c7106 2025-03-04T20:11:04.2016349Z * [new tag] ciflow/slow/0577043 -> ciflow/slow/0577043 2025-03-04T20:11:04.2016833Z * [new tag] ciflow/slow/0d5b74da0cab798fbfdb9caa53fad816999c8386-sdym -> ciflow/slow/0d5b74da0cab798fbfdb9caa53fad816999c8386-sdym 2025-03-04T20:11:04.2017130Z * [new tag] ciflow/slow/0e81104 -> ciflow/slow/0e81104 2025-03-04T20:11:04.2017707Z * [new tag] ciflow/slow/139975 -> ciflow/slow/139975 2025-03-04T20:11:04.2018402Z * [new tag] ciflow/slow/146256 -> ciflow/slow/146256 2025-03-04T20:11:04.2018783Z * [new tag] ciflow/slow/146903 -> ciflow/slow/146903 2025-03-04T20:11:04.2019599Z * [new tag] ciflow/slow/1732077 -> ciflow/slow/1732077 2025-03-04T20:11:04.2020384Z * [new tag] ciflow/slow/187eb7c -> ciflow/slow/187eb7c 2025-03-04T20:11:04.2020846Z * [new tag] ciflow/slow/1faef89 -> ciflow/slow/1faef89 2025-03-04T20:11:04.2021994Z * [new tag] ciflow/slow/3920ec1 -> ciflow/slow/3920ec1 2025-03-04T20:11:04.2022342Z * [new tag] ciflow/slow/3b7c6b2 -> ciflow/slow/3b7c6b2 2025-03-04T20:11:04.2022924Z * [new tag] ciflow/slow/59a3759 -> ciflow/slow/59a3759 2025-03-04T20:11:04.2027191Z * [new tag] ciflow/slow/70ef0bb -> ciflow/slow/70ef0bb 2025-03-04T20:11:04.2027356Z * [new tag] ciflow/slow/788ff06 -> ciflow/slow/788ff06 2025-03-04T20:11:04.2027660Z * [new tag] ciflow/slow/8751002215790a3a88750faa8f4366933e296693-sdym -> ciflow/slow/8751002215790a3a88750faa8f4366933e296693-sdym 2025-03-04T20:11:04.2027987Z * [new tag] ciflow/slow/9d85864 -> ciflow/slow/9d85864 2025-03-04T20:11:04.2028168Z * [new tag] ciflow/slow/9ffad5b -> ciflow/slow/9ffad5b 2025-03-04T20:11:04.2028417Z * [new tag] ciflow/slow/a206e8b -> ciflow/slow/a206e8b 2025-03-04T20:11:04.2028899Z * [new tag] ciflow/slow/a837609 -> ciflow/slow/a837609 2025-03-04T20:11:04.2029027Z * [new tag] ciflow/slow/af841f3 -> ciflow/slow/af841f3 2025-03-04T20:11:04.2029417Z * [new tag] ciflow/slow/da3aba1e46157c4df504b067477cdf2b3c96b194-sdym -> ciflow/slow/da3aba1e46157c4df504b067477cdf2b3c96b194-sdym 2025-03-04T20:11:04.2029708Z * [new tag] ciflow/trunk/108303 -> ciflow/trunk/108303 2025-03-04T20:11:04.2029829Z * [new tag] ciflow/trunk/113257 -> ciflow/trunk/113257 2025-03-04T20:11:04.2029945Z * [new tag] ciflow/trunk/113258 -> ciflow/trunk/113258 2025-03-04T20:11:04.2030065Z * [new tag] ciflow/trunk/120076 -> ciflow/trunk/120076 2025-03-04T20:11:04.2030180Z * [new tag] ciflow/trunk/121445 -> ciflow/trunk/121445 2025-03-04T20:11:04.2030536Z * [new tag] ciflow/trunk/123020 -> ciflow/trunk/123020 2025-03-04T20:11:04.2030653Z * [new tag] ciflow/trunk/124424 -> ciflow/trunk/124424 2025-03-04T20:11:04.2030765Z * [new tag] ciflow/trunk/124490 -> ciflow/trunk/124490 2025-03-04T20:11:04.2030882Z * [new tag] ciflow/trunk/125469 -> ciflow/trunk/125469 2025-03-04T20:11:04.2030990Z * [new tag] ciflow/trunk/125806 -> ciflow/trunk/125806 2025-03-04T20:11:04.2031108Z * [new tag] ciflow/trunk/125888 -> ciflow/trunk/125888 2025-03-04T20:11:04.2031218Z * [new tag] ciflow/trunk/125995 -> ciflow/trunk/125995 2025-03-04T20:11:04.2031340Z * [new tag] ciflow/trunk/126050 -> ciflow/trunk/126050 2025-03-04T20:11:04.2031449Z * [new tag] ciflow/trunk/126054 -> ciflow/trunk/126054 2025-03-04T20:11:04.2031582Z * [new tag] ciflow/trunk/126635 -> ciflow/trunk/126635 2025-03-04T20:11:04.2031736Z * [new tag] ciflow/trunk/127171 -> ciflow/trunk/127171 2025-03-04T20:11:04.2031856Z * [new tag] ciflow/trunk/127919 -> ciflow/trunk/127919 2025-03-04T20:11:04.2031967Z * [new tag] ciflow/trunk/129352 -> ciflow/trunk/129352 2025-03-04T20:11:04.2032086Z * [new tag] ciflow/trunk/129420 -> ciflow/trunk/129420 2025-03-04T20:11:04.2032289Z * [new tag] ciflow/trunk/130141 -> ciflow/trunk/130141 2025-03-04T20:11:04.2032416Z * [new tag] ciflow/trunk/130752 -> ciflow/trunk/130752 2025-03-04T20:11:04.2032533Z * [new tag] ciflow/trunk/131354 -> ciflow/trunk/131354 2025-03-04T20:11:04.2032657Z * [new tag] ciflow/trunk/131507 -> ciflow/trunk/131507 2025-03-04T20:11:04.2032769Z * [new tag] ciflow/trunk/132021 -> ciflow/trunk/132021 2025-03-04T20:11:04.2032919Z * [new tag] ciflow/trunk/133044 -> ciflow/trunk/133044 2025-03-04T20:11:04.2033032Z * [new tag] ciflow/trunk/133289 -> ciflow/trunk/133289 2025-03-04T20:11:04.2033153Z * [new tag] ciflow/trunk/133296 -> ciflow/trunk/133296 2025-03-04T20:11:04.2033267Z * [new tag] ciflow/trunk/133297 -> ciflow/trunk/133297 2025-03-04T20:11:04.2033389Z * [new tag] ciflow/trunk/133315 -> ciflow/trunk/133315 2025-03-04T20:11:04.2033502Z * [new tag] ciflow/trunk/133392 -> ciflow/trunk/133392 2025-03-04T20:11:04.2033625Z * [new tag] ciflow/trunk/133419 -> ciflow/trunk/133419 2025-03-04T20:11:04.2033789Z * [new tag] ciflow/trunk/133423 -> ciflow/trunk/133423 2025-03-04T20:11:04.2033902Z * [new tag] ciflow/trunk/133667 -> ciflow/trunk/133667 2025-03-04T20:11:04.2034031Z * [new tag] ciflow/trunk/133753 -> ciflow/trunk/133753 2025-03-04T20:11:04.2034140Z * [new tag] ciflow/trunk/134219 -> ciflow/trunk/134219 2025-03-04T20:11:04.2034258Z * [new tag] ciflow/trunk/134515 -> ciflow/trunk/134515 2025-03-04T20:11:04.2034369Z * [new tag] ciflow/trunk/135058 -> ciflow/trunk/135058 2025-03-04T20:11:04.2034484Z * [new tag] ciflow/trunk/135631 -> ciflow/trunk/135631 2025-03-04T20:11:04.2034593Z * [new tag] ciflow/trunk/136780 -> ciflow/trunk/136780 2025-03-04T20:11:04.2034710Z * [new tag] ciflow/trunk/136824 -> ciflow/trunk/136824 2025-03-04T20:11:04.2034822Z * [new tag] ciflow/trunk/136835 -> ciflow/trunk/136835 2025-03-04T20:11:04.2034939Z * [new tag] ciflow/trunk/136993 -> ciflow/trunk/136993 2025-03-04T20:11:04.2035047Z * [new tag] ciflow/trunk/137400 -> ciflow/trunk/137400 2025-03-04T20:11:04.2035165Z * [new tag] ciflow/trunk/137580 -> ciflow/trunk/137580 2025-03-04T20:11:04.2035273Z * [new tag] ciflow/trunk/138213 -> ciflow/trunk/138213 2025-03-04T20:11:04.2035389Z * [new tag] ciflow/trunk/138436 -> ciflow/trunk/138436 2025-03-04T20:11:04.2035498Z * [new tag] ciflow/trunk/138626 -> ciflow/trunk/138626 2025-03-04T20:11:04.2035614Z * [new tag] ciflow/trunk/138834 -> ciflow/trunk/138834 2025-03-04T20:11:04.2035723Z * [new tag] ciflow/trunk/138889 -> ciflow/trunk/138889 2025-03-04T20:11:04.2035842Z * [new tag] ciflow/trunk/138996 -> ciflow/trunk/138996 2025-03-04T20:11:04.2035951Z * [new tag] ciflow/trunk/139070 -> ciflow/trunk/139070 2025-03-04T20:11:04.2036067Z * [new tag] ciflow/trunk/139094 -> ciflow/trunk/139094 2025-03-04T20:11:04.2036207Z * [new tag] ciflow/trunk/139971 -> ciflow/trunk/139971 2025-03-04T20:11:04.2036337Z * [new tag] ciflow/trunk/139975 -> ciflow/trunk/139975 2025-03-04T20:11:04.2036875Z * [new tag] ciflow/trunk/140084 -> ciflow/trunk/140084 2025-03-04T20:11:04.2037042Z * [new tag] ciflow/trunk/140159 -> ciflow/trunk/140159 2025-03-04T20:11:04.2037163Z * [new tag] ciflow/trunk/140211 -> ciflow/trunk/140211 2025-03-04T20:11:04.2037282Z * [new tag] ciflow/trunk/140298 -> ciflow/trunk/140298 2025-03-04T20:11:04.2037391Z * [new tag] ciflow/trunk/140323 -> ciflow/trunk/140323 2025-03-04T20:11:04.2037528Z * [new tag] ciflow/trunk/140365 -> ciflow/trunk/140365 2025-03-04T20:11:04.2037639Z * [new tag] ciflow/trunk/140399 -> ciflow/trunk/140399 2025-03-04T20:11:04.2037757Z * [new tag] ciflow/trunk/140793 -> ciflow/trunk/140793 2025-03-04T20:11:04.2037868Z * [new tag] ciflow/trunk/140979 -> ciflow/trunk/140979 2025-03-04T20:11:04.2037986Z * [new tag] ciflow/trunk/140989 -> ciflow/trunk/140989 2025-03-04T20:11:04.2038097Z * [new tag] ciflow/trunk/141178 -> ciflow/trunk/141178 2025-03-04T20:11:04.2038216Z * [new tag] ciflow/trunk/141257 -> ciflow/trunk/141257 2025-03-04T20:11:04.2038325Z * [new tag] ciflow/trunk/141309 -> ciflow/trunk/141309 2025-03-04T20:11:04.2038444Z * [new tag] ciflow/trunk/141730 -> ciflow/trunk/141730 2025-03-04T20:11:04.2038594Z * [new tag] ciflow/trunk/141796 -> ciflow/trunk/141796 2025-03-04T20:11:04.2038714Z * [new tag] ciflow/trunk/141842 -> ciflow/trunk/141842 2025-03-04T20:11:04.2038824Z * [new tag] ciflow/trunk/141889 -> ciflow/trunk/141889 2025-03-04T20:11:04.2038946Z * [new tag] ciflow/trunk/141910 -> ciflow/trunk/141910 2025-03-04T20:11:04.2039062Z * [new tag] ciflow/trunk/141914 -> ciflow/trunk/141914 2025-03-04T20:11:04.2039172Z * [new tag] ciflow/trunk/141961 -> ciflow/trunk/141961 2025-03-04T20:11:04.2039287Z * [new tag] ciflow/trunk/142091 -> ciflow/trunk/142091 2025-03-04T20:11:04.2039394Z * [new tag] ciflow/trunk/142092 -> ciflow/trunk/142092 2025-03-04T20:11:04.2039819Z * [new tag] ciflow/trunk/142097 -> ciflow/trunk/142097 2025-03-04T20:11:04.2040026Z * [new tag] ciflow/trunk/142179 -> ciflow/trunk/142179 2025-03-04T20:11:04.2040886Z * [new tag] ciflow/trunk/142272 -> ciflow/trunk/142272 2025-03-04T20:11:04.2041755Z * [new tag] ciflow/trunk/142273 -> ciflow/trunk/142273 2025-03-04T20:11:04.2045202Z * [new tag] ciflow/trunk/142326 -> ciflow/trunk/142326 2025-03-04T20:11:04.2047501Z * [new tag] ciflow/trunk/142346 -> ciflow/trunk/142346 2025-03-04T20:11:04.2049662Z * [new tag] ciflow/trunk/142350 -> ciflow/trunk/142350 2025-03-04T20:11:04.2052250Z * [new tag] ciflow/trunk/142372 -> ciflow/trunk/142372 2025-03-04T20:11:04.2055093Z * [new tag] ciflow/trunk/142477 -> ciflow/trunk/142477 2025-03-04T20:11:04.2055406Z * [new tag] ciflow/trunk/142821 -> ciflow/trunk/142821 2025-03-04T20:11:04.2055527Z * [new tag] ciflow/trunk/142859 -> ciflow/trunk/142859 2025-03-04T20:11:04.2055652Z * [new tag] ciflow/trunk/142865 -> ciflow/trunk/142865 2025-03-04T20:11:04.2055762Z * [new tag] ciflow/trunk/143082 -> ciflow/trunk/143082 2025-03-04T20:11:04.2055867Z * [new tag] ciflow/trunk/143093 -> ciflow/trunk/143093 2025-03-04T20:11:04.2056102Z * [new tag] ciflow/trunk/143220 -> ciflow/trunk/143220 2025-03-04T20:11:04.2056216Z * [new tag] ciflow/trunk/143261 -> ciflow/trunk/143261 2025-03-04T20:11:04.2056340Z * [new tag] ciflow/trunk/143303 -> ciflow/trunk/143303 2025-03-04T20:11:04.2056823Z * [new tag] ciflow/trunk/143313 -> ciflow/trunk/143313 2025-03-04T20:11:04.2056955Z * [new tag] ciflow/trunk/143347 -> ciflow/trunk/143347 2025-03-04T20:11:04.2057092Z * [new tag] ciflow/trunk/143402 -> ciflow/trunk/143402 2025-03-04T20:11:04.2057207Z * [new tag] ciflow/trunk/143416 -> ciflow/trunk/143416 2025-03-04T20:11:04.2057324Z * [new tag] ciflow/trunk/143451 -> ciflow/trunk/143451 2025-03-04T20:11:04.2057430Z * [new tag] ciflow/trunk/143475 -> ciflow/trunk/143475 2025-03-04T20:11:04.2057548Z * [new tag] ciflow/trunk/143630 -> ciflow/trunk/143630 2025-03-04T20:11:04.2057651Z * [new tag] ciflow/trunk/143666 -> ciflow/trunk/143666 2025-03-04T20:11:04.2057764Z * [new tag] ciflow/trunk/143671 -> ciflow/trunk/143671 2025-03-04T20:11:04.2057869Z * [new tag] ciflow/trunk/143689 -> ciflow/trunk/143689 2025-03-04T20:11:04.2057982Z * [new tag] ciflow/trunk/143712 -> ciflow/trunk/143712 2025-03-04T20:11:04.2058087Z * [new tag] ciflow/trunk/143733 -> ciflow/trunk/143733 2025-03-04T20:11:04.2058200Z * [new tag] ciflow/trunk/143822 -> ciflow/trunk/143822 2025-03-04T20:11:04.2058367Z * [new tag] ciflow/trunk/143833 -> ciflow/trunk/143833 2025-03-04T20:11:04.2058484Z * [new tag] ciflow/trunk/143894 -> ciflow/trunk/143894 2025-03-04T20:11:04.2058592Z * [new tag] ciflow/trunk/143896 -> ciflow/trunk/143896 2025-03-04T20:11:04.2058711Z * [new tag] ciflow/trunk/143961 -> ciflow/trunk/143961 2025-03-04T20:11:04.2058820Z * [new tag] ciflow/trunk/143966 -> ciflow/trunk/143966 2025-03-04T20:11:04.2058935Z * [new tag] ciflow/trunk/144017 -> ciflow/trunk/144017 2025-03-04T20:11:04.2059043Z * [new tag] ciflow/trunk/144019 -> ciflow/trunk/144019 2025-03-04T20:11:04.2059159Z * [new tag] ciflow/trunk/144120 -> ciflow/trunk/144120 2025-03-04T20:11:04.2059267Z * [new tag] ciflow/trunk/144138 -> ciflow/trunk/144138 2025-03-04T20:11:04.2059385Z * [new tag] ciflow/trunk/144172 -> ciflow/trunk/144172 2025-03-04T20:11:04.2059497Z * [new tag] ciflow/trunk/144177 -> ciflow/trunk/144177 2025-03-04T20:11:04.2059613Z * [new tag] ciflow/trunk/144268 -> ciflow/trunk/144268 2025-03-04T20:11:04.2059722Z * [new tag] ciflow/trunk/144272 -> ciflow/trunk/144272 2025-03-04T20:11:04.2059837Z * [new tag] ciflow/trunk/144293 -> ciflow/trunk/144293 2025-03-04T20:11:04.2059947Z * [new tag] ciflow/trunk/144452 -> ciflow/trunk/144452 2025-03-04T20:11:04.2060391Z * [new tag] ciflow/trunk/144468 -> ciflow/trunk/144468 2025-03-04T20:11:04.2060499Z * [new tag] ciflow/trunk/144557 -> ciflow/trunk/144557 2025-03-04T20:11:04.2060710Z * [new tag] ciflow/trunk/144572 -> ciflow/trunk/144572 2025-03-04T20:11:04.2061209Z * [new tag] ciflow/trunk/144590 -> ciflow/trunk/144590 2025-03-04T20:11:04.2062551Z * [new tag] ciflow/trunk/144616 -> ciflow/trunk/144616 2025-03-04T20:11:04.2062965Z * [new tag] ciflow/trunk/144620 -> ciflow/trunk/144620 2025-03-04T20:11:04.2063154Z * [new tag] ciflow/trunk/144664 -> ciflow/trunk/144664 2025-03-04T20:11:04.2063271Z * [new tag] ciflow/trunk/144708 -> ciflow/trunk/144708 2025-03-04T20:11:04.2063678Z * [new tag] ciflow/trunk/144721 -> ciflow/trunk/144721 2025-03-04T20:11:04.2063876Z * [new tag] ciflow/trunk/144733 -> ciflow/trunk/144733 2025-03-04T20:11:04.2064657Z * [new tag] ciflow/trunk/144763 -> ciflow/trunk/144763 2025-03-04T20:11:04.2065433Z * [new tag] ciflow/trunk/144771 -> ciflow/trunk/144771 2025-03-04T20:11:04.2066166Z * [new tag] ciflow/trunk/144844 -> ciflow/trunk/144844 2025-03-04T20:11:04.2067008Z * [new tag] ciflow/trunk/144880 -> ciflow/trunk/144880 2025-03-04T20:11:04.2067470Z * [new tag] ciflow/trunk/144925 -> ciflow/trunk/144925 2025-03-04T20:11:04.2067595Z * [new tag] ciflow/trunk/144953 -> ciflow/trunk/144953 2025-03-04T20:11:04.2067805Z * [new tag] ciflow/trunk/144975 -> ciflow/trunk/144975 2025-03-04T20:11:04.2068443Z * [new tag] ciflow/trunk/144992 -> ciflow/trunk/144992 2025-03-04T20:11:04.2069421Z * [new tag] ciflow/trunk/145061 -> ciflow/trunk/145061 2025-03-04T20:11:04.2069899Z * [new tag] ciflow/trunk/145116 -> ciflow/trunk/145116 2025-03-04T20:11:04.2070019Z * [new tag] ciflow/trunk/145119 -> ciflow/trunk/145119 2025-03-04T20:11:04.2070128Z * [new tag] ciflow/trunk/145136 -> ciflow/trunk/145136 2025-03-04T20:11:04.2070295Z * [new tag] ciflow/trunk/145153 -> ciflow/trunk/145153 2025-03-04T20:11:04.2070695Z * [new tag] ciflow/trunk/145224 -> ciflow/trunk/145224 2025-03-04T20:11:04.2070901Z * [new tag] ciflow/trunk/145241 -> ciflow/trunk/145241 2025-03-04T20:11:04.2071672Z * [new tag] ciflow/trunk/145254 -> ciflow/trunk/145254 2025-03-04T20:11:04.2072521Z * [new tag] ciflow/trunk/145331 -> ciflow/trunk/145331 2025-03-04T20:11:04.2072750Z * [new tag] ciflow/trunk/145406 -> ciflow/trunk/145406 2025-03-04T20:11:04.2073552Z * [new tag] ciflow/trunk/145523 -> ciflow/trunk/145523 2025-03-04T20:11:04.2074348Z * [new tag] ciflow/trunk/145559 -> ciflow/trunk/145559 2025-03-04T20:11:04.2074907Z * [new tag] ciflow/trunk/145677 -> ciflow/trunk/145677 2025-03-04T20:11:04.2075113Z * [new tag] ciflow/trunk/145717 -> ciflow/trunk/145717 2025-03-04T20:11:04.2075243Z * [new tag] ciflow/trunk/145936 -> ciflow/trunk/145936 2025-03-04T20:11:04.2075442Z * [new tag] ciflow/trunk/145946 -> ciflow/trunk/145946 2025-03-04T20:11:04.2076197Z * [new tag] ciflow/trunk/145966 -> ciflow/trunk/145966 2025-03-04T20:11:04.2077243Z * [new tag] ciflow/trunk/145979 -> ciflow/trunk/145979 2025-03-04T20:11:04.2077695Z * [new tag] ciflow/trunk/146051 -> ciflow/trunk/146051 2025-03-04T20:11:04.2077819Z * [new tag] ciflow/trunk/146069 -> ciflow/trunk/146069 2025-03-04T20:11:04.2078017Z * [new tag] ciflow/trunk/146090 -> ciflow/trunk/146090 2025-03-04T20:11:04.2078484Z * [new tag] ciflow/trunk/146098 -> ciflow/trunk/146098 2025-03-04T20:11:04.2078600Z * [new tag] ciflow/trunk/146110 -> ciflow/trunk/146110 2025-03-04T20:11:04.2078791Z * [new tag] ciflow/trunk/146115 -> ciflow/trunk/146115 2025-03-04T20:11:04.2079476Z * [new tag] ciflow/trunk/146176 -> ciflow/trunk/146176 2025-03-04T20:11:04.2080301Z * [new tag] ciflow/trunk/146182 -> ciflow/trunk/146182 2025-03-04T20:11:04.2081120Z * [new tag] ciflow/trunk/146256 -> ciflow/trunk/146256 2025-03-04T20:11:04.2081571Z * [new tag] ciflow/trunk/146275 -> ciflow/trunk/146275 2025-03-04T20:11:04.2081688Z * [new tag] ciflow/trunk/146289 -> ciflow/trunk/146289 2025-03-04T20:11:04.2081882Z * [new tag] ciflow/trunk/146335 -> ciflow/trunk/146335 2025-03-04T20:11:04.2082477Z * [new tag] ciflow/trunk/146421 -> ciflow/trunk/146421 2025-03-04T20:11:04.2082620Z * [new tag] ciflow/trunk/146489 -> ciflow/trunk/146489 2025-03-04T20:11:04.2082762Z * [new tag] ciflow/trunk/146517 -> ciflow/trunk/146517 2025-03-04T20:11:04.2082874Z * [new tag] ciflow/trunk/146530 -> ciflow/trunk/146530 2025-03-04T20:11:04.2082993Z * [new tag] ciflow/trunk/146561 -> ciflow/trunk/146561 2025-03-04T20:11:04.2083688Z * [new tag] ciflow/trunk/146573 -> ciflow/trunk/146573 2025-03-04T20:11:04.2084450Z * [new tag] ciflow/trunk/146582 -> ciflow/trunk/146582 2025-03-04T20:11:04.2085201Z * [new tag] ciflow/trunk/146661 -> ciflow/trunk/146661 2025-03-04T20:11:04.2086000Z * [new tag] ciflow/trunk/146718 -> ciflow/trunk/146718 2025-03-04T20:11:04.2086724Z * [new tag] ciflow/trunk/146777 -> ciflow/trunk/146777 2025-03-04T20:11:04.2088095Z * [new tag] ciflow/trunk/146807 -> ciflow/trunk/146807 2025-03-04T20:11:04.2088591Z * [new tag] ciflow/trunk/146823 -> ciflow/trunk/146823 2025-03-04T20:11:04.2088840Z * [new tag] ciflow/trunk/146826 -> ciflow/trunk/146826 2025-03-04T20:11:04.2089302Z * [new tag] ciflow/trunk/146827 -> ciflow/trunk/146827 2025-03-04T20:11:04.2089413Z * [new tag] ciflow/trunk/146845 -> ciflow/trunk/146845 2025-03-04T20:11:04.2089535Z * [new tag] ciflow/trunk/146870 -> ciflow/trunk/146870 2025-03-04T20:11:04.2089652Z * [new tag] ciflow/trunk/146873 -> ciflow/trunk/146873 2025-03-04T20:11:04.2090098Z * [new tag] ciflow/trunk/146874 -> ciflow/trunk/146874 2025-03-04T20:11:04.2090217Z * [new tag] ciflow/trunk/146903 -> ciflow/trunk/146903 2025-03-04T20:11:04.2090410Z * [new tag] ciflow/trunk/146928 -> ciflow/trunk/146928 2025-03-04T20:11:04.2091174Z * [new tag] ciflow/trunk/146970 -> ciflow/trunk/146970 2025-03-04T20:11:04.2091942Z * [new tag] ciflow/trunk/147014 -> ciflow/trunk/147014 2025-03-04T20:11:04.2092781Z * [new tag] ciflow/trunk/147072 -> ciflow/trunk/147072 2025-03-04T20:11:04.2093526Z * [new tag] ciflow/trunk/147105 -> ciflow/trunk/147105 2025-03-04T20:11:04.2094337Z * [new tag] ciflow/trunk/147155 -> ciflow/trunk/147155 2025-03-04T20:11:04.2095001Z * [new tag] ciflow/trunk/147243 -> ciflow/trunk/147243 2025-03-04T20:11:04.2095756Z * [new tag] ciflow/trunk/147272 -> ciflow/trunk/147272 2025-03-04T20:11:04.2096750Z * [new tag] ciflow/trunk/147314 -> ciflow/trunk/147314 2025-03-04T20:11:04.2097001Z * [new tag] ciflow/trunk/147320 -> ciflow/trunk/147320 2025-03-04T20:11:04.2097126Z * [new tag] ciflow/trunk/147334 -> ciflow/trunk/147334 2025-03-04T20:11:04.2097238Z * [new tag] ciflow/trunk/147349 -> ciflow/trunk/147349 2025-03-04T20:11:04.2097357Z * [new tag] ciflow/trunk/147368 -> ciflow/trunk/147368 2025-03-04T20:11:04.2097466Z * [new tag] ciflow/trunk/147403 -> ciflow/trunk/147403 2025-03-04T20:11:04.2097584Z * [new tag] ciflow/trunk/147422 -> ciflow/trunk/147422 2025-03-04T20:11:04.2097735Z * [new tag] ciflow/trunk/147448 -> ciflow/trunk/147448 2025-03-04T20:11:04.2097853Z * [new tag] ciflow/trunk/147452 -> ciflow/trunk/147452 2025-03-04T20:11:04.2098052Z * [new tag] ciflow/trunk/147481 -> ciflow/trunk/147481 2025-03-04T20:11:04.2098530Z * [new tag] ciflow/trunk/147498 -> ciflow/trunk/147498 2025-03-04T20:11:04.2098725Z * [new tag] ciflow/trunk/147574 -> ciflow/trunk/147574 2025-03-04T20:11:04.2099674Z * [new tag] ciflow/trunk/147583 -> ciflow/trunk/147583 2025-03-04T20:11:04.2100337Z * [new tag] ciflow/trunk/147660 -> ciflow/trunk/147660 2025-03-04T20:11:04.2101115Z * [new tag] ciflow/trunk/147664 -> ciflow/trunk/147664 2025-03-04T20:11:04.2101863Z * [new tag] ciflow/trunk/147741 -> ciflow/trunk/147741 2025-03-04T20:11:04.2102659Z * [new tag] ciflow/trunk/147742 -> ciflow/trunk/147742 2025-03-04T20:11:04.2103514Z * [new tag] ciflow/trunk/147752 -> ciflow/trunk/147752 2025-03-04T20:11:04.2104275Z * [new tag] ciflow/trunk/147797 -> ciflow/trunk/147797 2025-03-04T20:11:04.2105082Z * [new tag] ciflow/trunk/147798 -> ciflow/trunk/147798 2025-03-04T20:11:04.2105741Z * [new tag] ciflow/trunk/147808 -> ciflow/trunk/147808 2025-03-04T20:11:04.2106493Z * [new tag] ciflow/trunk/147817 -> ciflow/trunk/147817 2025-03-04T20:11:04.2106991Z * [new tag] ciflow/trunk/147820 -> ciflow/trunk/147820 2025-03-04T20:11:04.2107111Z * [new tag] ciflow/trunk/147821 -> ciflow/trunk/147821 2025-03-04T20:11:04.2107221Z * [new tag] ciflow/trunk/147836 -> ciflow/trunk/147836 2025-03-04T20:11:04.2107345Z * [new tag] ciflow/trunk/147862 -> ciflow/trunk/147862 2025-03-04T20:11:04.2108100Z * [new tag] ciflow/trunk/147870 -> ciflow/trunk/147870 2025-03-04T20:11:04.2108233Z * [new tag] ciflow/trunk/147881 -> ciflow/trunk/147881 2025-03-04T20:11:04.2108342Z * [new tag] ciflow/trunk/147897 -> ciflow/trunk/147897 2025-03-04T20:11:04.2108468Z * [new tag] ciflow/trunk/147910 -> ciflow/trunk/147910 2025-03-04T20:11:04.2108587Z * [new tag] ciflow/trunk/147917 -> ciflow/trunk/147917 2025-03-04T20:11:04.2108700Z * [new tag] ciflow/trunk/147945 -> ciflow/trunk/147945 2025-03-04T20:11:04.2108823Z * [new tag] ciflow/trunk/147955 -> ciflow/trunk/147955 2025-03-04T20:11:04.2108932Z * [new tag] ciflow/trunk/147956 -> ciflow/trunk/147956 2025-03-04T20:11:04.2109050Z * [new tag] ciflow/trunk/147957 -> ciflow/trunk/147957 2025-03-04T20:11:04.2109162Z * [new tag] ciflow/trunk/147958 -> ciflow/trunk/147958 2025-03-04T20:11:04.2109285Z * [new tag] ciflow/trunk/147959 -> ciflow/trunk/147959 2025-03-04T20:11:04.2109392Z * [new tag] ciflow/trunk/147962 -> ciflow/trunk/147962 2025-03-04T20:11:04.2109509Z * [new tag] ciflow/trunk/147964 -> ciflow/trunk/147964 2025-03-04T20:11:04.2109917Z * [new tag] ciflow/trunk/147994 -> ciflow/trunk/147994 2025-03-04T20:11:04.2110121Z * [new tag] ciflow/trunk/147997 -> ciflow/trunk/147997 2025-03-04T20:11:04.2111092Z * [new tag] ciflow/trunk/148049 -> ciflow/trunk/148049 2025-03-04T20:11:04.2111328Z * [new tag] ciflow/trunk/148076 -> ciflow/trunk/148076 2025-03-04T20:11:04.2111995Z * [new tag] ciflow/trunk/148083 -> ciflow/trunk/148083 2025-03-04T20:11:04.2112247Z * [new tag] ciflow/trunk/148131 -> ciflow/trunk/148131 2025-03-04T20:11:04.2112453Z * [new tag] ciflow/trunk/148163 -> ciflow/trunk/148163 2025-03-04T20:11:04.2112897Z * [new tag] ciflow/trunk/148173 -> ciflow/trunk/148173 2025-03-04T20:11:04.2113013Z * [new tag] ciflow/trunk/148180 -> ciflow/trunk/148180 2025-03-04T20:11:04.2113328Z * [new tag] ciflow/trunk/148231 -> ciflow/trunk/148231 2025-03-04T20:11:04.2114708Z * [new tag] ciflow/trunk/148261 -> ciflow/trunk/148261 2025-03-04T20:11:04.2115342Z * [new tag] ciflow/trunk/148266 -> ciflow/trunk/148266 2025-03-04T20:11:04.2115749Z * [new tag] ciflow/trunk/148279 -> ciflow/trunk/148279 2025-03-04T20:11:04.2115924Z * [new tag] ciflow/trunk/148290 -> ciflow/trunk/148290 2025-03-04T20:11:04.2116668Z * [new tag] ciflow/trunk/148292 -> ciflow/trunk/148292 2025-03-04T20:11:04.2117092Z * [new tag] ciflow/trunk/148305 -> ciflow/trunk/148305 2025-03-04T20:11:04.2117474Z * [new tag] ciflow/trunk/148343 -> ciflow/trunk/148343 2025-03-04T20:11:04.2117828Z * [new tag] ciflow/trunk/148350 -> ciflow/trunk/148350 2025-03-04T20:11:04.2118208Z * [new tag] ciflow/trunk/148364 -> ciflow/trunk/148364 2025-03-04T20:11:04.2118379Z * [new tag] ciflow/trunk/148366 -> ciflow/trunk/148366 2025-03-04T20:11:04.2118798Z * [new tag] ciflow/trunk/148371 -> ciflow/trunk/148371 2025-03-04T20:11:04.2119085Z * [new tag] ciflow/trunk/148388 -> ciflow/trunk/148388 2025-03-04T20:11:04.2119878Z * [new tag] ciflow/trunk/148423 -> ciflow/trunk/148423 2025-03-04T20:11:04.2120015Z * [new tag] ciflow/trunk/70978 -> ciflow/trunk/70978 2025-03-04T20:11:04.2120138Z * [new tag] ciflow/trunk/70979 -> ciflow/trunk/70979 2025-03-04T20:11:04.2123437Z * [new tag] ciflow/unstable/123 -> ciflow/unstable/123 2025-03-04T20:11:04.2123597Z * [new tag] ciflow/unstable/146104 -> ciflow/unstable/146104 2025-03-04T20:11:04.2124684Z * [new tag] ciflow/unstable/146264 -> ciflow/unstable/146264 2025-03-04T20:11:04.2124810Z * [new tag] ciflow/unstable/147320 -> ciflow/unstable/147320 2025-03-04T20:11:04.2124933Z * [new tag] ciflow/xpu/137566 -> ciflow/xpu/137566 2025-03-04T20:11:04.2125054Z * [new tag] ciflow/xpu/137580 -> ciflow/xpu/137580 2025-03-04T20:11:04.2125158Z * [new tag] ciflow/xpu/138889 -> ciflow/xpu/138889 2025-03-04T20:11:04.2125268Z * [new tag] ciflow/xpu/138996 -> ciflow/xpu/138996 2025-03-04T20:11:04.2125378Z * [new tag] ciflow/xpu/139469 -> ciflow/xpu/139469 2025-03-04T20:11:04.2125490Z * [new tag] ciflow/xpu/139971 -> ciflow/xpu/139971 2025-03-04T20:11:04.2126004Z * [new tag] ciflow/xpu/140365 -> ciflow/xpu/140365 2025-03-04T20:11:04.2126178Z * [new tag] ciflow/xpu/140372 -> ciflow/xpu/140372 2025-03-04T20:11:04.2126288Z * [new tag] ciflow/xpu/140686 -> ciflow/xpu/140686 2025-03-04T20:11:04.2126399Z * [new tag] ciflow/xpu/140972 -> ciflow/xpu/140972 2025-03-04T20:11:04.2126813Z * [new tag] ciflow/xpu/142040 -> ciflow/xpu/142040 2025-03-04T20:11:04.2127823Z * [new tag] ciflow/xpu/142097 -> ciflow/xpu/142097 2025-03-04T20:11:04.2127945Z * [new tag] ciflow/xpu/143597 -> ciflow/xpu/143597 2025-03-04T20:11:04.2128693Z * [new tag] ciflow/xpu/143833 -> ciflow/xpu/143833 2025-03-04T20:11:04.2129174Z * [new tag] ciflow/xpu/144240 -> ciflow/xpu/144240 2025-03-04T20:11:04.2129329Z * [new tag] ciflow/xpu/144452 -> ciflow/xpu/144452 2025-03-04T20:11:04.2131739Z * [new tag] ciflow/xpu/144664 -> ciflow/xpu/144664 2025-03-04T20:11:04.2132164Z * [new tag] ciflow/xpu/146098 -> ciflow/xpu/146098 2025-03-04T20:11:04.2132519Z * [new tag] ciflow/xpu/147161 -> ciflow/xpu/147161 2025-03-04T20:11:04.2132879Z * [new tag] ciflow/xpu/147349 -> ciflow/xpu/147349 2025-03-04T20:11:04.2133235Z * [new tag] ciflow/xpu/147355 -> ciflow/xpu/147355 2025-03-04T20:11:04.2133940Z * [new tag] ciflow/xpu/147403 -> ciflow/xpu/147403 2025-03-04T20:11:04.2134292Z * [new tag] ciflow/xpu/147448 -> ciflow/xpu/147448 2025-03-04T20:11:04.2134573Z * [new tag] ciflow/xpu/147498 -> ciflow/xpu/147498 2025-03-04T20:11:04.2134868Z * [new tag] ciflow/xpu/147507 -> ciflow/xpu/147507 2025-03-04T20:11:04.2135144Z * [new tag] ciflow/xpu/147583 -> ciflow/xpu/147583 2025-03-04T20:11:04.2135425Z * [new tag] ciflow/xpu/147593 -> ciflow/xpu/147593 2025-03-04T20:11:04.2135703Z * [new tag] ciflow/xpu/147664 -> ciflow/xpu/147664 2025-03-04T20:11:04.2135984Z * [new tag] ciflow/xpu/147727 -> ciflow/xpu/147727 2025-03-04T20:11:04.2136259Z * [new tag] ciflow/xpu/147821 -> ciflow/xpu/147821 2025-03-04T20:11:04.2137377Z * [new tag] ciflow/xpu/147945 -> ciflow/xpu/147945 2025-03-04T20:11:04.2137718Z * [new tag] ciflow/xpu/147955 -> ciflow/xpu/147955 2025-03-04T20:11:04.2138006Z * [new tag] ciflow/xpu/147956 -> ciflow/xpu/147956 2025-03-04T20:11:04.2138288Z * [new tag] ciflow/xpu/147957 -> ciflow/xpu/147957 2025-03-04T20:11:04.2138548Z * [new tag] ciflow/xpu/147958 -> ciflow/xpu/147958 2025-03-04T20:11:04.2138811Z * [new tag] ciflow/xpu/147959 -> ciflow/xpu/147959 2025-03-04T20:11:04.2139072Z * [new tag] ciflow/xpu/147962 -> ciflow/xpu/147962 2025-03-04T20:11:04.2139336Z * [new tag] ciflow/xpu/148076 -> ciflow/xpu/148076 2025-03-04T20:11:04.2139599Z * [new tag] ciflow/xpu/148081 -> ciflow/xpu/148081 2025-03-04T20:11:04.2139872Z * [new tag] ciflow/xpu/148305 -> ciflow/xpu/148305 2025-03-04T20:11:04.2140134Z * [new tag] ciflow/xpu/148313 -> ciflow/xpu/148313 2025-03-04T20:11:04.2140395Z * [new tag] ciflow/xpu/148366 -> ciflow/xpu/148366 2025-03-04T20:11:04.2140654Z * [new tag] ciflow/xpu/148403 -> ciflow/xpu/148403 2025-03-04T20:11:04.2140919Z * [new tag] ciflow/xpu/148423 -> ciflow/xpu/148423 2025-03-04T20:11:04.2141183Z * [new tag] cslpull75 -> cslpull75 2025-03-04T20:11:04.2141436Z * [new tag] cslpull76 -> cslpull76 2025-03-04T20:11:04.2141685Z * [new tag] cslpull77 -> cslpull77 2025-03-04T20:11:04.2141930Z * [new tag] cslpull78 -> cslpull78 2025-03-04T20:11:04.2142586Z * [new tag] cslpull79 -> cslpull79 2025-03-04T20:11:04.2143039Z * [new tag] cslpull80 -> cslpull80 2025-03-04T20:11:04.2143573Z * [new tag] cslpull81 -> cslpull81 2025-03-04T20:11:04.2144045Z * [new tag] cslpull82 -> cslpull82 2025-03-04T20:11:04.2144494Z * [new tag] cslpull83 -> cslpull83 2025-03-04T20:11:04.2145022Z * [new tag] cslpull84 -> cslpull84 2025-03-04T20:11:04.2145443Z * [new tag] cslpull85 -> cslpull85 2025-03-04T20:11:04.2146369Z * [new tag] cslpull86 -> cslpull86 2025-03-04T20:11:04.2146624Z * [new tag] cslpull87 -> cslpull87 2025-03-04T20:11:04.2147552Z * [new tag] cslpull88 -> cslpull88 2025-03-04T20:11:04.2147913Z * [new tag] cslpull89 -> cslpull89 2025-03-04T20:11:04.2148222Z * [new tag] cslpull90 -> cslpull90 2025-03-04T20:11:04.2149501Z * [new tag] cslpull91 -> cslpull91 2025-03-04T20:11:04.2149783Z * [new tag] cslpull92 -> cslpull92 2025-03-04T20:11:04.2150065Z * [new tag] flight_5 -> flight_5 2025-03-04T20:11:04.2150668Z * [new tag] flight_5.1 -> flight_5.1 2025-03-04T20:11:04.2151216Z * [new tag] flight_5.2 -> flight_5.2 2025-03-04T20:11:04.2151773Z * [new tag] flight_5.3 -> flight_5.3 2025-03-04T20:11:04.2152177Z * [new tag] forpull1 -> forpull1 2025-03-04T20:11:04.2152823Z * [new tag] malfet/tag-2ef5611 -> malfet/tag-2ef5611 2025-03-04T20:11:04.2153432Z * [new tag] malfet/tag-317b1a0 -> malfet/tag-317b1a0 2025-03-04T20:11:04.2153768Z * [new tag] malfet/tag-ec6f767 -> malfet/tag-ec6f767 2025-03-04T20:11:04.2154483Z * [new tag] nightly-binary -> nightly-binary 2025-03-04T20:11:04.2154778Z * [new tag] sqzhang_flight4_plus -> sqzhang_flight4_plus 2025-03-04T20:11:04.2155399Z * [new tag] sqzhang_flight_3 -> sqzhang_flight_3 2025-03-04T20:11:04.2155809Z * [new tag] v0.1.1 -> v0.1.1 2025-03-04T20:11:04.2156918Z * [new tag] v0.1.10 -> v0.1.10 2025-03-04T20:11:04.2157206Z * [new tag] v0.1.11 -> v0.1.11 2025-03-04T20:11:04.2157454Z * [new tag] v0.1.12 -> v0.1.12 2025-03-04T20:11:04.2157868Z * [new tag] v0.1.2 -> v0.1.2 2025-03-04T20:11:04.2158290Z * [new tag] v0.1.3 -> v0.1.3 2025-03-04T20:11:04.2158708Z * [new tag] v0.1.4 -> v0.1.4 2025-03-04T20:11:04.2159289Z * [new tag] v0.1.5 -> v0.1.5 2025-03-04T20:11:04.2159696Z * [new tag] v0.1.6 -> v0.1.6 2025-03-04T20:11:04.2160108Z * [new tag] v0.1.7 -> v0.1.7 2025-03-04T20:11:04.2160661Z * [new tag] v0.1.8 -> v0.1.8 2025-03-04T20:11:04.2160939Z * [new tag] v0.1.9 -> v0.1.9 2025-03-04T20:11:04.2161574Z * [new tag] v0.2.0 -> v0.2.0 2025-03-04T20:11:04.2161985Z * [new tag] v0.3.0 -> v0.3.0 2025-03-04T20:11:04.2162881Z * [new tag] v0.3.1 -> v0.3.1 2025-03-04T20:11:04.2163266Z * [new tag] v0.4.0 -> v0.4.0 2025-03-04T20:11:04.2163577Z * [new tag] v0.4.1 -> v0.4.1 2025-03-04T20:11:04.2164063Z * [new tag] v1.0.0 -> v1.0.0 2025-03-04T20:11:04.2164601Z * [new tag] v1.0.0a0 -> v1.0.0a0 2025-03-04T20:11:04.2165132Z * [new tag] v1.0.1 -> v1.0.1 2025-03-04T20:11:04.2165656Z * [new tag] v1.0rc0 -> v1.0rc0 2025-03-04T20:11:04.2166480Z * [new tag] v1.0rc1 -> v1.0rc1 2025-03-04T20:11:04.2166842Z * [new tag] v1.1.0 -> v1.1.0 2025-03-04T20:11:04.2167228Z * [new tag] v1.1.0a0 -> v1.1.0a0 2025-03-04T20:11:04.2168221Z * [new tag] v1.10.0 -> v1.10.0 2025-03-04T20:11:04.2168614Z * [new tag] v1.10.0-rc1 -> v1.10.0-rc1 2025-03-04T20:11:04.2168969Z * [new tag] v1.10.0-rc2 -> v1.10.0-rc2 2025-03-04T20:11:04.2171428Z * [new tag] v1.10.0-rc3 -> v1.10.0-rc3 2025-03-04T20:11:04.2171775Z * [new tag] v1.10.1 -> v1.10.1 2025-03-04T20:11:04.2172036Z * [new tag] v1.10.1-rc1 -> v1.10.1-rc1 2025-03-04T20:11:04.2172296Z * [new tag] v1.10.2 -> v1.10.2 2025-03-04T20:11:04.2172551Z * [new tag] v1.10.2-rc1 -> v1.10.2-rc1 2025-03-04T20:11:04.2172811Z * [new tag] v1.11.0 -> v1.11.0 2025-03-04T20:11:04.2173077Z * [new tag] v1.11.0-rc1 -> v1.11.0-rc1 2025-03-04T20:11:04.2173327Z * [new tag] v1.11.0-rc2 -> v1.11.0-rc2 2025-03-04T20:11:04.2173579Z * [new tag] v1.11.0-rc3 -> v1.11.0-rc3 2025-03-04T20:11:04.2173829Z * [new tag] v1.11.0-rc4 -> v1.11.0-rc4 2025-03-04T20:11:04.2174536Z * [new tag] v1.11.0-rc5 -> v1.11.0-rc5 2025-03-04T20:11:04.2174797Z * [new tag] v1.11.0-rc6 -> v1.11.0-rc6 2025-03-04T20:11:04.2175206Z * [new tag] v1.11.0-rc7 -> v1.11.0-rc7 2025-03-04T20:11:04.2175782Z * [new tag] v1.12.0 -> v1.12.0 2025-03-04T20:11:04.2176221Z * [new tag] v1.12.0-rc1 -> v1.12.0-rc1 2025-03-04T20:11:04.2177040Z * [new tag] v1.12.0-rc2 -> v1.12.0-rc2 2025-03-04T20:11:04.2177326Z * [new tag] v1.12.0-rc3 -> v1.12.0-rc3 2025-03-04T20:11:04.2177865Z * [new tag] v1.12.0-rc4 -> v1.12.0-rc4 2025-03-04T20:11:04.2178319Z * [new tag] v1.12.0-rc5 -> v1.12.0-rc5 2025-03-04T20:11:04.2179198Z * [new tag] v1.12.0-rc6 -> v1.12.0-rc6 2025-03-04T20:11:04.2179455Z * [new tag] v1.12.0-rc7 -> v1.12.0-rc7 2025-03-04T20:11:04.2180088Z * [new tag] v1.12.0-rc8 -> v1.12.0-rc8 2025-03-04T20:11:04.2180370Z * [new tag] v1.12.1 -> v1.12.1 2025-03-04T20:11:04.2181396Z * [new tag] v1.12.1-rc1 -> v1.12.1-rc1 2025-03-04T20:11:04.2181929Z * [new tag] v1.12.1-rc2 -> v1.12.1-rc2 2025-03-04T20:11:04.2182508Z * [new tag] v1.12.1-rc3 -> v1.12.1-rc3 2025-03-04T20:11:04.2183011Z * [new tag] v1.12.1-rc4 -> v1.12.1-rc4 2025-03-04T20:11:04.2183422Z * [new tag] v1.12.1-rc5 -> v1.12.1-rc5 2025-03-04T20:11:04.2184042Z * [new tag] v1.13.0 -> v1.13.0 2025-03-04T20:11:04.2184562Z * [new tag] v1.13.0-rc1 -> v1.13.0-rc1 2025-03-04T20:11:04.2184985Z * [new tag] v1.13.0-rc2 -> v1.13.0-rc2 2025-03-04T20:11:04.2185528Z * [new tag] v1.13.0-rc3 -> v1.13.0-rc3 2025-03-04T20:11:04.2186500Z * [new tag] v1.13.0-rc4 -> v1.13.0-rc4 2025-03-04T20:11:04.2186768Z * [new tag] v1.13.0-rc5 -> v1.13.0-rc5 2025-03-04T20:11:04.2187027Z * [new tag] v1.13.0-rc6 -> v1.13.0-rc6 2025-03-04T20:11:04.2187492Z * [new tag] v1.13.1 -> v1.13.1 2025-03-04T20:11:04.2187759Z * [new tag] v1.13.1-rc1 -> v1.13.1-rc1 2025-03-04T20:11:04.2188302Z * [new tag] v1.2.0 -> v1.2.0 2025-03-04T20:11:04.2188760Z * [new tag] v1.2.0a0 -> v1.2.0a0 2025-03-04T20:11:04.2189296Z * [new tag] v1.3.0 -> v1.3.0 2025-03-04T20:11:04.2189757Z * [new tag] v1.3.0a0 -> v1.3.0a0 2025-03-04T20:11:04.2190181Z * [new tag] v1.3.1 -> v1.3.1 2025-03-04T20:11:04.2190681Z * [new tag] v1.4.0 -> v1.4.0 2025-03-04T20:11:04.2191125Z * [new tag] v1.4.0a0 -> v1.4.0a0 2025-03-04T20:11:04.2191484Z * [new tag] v1.4.1 -> v1.4.1 2025-03-04T20:11:04.2192094Z * [new tag] v1.5.0 -> v1.5.0 2025-03-04T20:11:04.2192657Z * [new tag] v1.5.0-rc1 -> v1.5.0-rc1 2025-03-04T20:11:04.2193325Z * [new tag] v1.5.0-rc2 -> v1.5.0-rc2 2025-03-04T20:11:04.2193897Z * [new tag] v1.5.0-rc3 -> v1.5.0-rc3 2025-03-04T20:11:04.2194258Z * [new tag] v1.5.0-rc4 -> v1.5.0-rc4 2025-03-04T20:11:04.2194629Z * [new tag] v1.5.0-rc5 -> v1.5.0-rc5 2025-03-04T20:11:04.2195597Z * [new tag] v1.5.1 -> v1.5.1 2025-03-04T20:11:04.2195850Z * [new tag] v1.5.1-rc1 -> v1.5.1-rc1 2025-03-04T20:11:04.2196175Z * [new tag] v1.6.0 -> v1.6.0 2025-03-04T20:11:04.2196538Z * [new tag] v1.6.0-rc1 -> v1.6.0-rc1 2025-03-04T20:11:04.2197682Z * [new tag] v1.6.0-rc2 -> v1.6.0-rc2 2025-03-04T20:11:04.2197981Z * [new tag] v1.6.0-rc3 -> v1.6.0-rc3 2025-03-04T20:11:04.2198246Z * [new tag] v1.6.0-rc4 -> v1.6.0-rc4 2025-03-04T20:11:04.2199165Z * [new tag] v1.6.0-rc5 -> v1.6.0-rc5 2025-03-04T20:11:04.2199564Z * [new tag] v1.6.0-rc6 -> v1.6.0-rc6 2025-03-04T20:11:04.2199829Z * [new tag] v1.6.0-rc7 -> v1.6.0-rc7 2025-03-04T20:11:04.2200090Z * [new tag] v1.7.0 -> v1.7.0 2025-03-04T20:11:04.2200618Z * [new tag] v1.7.0-rc1 -> v1.7.0-rc1 2025-03-04T20:11:04.2201609Z * [new tag] v1.7.0-rc2 -> v1.7.0-rc2 2025-03-04T20:11:04.2201979Z * [new tag] v1.7.0-rc3 -> v1.7.0-rc3 2025-03-04T20:11:04.2202245Z * [new tag] v1.7.0-rc4 -> v1.7.0-rc4 2025-03-04T20:11:04.2202819Z * [new tag] v1.7.1 -> v1.7.1 2025-03-04T20:11:04.2203350Z * [new tag] v1.7.1-rc1 -> v1.7.1-rc1 2025-03-04T20:11:04.2203865Z * [new tag] v1.7.1-rc2 -> v1.7.1-rc2 2025-03-04T20:11:04.2204194Z * [new tag] v1.7.1-rc3 -> v1.7.1-rc3 2025-03-04T20:11:04.2204820Z * [new tag] v1.8.0 -> v1.8.0 2025-03-04T20:11:04.2205415Z * [new tag] v1.8.0-rc1 -> v1.8.0-rc1 2025-03-04T20:11:04.2205861Z * [new tag] v1.8.0-rc2 -> v1.8.0-rc2 2025-03-04T20:11:04.2206283Z * [new tag] v1.8.0-rc3 -> v1.8.0-rc3 2025-03-04T20:11:04.2206714Z * [new tag] v1.8.0-rc4 -> v1.8.0-rc4 2025-03-04T20:11:04.2207115Z * [new tag] v1.8.0-rc5 -> v1.8.0-rc5 2025-03-04T20:11:04.2207429Z * [new tag] v1.8.1 -> v1.8.1 2025-03-04T20:11:04.2208014Z * [new tag] v1.8.1-rc1 -> v1.8.1-rc1 2025-03-04T20:11:04.2208409Z * [new tag] v1.8.1-rc2 -> v1.8.1-rc2 2025-03-04T20:11:04.2208884Z * [new tag] v1.8.1-rc3 -> v1.8.1-rc3 2025-03-04T20:11:04.2209387Z * [new tag] v1.8.2 -> v1.8.2 2025-03-04T20:11:04.2209890Z * [new tag] v1.8.2-rc1 -> v1.8.2-rc1 2025-03-04T20:11:04.2210265Z * [new tag] v1.9.0 -> v1.9.0 2025-03-04T20:11:04.2210935Z * [new tag] v1.9.0-rc1 -> v1.9.0-rc1 2025-03-04T20:11:04.2211315Z * [new tag] v1.9.0-rc2 -> v1.9.0-rc2 2025-03-04T20:11:04.2211985Z * [new tag] v1.9.0-rc3 -> v1.9.0-rc3 2025-03-04T20:11:04.2212277Z * [new tag] v1.9.0-rc4 -> v1.9.0-rc4 2025-03-04T20:11:04.2212648Z * [new tag] v1.9.1 -> v1.9.1 2025-03-04T20:11:04.2213957Z * [new tag] v1.9.1-rc1 -> v1.9.1-rc1 2025-03-04T20:11:04.2214345Z * [new tag] v1.9.1-rc2 -> v1.9.1-rc2 2025-03-04T20:11:04.2214792Z * [new tag] v2.0.0 -> v2.0.0 2025-03-04T20:11:04.2215233Z * [new tag] v2.0.0-rc1 -> v2.0.0-rc1 2025-03-04T20:11:04.2215657Z * [new tag] v2.0.0-rc2 -> v2.0.0-rc2 2025-03-04T20:11:04.2217025Z * [new tag] v2.0.0-rc3 -> v2.0.0-rc3 2025-03-04T20:11:04.2217630Z * [new tag] v2.0.0-rc4 -> v2.0.0-rc4 2025-03-04T20:11:04.2217886Z * [new tag] v2.0.0-rc5 -> v2.0.0-rc5 2025-03-04T20:11:04.2218136Z * [new tag] v2.0.0-rc6 -> v2.0.0-rc6 2025-03-04T20:11:04.2218399Z * [new tag] v2.0.1 -> v2.0.1 2025-03-04T20:11:04.2218662Z * [new tag] v2.0.1-rc1 -> v2.0.1-rc1 2025-03-04T20:11:04.2218916Z * [new tag] v2.0.1-rc2 -> v2.0.1-rc2 2025-03-04T20:11:04.2219171Z * [new tag] v2.0.1-rc3 -> v2.0.1-rc3 2025-03-04T20:11:04.2219409Z * [new tag] v2.0.1-rc4 -> v2.0.1-rc4 2025-03-04T20:11:04.2220617Z * [new tag] v2.1.0 -> v2.1.0 2025-03-04T20:11:04.2220871Z * [new tag] v2.1.0-rc1 -> v2.1.0-rc1 2025-03-04T20:11:04.2221344Z * [new tag] v2.1.0-rc2 -> v2.1.0-rc2 2025-03-04T20:11:04.2221808Z * [new tag] v2.1.0-rc3 -> v2.1.0-rc3 2025-03-04T20:11:04.2222508Z * [new tag] v2.1.0-rc4 -> v2.1.0-rc4 2025-03-04T20:11:04.2222784Z * [new tag] v2.1.0-rc5 -> v2.1.0-rc5 2025-03-04T20:11:04.2223051Z * [new tag] v2.1.0-rc6 -> v2.1.0-rc6 2025-03-04T20:11:04.2223496Z * [new tag] v2.1.1 -> v2.1.1 2025-03-04T20:11:04.2223901Z * [new tag] v2.1.1-rc1 -> v2.1.1-rc1 2025-03-04T20:11:04.2224417Z * [new tag] v2.1.1-rc2 -> v2.1.1-rc2 2025-03-04T20:11:04.2224960Z * [new tag] v2.1.1-rc3 -> v2.1.1-rc3 2025-03-04T20:11:04.2225474Z * [new tag] v2.1.1-rc4 -> v2.1.1-rc4 2025-03-04T20:11:04.2226095Z * [new tag] v2.1.1-rc5 -> v2.1.1-rc5 2025-03-04T20:11:04.2226421Z * [new tag] v2.1.1-rc6 -> v2.1.1-rc6 2025-03-04T20:11:04.2226758Z * [new tag] v2.1.2 -> v2.1.2 2025-03-04T20:11:04.2227293Z * [new tag] v2.1.2-rc1 -> v2.1.2-rc1 2025-03-04T20:11:04.2227817Z * [new tag] v2.1.2-rc2 -> v2.1.2-rc2 2025-03-04T20:11:04.2228171Z * [new tag] v2.1.2-rc3 -> v2.1.2-rc3 2025-03-04T20:11:04.2228539Z * [new tag] v2.2.0 -> v2.2.0 2025-03-04T20:11:04.2228958Z * [new tag] v2.2.0-rc1 -> v2.2.0-rc1 2025-03-04T20:11:04.2229585Z * [new tag] v2.2.0-rc2 -> v2.2.0-rc2 2025-03-04T20:11:04.2230061Z * [new tag] v2.2.0-rc3 -> v2.2.0-rc3 2025-03-04T20:11:04.2230381Z * [new tag] v2.2.0-rc4 -> v2.2.0-rc4 2025-03-04T20:11:04.2230915Z * [new tag] v2.2.0-rc5 -> v2.2.0-rc5 2025-03-04T20:11:04.2231522Z * [new tag] v2.2.0-rc6 -> v2.2.0-rc6 2025-03-04T20:11:04.2231909Z * [new tag] v2.2.0-rc7 -> v2.2.0-rc7 2025-03-04T20:11:04.2232343Z * [new tag] v2.2.0-rc8 -> v2.2.0-rc8 2025-03-04T20:11:04.2232893Z * [new tag] v2.2.1 -> v2.2.1 2025-03-04T20:11:04.2233293Z * [new tag] v2.2.1-rc1 -> v2.2.1-rc1 2025-03-04T20:11:04.2233787Z * [new tag] v2.2.1-rc2 -> v2.2.1-rc2 2025-03-04T20:11:04.2234095Z * [new tag] v2.2.1-rc3 -> v2.2.1-rc3 2025-03-04T20:11:04.2234626Z * [new tag] v2.2.2 -> v2.2.2 2025-03-04T20:11:04.2235170Z * [new tag] v2.2.2-rc1 -> v2.2.2-rc1 2025-03-04T20:11:04.2235524Z * [new tag] v2.2.2-rc2 -> v2.2.2-rc2 2025-03-04T20:11:04.2235795Z * [new tag] v2.2.2-rc3 -> v2.2.2-rc3 2025-03-04T20:11:04.2236261Z * [new tag] v2.3.0 -> v2.3.0 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2025-03-04T20:11:04.2251811Z * [new tag] v2.5.0 -> v2.5.0 2025-03-04T20:11:04.2252559Z * [new tag] v2.5.0-rc1 -> v2.5.0-rc1 2025-03-04T20:11:04.2252831Z * [new tag] v2.5.0-rc10 -> v2.5.0-rc10 2025-03-04T20:11:04.2253305Z * [new tag] v2.5.0-rc2 -> v2.5.0-rc2 2025-03-04T20:11:04.2253714Z * [new tag] v2.5.0-rc3 -> v2.5.0-rc3 2025-03-04T20:11:04.2254731Z * [new tag] v2.5.0-rc4 -> v2.5.0-rc4 2025-03-04T20:11:04.2254983Z * [new tag] v2.5.0-rc5 -> v2.5.0-rc5 2025-03-04T20:11:04.2255719Z * [new tag] v2.5.0-rc6 -> v2.5.0-rc6 2025-03-04T20:11:04.2256049Z * [new tag] v2.5.0-rc7 -> v2.5.0-rc7 2025-03-04T20:11:04.2257010Z * [new tag] v2.5.0-rc8 -> v2.5.0-rc8 2025-03-04T20:11:04.2257394Z * [new tag] v2.5.0-rc9 -> v2.5.0-rc9 2025-03-04T20:11:04.2257930Z * [new tag] v2.5.1 -> v2.5.1 2025-03-04T20:11:04.2258219Z * [new tag] v2.5.1-rc1 -> v2.5.1-rc1 2025-03-04T20:11:04.2258575Z * [new tag] v2.6.0 -> v2.6.0 2025-03-04T20:11:04.2258916Z * [new tag] v2.6.0-rc1 -> v2.6.0-rc1 2025-03-04T20:11:04.2259276Z * [new tag] v2.6.0-rc2 -> v2.6.0-rc2 2025-03-04T20:11:04.2259822Z * [new tag] v2.6.0-rc3 -> v2.6.0-rc3 2025-03-04T20:11:04.2260292Z * [new tag] v2.6.0-rc4 -> v2.6.0-rc4 2025-03-04T20:11:04.2261010Z * [new tag] v2.6.0-rc5 -> v2.6.0-rc5 2025-03-04T20:11:04.2261644Z * [new tag] v2.6.0-rc6 -> v2.6.0-rc6 2025-03-04T20:11:04.2262211Z * [new tag] v2.6.0-rc7 -> v2.6.0-rc7 2025-03-04T20:11:04.2262746Z * [new tag] v2.6.0-rc8 -> v2.6.0-rc8 2025-03-04T20:11:04.2263126Z * [new tag] v2.6.0-rc9 -> v2.6.0-rc9 2025-03-04T20:11:04.2263729Z * [new tag] whc_flight_1 -> whc_flight_1 2025-03-04T20:11:04.2264259Z * [new tag] whc_flight_2 -> whc_flight_2 2025-03-04T20:11:04.2264559Z * [new tag] whc_flight_4 -> whc_flight_4 2025-03-04T20:11:04.2628849Z [command]/usr/bin/git rev-parse --verify --quiet 1b7498080987913ecb3aff6253c5e88f3540d911^{object} 2025-03-04T20:11:04.2655151Z 1b7498080987913ecb3aff6253c5e88f3540d911 2025-03-04T20:11:04.2669154Z ##[endgroup] 2025-03-04T20:11:04.2669490Z ##[group]Determining the checkout info 2025-03-04T20:11:04.2669836Z ##[endgroup] 2025-03-04T20:11:04.2670037Z [command]/usr/bin/git sparse-checkout disable 2025-03-04T20:11:04.2698988Z [command]/usr/bin/git config --local --unset-all extensions.worktreeConfig 2025-03-04T20:11:04.2724735Z ##[group]Checking out the ref 2025-03-04T20:11:04.2725426Z [command]/usr/bin/git checkout --progress --force 1b7498080987913ecb3aff6253c5e88f3540d911 2025-03-04T20:11:05.0852340Z Note: switching to '1b7498080987913ecb3aff6253c5e88f3540d911'. 2025-03-04T20:11:05.0852846Z 2025-03-04T20:11:05.0853099Z You are in 'detached HEAD' state. You can look around, make experimental 2025-03-04T20:11:05.0853571Z changes and commit them, and you can discard any commits you make in this 2025-03-04T20:11:05.0853923Z state without impacting any branches by switching back to a branch. 2025-03-04T20:11:05.0854136Z 2025-03-04T20:11:05.0854282Z If you want to create a new branch to retain commits you create, you may 2025-03-04T20:11:05.0854601Z do so (now or later) by using -c with the switch command. Example: 2025-03-04T20:11:05.0854823Z 2025-03-04T20:11:05.0854919Z git switch -c 2025-03-04T20:11:05.0855051Z 2025-03-04T20:11:05.0855138Z Or undo this operation with: 2025-03-04T20:11:05.0855257Z 2025-03-04T20:11:05.0855334Z git switch - 2025-03-04T20:11:05.0855430Z 2025-03-04T20:11:05.0855611Z Turn off this advice by setting config variable advice.detachedHead to false 2025-03-04T20:11:05.0855826Z 2025-03-04T20:11:05.0856025Z HEAD is now at 1b749808098 Update on "[dynamo] remove internal stack trace for fullgraph=True graph breaks" 2025-03-04T20:11:05.0887009Z ##[endgroup] 2025-03-04T20:11:05.0887350Z ##[group]Setting up auth for fetching submodules 2025-03-04T20:11:05.0898870Z [command]/usr/bin/git config --global http.https://github.com/.extraheader AUTHORIZATION: basic *** 2025-03-04T20:11:05.0977626Z [command]/usr/bin/git config --global --unset-all url.https://github.com/.insteadOf 2025-03-04T20:11:05.1012518Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf git@github.com: 2025-03-04T20:11:05.1041555Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf org-21003710@github.com: 2025-03-04T20:11:05.1066357Z ##[endgroup] 2025-03-04T20:11:05.1066846Z ##[group]Fetching submodules 2025-03-04T20:11:05.1067225Z [command]/usr/bin/git submodule sync --recursive 2025-03-04T20:11:05.1375450Z [command]/usr/bin/git -c protocol.version=2 submodule update --init --force --recursive 2025-03-04T20:11:05.1665929Z Submodule 'android/libs/fbjni' (https://github.com/facebookincubator/fbjni.git) registered for path 'android/libs/fbjni' 2025-03-04T20:11:05.1666782Z Submodule 'third_party/NNPACK_deps/FP16' (https://github.com/Maratyszcza/FP16.git) registered for path 'third_party/FP16' 2025-03-04T20:11:05.2019265Z Submodule 'third_party/NNPACK_deps/FXdiv' (https://github.com/Maratyszcza/FXdiv.git) registered for path 'third_party/FXdiv' 2025-03-04T20:11:05.2024307Z Submodule 'third_party/NNPACK' (https://github.com/Maratyszcza/NNPACK.git) registered for path 'third_party/NNPACK' 2025-03-04T20:11:05.2028872Z Submodule 'third_party/NVTX' (https://github.com/NVIDIA/NVTX.git) registered for path 'third_party/NVTX' 2025-03-04T20:11:05.2029627Z Submodule 'third_party/VulkanMemoryAllocator' (https://github.com/GPUOpen-LibrariesAndSDKs/VulkanMemoryAllocator.git) registered for path 'third_party/VulkanMemoryAllocator' 2025-03-04T20:11:05.2030302Z Submodule 'third_party/XNNPACK' (https://github.com/google/XNNPACK.git) registered for path 'third_party/XNNPACK' 2025-03-04T20:11:05.2030840Z Submodule 'third_party/benchmark' (https://github.com/google/benchmark.git) registered for path 'third_party/benchmark' 2025-03-04T20:11:05.2031778Z Submodule 'third_party/composable_kernel' (https://github.com/ROCm/composable_kernel.git) registered for path 'third_party/composable_kernel' 2025-03-04T20:11:05.2039479Z Submodule 'third_party/cpp-httplib' (https://github.com/yhirose/cpp-httplib.git) registered for path 'third_party/cpp-httplib' 2025-03-04T20:11:05.2042732Z Submodule 'third_party/cpuinfo' (https://github.com/pytorch/cpuinfo.git) registered for path 'third_party/cpuinfo' 2025-03-04T20:11:05.2046749Z Submodule 'third_party/cudnn_frontend' (https://github.com/NVIDIA/cudnn-frontend.git) registered for path 'third_party/cudnn_frontend' 2025-03-04T20:11:05.2049074Z Submodule 'third_party/cutlass' (https://github.com/NVIDIA/cutlass.git) registered for path 'third_party/cutlass' 2025-03-04T20:11:05.2049654Z Submodule 'third_party/eigen' (https://gitlab.com/libeigen/eigen.git) registered for path 'third_party/eigen' 2025-03-04T20:11:05.2058163Z Submodule 'third_party/fbgemm' (https://github.com/pytorch/fbgemm) registered for path 'third_party/fbgemm' 2025-03-04T20:11:05.2060049Z Submodule 'third_party/flash-attention' (https://github.com/Dao-AILab/flash-attention.git) registered for path 'third_party/flash-attention' 2025-03-04T20:11:05.2060949Z Submodule 'third_party/flatbuffers' (https://github.com/google/flatbuffers.git) registered for path 'third_party/flatbuffers' 2025-03-04T20:11:05.2064643Z Submodule 'third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/fmt' 2025-03-04T20:11:05.2065231Z Submodule 'third_party/gemmlowp/gemmlowp' (https://github.com/google/gemmlowp.git) registered for path 'third_party/gemmlowp/gemmlowp' 2025-03-04T20:11:05.2065834Z Submodule 'third_party/gloo' (https://github.com/facebookincubator/gloo) registered for path 'third_party/gloo' 2025-03-04T20:11:05.2076368Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/googletest' 2025-03-04T20:11:05.2076978Z Submodule 'third_party/ideep' (https://github.com/intel/ideep) registered for path 'third_party/ideep' 2025-03-04T20:11:05.2080977Z Submodule 'third_party/ittapi' (https://github.com/intel/ittapi.git) registered for path 'third_party/ittapi' 2025-03-04T20:11:05.2081480Z Submodule 'third_party/kineto' (https://github.com/pytorch/kineto) registered for path 'third_party/kineto' 2025-03-04T20:11:05.2085296Z Submodule 'third_party/kleidiai' (https://github.com/ARM-software/kleidiai.git) registered for path 'third_party/kleidiai' 2025-03-04T20:11:05.2085870Z Submodule 'third_party/mimalloc' (https://github.com/microsoft/mimalloc.git) registered for path 'third_party/mimalloc' 2025-03-04T20:11:05.2099436Z Submodule 'third_party/nlohmann' (https://github.com/nlohmann/json.git) registered for path 'third_party/nlohmann' 2025-03-04T20:11:05.2104311Z Submodule 'third_party/onnx' (https://github.com/onnx/onnx.git) registered for path 'third_party/onnx' 2025-03-04T20:11:05.2107488Z Submodule 'third_party/opentelemetry-cpp' (https://github.com/open-telemetry/opentelemetry-cpp.git) registered for path 'third_party/opentelemetry-cpp' 2025-03-04T20:11:05.2108179Z Submodule 'third_party/pocketfft' (https://github.com/mreineck/pocketfft) registered for path 'third_party/pocketfft' 2025-03-04T20:11:05.2108802Z Submodule 'third_party/protobuf' (https://github.com/protocolbuffers/protobuf.git) registered for path 'third_party/protobuf' 2025-03-04T20:11:05.2111651Z Submodule 'third_party/NNPACK_deps/psimd' (https://github.com/Maratyszcza/psimd.git) registered for path 'third_party/psimd' 2025-03-04T20:11:05.2127076Z Submodule 'third_party/NNPACK_deps/pthreadpool' (https://github.com/Maratyszcza/pthreadpool.git) registered for path 'third_party/pthreadpool' 2025-03-04T20:11:05.2133252Z Submodule 'third_party/pybind11' (https://github.com/pybind/pybind11.git) registered for path 'third_party/pybind11' 2025-03-04T20:11:05.2135097Z Submodule 'third_party/python-peachpy' (https://github.com/malfet/PeachPy.git) registered for path 'third_party/python-peachpy' 2025-03-04T20:11:05.2139990Z Submodule 'third_party/sleef' (https://github.com/shibatch/sleef) registered for path 'third_party/sleef' 2025-03-04T20:11:05.2144519Z Submodule 'third_party/tensorpipe' (https://github.com/pytorch/tensorpipe.git) registered for path 'third_party/tensorpipe' 2025-03-04T20:11:05.2166509Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/android/libs/fbjni'... 2025-03-04T20:11:05.4820044Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/FXdiv'... 2025-03-04T20:11:05.4820802Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/FP16'... 2025-03-04T20:11:05.4821767Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/pocketfft'... 2025-03-04T20:11:05.4822479Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/NNPACK'... 2025-03-04T20:11:05.4823154Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/psimd'... 2025-03-04T20:11:05.4973695Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/pybind11'... 2025-03-04T20:11:06.4623628Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/ittapi'... 2025-03-04T20:11:06.4624756Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/NVTX'... 2025-03-04T20:11:06.4625590Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/gloo'... 2025-03-04T20:11:06.4626202Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/pthreadpool'... 2025-03-04T20:11:06.4626689Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kleidiai'... 2025-03-04T20:11:06.4627374Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/gemmlowp/gemmlowp'... 2025-03-04T20:11:06.4627826Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/ideep'... 2025-03-04T20:11:06.4628442Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cpp-httplib'... 2025-03-04T20:11:06.4628980Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/benchmark'... 2025-03-04T20:11:06.4629433Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/python-peachpy'... 2025-03-04T20:11:06.4629961Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/flash-attention'... 2025-03-04T20:11:06.4631072Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cpuinfo'... 2025-03-04T20:11:06.4631817Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/tensorpipe'... 2025-03-04T20:11:06.4632544Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/mimalloc'... 2025-03-04T20:11:06.4702021Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/sleef'... 2025-03-04T20:11:06.5360392Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/VulkanMemoryAllocator'... 2025-03-04T20:11:07.4883371Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/googletest'... 2025-03-04T20:11:07.4884454Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cudnn_frontend'... 2025-03-04T20:11:07.4885381Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fmt'... 2025-03-04T20:11:07.4886445Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto'... 2025-03-04T20:11:07.4887480Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/flatbuffers'... 2025-03-04T20:11:07.5423933Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/XNNPACK'... 2025-03-04T20:11:16.9072899Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm'... 2025-03-04T20:11:16.9073438Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/composable_kernel'... 2025-03-04T20:11:16.9073879Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/onnx'... 2025-03-04T20:11:16.9074271Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cutlass'... 2025-03-04T20:11:16.9074690Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp'... 2025-03-04T20:11:16.9075111Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/nlohmann'... 2025-03-04T20:11:16.9075518Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/eigen'... 2025-03-04T20:11:16.9075909Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/protobuf'... 2025-03-04T20:11:16.9196811Z Submodule path 'android/libs/fbjni': checked out '7e1e1fe3858c63c251c637ae41a20de425dde96f' 2025-03-04T20:11:16.9291919Z Submodule path 'third_party/FP16': checked out '4dfe081cf6bcd15db339cf2680b9281b8451eeb3' 2025-03-04T20:11:16.9368566Z Submodule path 'third_party/FXdiv': checked out 'b408327ac2a15ec3e43352421954f5b1967701d1' 2025-03-04T20:11:16.9552056Z Submodule path 'third_party/NNPACK': checked out 'c07e3a0400713d546e0dea2d5466dd22ea389c73' 2025-03-04T20:11:16.9800016Z Submodule path 'third_party/NVTX': checked out 'e170594ac7cf1dac584da473d4ca9301087090c1' 2025-03-04T20:11:17.0085246Z Submodule path 'third_party/VulkanMemoryAllocator': checked out 'a6bfc237255a6bac1513f7c1ebde6d8aed6b5191' 2025-03-04T20:11:17.5017891Z Submodule path 'third_party/XNNPACK': checked out '51a0103656eff6fc9bfd39a4597923c4b542c883' 2025-03-04T20:11:17.5197569Z Submodule path 'third_party/benchmark': checked out '0d98dba29d66e93259db7daa53a9327df767a415' 2025-03-04T20:11:17.6881109Z Submodule path 'third_party/composable_kernel': checked out '8086bbe3a78d931eb96fe12fdc014082e18d18d3' 2025-03-04T20:11:17.7244774Z Submodule path 'third_party/cpp-httplib': checked out '3b6597bba913d51161383657829b7e644e59c006' 2025-03-04T20:11:17.8023866Z Submodule path 'third_party/cpuinfo': checked out '1e83a2fdd3102f65c6f1fb602c1b320486218a99' 2025-03-04T20:11:17.8293738Z Submodule path 'third_party/cudnn_frontend': checked out '91b7532f3386768bba4f444ee7672b497f34da8a' 2025-03-04T20:11:18.2720009Z Submodule path 'third_party/cutlass': checked out 'afa1772203677c5118fcd82537a9c8fefbcc7008' 2025-03-04T20:11:18.4782727Z Submodule path 'third_party/eigen': checked out '3147391d946bb4b6c68edd901f2add6ac1f31f8c' 2025-03-04T20:11:18.5598615Z Submodule path 'third_party/fbgemm': checked out 'dbc3157bf256f1339b3fa1fef2be89ac4078be0e' 2025-03-04T20:11:18.5614818Z Submodule 'third_party/asmjit' (https://github.com/asmjit/asmjit.git) registered for path 'third_party/fbgemm/third_party/asmjit' 2025-03-04T20:11:18.5615713Z Submodule 'third_party/cpuinfo' (https://github.com/pytorch/cpuinfo) registered for path 'third_party/fbgemm/third_party/cpuinfo' 2025-03-04T20:11:18.5616390Z Submodule 'third_party/cutlass' (https://github.com/NVIDIA/cutlass.git) registered for path 'third_party/fbgemm/third_party/cutlass' 2025-03-04T20:11:18.5618360Z Submodule 'third_party/googletest' (https://github.com/google/googletest) registered for path 'third_party/fbgemm/third_party/googletest' 2025-03-04T20:11:18.5619276Z Submodule 'third_party/hipify_torch' (https://github.com/ROCmSoftwarePlatform/hipify_torch.git) registered for path 'third_party/fbgemm/third_party/hipify_torch' 2025-03-04T20:11:18.5644612Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/asmjit'... 2025-03-04T20:11:19.2675351Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/hipify_torch'... 2025-03-04T20:11:19.2677043Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/cpuinfo'... 2025-03-04T20:11:19.3676776Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/cutlass'... 2025-03-04T20:11:20.7464805Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/googletest'... 2025-03-04T20:11:20.7827308Z Submodule path 'third_party/fbgemm/third_party/asmjit': checked out 'd3fbf7c9bc7c1d1365a94a45614b91c5a3706b81' 2025-03-04T20:11:20.8605111Z Submodule path 'third_party/fbgemm/third_party/cpuinfo': checked out 'ed8b86a253800bafdb7b25c5c399f91bff9cb1f3' 2025-03-04T20:11:21.1562940Z Submodule path 'third_party/fbgemm/third_party/cutlass': checked out 'fc9ebc645b63f3a6bc80aaefde5c063fb72110d6' 2025-03-04T20:11:21.2065802Z Submodule path 'third_party/fbgemm/third_party/googletest': checked out 'cbf019de22c8dd37b2108da35b2748fd702d1796' 2025-03-04T20:11:21.2168472Z Submodule path 'third_party/fbgemm/third_party/hipify_torch': checked out '23f53b025b466d8ec3c45d52290d3442f7fbe6b1' 2025-03-04T20:11:21.2703689Z Submodule path 'third_party/flash-attention': checked out '979702c87a8713a8e0a5e9fee122b90d2ef13be5' 2025-03-04T20:11:21.2716101Z Submodule 'csrc/composable_kernel' (https://github.com/ROCm/composable_kernel.git) registered for path 'third_party/flash-attention/csrc/composable_kernel' 2025-03-04T20:11:21.2721480Z Submodule 'csrc/cutlass' (https://github.com/NVIDIA/cutlass.git) registered for path 'third_party/flash-attention/csrc/cutlass' 2025-03-04T20:11:21.2736173Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/flash-attention/csrc/composable_kernel'... 2025-03-04T20:11:23.3786103Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/flash-attention/csrc/cutlass'... 2025-03-04T20:11:23.5939537Z Submodule path 'third_party/flash-attention/csrc/composable_kernel': checked out '888317e698e9803c62bd38568abc9e05d7709f33' 2025-03-04T20:11:24.0255068Z Submodule path 'third_party/flash-attention/csrc/cutlass': checked out 'c506e16788cb08416a4a57e11a9067beeee29420' 2025-03-04T20:11:24.1214894Z Submodule path 'third_party/flatbuffers': checked out '01834de25e4bf3975a9a00e816292b1ad0fe184b' 2025-03-04T20:11:24.1491793Z Submodule path 'third_party/fmt': checked out '123913715afeb8a437e6388b4473fcc4753e1c9a' 2025-03-04T20:11:24.1793003Z Submodule path 'third_party/gemmlowp/gemmlowp': checked out '3fb5c176c17c765a3492cd2f0321b0dab712f350' 2025-03-04T20:11:24.1992993Z Submodule path 'third_party/gloo': checked out '5354032ea08eadd7fc4456477f7f7c6308818509' 2025-03-04T20:11:24.2338887Z Submodule path 'third_party/googletest': checked out 'b514bdc898e2951020cbdca1304b75f5950d1f59' 2025-03-04T20:11:24.2441487Z Submodule path 'third_party/ideep': checked out 'e026f3b0318087fe19e2b062e8edf55bfe7a522c' 2025-03-04T20:11:24.2454756Z Submodule 'mkl-dnn' (https://github.com/intel/mkl-dnn.git) registered for path 'third_party/ideep/mkl-dnn' 2025-03-04T20:11:24.2478638Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/ideep/mkl-dnn'... 2025-03-04T20:11:36.5594109Z Submodule path 'third_party/ideep/mkl-dnn': checked out '66f0cb9eb66affd2da3bf5f8d897376f04aae6af' 2025-03-04T20:11:36.5749890Z Submodule path 'third_party/ittapi': checked out '5b8a7d7422611c3a0d799fb5fc5dd4abfae35b42' 2025-03-04T20:11:36.6510349Z Submodule path 'third_party/kineto': checked out 'a054a4be0db117c579a21747debf19c863631f26' 2025-03-04T20:11:36.6523773Z Submodule 'libkineto/third_party/dynolog' (https://github.com/facebookincubator/dynolog.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog' 2025-03-04T20:11:36.6525221Z Submodule 'libkineto/third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/kineto/libkineto/third_party/fmt' 2025-03-04T20:11:36.6526010Z Submodule 'libkineto/third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/kineto/libkineto/third_party/googletest' 2025-03-04T20:11:36.6588431Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog'... 2025-03-04T20:11:37.2666683Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/fmt'... 2025-03-04T20:11:37.8546909Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/googletest'... 2025-03-04T20:11:37.9206803Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog': checked out '7d04a0053a845370ae06ce317a22a48e9edcc74e' 2025-03-04T20:11:37.9222637Z Submodule 'third_party/DCGM' (https://github.com/NVIDIA/DCGM.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2025-03-04T20:11:37.9225585Z Submodule 'third_party/cpr' (https://github.com/libcpr/cpr.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2025-03-04T20:11:37.9227657Z Submodule 'third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2025-03-04T20:11:37.9228794Z Submodule 'third_party/gflags' (https://github.com/gflags/gflags.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2025-03-04T20:11:37.9233359Z Submodule 'third_party/glog' (https://github.com/google/glog.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2025-03-04T20:11:37.9235444Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2025-03-04T20:11:37.9236321Z Submodule 'third_party/json' (https://github.com/nlohmann/json.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2025-03-04T20:11:37.9238679Z Submodule 'third_party/pfs' (https://github.com/dtrugman/pfs.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2025-03-04T20:11:37.9256722Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM'... 2025-03-04T20:11:38.9540753Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/pfs'... 2025-03-04T20:11:38.9541502Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/gflags'... 2025-03-04T20:11:38.9542218Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/cpr'... 2025-03-04T20:11:38.9542904Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/glog'... 2025-03-04T20:11:38.9543867Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/googletest'... 2025-03-04T20:11:39.0544770Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/fmt'... 2025-03-04T20:11:39.2645841Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/json'... 2025-03-04T20:11:43.6369459Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM': checked out 'ffde4e54bc7249a6039a5e6b45b395141e1217f9' 2025-03-04T20:11:43.6513183Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr': checked out '871ed52d350214a034f6ef8a3b8f51c5ce1bd400' 2025-03-04T20:11:43.6794130Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt': checked out 'cd4af11efc9c622896a3e4cb599fa28668ca3d05' 2025-03-04T20:11:43.6904665Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags': checked out 'e171aa2d15ed9eb17054558e0b3a6a413bb01067' 2025-03-04T20:11:43.6919845Z Submodule 'doc' (https://github.com/gflags/gflags.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2025-03-04T20:11:43.6943108Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc'... 2025-03-04T20:11:43.9731305Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc': checked out '8411df715cf522606e3b1aca386ddfc0b63d34b4' 2025-03-04T20:11:43.9881743Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog': checked out 'b33e3bad4c46c8a6345525fd822af355e5ef9446' 2025-03-04T20:11:44.0219646Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest': checked out '58d77fa8070e8cec2dc1ed015d66b454c8d78850' 2025-03-04T20:11:44.1045718Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/json': checked out '4f8fba14066156b73f1189a2b8bd568bde5284c5' 2025-03-04T20:11:44.1178411Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs': checked out 'f68a2fa8ea36c783bdd760371411fcb495aa3150' 2025-03-04T20:11:44.1499611Z Submodule path 'third_party/kineto/libkineto/third_party/fmt': checked out '0041a40c1350ba702d475b9c4ad62da77caea164' 2025-03-04T20:11:44.1974094Z Submodule path 'third_party/kineto/libkineto/third_party/googletest': checked out '7aca84427f224eeed3144123d5230d5871e93347' 2025-03-04T20:11:44.2280506Z Submodule path 'third_party/kleidiai': checked out 'ef685a13cfbe8d418aa2ed34350e21e4938358b6' 2025-03-04T20:11:44.2563044Z Submodule path 'third_party/mimalloc': checked out 'b66e3214d8a104669c2ec05ae91ebc26a8f5ab78' 2025-03-04T20:11:44.3418897Z Submodule path 'third_party/nlohmann': checked out '87cda1d6646592ac5866dc703c8e1839046a6806' 2025-03-04T20:11:44.5782618Z Submodule path 'third_party/onnx': checked out 'b8baa8446686496da4cc8fda09f2b6fe65c2a02c' 2025-03-04T20:11:44.5806095Z Submodule 'third_party/pybind11' (https://github.com/pybind/pybind11.git) registered for path 'third_party/onnx/third_party/pybind11' 2025-03-04T20:11:44.5830501Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/onnx/third_party/pybind11'... 2025-03-04T20:11:45.4194301Z Submodule path 'third_party/onnx/third_party/pybind11': checked out '3e9dfa2866941655c56877882565e7577de6fc7b' 2025-03-04T20:11:45.4677593Z Submodule path 'third_party/opentelemetry-cpp': checked out 'a799f4aed9c94b765dcdaabaeab7d5e7e2310878' 2025-03-04T20:11:45.4693093Z Submodule 'third_party/benchmark' (https://github.com/google/benchmark) registered for path 'third_party/opentelemetry-cpp/third_party/benchmark' 2025-03-04T20:11:45.4694101Z Submodule 'third_party/googletest' (https://github.com/google/googletest) registered for path 'third_party/opentelemetry-cpp/third_party/googletest' 2025-03-04T20:11:45.4695053Z Submodule 'third_party/ms-gsl' (https://github.com/microsoft/GSL) registered for path 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2025-03-04T20:11:45.4695678Z Submodule 'third_party/nlohmann-json' (https://github.com/nlohmann/json) registered for path 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2025-03-04T20:11:45.4696427Z Submodule 'third_party/opentelemetry-proto' (https://github.com/open-telemetry/opentelemetry-proto) registered for path 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2025-03-04T20:11:45.4698603Z Submodule 'third_party/opentracing-cpp' (https://github.com/opentracing/opentracing-cpp.git) registered for path 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2025-03-04T20:11:45.4699371Z Submodule 'third_party/prometheus-cpp' (https://github.com/jupp0r/prometheus-cpp) registered for path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2025-03-04T20:11:45.4700020Z Submodule 'tools/vcpkg' (https://github.com/Microsoft/vcpkg) registered for path 'third_party/opentelemetry-cpp/tools/vcpkg' 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Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2025-03-04T20:12:00.7658306Z Entering 'third_party/pocketfft' 2025-03-04T20:12:00.7701610Z Entering 'third_party/protobuf' 2025-03-04T20:12:00.7753438Z Entering 'third_party/protobuf/third_party/benchmark' 2025-03-04T20:12:00.7798510Z Entering 'third_party/protobuf/third_party/googletest' 2025-03-04T20:12:00.7851118Z Entering 'third_party/psimd' 2025-03-04T20:12:00.7897926Z Entering 'third_party/pthreadpool' 2025-03-04T20:12:00.7947578Z Entering 'third_party/pybind11' 2025-03-04T20:12:00.7998713Z Entering 'third_party/python-peachpy' 2025-03-04T20:12:00.8047230Z Entering 'third_party/sleef' 2025-03-04T20:12:00.8093914Z Entering 'third_party/tensorpipe' 2025-03-04T20:12:00.8140786Z Entering 'third_party/tensorpipe/third_party/googletest' 2025-03-04T20:12:00.8188883Z Entering 'third_party/tensorpipe/third_party/libnop' 2025-03-04T20:12:00.8234779Z Entering 'third_party/tensorpipe/third_party/libuv' 2025-03-04T20:12:00.8285184Z Entering 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'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2025-03-04T20:12:01.9748724Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2025-03-04T20:12:01.9785265Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2025-03-04T20:12:01.9821735Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2025-03-04T20:12:01.9875859Z Entering 'third_party/pocketfft' 2025-03-04T20:12:01.9928557Z Entering 'third_party/protobuf' 2025-03-04T20:12:01.9947428Z Entering 'third_party/protobuf/third_party/benchmark' 2025-03-04T20:12:01.9983841Z Entering 'third_party/protobuf/third_party/googletest' 2025-03-04T20:12:02.0029809Z Entering 'third_party/psimd' 2025-03-04T20:12:02.0063771Z Entering 'third_party/pthreadpool' 2025-03-04T20:12:02.0099265Z Entering 'third_party/pybind11' 2025-03-04T20:12:02.0132677Z Entering 'third_party/python-peachpy' 2025-03-04T20:12:02.0164379Z Entering 'third_party/sleef' 2025-03-04T20:12:02.0197591Z Entering 'third_party/tensorpipe' 2025-03-04T20:12:02.0229581Z Entering 'third_party/tensorpipe/third_party/googletest' 2025-03-04T20:12:02.0269464Z Entering 'third_party/tensorpipe/third_party/libnop' 2025-03-04T20:12:02.0300941Z Entering 'third_party/tensorpipe/third_party/libuv' 2025-03-04T20:12:02.0335975Z Entering 'third_party/tensorpipe/third_party/pybind11' 2025-03-04T20:12:02.0367536Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2025-03-04T20:12:02.0419799Z ##[endgroup] 2025-03-04T20:12:02.0461535Z [command]/usr/bin/git log -1 --format=%H 2025-03-04T20:12:02.0490026Z 1b7498080987913ecb3aff6253c5e88f3540d911 2025-03-04T20:12:02.0638501Z Prepare all required actions 2025-03-04T20:12:02.0638987Z Getting action download info 2025-03-04T20:12:02.2058188Z ##[group]Run ./.github/actions/setup-linux 2025-03-04T20:12:02.2058410Z env: 2025-03-04T20:12:02.2058573Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:12:02.2058753Z ##[endgroup] 2025-03-04T20:12:02.2100085Z ##[group]Run set -euo pipefail 2025-03-04T20:12:02.2100345Z set -euo pipefail 2025-03-04T20:12:02.2100546Z function get_ec2_metadata() { 2025-03-04T20:12:02.2100812Z  # Pulled from instance metadata endpoint for EC2 2025-03-04T20:12:02.2101198Z  # see https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/instancedata-data-retrieval.html 2025-03-04T20:12:02.2101530Z  category=$1 2025-03-04T20:12:02.2101776Z  # If it is GCP runner (runner name contains gcp), do not run this 2025-03-04T20:12:02.2102709Z  runner_name_str=i-034d656ab88aab9e9 2025-03-04T20:12:02.2102985Z  if [[ -f /.inarc ]]; then 2025-03-04T20:12:02.2103219Z  echo "ARC Runner, no info on ec2 metadata" 2025-03-04T20:12:02.2103474Z  elif [[ $runner_name_str == *"gcp"* ]]; then 2025-03-04T20:12:02.2103772Z  echo "Runner is from Google Cloud Platform, No info on ec2 metadata" 2025-03-04T20:12:02.2104030Z  else 2025-03-04T20:12:02.2104534Z  curl -H "X-aws-ec2-metadata-token: $(curl -s -X PUT "http://169.254.169.254/latest/api/token" -H "X-aws-ec2-metadata-token-ttl-seconds: 30")" -fsSL "http://169.254.169.254/latest/meta-data/${category}" 2025-03-04T20:12:02.2105193Z  fi 2025-03-04T20:12:02.2105353Z } 2025-03-04T20:12:02.2105541Z echo "ami-id: $(get_ec2_metadata ami-id)" 2025-03-04T20:12:02.2105813Z echo "instance-id: $(get_ec2_metadata instance-id)" 2025-03-04T20:12:02.2106101Z echo "instance-type: $(get_ec2_metadata instance-type)" 2025-03-04T20:12:02.2106362Z echo "system info $(uname -a)" 2025-03-04T20:12:02.2112046Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T20:12:02.2112375Z env: 2025-03-04T20:12:02.2112547Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:12:02.2112729Z ##[endgroup] 2025-03-04T20:12:02.2244506Z ami-id: ami-05b10e08d247fb927 2025-03-04T20:12:02.2334259Z instance-id: i-034d656ab88aab9e9 2025-03-04T20:12:02.2416948Z instance-type: m7i-flex.8xlarge 2025-03-04T20:12:02.2428589Z system info Linux ip-10-0-52-54.ec2.internal 6.1.128-136.201.amzn2023.x86_64 #1 SMP PREEMPT_DYNAMIC Mon Feb 10 16:18:01 UTC 2025 x86_64 x86_64 x86_64 GNU/Linux 2025-03-04T20:12:02.2455161Z ##[group]Run echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-03-04T20:12:02.2455712Z echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-03-04T20:12:02.2460262Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T20:12:02.2460516Z env: 2025-03-04T20:12:02.2460676Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:12:02.2460858Z ##[endgroup] 2025-03-04T20:12:02.2508117Z ##[group]Run if systemctl is-active --quiet docker; then 2025-03-04T20:12:02.2508418Z if systemctl is-active --quiet docker; then 2025-03-04T20:12:02.2508666Z  echo "Docker daemon is running..."; 2025-03-04T20:12:02.2508882Z else 2025-03-04T20:12:02.2509115Z  echo "Starting docker deamon..." && sudo systemctl start docker; 2025-03-04T20:12:02.2509393Z fi 2025-03-04T20:12:02.2513249Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T20:12:02.2513493Z env: 2025-03-04T20:12:02.2513680Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:12:02.2513857Z ##[endgroup] 2025-03-04T20:12:02.2588742Z Docker daemon is running... 2025-03-04T20:12:02.2620580Z ##[group]Run nick-fields/retry@v3.0.0 2025-03-04T20:12:02.2620784Z with: 2025-03-04T20:12:02.2620937Z shell: bash 2025-03-04T20:12:02.2621283Z timeout_minutes: 5 2025-03-04T20:12:02.2621468Z max_attempts: 3 2025-03-04T20:12:02.2621640Z retry_wait_seconds: 30 2025-03-04T20:12:02.2622874Z command: AWS_ACCOUNT_ID=$(aws sts get-caller-identity|grep Account|cut -f4 -d\") aws ecr get-login-password --region "$AWS_DEFAULT_REGION" | docker login --username AWS \ --password-stdin "$AWS_ACCOUNT_ID.dkr.ecr.$AWS_DEFAULT_REGION.amazonaws.com" # For LF Runners we need to make sure we also login to Meta's ECR docker registry too. META_AWS_ACCOUNT_ID=308535385114 if [ "$AWS_ACCOUNT_ID" != "$META_AWS_ACCOUNT_ID" ] ; then aws ecr get-login-password --region "$AWS_DEFAULT_REGION" | docker login --username AWS \ --password-stdin "$META_AWS_ACCOUNT_ID.dkr.ecr.$AWS_DEFAULT_REGION.amazonaws.com" fi 2025-03-04T20:12:02.2624093Z polling_interval_seconds: 1 2025-03-04T20:12:02.2624283Z warning_on_retry: true 2025-03-04T20:12:02.2624463Z continue_on_error: false 2025-03-04T20:12:02.2624635Z env: 2025-03-04T20:12:02.2624789Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:12:02.2624968Z AWS_RETRY_MODE: standard 2025-03-04T20:12:02.2625137Z AWS_MAX_ATTEMPTS: 5 2025-03-04T20:12:02.2625314Z AWS_DEFAULT_REGION: us-east-1 2025-03-04T20:12:02.2625500Z ##[endgroup] 2025-03-04T20:12:03.1293605Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2025-03-04T20:12:03.1294159Z Configure a credential helper to remove this warning. See 2025-03-04T20:12:03.1298454Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2025-03-04T20:12:03.1299068Z 2025-03-04T20:12:03.1303751Z Login Succeeded 2025-03-04T20:12:03.3319410Z Command completed after 1 attempt(s). 2025-03-04T20:12:03.3375881Z ##[group]Run env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2025-03-04T20:12:03.3376233Z env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2025-03-04T20:12:03.3376515Z env | grep '^CI' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2025-03-04T20:12:03.3381870Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T20:12:03.3382112Z env: 2025-03-04T20:12:03.3382276Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:12:03.3382462Z ##[endgroup] 2025-03-04T20:12:03.3467282Z ##[group]Run # ignore expansion of "docker ps -q" since it could be empty 2025-03-04T20:12:03.3467655Z # ignore expansion of "docker ps -q" since it could be empty 2025-03-04T20:12:03.3467930Z # shellcheck disable=SC2046 2025-03-04T20:12:03.3468158Z docker stop $(docker ps -q) || true 2025-03-04T20:12:03.3468403Z # Prune all of the docker images 2025-03-04T20:12:03.3468621Z docker system prune -af 2025-03-04T20:12:03.3472499Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T20:12:03.3472741Z env: 2025-03-04T20:12:03.3472909Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:12:03.3473098Z ##[endgroup] 2025-03-04T20:12:03.3743657Z "docker stop" requires at least 1 argument. 2025-03-04T20:12:03.3744057Z See 'docker stop --help'. 2025-03-04T20:12:03.3744264Z 2025-03-04T20:12:03.3744449Z Usage: docker stop [OPTIONS] CONTAINER [CONTAINER...] 2025-03-04T20:12:03.3744710Z 2025-03-04T20:12:03.3744836Z Stop one or more running containers 2025-03-04T20:12:03.3919832Z Total reclaimed space: 0B 2025-03-04T20:12:03.3953998Z ##[group]Run set +e 2025-03-04T20:12:03.3954238Z set +e 2025-03-04T20:12:03.3954417Z set -x 2025-03-04T20:12:03.3954584Z  2025-03-04T20:12:03.3954774Z PT_DOMAIN=download.pytorch.org 2025-03-04T20:12:03.3955187Z # TODO: Flaky access to download.pytorch.org https://github.com/pytorch/pytorch/issues/100400, 2025-03-04T20:12:03.3955654Z # cleaning this up once the issue is fixed. There are more than one resolved IP here, the last 2025-03-04T20:12:03.3955987Z # one is returned at random 2025-03-04T20:12:03.3956265Z RESOLVED_IP=$(dig -4 +short "${PT_DOMAIN}" | tail -n1) 2025-03-04T20:12:03.3956523Z  2025-03-04T20:12:03.3956832Z if [ -z "${RESOLVED_IP}" ]; then 2025-03-04T20:12:03.3957127Z  echo "Couldn't resolve ${PT_DOMAIN}, retrying with Google DNS..." 2025-03-04T20:12:03.3957466Z  RESOLVED_IP=$(dig -4 +short "${PT_DOMAIN}" @8.8.8.8 | tail -n1) 2025-03-04T20:12:03.3957727Z  2025-03-04T20:12:03.3957907Z  if [ -z "${RESOLVED_IP}" ]; then 2025-03-04T20:12:03.3958174Z  echo "Couldn't resolve ${PT_DOMAIN}, exiting..." 2025-03-04T20:12:03.3958421Z  exit 1 2025-03-04T20:12:03.3958597Z  fi 2025-03-04T20:12:03.3958755Z fi 2025-03-04T20:12:03.3958942Z  2025-03-04T20:12:03.3959130Z if grep -r "${PT_DOMAIN}" /etc/hosts; then 2025-03-04T20:12:03.3959379Z  # Clean up any old records first 2025-03-04T20:12:03.3959626Z  sudo sed -i "/${PT_DOMAIN}/d" /etc/hosts 2025-03-04T20:12:03.3959847Z fi 2025-03-04T20:12:03.3960002Z  2025-03-04T20:12:03.3960234Z echo "${RESOLVED_IP} ${PT_DOMAIN}" | sudo tee -a /etc/hosts 2025-03-04T20:12:03.3960480Z cat /etc/hosts 2025-03-04T20:12:03.3964551Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T20:12:03.3964788Z env: 2025-03-04T20:12:03.3964954Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:12:03.3965135Z ##[endgroup] 2025-03-04T20:12:03.3985020Z + PT_DOMAIN=download.pytorch.org 2025-03-04T20:12:03.3988427Z ++ dig -4 +short download.pytorch.org 2025-03-04T20:12:03.3988731Z ++ tail -n1 2025-03-04T20:12:03.4560860Z + RESOLVED_IP=18.160.10.28 2025-03-04T20:12:03.4561126Z + '[' -z 18.160.10.28 ']' 2025-03-04T20:12:03.4561336Z + grep -r download.pytorch.org /etc/hosts 2025-03-04T20:12:03.4573321Z + echo '18.160.10.28 download.pytorch.org' 2025-03-04T20:12:03.4575966Z + sudo tee -a /etc/hosts 2025-03-04T20:12:03.7109354Z 18.160.10.28 download.pytorch.org 2025-03-04T20:12:03.7132159Z + cat /etc/hosts 2025-03-04T20:12:03.7142919Z 127.0.0.1 localhost localhost.localdomain localhost4 localhost4.localdomain4 2025-03-04T20:12:03.7148376Z ::1 localhost6 localhost6.localdomain6 2025-03-04T20:12:03.7148636Z 18.160.10.28 download.pytorch.org 2025-03-04T20:12:03.7251451Z ##[group]Run pytorch/test-infra/.github/actions/calculate-docker-image@main 2025-03-04T20:12:03.7251741Z with: 2025-03-04T20:12:03.7252199Z docker-image-name: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e 2025-03-04T20:12:03.7252696Z docker-build-dir: .ci/docker 2025-03-04T20:12:03.7252938Z working-directory: . 2025-03-04T20:12:03.7253175Z docker-registry: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-03-04T20:12:03.7253432Z force-push: false 2025-03-04T20:12:03.7253596Z env: 2025-03-04T20:12:03.7253763Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:12:03.7253943Z ##[endgroup] 2025-03-04T20:12:03.7415946Z ##[group]Run set -ex 2025-03-04T20:12:03.7416185Z set -ex 2025-03-04T20:12:03.7416341Z  2025-03-04T20:12:03.7416592Z # If the docker build directory or the build script doesn't exist, the action will 2025-03-04T20:12:03.7417018Z # gracefully return the docker image name as it is. Pulling docker image in Linux 2025-03-04T20:12:03.7417355Z # job could then download the pre-built image as usual 2025-03-04T20:12:03.7417663Z if [[ ! -d "${DOCKER_BUILD_DIR}" ]] || [[ ! -f "${DOCKER_BUILD_DIR}/build.sh" ]]; then 2025-03-04T20:12:03.7417949Z  echo "skip=true" >> "${GITHUB_OUTPUT}" 2025-03-04T20:12:03.7418233Z  echo "docker-image=${DOCKER_IMAGE_NAME}" >> "${GITHUB_OUTPUT}" 2025-03-04T20:12:03.7418480Z  2025-03-04T20:12:03.7418707Z  echo "There is no Docker build script in ${REPO_NAME} repo, skipping..." 2025-03-04T20:12:03.7418969Z  exit 0 2025-03-04T20:12:03.7419124Z else 2025-03-04T20:12:03.7419301Z  echo "skip=false" >> "${GITHUB_OUTPUT}" 2025-03-04T20:12:03.7419506Z fi 2025-03-04T20:12:03.7419648Z  2025-03-04T20:12:03.7419866Z if [[ "${DOCKER_IMAGE_NAME}" == *"${DOCKER_REGISTRY}/${REPO_NAME}"* ]]; then 2025-03-04T20:12:03.7420214Z  # The docker image name already includes the ECR prefix and tag, so we can just 2025-03-04T20:12:03.7420530Z  # use it as it is, but first let's extract the tag 2025-03-04T20:12:03.7420823Z  DOCKER_TAG=$(echo "${DOCKER_IMAGE_NAME}" | awk -F '[:,]' '{print $2}') 2025-03-04T20:12:03.7421126Z  echo "docker-tag=${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2025-03-04T20:12:03.7421420Z  echo "docker-image=${DOCKER_IMAGE_NAME}" >> "${GITHUB_OUTPUT}" 2025-03-04T20:12:03.7421666Z else 2025-03-04T20:12:03.7421875Z  DOCKER_TAG=$(git rev-parse HEAD:"${DOCKER_BUILD_DIR}") 2025-03-04T20:12:03.7422150Z  echo "docker-tag=${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2025-03-04T20:12:03.7422524Z  echo "docker-image=${DOCKER_REGISTRY}/${REPO_NAME}/${DOCKER_IMAGE_NAME}:${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2025-03-04T20:12:03.7422854Z fi 2025-03-04T20:12:03.7428882Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T20:12:03.7429120Z env: 2025-03-04T20:12:03.7429278Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:12:03.7429460Z REPO_NAME: pytorch 2025-03-04T20:12:03.7429922Z DOCKER_IMAGE_NAME: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e 2025-03-04T20:12:03.7430515Z DOCKER_BUILD_DIR: .ci/docker 2025-03-04T20:12:03.7430754Z DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-03-04T20:12:03.7430996Z ##[endgroup] 2025-03-04T20:12:03.7453357Z + [[ ! -d .ci/docker ]] 2025-03-04T20:12:03.7457546Z + [[ ! -f .ci/docker/build.sh ]] 2025-03-04T20:12:03.7461733Z + echo skip=false 2025-03-04T20:12:03.7466370Z + [[ 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e == *\3\0\8\5\3\5\3\8\5\1\1\4\.\d\k\r\.\e\c\r\.\u\s\-\e\a\s\t\-\1\.\a\m\a\z\o\n\a\w\s\.\c\o\m\/\p\y\t\o\r\c\h* ]] 2025-03-04T20:12:03.7467603Z ++ awk -F '[:,]' '{print $2}' 2025-03-04T20:12:03.7474403Z ++ echo 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e 2025-03-04T20:12:03.7483029Z + DOCKER_TAG=e4800fd93ba7d48bf4197a488fd32c12de647b0e 2025-03-04T20:12:03.7483500Z + echo docker-tag=e4800fd93ba7d48bf4197a488fd32c12de647b0e 2025-03-04T20:12:03.7484073Z + echo docker-image=308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e 2025-03-04T20:12:03.7506068Z ##[group]Run set +e 2025-03-04T20:12:03.7506310Z set +e 2025-03-04T20:12:03.7506478Z set -x 2025-03-04T20:12:03.7506636Z  2025-03-04T20:12:03.7506786Z login() { 2025-03-04T20:12:03.7507096Z  aws ecr get-login-password --region us-east-1 | docker login -u AWS --password-stdin "$1" 2025-03-04T20:12:03.7507419Z } 2025-03-04T20:12:03.7507581Z  2025-03-04T20:12:03.7507733Z retry () { 2025-03-04T20:12:03.7507926Z  $* || (sleep 1 && $*) || (sleep 2 && $*) 2025-03-04T20:12:03.7508136Z } 2025-03-04T20:12:03.7508286Z  2025-03-04T20:12:03.7508444Z retry login "${DOCKER_REGISTRY}" 2025-03-04T20:12:03.7508650Z  2025-03-04T20:12:03.7508813Z START_TIME=$(date +%s) 2025-03-04T20:12:03.7509022Z # Wait up to 120 minutes 2025-03-04T20:12:03.7509270Z while [[ $(( $(date +%s) - 7200 )) -lt $START_TIME ]]; do 2025-03-04T20:12:03.7509578Z  # Check if image already exists, if it does then skip building it 2025-03-04T20:12:03.7509888Z  if docker manifest inspect "${DOCKER_IMAGE}"; then 2025-03-04T20:12:03.7510125Z  exit 0 2025-03-04T20:12:03.7510290Z  fi 2025-03-04T20:12:03.7510446Z  2025-03-04T20:12:03.7510699Z  # NB: This flag is used by Docker build workflow to push the image to ECR, so we can 2025-03-04T20:12:03.7511099Z  # use this to differentiate between the Docker build and regular build jobs. For the 2025-03-04T20:12:03.7511495Z  # latter, it will wait for the Docker images to become available before continuing 2025-03-04T20:12:03.7511818Z  if [ "${DOCKER_PUSH:-false}" == "true" ]; then 2025-03-04T20:12:03.7512083Z  # It's a Docker build job, let's build the image 2025-03-04T20:12:03.7512312Z  break 2025-03-04T20:12:03.7512478Z  else 2025-03-04T20:12:03.7512724Z  # It's a regular build job, wait for the image to become available 2025-03-04T20:12:03.7512985Z  sleep 300 2025-03-04T20:12:03.7513161Z  fi 2025-03-04T20:12:03.7513319Z done 2025-03-04T20:12:03.7513473Z  2025-03-04T20:12:03.7513701Z # NB: This part requires a full checkout. Otherwise, the merge base will 2025-03-04T20:12:03.7514053Z # be empty. The default action would be to continue rebuild the image 2025-03-04T20:12:03.7514380Z if [[ "$BASE_REVISION" = "$(git rev-parse HEAD)" ]]; then 2025-03-04T20:12:03.7514672Z  # if we're on the base branch then use the parent commit 2025-03-04T20:12:03.7514943Z  MERGE_BASE=$(git rev-parse HEAD~) 2025-03-04T20:12:03.7515148Z else 2025-03-04T20:12:03.7515641Z  # otherwise we're on a PR, so use the most recent base commit 2025-03-04T20:12:03.7516087Z  MERGE_BASE=$(git merge-base HEAD "$BASE_REVISION") 2025-03-04T20:12:03.7516333Z fi 2025-03-04T20:12:03.7516488Z  2025-03-04T20:12:03.7516661Z if [[ -z "${MERGE_BASE}" ]]; then 2025-03-04T20:12:03.7516903Z  echo "rebuild=true" >> "${GITHUB_OUTPUT}" 2025-03-04T20:12:03.7517127Z  2025-03-04T20:12:03.7517415Z  echo "Finding merge base only works with full checkout, please set fetch-depth to 0, continuing ..." 2025-03-04T20:12:03.7517817Z  exit 0 2025-03-04T20:12:03.7517980Z fi 2025-03-04T20:12:03.7518128Z  2025-03-04T20:12:03.7518339Z if ! git rev-parse "${MERGE_BASE}:${DOCKER_BUILD_DIR}"; then 2025-03-04T20:12:03.7518738Z  echo "Directory '${DOCKER_BUILD_DIR}' not found in commit $MERGE_BASE, you should rebase onto a more recent commit" 2025-03-04T20:12:03.7519086Z  exit 1 2025-03-04T20:12:03.7519242Z fi 2025-03-04T20:12:03.7519392Z  2025-03-04T20:12:03.7519628Z PREVIOUS_DOCKER_TAG=$(git rev-parse "${MERGE_BASE}:${DOCKER_BUILD_DIR}") 2025-03-04T20:12:03.7520018Z # If no image exists but the hash is the same as the previous hash then we should error out here 2025-03-04T20:12:03.7520367Z if [[ "${PREVIOUS_DOCKER_TAG}" == "${DOCKER_TAG}" ]]; then 2025-03-04T20:12:03.7520761Z  echo "WARNING: Something has gone wrong and the previous image isn't available for the merge-base of your branch" 2025-03-04T20:12:03.7521203Z  echo " Will re-build docker image to store in local cache, TTS may be longer" 2025-03-04T20:12:03.7521479Z fi 2025-03-04T20:12:03.7521623Z  2025-03-04T20:12:03.7521804Z echo "rebuild=true" >> "${GITHUB_OUTPUT}" 2025-03-04T20:12:03.7525578Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T20:12:03.7525827Z env: 2025-03-04T20:12:03.7525989Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:12:03.7526181Z DOCKER_BUILD_DIR: .ci/docker 2025-03-04T20:12:03.7526415Z BASE_REVISION: 1b7498080987913ecb3aff6253c5e88f3540d911 2025-03-04T20:12:03.7526944Z DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e 2025-03-04T20:12:03.7527460Z DOCKER_TAG: e4800fd93ba7d48bf4197a488fd32c12de647b0e 2025-03-04T20:12:03.7527751Z DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-03-04T20:12:03.7527992Z DOCKER_PUSH: 2025-03-04T20:12:03.7528161Z ##[endgroup] 2025-03-04T20:12:03.7548606Z + retry login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-03-04T20:12:03.7550751Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-03-04T20:12:03.7551157Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-03-04T20:12:03.7551489Z + aws ecr get-login-password --region us-east-1 2025-03-04T20:12:04.1466179Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2025-03-04T20:12:04.1466538Z Login Succeeded 2025-03-04T20:12:04.1469607Z Configure a credential helper to remove this warning. See 2025-03-04T20:12:04.1469982Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2025-03-04T20:12:04.1470206Z 2025-03-04T20:12:04.1482953Z ++ date +%s 2025-03-04T20:12:04.1492644Z + START_TIME=1741119124 2025-03-04T20:12:04.1496805Z ++ date +%s 2025-03-04T20:12:04.1504004Z + [[ 1741111924 -lt 1741119124 ]] 2025-03-04T20:12:04.1506469Z + docker manifest inspect 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e 2025-03-04T20:12:04.3652745Z { 2025-03-04T20:12:04.3654565Z "schemaVersion": 2, 2025-03-04T20:12:04.3655053Z "mediaType": "application/vnd.docker.distribution.manifest.v2+json", 2025-03-04T20:12:04.3660292Z "config": { 2025-03-04T20:12:04.3662588Z "mediaType": "application/vnd.docker.container.image.v1+json", 2025-03-04T20:12:04.3663050Z "size": 42070, 2025-03-04T20:12:04.3664665Z + exit 0 2025-03-04T20:12:04.3666482Z "digest": "sha256:9eba596d1817cef362b8eee5b1a58c3110c752e1c111809994c788e5df204b1e" 2025-03-04T20:12:04.3666858Z }, 2025-03-04T20:12:04.3667034Z "layers": [ 2025-03-04T20:12:04.3667206Z { 2025-03-04T20:12:04.3667486Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:04.3667819Z "size": 30440118, 2025-03-04T20:12:04.3668455Z "digest": "sha256:8f84a9f2102e97a4a6bf673b150fc9894df5acc9618ad3484c6c36f768c1caa0" 2025-03-04T20:12:04.3668811Z }, 2025-03-04T20:12:04.3668986Z { 2025-03-04T20:12:04.3669250Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:04.3669562Z "size": 1894, 2025-03-04T20:12:04.3669891Z "digest": "sha256:546bd73ecfd84e7a8723205acc797488833a44ca24cc0c25bb4a642fd2667f85" 2025-03-04T20:12:04.3670314Z }, 2025-03-04T20:12:04.3670491Z { 2025-03-04T20:12:04.3670775Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:04.3671133Z "size": 319404697, 2025-03-04T20:12:04.3671469Z "digest": "sha256:4ba5b16f22b030e00b386b41fe894de1df517e23c8ba07ef6dfb9196a5698b7d" 2025-03-04T20:12:04.3671825Z }, 2025-03-04T20:12:04.3671988Z { 2025-03-04T20:12:04.3672268Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:04.3672534Z "size": 864, 2025-03-04T20:12:04.3672812Z "digest": "sha256:64de9d43f178279dec12e258b22e9ef47d316393fabbc0cd1a4912a8d0815070" 2025-03-04T20:12:04.3673113Z }, 2025-03-04T20:12:04.3673257Z { 2025-03-04T20:12:04.3673476Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:04.3673748Z "size": 106, 2025-03-04T20:12:04.3674023Z "digest": "sha256:db0a277d2bad9ea270266b2209b210e03d7943fdd0e281d26ba2602c3ba392af" 2025-03-04T20:12:04.3674319Z }, 2025-03-04T20:12:04.3674450Z { 2025-03-04T20:12:04.3674677Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:04.3674946Z "size": 704, 2025-03-04T20:12:04.3675216Z "digest": "sha256:70c60a789cc0da5305c8d4abb20617b35dc7e1f9c07988f57bb373e37c08294b" 2025-03-04T20:12:04.3675513Z }, 2025-03-04T20:12:04.3675653Z { 2025-03-04T20:12:04.3675962Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:04.3676225Z "size": 1216, 2025-03-04T20:12:04.3676575Z "digest": "sha256:25cadc1cd3aad6be708772f38bf0236b0658ce0fab2392354a86078c405c26d2" 2025-03-04T20:12:04.3676876Z }, 2025-03-04T20:12:04.3677013Z { 2025-03-04T20:12:04.3677246Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:04.3677525Z "size": 485, 2025-03-04T20:12:04.3677818Z "digest": "sha256:374d8cd6d29fd9af63924ff454faf0e4122fe2d8c0ce854a85bb2bc6c111ec6c" 2025-03-04T20:12:04.3678114Z }, 2025-03-04T20:12:04.3678254Z { 2025-03-04T20:12:04.3678470Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:04.3678790Z "size": 110341876, 2025-03-04T20:12:04.3679233Z "digest": "sha256:bcb2ba3f25a9c3ac7f4571fee0b939e1c77d500f6ea1047726ede05abd72df78" 2025-03-04T20:12:04.3679587Z }, 2025-03-04T20:12:04.3679766Z { 2025-03-04T20:12:04.3679998Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:04.3680277Z "size": 4180, 2025-03-04T20:12:04.3680575Z "digest": "sha256:fbe64186fd30f6eea8c73fd9de7d86af228d9c64e4ef944a9b187ceaa7809d08" 2025-03-04T20:12:04.3680911Z }, 2025-03-04T20:12:04.3681070Z { 2025-03-04T20:12:04.3681420Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:04.3681734Z "size": 1860, 2025-03-04T20:12:04.3682024Z "digest": "sha256:a4b05775098ed2bc34309b98a4e7e91f5857374bc060cb29f4e5bbf6264a15f5" 2025-03-04T20:12:04.3682334Z }, 2025-03-04T20:12:04.3682484Z { 2025-03-04T20:12:04.3682711Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:04.3683065Z "size": 700, 2025-03-04T20:12:04.3683363Z "digest": "sha256:8d1cf83bb67fdebcda602a033c508ed2e543bf42e81f61df50b0e481a4d9b8cd" 2025-03-04T20:12:04.3683681Z }, 2025-03-04T20:12:04.3683834Z { 2025-03-04T20:12:04.3684068Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:04.3684352Z "size": 477, 2025-03-04T20:12:04.3684644Z "digest": "sha256:8dfef80cec656f1902a5beae7e2452a246ba8af6899f5f2144211642acef4b30" 2025-03-04T20:12:04.3684971Z }, 2025-03-04T20:12:04.3685109Z { 2025-03-04T20:12:04.3685373Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:04.3685632Z "size": 2816726602, 2025-03-04T20:12:04.3685903Z "digest": "sha256:18cc35ebce84d4de1e3853e530c0876eaf2e0034444663f025602c8d1666ff6b" 2025-03-04T20:12:04.3686179Z }, 2025-03-04T20:12:04.3686315Z { 2025-03-04T20:12:04.3686523Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:04.3686774Z "size": 32, 2025-03-04T20:12:04.3687031Z "digest": 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"sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2025-03-04T20:12:04.3742864Z }, 2025-03-04T20:12:04.3742999Z { 2025-03-04T20:12:04.3743210Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:04.3743494Z "size": 161, 2025-03-04T20:12:04.3743748Z "digest": "sha256:67823c8824491bbcf498f0a827acd0289152643ea7b5906383d22171357f3951" 2025-03-04T20:12:04.3744023Z }, 2025-03-04T20:12:04.3744171Z { 2025-03-04T20:12:04.3744375Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:04.3744622Z "size": 765, 2025-03-04T20:12:04.3744874Z "digest": "sha256:c196c67b067c2da2bbd1ef57296cb4a7549b0574f323f572d8ae6ce6c0d89dde" 2025-03-04T20:12:04.3745151Z }, 2025-03-04T20:12:04.3745284Z { 2025-03-04T20:12:04.3745489Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:04.3745740Z "size": 700, 2025-03-04T20:12:04.3745991Z "digest": "sha256:8d1cf83bb67fdebcda602a033c508ed2e543bf42e81f61df50b0e481a4d9b8cd" 2025-03-04T20:12:04.3746264Z }, 2025-03-04T20:12:04.3746394Z { 2025-03-04T20:12:04.3746595Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:04.3746839Z "size": 139, 2025-03-04T20:12:04.3747098Z "digest": "sha256:3347939b4ff440cbabd3f7b1cfa6c15ea5bbae6ec30bc24a98f9375af3bc8990" 2025-03-04T20:12:04.3747368Z }, 2025-03-04T20:12:04.3747501Z { 2025-03-04T20:12:04.3747706Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:04.3747951Z "size": 32, 2025-03-04T20:12:04.3748202Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2025-03-04T20:12:04.3748478Z }, 2025-03-04T20:12:04.3748613Z { 2025-03-04T20:12:04.3748817Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:04.3749061Z "size": 159, 2025-03-04T20:12:04.3749318Z "digest": "sha256:01827e1cb87244e15ded148888c0eb9bf540965b8c29be07e853e9ba4cf3b327" 2025-03-04T20:12:04.3749600Z }, 2025-03-04T20:12:04.3749738Z { 2025-03-04T20:12:04.3749948Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:04.3750196Z "size": 907, 2025-03-04T20:12:04.3750455Z "digest": "sha256:9118a8d55db318a9fe5af23e1d47f21e5e622c2e1047e79cb3e3b2930e597a9d" 2025-03-04T20:12:04.3750733Z }, 2025-03-04T20:12:04.3750862Z { 2025-03-04T20:12:04.3751069Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:04.3751317Z "size": 700, 2025-03-04T20:12:04.3751575Z "digest": "sha256:8d1cf83bb67fdebcda602a033c508ed2e543bf42e81f61df50b0e481a4d9b8cd" 2025-03-04T20:12:04.3751862Z }, 2025-03-04T20:12:04.3751999Z { 2025-03-04T20:12:04.3752210Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:04.3752466Z "size": 135, 2025-03-04T20:12:04.3752736Z "digest": "sha256:aeac28871f722d7dbda3e05aed38c4b0bcdc2a4d8e804ec84a976bc76a99aabc" 2025-03-04T20:12:04.3753032Z }, 2025-03-04T20:12:04.3753171Z { 2025-03-04T20:12:04.3753386Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:04.3753640Z "size": 32, 2025-03-04T20:12:04.3753905Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2025-03-04T20:12:04.3754193Z }, 2025-03-04T20:12:04.3754335Z { 2025-03-04T20:12:04.3754544Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:04.3754802Z "size": 157, 2025-03-04T20:12:04.3755072Z "digest": "sha256:ffdbe6e013f1afea6a7c9311dfae3ecc02983a02a121a6ff568ae70373aafbbf" 2025-03-04T20:12:04.3755367Z }, 2025-03-04T20:12:04.3755580Z { 2025-03-04T20:12:04.3755801Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:04.3756068Z "size": 1484, 2025-03-04T20:12:04.3756333Z "digest": "sha256:0c5a33fac2aca608f4473808d0d00a68e18f732220bedee1952d2a926bdaf47a" 2025-03-04T20:12:04.3756716Z }, 2025-03-04T20:12:04.3756857Z { 2025-03-04T20:12:04.3757068Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:04.3757327Z "size": 32, 2025-03-04T20:12:04.3757595Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2025-03-04T20:12:04.3757882Z }, 2025-03-04T20:12:04.3758065Z { 2025-03-04T20:12:04.3758296Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:04.3758535Z "size": 136, 2025-03-04T20:12:04.3758784Z "digest": "sha256:a7db55a83f84a7bc071472509e2b539f38420428c02ac506ee4f3e16542e9e55" 2025-03-04T20:12:04.3759059Z }, 2025-03-04T20:12:04.3759194Z { 2025-03-04T20:12:04.3759399Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:04.3759649Z "size": 381, 2025-03-04T20:12:04.3759904Z "digest": "sha256:73c37d17434fd27ec9e2bad8f0241ae3342cd8e22d684df9558608bc96c488ea" 2025-03-04T20:12:04.3760180Z }, 2025-03-04T20:12:04.3760311Z { 2025-03-04T20:12:04.3760517Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:04.3760773Z "size": 32, 2025-03-04T20:12:04.3761023Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2025-03-04T20:12:04.3761298Z }, 2025-03-04T20:12:04.3761429Z { 2025-03-04T20:12:04.3761634Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:04.3761878Z "size": 104, 2025-03-04T20:12:04.3762129Z "digest": "sha256:57a20cb92dc71209bf728a591bfcc5d70752bd9025bb2cf63011a05659343445" 2025-03-04T20:12:04.3762397Z }, 2025-03-04T20:12:04.3762532Z { 2025-03-04T20:12:04.3762738Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:04.3762987Z "size": 1898, 2025-03-04T20:12:04.3763239Z "digest": "sha256:b2d315c99c689b3481609769bba07036f26cad0eef40b61f83a8011d5e835fda" 2025-03-04T20:12:04.3763511Z }, 2025-03-04T20:12:04.3763641Z { 2025-03-04T20:12:04.3763844Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:04.3764092Z "size": 196973761, 2025-03-04T20:12:04.3764356Z "digest": "sha256:e395393695a2807512bcfccbc23881949b5ff55b5a14a898ebec2f997b99525b" 2025-03-04T20:12:04.3764630Z }, 2025-03-04T20:12:04.3764759Z { 2025-03-04T20:12:04.3764967Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:04.3765211Z "size": 106, 2025-03-04T20:12:04.3765458Z "digest": "sha256:88bffb73f635ef4f012ce6418ab29266c4f99072c5b3f18eb955c682a0772361" 2025-03-04T20:12:04.3765731Z }, 2025-03-04T20:12:04.3765856Z { 2025-03-04T20:12:04.3766058Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:04.3766306Z "size": 165, 2025-03-04T20:12:04.3766559Z "digest": "sha256:e51fdae441f2679f71b02dea9e5815f1c9774288f5b47f77c31fb746a4242ef8" 2025-03-04T20:12:04.3766837Z }, 2025-03-04T20:12:04.3766971Z { 2025-03-04T20:12:04.3767175Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:04.3767421Z "size": 7943, 2025-03-04T20:12:04.3767677Z "digest": "sha256:0b5081186bc1f9d038a133cf3f4e9ea4252dc51ff4f37c0825d6486545538907" 2025-03-04T20:12:04.3767953Z }, 2025-03-04T20:12:04.3768087Z { 2025-03-04T20:12:04.3768291Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:04.3768540Z "size": 8070, 2025-03-04T20:12:04.3768796Z "digest": "sha256:b7e9be3cf393246b723dbe14ffa9b7ec8e7d0d0ea7853f0d5e32d716f33e9f2d" 2025-03-04T20:12:04.3769078Z }, 2025-03-04T20:12:04.3769213Z { 2025-03-04T20:12:04.3769413Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:04.3769657Z "size": 301, 2025-03-04T20:12:04.3769955Z "digest": "sha256:8a9d1164fe66bfa891980deecb5bef8d78ea8d875361fc3ffdcaf97c87dc2359" 2025-03-04T20:12:04.3770236Z }, 2025-03-04T20:12:04.3770372Z { 2025-03-04T20:12:04.3770576Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:04.3770825Z "size": 32, 2025-03-04T20:12:04.3771076Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2025-03-04T20:12:04.3771354Z }, 2025-03-04T20:12:04.3771487Z { 2025-03-04T20:12:04.3771696Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:04.3771945Z "size": 108, 2025-03-04T20:12:04.3772245Z "digest": "sha256:6802a480248fa1140e9cea64341ce0b85f49f65d4785ddbab23dd5b9f15da80b" 2025-03-04T20:12:04.3772523Z }, 2025-03-04T20:12:04.3772659Z { 2025-03-04T20:12:04.3772865Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:04.3773112Z "size": 54145661, 2025-03-04T20:12:04.3773386Z "digest": "sha256:007eadabfae0f9c12087b7c79a6a4982efadd5b0b56ad6cdbdc1cbbac10fda94" 2025-03-04T20:12:04.3773676Z }, 2025-03-04T20:12:04.3773809Z { 2025-03-04T20:12:04.3774017Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T20:12:04.3774268Z "size": 32, 2025-03-04T20:12:04.3774524Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2025-03-04T20:12:04.3774798Z } 2025-03-04T20:12:04.3774930Z ] 2025-03-04T20:12:04.3775063Z } 2025-03-04T20:12:04.3804681Z ##[group]Run set -eux 2025-03-04T20:12:04.3804889Z set -eux 2025-03-04T20:12:04.3805412Z aws secretsmanager get-secret-value --secret-id docker_hub_readonly_token | jq --raw-output '.SecretString' | jq -r .docker_hub_readonly_token | docker login --username pytorchbot --password-stdin 2025-03-04T20:12:04.3810692Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T20:12:04.3810949Z env: 2025-03-04T20:12:04.3811116Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:12:04.3811311Z ##[endgroup] 2025-03-04T20:12:04.3835837Z + aws secretsmanager get-secret-value --secret-id docker_hub_readonly_token 2025-03-04T20:12:04.3836332Z + jq --raw-output .SecretString 2025-03-04T20:12:04.3836758Z + jq -r .docker_hub_readonly_token 2025-03-04T20:12:04.3837111Z + docker login --username pytorchbot --password-stdin 2025-03-04T20:12:04.8541060Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2025-03-04T20:12:04.8541884Z Configure a credential helper to remove this warning. See 2025-03-04T20:12:04.8542141Z Login Succeeded 2025-03-04T20:12:04.8542494Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2025-03-04T20:12:04.8542737Z 2025-03-04T20:12:04.8619511Z ##[group]Run tag=${ECR_DOCKER_IMAGE##*/} 2025-03-04T20:12:04.8619767Z tag=${ECR_DOCKER_IMAGE##*/} 2025-03-04T20:12:04.8620024Z echo "docker pull ghcr.io/pytorch/ci-image:${tag/:/-}" 2025-03-04T20:12:04.8624546Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T20:12:04.8624805Z env: 2025-03-04T20:12:04.8624971Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:12:04.8625444Z ECR_DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e 2025-03-04T20:12:04.8625913Z ##[endgroup] 2025-03-04T20:12:04.8651134Z docker pull ghcr.io/pytorch/ci-image:pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks-e4800fd93ba7d48bf4197a488fd32c12de647b0e 2025-03-04T20:12:04.8687729Z ##[group]Run pytorch/test-infra/.github/actions/pull-docker-image@main 2025-03-04T20:12:04.8688032Z with: 2025-03-04T20:12:04.8688538Z docker-image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e 2025-03-04T20:12:04.8689135Z docker-registry: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-03-04T20:12:04.8689409Z env: 2025-03-04T20:12:04.8689591Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:12:04.8689882Z ##[endgroup] 2025-03-04T20:12:04.8705534Z ##[group]Run set -x 2025-03-04T20:12:04.8705751Z set -x 2025-03-04T20:12:04.8705929Z set +e 2025-03-04T20:12:04.8706103Z  2025-03-04T20:12:04.8706270Z login() { 2025-03-04T20:12:04.8706603Z  aws ecr get-login-password --region us-east-1 | docker login -u AWS --password-stdin "$1" 2025-03-04T20:12:04.8706942Z } 2025-03-04T20:12:04.8707108Z  2025-03-04T20:12:04.8707313Z retry () { 2025-03-04T20:12:04.8707522Z  $* || (sleep 1 && $*) || (sleep 2 && $*) 2025-03-04T20:12:04.8707749Z } 2025-03-04T20:12:04.8707911Z  2025-03-04T20:12:04.8708088Z retry login "${DOCKER_REGISTRY}" 2025-03-04T20:12:04.8708306Z  2025-03-04T20:12:04.8708466Z set -e 2025-03-04T20:12:04.8708706Z # ignore output since only exit code is used for conditional 2025-03-04T20:12:04.8709037Z # only pull docker image if it's not available locally 2025-03-04T20:12:04.8709404Z if ! docker inspect --type=image "${DOCKER_IMAGE}" >/dev/null 2>/dev/null; then 2025-03-04T20:12:04.8709734Z  retry docker pull "${DOCKER_IMAGE}" 2025-03-04T20:12:04.8709960Z fi 2025-03-04T20:12:04.8713681Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T20:12:04.8713932Z env: 2025-03-04T20:12:04.8714112Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:12:04.8714634Z DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e 2025-03-04T20:12:04.8715203Z DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-03-04T20:12:04.8715472Z ##[endgroup] 2025-03-04T20:12:04.8735953Z + set +e 2025-03-04T20:12:04.8740907Z + retry login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-03-04T20:12:04.8741238Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-03-04T20:12:04.8741574Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-03-04T20:12:04.8741922Z + aws ecr get-login-password --region us-east-1 2025-03-04T20:12:05.3443766Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2025-03-04T20:12:05.3444132Z Login Succeeded 2025-03-04T20:12:05.3444376Z Configure a credential helper to remove this warning. See 2025-03-04T20:12:05.3444784Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2025-03-04T20:12:05.3445019Z 2025-03-04T20:12:05.3460321Z + set -e 2025-03-04T20:12:05.3460982Z + docker inspect --type=image 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e 2025-03-04T20:12:05.3769600Z + retry docker pull 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e 2025-03-04T20:12:05.3770549Z + docker pull 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e 2025-03-04T20:12:05.5998965Z e4800fd93ba7d48bf4197a488fd32c12de647b0e: Pulling from pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks 2025-03-04T20:12:05.5999517Z 8f84a9f2102e: Pulling fs layer 2025-03-04T20:12:05.5999768Z 546bd73ecfd8: Pulling fs layer 2025-03-04T20:12:05.6000036Z 4ba5b16f22b0: Pulling fs layer 2025-03-04T20:12:05.6000581Z 64de9d43f178: Pulling fs layer 2025-03-04T20:12:05.6000862Z db0a277d2bad: Pulling fs layer 2025-03-04T20:12:05.6001105Z 70c60a789cc0: Pulling fs layer 2025-03-04T20:12:05.6001356Z 25cadc1cd3aa: Pulling fs layer 2025-03-04T20:12:05.6001645Z 374d8cd6d29f: Pulling fs layer 2025-03-04T20:12:05.6002066Z bcb2ba3f25a9: Pulling fs layer 2025-03-04T20:12:05.6002333Z fbe64186fd30: Pulling fs layer 2025-03-04T20:12:05.6002551Z a4b05775098e: Pulling fs layer 2025-03-04T20:12:05.6002778Z 8d1cf83bb67f: Pulling fs layer 2025-03-04T20:12:05.6003016Z 8dfef80cec65: Pulling fs layer 2025-03-04T20:12:05.6003414Z 18cc35ebce84: Pulling fs layer 2025-03-04T20:12:05.6003659Z 4f4fb700ef54: Pulling fs layer 2025-03-04T20:12:05.6003894Z a9ea0ac84712: Pulling fs layer 2025-03-04T20:12:05.6004198Z 0a3e73917936: Pulling fs layer 2025-03-04T20:12:05.6004434Z 64de9d43f178: Waiting 2025-03-04T20:12:05.6004630Z b8527246f7bb: Pulling fs layer 2025-03-04T20:12:05.6004885Z 7452d6d63f3b: Pulling fs layer 2025-03-04T20:12:05.6005117Z db0a277d2bad: Waiting 2025-03-04T20:12:05.6005338Z 0af9598ff0a6: Pulling fs layer 2025-03-04T20:12:05.6005575Z d3bd66660967: Pulling fs layer 2025-03-04T20:12:05.6005818Z 607b55cff127: Pulling fs layer 2025-03-04T20:12:05.6006062Z 7bba48dbd356: Pulling fs layer 2025-03-04T20:12:05.6006329Z d0ae79a6dc1b: Pulling fs layer 2025-03-04T20:12:05.6006561Z 8b2862ae18f2: Pulling fs layer 2025-03-04T20:12:05.6006806Z cd77873dfe7d: Pulling fs layer 2025-03-04T20:12:05.6007050Z cba5242bc2d3: Pulling fs layer 2025-03-04T20:12:05.6007296Z 2a41a4419d5d: Pulling fs layer 2025-03-04T20:12:05.6007533Z 2e7b43d07246: Pulling fs layer 2025-03-04T20:12:05.6007782Z 4df3af6ab4be: Pulling fs layer 2025-03-04T20:12:05.6008005Z fc852da9ad99: Pulling fs layer 2025-03-04T20:12:05.6008202Z 83e82bafb72c: Pulling fs layer 2025-03-04T20:12:05.6008383Z b94a63019124: Pulling fs layer 2025-03-04T20:12:05.6008558Z 814cabf595d9: Pulling fs layer 2025-03-04T20:12:05.6008741Z 9f1d98c20a29: Pulling fs layer 2025-03-04T20:12:05.6008921Z 567cde61f699: Pulling fs layer 2025-03-04T20:12:05.6009109Z dd11d363b70b: Pulling fs layer 2025-03-04T20:12:05.6009296Z 2befdfdc7dfc: Pulling fs layer 2025-03-04T20:12:05.6009475Z 70c60a789cc0: Waiting 2025-03-04T20:12:05.6009644Z 47fc8d947ed6: Pulling fs layer 2025-03-04T20:12:05.6009826Z 615680495c63: Pulling fs layer 2025-03-04T20:12:05.6010004Z 25cadc1cd3aa: Waiting 2025-03-04T20:12:05.6010173Z 56b65d8702b3: Pulling fs layer 2025-03-04T20:12:05.6010349Z 374d8cd6d29f: Waiting 2025-03-04T20:12:05.6010514Z e007be01a8bc: Pulling fs layer 2025-03-04T20:12:05.6010699Z b1c484085750: Pulling fs layer 2025-03-04T20:12:05.6010882Z 3308f34b17a2: Pulling fs layer 2025-03-04T20:12:05.6011063Z a12c1518a685: Pulling fs layer 2025-03-04T20:12:05.6011239Z 844abfd9220f: Pulling fs layer 2025-03-04T20:12:05.6011418Z bcb2ba3f25a9: Waiting 2025-03-04T20:12:05.6011585Z 35848874efa0: Pulling fs layer 2025-03-04T20:12:05.6011770Z 3d43d95bb625: Pulling fs layer 2025-03-04T20:12:05.6011949Z fbe64186fd30: Waiting 2025-03-04T20:12:05.6012122Z 2c87361802e8: Pulling fs layer 2025-03-04T20:12:05.6012309Z 4d646f8404a6: Pulling fs layer 2025-03-04T20:12:05.6012486Z a4b05775098e: Waiting 2025-03-04T20:12:05.6012660Z fd4c3c7d17dc: Pulling fs layer 2025-03-04T20:12:05.6012848Z b6c21a5c52c9: Pulling fs layer 2025-03-04T20:12:05.6013032Z 8d1cf83bb67f: Waiting 2025-03-04T20:12:05.6013204Z ce4dd732b23a: Pulling fs layer 2025-03-04T20:12:05.6013388Z 8dfef80cec65: Waiting 2025-03-04T20:12:05.6013552Z 7452d6d63f3b: Waiting 2025-03-04T20:12:05.6013724Z 5c7d6b4fa1e9: Pulling fs layer 2025-03-04T20:12:05.6013903Z 0af9598ff0a6: Waiting 2025-03-04T20:12:05.6014068Z b8527246f7bb: Waiting 2025-03-04T20:12:05.6014237Z 67823c882449: Pulling fs layer 2025-03-04T20:12:05.6014420Z 18cc35ebce84: Waiting 2025-03-04T20:12:05.6014593Z c196c67b067c: Pulling fs layer 2025-03-04T20:12:05.6014774Z d3bd66660967: Waiting 2025-03-04T20:12:05.6014949Z 3347939b4ff4: Pulling fs layer 2025-03-04T20:12:05.6015125Z 607b55cff127: Waiting 2025-03-04T20:12:05.6015286Z 4f4fb700ef54: Waiting 2025-03-04T20:12:05.6015554Z 01827e1cb872: Pulling fs layer 2025-03-04T20:12:05.6015741Z 9118a8d55db3: Pulling fs layer 2025-03-04T20:12:05.6015916Z a9ea0ac84712: Waiting 2025-03-04T20:12:05.6016085Z aeac28871f72: Pulling fs layer 2025-03-04T20:12:05.6016264Z 0a3e73917936: Waiting 2025-03-04T20:12:05.6016428Z 7bba48dbd356: Waiting 2025-03-04T20:12:05.6016596Z ffdbe6e013f1: Pulling fs layer 2025-03-04T20:12:05.6016793Z 4df3af6ab4be: Waiting 2025-03-04T20:12:05.6016966Z 0c5a33fac2ac: Pulling fs layer 2025-03-04T20:12:05.6017147Z cba5242bc2d3: Waiting 2025-03-04T20:12:05.6017363Z fc852da9ad99: Waiting 2025-03-04T20:12:05.6017526Z d0ae79a6dc1b: Waiting 2025-03-04T20:12:05.6017685Z 2a41a4419d5d: Waiting 2025-03-04T20:12:05.6017840Z 2e7b43d07246: Waiting 2025-03-04T20:12:05.6017999Z 83e82bafb72c: Waiting 2025-03-04T20:12:05.6018165Z a7db55a83f84: Pulling fs layer 2025-03-04T20:12:05.6018339Z 567cde61f699: Waiting 2025-03-04T20:12:05.6018503Z 73c37d17434f: Pulling fs layer 2025-03-04T20:12:05.6018684Z 8b2862ae18f2: Waiting 2025-03-04T20:12:05.6018848Z cd77873dfe7d: Waiting 2025-03-04T20:12:05.6019003Z b94a63019124: Waiting 2025-03-04T20:12:05.6019180Z 57a20cb92dc7: Pulling fs layer 2025-03-04T20:12:05.6019365Z b2d315c99c68: Pulling fs layer 2025-03-04T20:12:05.6019541Z dd11d363b70b: Waiting 2025-03-04T20:12:05.6019701Z 3308f34b17a2: Waiting 2025-03-04T20:12:05.6019864Z e395393695a2: Pulling fs layer 2025-03-04T20:12:05.6020047Z 88bffb73f635: Pulling fs layer 2025-03-04T20:12:05.6020228Z 814cabf595d9: Waiting 2025-03-04T20:12:05.6020395Z e51fdae441f2: Pulling fs layer 2025-03-04T20:12:05.6020583Z 0b5081186bc1: Pulling fs layer 2025-03-04T20:12:05.6020764Z 2befdfdc7dfc: Waiting 2025-03-04T20:12:05.6020929Z a12c1518a685: Waiting 2025-03-04T20:12:05.6021097Z b7e9be3cf393: Pulling fs layer 2025-03-04T20:12:05.6021274Z 615680495c63: Waiting 2025-03-04T20:12:05.6021432Z 56b65d8702b3: Waiting 2025-03-04T20:12:05.6021592Z 8a9d1164fe66: Pulling fs layer 2025-03-04T20:12:05.6021771Z b1c484085750: Waiting 2025-03-04T20:12:05.6021938Z 6802a480248f: Pulling fs layer 2025-03-04T20:12:05.6022118Z 844abfd9220f: Waiting 2025-03-04T20:12:05.6022283Z e007be01a8bc: Waiting 2025-03-04T20:12:05.6022453Z 007eadabfae0: Pulling fs layer 2025-03-04T20:12:05.6022632Z 57a20cb92dc7: Waiting 2025-03-04T20:12:05.6022791Z b2d315c99c68: Waiting 2025-03-04T20:12:05.6022951Z 47fc8d947ed6: Waiting 2025-03-04T20:12:05.6023115Z e395393695a2: Waiting 2025-03-04T20:12:05.6023273Z 88bffb73f635: Waiting 2025-03-04T20:12:05.6023432Z 73c37d17434f: Waiting 2025-03-04T20:12:05.6023589Z e51fdae441f2: Waiting 2025-03-04T20:12:05.6023751Z 0b5081186bc1: Waiting 2025-03-04T20:12:05.6023903Z 4d646f8404a6: Waiting 2025-03-04T20:12:05.6024061Z b7e9be3cf393: Waiting 2025-03-04T20:12:05.6024222Z 8a9d1164fe66: Waiting 2025-03-04T20:12:05.6024381Z aeac28871f72: Waiting 2025-03-04T20:12:05.6024541Z ffdbe6e013f1: Waiting 2025-03-04T20:12:05.6024700Z 3d43d95bb625: Waiting 2025-03-04T20:12:05.6024854Z 6802a480248f: Waiting 2025-03-04T20:12:05.6025015Z 007eadabfae0: Waiting 2025-03-04T20:12:05.6025181Z a7db55a83f84: Waiting 2025-03-04T20:12:05.6025348Z fd4c3c7d17dc: Waiting 2025-03-04T20:12:05.6025508Z b6c21a5c52c9: Waiting 2025-03-04T20:12:05.6025669Z c196c67b067c: Waiting 2025-03-04T20:12:05.6025833Z ce4dd732b23a: Waiting 2025-03-04T20:12:05.6025992Z 3347939b4ff4: Waiting 2025-03-04T20:12:05.6026147Z 67823c882449: Waiting 2025-03-04T20:12:05.6026308Z 9118a8d55db3: Waiting 2025-03-04T20:12:05.6026473Z 0c5a33fac2ac: Waiting 2025-03-04T20:12:05.6703157Z 546bd73ecfd8: Verifying Checksum 2025-03-04T20:12:05.6707740Z 546bd73ecfd8: Download complete 2025-03-04T20:12:05.7675102Z 64de9d43f178: Download complete 2025-03-04T20:12:05.8672271Z db0a277d2bad: Download complete 2025-03-04T20:12:05.9397533Z 70c60a789cc0: Download complete 2025-03-04T20:12:05.9602332Z 8f84a9f2102e: Verifying Checksum 2025-03-04T20:12:05.9602701Z 8f84a9f2102e: Download complete 2025-03-04T20:12:06.0258422Z 25cadc1cd3aa: Verifying Checksum 2025-03-04T20:12:06.0263046Z 25cadc1cd3aa: Download complete 2025-03-04T20:12:06.0482880Z 374d8cd6d29f: Verifying Checksum 2025-03-04T20:12:06.0483250Z 374d8cd6d29f: Download complete 2025-03-04T20:12:06.1376980Z fbe64186fd30: Verifying Checksum 2025-03-04T20:12:06.1381131Z fbe64186fd30: Download complete 2025-03-04T20:12:06.2033580Z a4b05775098e: Verifying Checksum 2025-03-04T20:12:06.2033969Z a4b05775098e: Download complete 2025-03-04T20:12:06.2797236Z 8d1cf83bb67f: Download complete 2025-03-04T20:12:06.3591146Z 8dfef80cec65: Verifying Checksum 2025-03-04T20:12:06.3591440Z 8dfef80cec65: Download complete 2025-03-04T20:12:07.0550649Z 8f84a9f2102e: Pull complete 2025-03-04T20:12:07.0707462Z 546bd73ecfd8: Pull complete 2025-03-04T20:12:07.1789242Z bcb2ba3f25a9: Verifying Checksum 2025-03-04T20:12:07.1789600Z bcb2ba3f25a9: Download complete 2025-03-04T20:12:07.1905011Z 4f4fb700ef54: Verifying Checksum 2025-03-04T20:12:07.1910998Z 4f4fb700ef54: Download complete 2025-03-04T20:12:07.2918478Z a9ea0ac84712: Verifying Checksum 2025-03-04T20:12:07.2919237Z a9ea0ac84712: Download complete 2025-03-04T20:12:07.3738449Z 0a3e73917936: Verifying Checksum 2025-03-04T20:12:07.3738947Z 0a3e73917936: Download complete 2025-03-04T20:12:07.4584187Z b8527246f7bb: Verifying Checksum 2025-03-04T20:12:07.4587306Z b8527246f7bb: Download complete 2025-03-04T20:12:07.5654734Z 7452d6d63f3b: Verifying Checksum 2025-03-04T20:12:07.5655426Z 7452d6d63f3b: Download complete 2025-03-04T20:12:07.6593750Z 0af9598ff0a6: Download complete 2025-03-04T20:12:07.7334367Z d3bd66660967: Verifying Checksum 2025-03-04T20:12:07.7334661Z d3bd66660967: Download complete 2025-03-04T20:12:07.8036092Z 607b55cff127: Verifying Checksum 2025-03-04T20:12:07.8036517Z 607b55cff127: Download complete 2025-03-04T20:12:07.8743543Z 7bba48dbd356: Verifying Checksum 2025-03-04T20:12:07.8744116Z 7bba48dbd356: Download complete 2025-03-04T20:12:07.9655841Z d0ae79a6dc1b: Download complete 2025-03-04T20:12:08.8655330Z 4ba5b16f22b0: Verifying Checksum 2025-03-04T20:12:08.8655704Z 4ba5b16f22b0: Download complete 2025-03-04T20:12:08.9292654Z cd77873dfe7d: Verifying Checksum 2025-03-04T20:12:08.9293969Z cd77873dfe7d: Download complete 2025-03-04T20:12:09.0183612Z cba5242bc2d3: Download complete 2025-03-04T20:12:09.0247470Z 8b2862ae18f2: Verifying Checksum 2025-03-04T20:12:09.0251076Z 8b2862ae18f2: Download complete 2025-03-04T20:12:09.1120284Z 2e7b43d07246: Verifying Checksum 2025-03-04T20:12:09.1125302Z 2e7b43d07246: Download complete 2025-03-04T20:12:09.1298851Z 2a41a4419d5d: Verifying Checksum 2025-03-04T20:12:09.1299156Z 2a41a4419d5d: Download complete 2025-03-04T20:12:09.2179490Z 4df3af6ab4be: Verifying Checksum 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2025-03-04T20:16:32.3668698Z ffdbe6e013f1: Pull complete 2025-03-04T20:16:32.7777367Z 0c5a33fac2ac: Pull complete 2025-03-04T20:16:33.7157360Z a7db55a83f84: Pull complete 2025-03-04T20:16:34.1724336Z 73c37d17434f: Pull complete 2025-03-04T20:16:35.1161780Z 57a20cb92dc7: Pull complete 2025-03-04T20:16:35.5227278Z b2d315c99c68: Pull complete 2025-03-04T20:16:43.5389873Z e395393695a2: Pull complete 2025-03-04T20:16:43.9858475Z 88bffb73f635: Pull complete 2025-03-04T20:16:44.3778531Z e51fdae441f2: Pull complete 2025-03-04T20:16:44.8218934Z 0b5081186bc1: Pull complete 2025-03-04T20:16:45.3055430Z b7e9be3cf393: Pull complete 2025-03-04T20:16:45.7746173Z 8a9d1164fe66: Pull complete 2025-03-04T20:16:46.4386258Z 6802a480248f: Pull complete 2025-03-04T20:16:48.7033395Z 007eadabfae0: Pull complete 2025-03-04T20:16:49.3557285Z Digest: sha256:bd007eb2fe9e7d1c860264514799356825b802ed452803de01a654c76280cd51 2025-03-04T20:16:49.4539415Z Status: Downloaded newer image for 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e 2025-03-04T20:16:49.5000778Z 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e 2025-03-04T20:16:49.5045201Z ##[group]Run echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-03-04T20:16:49.5045753Z echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-03-04T20:16:49.5051398Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T20:16:49.5051647Z env: 2025-03-04T20:16:49.5051812Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:16:49.5052001Z ##[endgroup] 2025-03-04T20:16:49.5122046Z Prepare all required actions 2025-03-04T20:16:49.5320349Z ##[group]Run ./.github/actions/get-workflow-job-id 2025-03-04T20:16:49.5320585Z with: 2025-03-04T20:16:49.5320919Z github-token: *** 2025-03-04T20:16:49.5321091Z env: 2025-03-04T20:16:49.5321260Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:16:49.5321449Z ##[endgroup] 2025-03-04T20:16:49.5479048Z ##[group]Run set -eux 2025-03-04T20:16:49.5479265Z set -eux 2025-03-04T20:16:49.5479677Z python3 .github/scripts/get_workflow_job_id.py "${GITHUB_RUN_ID}" "${RUNNER_NAME}" 2025-03-04T20:16:49.5484040Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T20:16:49.5484279Z env: 2025-03-04T20:16:49.5484443Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:16:49.5484851Z GITHUB_TOKEN: *** 2025-03-04T20:16:49.5485037Z ##[endgroup] 2025-03-04T20:16:49.5509005Z + python3 .github/scripts/get_workflow_job_id.py 13661696663 i-034d656ab88aab9e9 2025-03-04T20:16:50.2004454Z setting job-id=38195233363 2025-03-04T20:16:50.2004989Z setting job-name=linux-jammy-cpu-py3.9-gcc11-inductor / test (cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx) 2025-03-04T20:16:50.2337198Z ##[group]Run python3 -m pip install psutil==5.9.1 nvidia-ml-py==11.525.84 dataclasses_json==0.6.7 2025-03-04T20:16:50.2337721Z python3 -m pip install psutil==5.9.1 nvidia-ml-py==11.525.84 dataclasses_json==0.6.7 2025-03-04T20:16:50.2338085Z python3 -m tools.stats.monitor > usage_log.txt 2>&1 & 2025-03-04T20:16:50.2338402Z echo "monitor-script-pid=${!}" >> "${GITHUB_OUTPUT}" 2025-03-04T20:16:50.2343371Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T20:16:50.2343621Z env: 2025-03-04T20:16:50.2343781Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:16:50.2343967Z JOB_ID: 38195233363 2025-03-04T20:16:50.2344296Z JOB_NAME: linux-jammy-cpu-py3.9-gcc11-inductor / test (cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx) 2025-03-04T20:16:50.2344648Z WORKFLOW_NAME: inductor 2025-03-04T20:16:50.2344840Z WORKFLOW_RUN_ID: 13661696663 2025-03-04T20:16:50.2345027Z ##[endgroup] 2025-03-04T20:16:51.0689008Z Defaulting to user installation because normal site-packages is not writeable 2025-03-04T20:16:51.3276912Z Collecting psutil==5.9.1 2025-03-04T20:16:51.3474397Z Downloading psutil-5.9.1-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (281 kB) 2025-03-04T20:16:51.4558519Z Collecting nvidia-ml-py==11.525.84 2025-03-04T20:16:51.4590983Z Downloading nvidia_ml_py-11.525.84-py3-none-any.whl (34 kB) 2025-03-04T20:16:51.5367229Z Collecting dataclasses_json==0.6.7 2025-03-04T20:16:51.5395187Z Downloading dataclasses_json-0.6.7-py3-none-any.whl (28 kB) 2025-03-04T20:16:51.5788250Z Collecting typing-inspect<1,>=0.4.0 2025-03-04T20:16:51.5816617Z Downloading typing_inspect-0.9.0-py3-none-any.whl (8.8 kB) 2025-03-04T20:16:51.6747340Z Collecting marshmallow<4.0.0,>=3.18.0 2025-03-04T20:16:51.6777811Z Downloading marshmallow-3.26.1-py3-none-any.whl (50 kB) 2025-03-04T20:16:51.7485595Z Collecting packaging>=17.0 2025-03-04T20:16:51.7514061Z Downloading packaging-24.2-py3-none-any.whl (65 kB) 2025-03-04T20:16:51.8534619Z Collecting mypy-extensions>=0.3.0 2025-03-04T20:16:51.8566750Z Downloading mypy_extensions-1.0.0-py3-none-any.whl (4.7 kB) 2025-03-04T20:16:52.0038112Z Collecting typing-extensions>=3.7.4 2025-03-04T20:16:52.0067929Z Downloading typing_extensions-4.12.2-py3-none-any.whl (37 kB) 2025-03-04T20:16:52.2683699Z Installing collected packages: typing-extensions, packaging, mypy-extensions, typing-inspect, marshmallow, psutil, nvidia-ml-py, dataclasses-json 2025-03-04T20:16:53.1256259Z Successfully installed dataclasses-json-0.6.7 marshmallow-3.26.1 mypy-extensions-1.0.0 nvidia-ml-py-11.525.84 packaging-24.2 psutil-5.9.1 typing-extensions-4.12.2 typing-inspect-0.9.0 2025-03-04T20:16:53.3934292Z Prepare all required actions 2025-03-04T20:16:53.3934650Z Getting action download info 2025-03-04T20:16:53.5316091Z Download action repository 'seemethere/download-artifact-s3@v4' (SHA:1da556a7aa0a088e3153970611f6c432d58e80e6) 2025-03-04T20:16:54.3536815Z Download action repository 'actions/download-artifact@v4' (SHA:cc203385981b70ca67e1cc392babf9cc229d5806) 2025-03-04T20:16:57.3400353Z ##[group]Run ./.github/actions/download-build-artifacts 2025-03-04T20:16:57.3400643Z with: 2025-03-04T20:16:57.3400844Z name: linux-jammy-py3.9-gcc11-build 2025-03-04T20:16:57.3401108Z s3-bucket: gha-artifacts 2025-03-04T20:16:57.3401327Z env: 2025-03-04T20:16:57.3401671Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:16:57.3402041Z ##[endgroup] 2025-03-04T20:16:57.3719915Z ##[group]Run seemethere/download-artifact-s3@v4 2025-03-04T20:16:57.3720234Z with: 2025-03-04T20:16:57.3720428Z name: linux-jammy-py3.9-gcc11-build 2025-03-04T20:16:57.3720643Z s3-bucket: gha-artifacts 2025-03-04T20:16:57.3720881Z region: us-east-1 2025-03-04T20:16:57.3721045Z env: 2025-03-04T20:16:57.3721195Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:16:57.3721374Z ##[endgroup] 2025-03-04T20:16:58.0452758Z (node:45126) NOTE: We are formalizing our plans to enter AWS SDK for JavaScript (v2) into maintenance mode in 2023. 2025-03-04T20:16:58.0456841Z 2025-03-04T20:16:58.0458855Z Please migrate your code to use AWS SDK for JavaScript (v3). 2025-03-04T20:16:58.0461832Z For more information, check the migration guide at https://a.co/7PzMCcy 2025-03-04T20:16:58.0462343Z (Use `node --trace-warnings ...` to show where the warning was created) 2025-03-04T20:16:59.5885069Z Found 1 objects with prefix pytorch/pytorch/13661696663/linux-jammy-py3.9-gcc11-build/ 2025-03-04T20:16:59.5885693Z Starting download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/artifacts.zip 2025-03-04T20:17:03.5468693Z Finished download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/artifacts.zip 2025-03-04T20:17:03.5471031Z Artifact download has finished successfully 2025-03-04T20:17:03.5832998Z ##[group]Run unzip -o artifacts.zip 2025-03-04T20:17:03.5833248Z unzip -o artifacts.zip 2025-03-04T20:17:03.5837782Z shell: /usr/bin/bash --noprofile --norc -e -o 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-H 2025-03-04T20:17:09.4335814Z df -H 2025-03-04T20:17:09.4339993Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T20:17:09.4340247Z env: 2025-03-04T20:17:09.4340424Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:17:09.4340604Z ##[endgroup] 2025-03-04T20:17:09.4390981Z Filesystem Size Used Avail Use% Mounted on 2025-03-04T20:17:09.4391271Z devtmpfs 4.2M 0 4.2M 0% /dev 2025-03-04T20:17:09.4391519Z tmpfs 67G 0 67G 0% /dev/shm 2025-03-04T20:17:09.4391746Z tmpfs 27G 783k 27G 1% /run 2025-03-04T20:17:09.4391961Z /dev/nvme0n1p1 215G 46G 169G 22% / 2025-03-04T20:17:09.4392181Z tmpfs 67G 13k 67G 1% /tmp 2025-03-04T20:17:09.4392423Z /dev/nvme0n1p128 11M 1.4M 9.2M 13% /boot/efi 2025-03-04T20:17:09.4414470Z Prepare all required actions 2025-03-04T20:17:09.4415307Z Getting action download info 2025-03-04T20:17:09.5934248Z ##[group]Run ./.github/actions/download-td-artifacts 2025-03-04T20:17:09.5934501Z with: 2025-03-04T20:17:09.5934665Z env: 2025-03-04T20:17:09.5934827Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:17:09.5935016Z ##[endgroup] 2025-03-04T20:17:09.6255152Z ##[group]Run seemethere/download-artifact-s3@v4 2025-03-04T20:17:09.6255390Z with: 2025-03-04T20:17:09.6255555Z name: td_results 2025-03-04T20:17:09.6255730Z s3-bucket: gha-artifacts 2025-03-04T20:17:09.6255918Z region: us-east-1 2025-03-04T20:17:09.6256096Z env: 2025-03-04T20:17:09.6256252Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:17:09.6256428Z ##[endgroup] 2025-03-04T20:17:09.9526355Z (node:45145) NOTE: We are formalizing our plans to enter AWS SDK for JavaScript (v2) into maintenance mode in 2023. 2025-03-04T20:17:09.9526805Z 2025-03-04T20:17:09.9527129Z Please migrate your code to use AWS SDK for JavaScript (v3). 2025-03-04T20:17:09.9527495Z For more information, check the migration guide at https://a.co/7PzMCcy 2025-03-04T20:17:09.9527950Z (Use `node --trace-warnings ...` to show where the warning was created) 2025-03-04T20:17:10.0443300Z Found 0 objects with prefix pytorch/pytorch/13661696663/td_results/ 2025-03-04T20:17:10.0450146Z Artifact download has finished successfully 2025-03-04T20:17:10.0788898Z ##[group]Run mkdir -p .additional_ci_files 2025-03-04T20:17:10.0789169Z mkdir -p .additional_ci_files 2025-03-04T20:17:10.0789451Z mv td_results.json .additional_ci_files/td_results.json || true 2025-03-04T20:17:10.0795227Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T20:17:10.0795477Z env: 2025-03-04T20:17:10.0795635Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:17:10.0795821Z ##[endgroup] 2025-03-04T20:17:10.0838110Z mv: cannot stat 'td_results.json': No such file or directory 2025-03-04T20:17:10.0991938Z ##[group]Run .github/scripts/parse_ref.py 2025-03-04T20:17:10.0992208Z .github/scripts/parse_ref.py 2025-03-04T20:17:10.0996866Z shell: /usr/bin/bash -e {0} 2025-03-04T20:17:10.0997064Z env: 2025-03-04T20:17:10.0997229Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:17:10.0997421Z ##[endgroup] 2025-03-04T20:17:10.1237826Z Prepare all required actions 2025-03-04T20:17:10.1238150Z Getting action download info 2025-03-04T20:17:10.2972153Z ##[group]Run ./.github/actions/filter-test-configs 2025-03-04T20:17:10.2972401Z with: 2025-03-04T20:17:10.2972755Z github-token: *** 2025-03-04T20:17:10.2974780Z test-matrix: {"include": [{"config": "cpu_inductor_torchbench", "shard": 1, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "cpu_inductor_torchbench", "shard": 2, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_huggingface", "shard": 1, "num_shards": 1, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_timm", "shard": 1, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_timm", "shard": 2, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_torchbench", "shard": 1, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_torchbench", "shard": 2, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "inductor_torchbench_cpu_smoketest_perf", "shard": 1, "num_shards": 1, "runner": "linux.24xl.spr-metal"}]} 2025-03-04T20:17:10.2976411Z job-name: linux-jammy-cpu-py3.9-gcc11-inductor / test (cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx) 2025-03-04T20:17:10.2976747Z env: 2025-03-04T20:17:10.2976905Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:17:10.2977088Z ##[endgroup] 2025-03-04T20:17:10.3151882Z ##[group]Run nick-fields/retry@v3.0.0 2025-03-04T20:17:10.3152187Z with: 2025-03-04T20:17:10.3152450Z shell: bash 2025-03-04T20:17:10.3153032Z timeout_minutes: 10 2025-03-04T20:17:10.3153405Z max_attempts: 5 2025-03-04T20:17:10.3153650Z retry_wait_seconds: 30 2025-03-04T20:17:10.3154362Z command: set -eux # PyYAML 6.0 doesn't work with MacOS x86 anymore # This must run on Python-3.7 (AmazonLinux2) so can't use request=3.32.2 python3 -m pip install requests==2.27.1 pyyaml==6.0.1 2025-03-04T20:17:10.3155123Z polling_interval_seconds: 1 2025-03-04T20:17:10.3155410Z warning_on_retry: true 2025-03-04T20:17:10.3155652Z continue_on_error: false 2025-03-04T20:17:10.3155887Z env: 2025-03-04T20:17:10.3156080Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:17:10.3156664Z GITHUB_TOKEN: *** 2025-03-04T20:17:10.3156886Z ##[endgroup] 2025-03-04T20:17:10.3933423Z + python3 -m pip install requests==2.27.1 pyyaml==6.0.1 2025-03-04T20:17:10.5617958Z Defaulting to user installation because normal site-packages is not writeable 2025-03-04T20:17:10.7530033Z Collecting requests==2.27.1 2025-03-04T20:17:10.7716235Z Downloading requests-2.27.1-py2.py3-none-any.whl (63 kB) 2025-03-04T20:17:10.9761671Z Collecting pyyaml==6.0.1 2025-03-04T20:17:10.9793342Z Downloading PyYAML-6.0.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (738 kB) 2025-03-04T20:17:11.0532028Z Requirement already satisfied: idna<4,>=2.5 in /usr/lib/python3.9/site-packages (from requests==2.27.1) (2.10) 2025-03-04T20:17:11.2922885Z Collecting charset-normalizer~=2.0.0 2025-03-04T20:17:11.2954284Z Downloading charset_normalizer-2.0.12-py3-none-any.whl (39 kB) 2025-03-04T20:17:11.3411262Z Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/lib/python3.9/site-packages (from requests==2.27.1) (1.25.10) 2025-03-04T20:17:11.4352066Z Collecting certifi>=2017.4.17 2025-03-04T20:17:11.4400429Z Downloading certifi-2025.1.31-py3-none-any.whl (166 kB) 2025-03-04T20:17:11.5547292Z Installing collected packages: charset-normalizer, certifi, requests, pyyaml 2025-03-04T20:17:11.9889649Z Successfully installed certifi-2025.1.31 charset-normalizer-2.0.12 pyyaml-6.0.1 requests-2.27.1 2025-03-04T20:17:12.3768135Z Command completed after 1 attempt(s). 2025-03-04T20:17:12.4011748Z ##[group]Run set -x 2025-03-04T20:17:12.4011976Z set -x 2025-03-04T20:17:12.4012142Z  2025-03-04T20:17:12.4012407Z # Use relative path here as this could be checked out anywhere, not necessarily 2025-03-04T20:17:12.4012716Z # in runner workspace 2025-03-04T20:17:12.4013120Z python3 "${GITHUB_ACTION_PATH}/../../scripts/parse_ref.py" 2025-03-04T20:17:12.4018056Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T20:17:12.4018300Z env: 2025-03-04T20:17:12.4018469Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:17:12.4018660Z ##[endgroup] 2025-03-04T20:17:12.4067076Z + python3 /home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/filter-test-configs/../../scripts/parse_ref.py 2025-03-04T20:17:12.4494236Z ##[group]Run echo "Workflow: ${GITHUB_WORKFLOW}" 2025-03-04T20:17:12.4494542Z echo "Workflow: ${GITHUB_WORKFLOW}" 2025-03-04T20:17:12.4494769Z echo "Job name: ${JOB_NAME}" 2025-03-04T20:17:12.4494974Z  2025-03-04T20:17:12.4495225Z # Use relative path here as this could be checked out anywhere, not necessarily 2025-03-04T20:17:12.4495529Z # in runner workspace 2025-03-04T20:17:12.4495815Z python3 "${GITHUB_ACTION_PATH}/../../scripts/filter_test_configs.py" \ 2025-03-04T20:17:12.4496132Z  --workflow "${GITHUB_WORKFLOW}" \ 2025-03-04T20:17:12.4496350Z  --job-name "${JOB_NAME}" \ 2025-03-04T20:17:12.4497859Z  --test-matrix "{"include": [{"config": "cpu_inductor_torchbench", "shard": 1, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "cpu_inductor_torchbench", "shard": 2, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_huggingface", "shard": 1, "num_shards": 1, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_timm", "shard": 1, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_timm", "shard": 2, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_torchbench", "shard": 1, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_torchbench", "shard": 2, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "inductor_torchbench_cpu_smoketest_perf", "shard": 1, "num_shards": 1, "runner": "linux.24xl.spr-metal"}]}" \ 2025-03-04T20:17:12.4499383Z  --selected-test-configs "" \ 2025-03-04T20:17:12.4499605Z  --pr-number "${PR_NUMBER}" \ 2025-03-04T20:17:12.4499808Z  --tag "${TAG}" \ 2025-03-04T20:17:12.4500010Z  --event-name "${EVENT_NAME}" \ 2025-03-04T20:17:12.4500222Z  --schedule "${SCHEDULE}" \ 2025-03-04T20:17:12.4500426Z  --branch "${HEAD_BRANCH}" 2025-03-04T20:17:12.4505727Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T20:17:12.4505989Z env: 2025-03-04T20:17:12.4506164Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:17:12.4506574Z GITHUB_TOKEN: *** 2025-03-04T20:17:12.4506906Z JOB_NAME: linux-jammy-cpu-py3.9-gcc11-inductor / test (cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx) 2025-03-04T20:17:12.4507255Z PR_NUMBER: 2025-03-04T20:17:12.4507434Z TAG: ciflow/inductor/148205 2025-03-04T20:17:12.4507635Z EVENT_NAME: push 2025-03-04T20:17:12.4507808Z SCHEDULE: 2025-03-04T20:17:12.4507968Z HEAD_BRANCH: 2025-03-04T20:17:12.4508144Z ##[endgroup] 2025-03-04T20:17:12.4530596Z Workflow: inductor 2025-03-04T20:17:12.4535124Z Job name: linux-jammy-cpu-py3.9-gcc11-inductor / test (cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx) 2025-03-04T20:17:12.6845560Z INFO:root:Found no test-config label on the PR, so all test configs are included 2025-03-04T20:17:12.8208831Z ##[group]Run echo "Filtered matrix:" 2025-03-04T20:17:12.8209211Z echo "Filtered matrix:" 2025-03-04T20:17:12.8210847Z echo "{"include": [{"config": "cpu_inductor_torchbench", "shard": 1, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "cpu_inductor_torchbench", "shard": 2, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_huggingface", "shard": 1, "num_shards": 1, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_timm", "shard": 1, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_timm", "shard": 2, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_torchbench", "shard": 1, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_torchbench", "shard": 2, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "inductor_torchbench_cpu_smoketest_perf", "shard": 1, "num_shards": 1, "runner": "linux.24xl.spr-metal"}]}" 2025-03-04T20:17:12.8212310Z  2025-03-04T20:17:12.8212461Z echo 2025-03-04T20:17:12.8212662Z echo "Is the current job unstable? False" 2025-03-04T20:17:12.8212887Z  2025-03-04T20:17:12.8213040Z echo 2025-03-04T20:17:12.8213228Z echo "Is keep-going label set? False" 2025-03-04T20:17:12.8213442Z  2025-03-04T20:17:12.8213590Z echo 2025-03-04T20:17:12.8213759Z echo "Renabled issues? " 2025-03-04T20:17:12.8218294Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T20:17:12.8218539Z env: 2025-03-04T20:17:12.8218706Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:17:12.8218890Z ##[endgroup] 2025-03-04T20:17:12.8239401Z Filtered matrix: 2025-03-04T20:17:12.8240958Z {include: [{config: cpu_inductor_torchbench, shard: 1, num_shards: 2, runner: linux.8xlarge.amx}, {config: cpu_inductor_torchbench, shard: 2, num_shards: 2, runner: linux.8xlarge.amx}, {config: dynamic_cpu_inductor_huggingface, shard: 1, num_shards: 1, runner: linux.8xlarge.amx}, {config: dynamic_cpu_inductor_timm, shard: 1, num_shards: 2, runner: linux.8xlarge.amx}, {config: dynamic_cpu_inductor_timm, shard: 2, num_shards: 2, runner: linux.8xlarge.amx}, {config: dynamic_cpu_inductor_torchbench, shard: 1, num_shards: 2, runner: linux.8xlarge.amx}, {config: dynamic_cpu_inductor_torchbench, shard: 2, num_shards: 2, runner: linux.8xlarge.amx}, {config: inductor_torchbench_cpu_smoketest_perf, shard: 1, num_shards: 1, runner: linux.24xl.spr-metal}]} 2025-03-04T20:17:12.8242398Z 2025-03-04T20:17:12.8242483Z Is the current job unstable? False 2025-03-04T20:17:12.8242626Z 2025-03-04T20:17:12.8242707Z Is keep-going label set? False 2025-03-04T20:17:12.8242842Z 2025-03-04T20:17:12.8242954Z Renabled issues? 2025-03-04T20:17:12.8526583Z ##[group]Run echo "timeout=$((JOB_TIMEOUT-30))" >> "${GITHUB_OUTPUT}" 2025-03-04T20:17:12.8526929Z echo "timeout=$((JOB_TIMEOUT-30))" >> "${GITHUB_OUTPUT}" 2025-03-04T20:17:12.8530940Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T20:17:12.8531183Z env: 2025-03-04T20:17:12.8531364Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:17:12.8531544Z JOB_TIMEOUT: 240 2025-03-04T20:17:12.8531701Z ##[endgroup] 2025-03-04T20:17:12.8844410Z ##[group]Run set -x 2025-03-04T20:17:12.8844706Z set -x 2025-03-04T20:17:12.8844864Z  2025-03-04T20:17:12.8845220Z if [[ $TEST_CONFIG == 'multigpu' ]]; then 2025-03-04T20:17:12.8845476Z  TEST_COMMAND=.ci/pytorch/multigpu-test.sh 2025-03-04T20:17:12.8845731Z elif [[ $BUILD_ENVIRONMENT == *onnx* ]]; then 2025-03-04T20:17:12.8845966Z  TEST_COMMAND=.ci/onnx/test.sh 2025-03-04T20:17:12.8846172Z else 2025-03-04T20:17:12.8846350Z  TEST_COMMAND=.ci/pytorch/test.sh 2025-03-04T20:17:12.8846553Z fi 2025-03-04T20:17:12.8846701Z  2025-03-04T20:17:12.8846885Z # Leaving 1GB for the runner and other things 2025-03-04T20:17:12.8847234Z TOTAL_AVAILABLE_MEMORY_IN_GB=$(awk '/MemTotal/ { printf "%.3f \n", $2/1024/1024 - 1 }' /proc/meminfo) 2025-03-04T20:17:12.8847865Z # https://docs.docker.com/engine/containers/resource_constraints/#--memory-swap-details, the 3GB swap 2025-03-04T20:17:12.8848274Z # comes from https://github.com/pytorch/test-infra/pull/6058 2025-03-04T20:17:12.8848594Z TOTAL_MEMORY_WITH_SWAP=$(("${TOTAL_AVAILABLE_MEMORY_IN_GB%.*}" + 3)) 2025-03-04T20:17:12.8848867Z  2025-03-04T20:17:12.8849106Z if [[ ${BUILD_ENVIRONMENT} == *"s390x"* ]]; then 2025-03-04T20:17:12.8849331Z  SHM_OPTS= 2025-03-04T20:17:12.8849507Z  JENKINS_USER= 2025-03-04T20:17:12.8849739Z  # ensure that docker container cleanly exits in 12 hours 2025-03-04T20:17:12.8850026Z  # if for some reason cleanup action doesn't stop container 2025-03-04T20:17:12.8850274Z  # when job is cancelled 2025-03-04T20:17:12.8850485Z  DOCKER_SHELL_CMD="sleep 12h" 2025-03-04T20:17:12.8850680Z  2025-03-04T20:17:12.8850917Z  # since some steps are skipped on s390x, if they are necessary, run them here 2025-03-04T20:17:12.8851247Z  env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2025-03-04T20:17:12.8851522Z  env | grep '^CI' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2025-03-04T20:17:12.8851745Z else 2025-03-04T20:17:12.8851924Z  SHM_OPTS="--shm-size=${SHM_SIZE}" 2025-03-04T20:17:12.8852141Z  JENKINS_USER="--user jenkins" 2025-03-04T20:17:12.8852346Z  DOCKER_SHELL_CMD= 2025-03-04T20:17:12.8852522Z fi 2025-03-04T20:17:12.8852667Z  2025-03-04T20:17:12.8852885Z # detached container should get cleaned up by teardown_ec2_linux 2025-03-04T20:17:12.8853205Z # TODO: Stop building test binaries as part of the build phase 2025-03-04T20:17:12.8853566Z # Used for GPU_FLAG, SHM_OPTS, JENKINS_USER and DOCKER_SHELL_CMD since that doesn't play nice 2025-03-04T20:17:12.8853883Z # shellcheck disable=SC2086,SC2090 2025-03-04T20:17:12.8854103Z container_name=$(docker run \ 2025-03-04T20:17:12.8854307Z  ${GPU_FLAG:-} \ 2025-03-04T20:17:12.8854514Z  ${SCCACHE_SERVER_PORT_DOCKER_FLAG:-} \ 2025-03-04T20:17:12.8854742Z  -e BUILD_ENVIRONMENT \ 2025-03-04T20:17:12.8854941Z  -e PR_NUMBER \ 2025-03-04T20:17:12.8855126Z  -e GITHUB_ACTIONS \ 2025-03-04T20:17:12.8855316Z  -e GITHUB_REPOSITORY \ 2025-03-04T20:17:12.8855516Z  -e GITHUB_WORKFLOW \ 2025-03-04T20:17:12.8855708Z  -e GITHUB_JOB \ 2025-03-04T20:17:12.8855891Z  -e GITHUB_RUN_ID \ 2025-03-04T20:17:12.8856078Z  -e GITHUB_RUN_NUMBER \ 2025-03-04T20:17:12.8856274Z  -e GITHUB_RUN_ATTEMPT \ 2025-03-04T20:17:12.8856497Z  -e JOB_ID \ 2025-03-04T20:17:12.8856678Z  -e JOB_NAME \ 2025-03-04T20:17:12.8856859Z  -e BASE_SHA \ 2025-03-04T20:17:12.8857033Z  -e BRANCH \ 2025-03-04T20:17:12.8857203Z  -e SHA1 \ 2025-03-04T20:17:12.8857380Z  -e AWS_DEFAULT_REGION \ 2025-03-04T20:17:12.8857655Z  -e IN_WHEEL_TEST \ 2025-03-04T20:17:12.8857847Z  -e SHARD_NUMBER \ 2025-03-04T20:17:12.8858034Z  -e TEST_CONFIG \ 2025-03-04T20:17:12.8858218Z  -e NUM_TEST_SHARDS \ 2025-03-04T20:17:12.8858411Z  -e REENABLED_ISSUES \ 2025-03-04T20:17:12.8858614Z  -e CONTINUE_THROUGH_ERROR \ 2025-03-04T20:17:12.8858817Z  -e VERBOSE_TEST_LOGS \ 2025-03-04T20:17:12.8859011Z  -e TEST_SHOWLOCALS \ 2025-03-04T20:17:12.8859203Z  -e NO_TEST_TIMEOUT \ 2025-03-04T20:17:12.8859390Z  -e NO_TD \ 2025-03-04T20:17:12.8859558Z  -e TD_DISTRIBUTED \ 2025-03-04T20:17:12.8859748Z  -e PR_LABELS \ 2025-03-04T20:17:12.8859950Z  -e MAX_JOBS="$(nproc --ignore=2)" \ 2025-03-04T20:17:12.8860168Z  -e SCCACHE_BUCKET \ 2025-03-04T20:17:12.8860354Z  -e SCCACHE_REGION \ 2025-03-04T20:17:12.8860580Z  -e XLA_CUDA \ 2025-03-04T20:17:12.8860773Z  -e XLA_CLANG_CACHE_S3_BUCKET_NAME \ 2025-03-04T20:17:12.8861004Z  -e PYTORCH_TEST_CUDA_MEM_LEAK_CHECK \ 2025-03-04T20:17:12.8861238Z  -e PYTORCH_TEST_RERUN_DISABLED_TESTS \ 2025-03-04T20:17:12.8861468Z  -e SKIP_SCCACHE_INITIALIZATION=1 \ 2025-03-04T20:17:12.8861688Z  -e HUGGING_FACE_HUB_TOKEN \ 2025-03-04T20:17:12.8861903Z  -e SCRIBE_GRAPHQL_ACCESS_TOKEN \ 2025-03-04T20:17:12.8862110Z  -e DASHBOARD_TAG \ 2025-03-04T20:17:12.8862299Z  -e IS_A100_RUNNER \ 2025-03-04T20:17:12.8862494Z  -e ARTIFACTS_FILE_SUFFIX \ 2025-03-04T20:17:12.8862728Z  --memory="${TOTAL_AVAILABLE_MEMORY_IN_GB%.*}g" \ 2025-03-04T20:17:12.8862989Z  --memory-swap="${TOTAL_MEMORY_WITH_SWAP}g" \ 2025-03-04T20:17:12.8863251Z  --env-file="/tmp/github_env_${GITHUB_RUN_ID}" \ 2025-03-04T20:17:12.8863499Z  --security-opt seccomp=unconfined \ 2025-03-04T20:17:12.8863724Z  --cap-add=SYS_PTRACE \ 2025-03-04T20:17:12.8863925Z  --ipc=host \ 2025-03-04T20:17:12.8864097Z  ${SHM_OPTS} \ 2025-03-04T20:17:12.8864274Z  --tty \ 2025-03-04T20:17:12.8864441Z  --detach \ 2025-03-04T20:17:12.8864623Z  --name="${container_name}" \ 2025-03-04T20:17:12.8864830Z  ${JENKINS_USER} \ 2025-03-04T20:17:12.8865060Z  -v "${GITHUB_WORKSPACE}:/var/lib/jenkins/workspace" \ 2025-03-04T20:17:12.8865308Z  -w /var/lib/jenkins/workspace \ 2025-03-04T20:17:12.8865514Z  "${DOCKER_IMAGE}" \ 2025-03-04T20:17:12.8865700Z  ${DOCKER_SHELL_CMD} 2025-03-04T20:17:12.8865877Z ) 2025-03-04T20:17:12.8866075Z # Propagate download.pytorch.org IP to container 2025-03-04T20:17:12.8866469Z grep download.pytorch.org /etc/hosts | docker exec -i "${container_name}" sudo bash -c "/bin/cat >> /etc/hosts" 2025-03-04T20:17:12.8866885Z echo "DOCKER_CONTAINER_ID=${container_name}" >> "${GITHUB_ENV}" 2025-03-04T20:17:12.8867138Z  2025-03-04T20:17:12.8867319Z if [[ ${BUILD_ENVIRONMENT} == *"s390x"* ]]; then 2025-03-04T20:17:12.8867662Z  docker exec -t "${container_name}" sh -c "python3 -m pip install -r .ci/docker/requirements-ci.txt" 2025-03-04T20:17:12.8867968Z fi 2025-03-04T20:17:12.8868117Z  2025-03-04T20:17:12.8868410Z docker exec -t "${container_name}" sh -c "python3 -m pip install $(echo dist/*.whl)[opt-einsum] && ${TEST_COMMAND}" 2025-03-04T20:17:12.8872339Z shell: /usr/bin/bash -e {0} 2025-03-04T20:17:12.8872523Z env: 2025-03-04T20:17:12.8872682Z GIT_DEFAULT_BRANCH: main 2025-03-04T20:17:12.8872898Z BUILD_ENVIRONMENT: linux-jammy-py3.9-gcc11-build 2025-03-04T20:17:12.8873276Z PR_NUMBER: 2025-03-04T20:17:12.8873455Z GITHUB_REPOSITORY: pytorch/pytorch 2025-03-04T20:17:12.8873668Z GITHUB_WORKFLOW: inductor 2025-03-04T20:17:12.8873846Z GITHUB_JOB: test 2025-03-04T20:17:12.8874034Z GITHUB_RUN_ID: 13661696663 2025-03-04T20:17:12.8874291Z GITHUB_RUN_NUMBER: 120837 2025-03-04T20:17:12.8874477Z GITHUB_RUN_ATTEMPT: 1 2025-03-04T20:17:12.8874654Z JOB_ID: 38195233363 2025-03-04T20:17:12.8874978Z JOB_NAME: linux-jammy-cpu-py3.9-gcc11-inductor / test (cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx) 2025-03-04T20:17:12.8875320Z BRANCH: 2025-03-04T20:17:12.8875502Z SHA1: 1b7498080987913ecb3aff6253c5e88f3540d911 2025-03-04T20:17:12.8875749Z BASE_SHA: 1b7498080987913ecb3aff6253c5e88f3540d911 2025-03-04T20:17:12.8875984Z TEST_CONFIG: cpu_inductor_torchbench 2025-03-04T20:17:12.8876192Z SHARD_NUMBER: 1 2025-03-04T20:17:12.8876360Z NUM_TEST_SHARDS: 2 2025-03-04T20:17:12.8876532Z REENABLED_ISSUES: 2025-03-04T20:17:12.8876710Z CONTINUE_THROUGH_ERROR: False 2025-03-04T20:17:12.8876905Z VERBOSE_TEST_LOGS: False 2025-03-04T20:17:12.8877087Z TEST_SHOWLOCALS: False 2025-03-04T20:17:12.8877266Z NO_TEST_TIMEOUT: False 2025-03-04T20:17:12.8877485Z NO_TD: False 2025-03-04T20:17:12.8877644Z TD_DISTRIBUTED: False 2025-03-04T20:17:12.8877861Z SCCACHE_BUCKET: ossci-compiler-cache-circleci-v2 2025-03-04T20:17:12.8878100Z SCCACHE_REGION: us-east-1 2025-03-04T20:17:12.8878283Z SHM_SIZE: 1g 2025-03-04T20:17:12.8878735Z DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e 2025-03-04T20:17:12.8879206Z XLA_CUDA: 2025-03-04T20:17:12.8879443Z XLA_CLANG_CACHE_S3_BUCKET_NAME: ossci-compiler-clang-cache-circleci-xla 2025-03-04T20:17:12.8879732Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK: 0 2025-03-04T20:17:12.8879949Z PYTORCH_TEST_RERUN_DISABLED_TESTS: 0 2025-03-04T20:17:12.8880147Z DASHBOARD_TAG: 2025-03-04T20:17:12.8880466Z HUGGING_FACE_HUB_TOKEN: *** 2025-03-04T20:17:12.8880733Z SCRIBE_GRAPHQL_ACCESS_TOKEN: *** 2025-03-04T20:17:12.8880933Z IS_A100_RUNNER: 0 2025-03-04T20:17:12.8881209Z ARTIFACTS_FILE_SUFFIX: test-cpu_inductor_torchbench-1-2-linux.8xlarge.amx_38195233363 2025-03-04T20:17:12.8881517Z ##[endgroup] 2025-03-04T20:17:12.8902747Z + [[ cpu_inductor_torchbench == \m\u\l\t\i\g\p\u ]] 2025-03-04T20:17:12.8908143Z + [[ linux-jammy-py3.9-gcc11-build == *onnx* ]] 2025-03-04T20:17:12.8908585Z + TEST_COMMAND=.ci/pytorch/test.sh 2025-03-04T20:17:12.8908988Z ++ awk '/MemTotal/ { printf "%.3f \n", $2/1024/1024 - 1 }' /proc/meminfo 2025-03-04T20:17:12.8925463Z + TOTAL_AVAILABLE_MEMORY_IN_GB='122.780 ' 2025-03-04T20:17:12.8930070Z + TOTAL_MEMORY_WITH_SWAP=125 2025-03-04T20:17:12.8934944Z + [[ linux-jammy-py3.9-gcc11-build == *\s\3\9\0\x* ]] 2025-03-04T20:17:12.8935394Z + SHM_OPTS=--shm-size=1g 2025-03-04T20:17:12.8935686Z + JENKINS_USER='--user jenkins' 2025-03-04T20:17:12.8936028Z + DOCKER_SHELL_CMD= 2025-03-04T20:17:12.8936400Z +++ nproc --ignore=2 2025-03-04T20:17:12.9596719Z ++ docker run -e BUILD_ENVIRONMENT -e PR_NUMBER -e GITHUB_ACTIONS -e GITHUB_REPOSITORY -e GITHUB_WORKFLOW -e GITHUB_JOB -e GITHUB_RUN_ID -e GITHUB_RUN_NUMBER -e GITHUB_RUN_ATTEMPT -e JOB_ID -e JOB_NAME -e BASE_SHA -e BRANCH -e SHA1 -e AWS_DEFAULT_REGION -e IN_WHEEL_TEST -e SHARD_NUMBER -e TEST_CONFIG -e NUM_TEST_SHARDS -e REENABLED_ISSUES -e CONTINUE_THROUGH_ERROR -e VERBOSE_TEST_LOGS -e TEST_SHOWLOCALS -e NO_TEST_TIMEOUT -e NO_TD -e TD_DISTRIBUTED -e PR_LABELS -e MAX_JOBS=30 -e SCCACHE_BUCKET -e SCCACHE_REGION -e XLA_CUDA -e XLA_CLANG_CACHE_S3_BUCKET_NAME -e PYTORCH_TEST_CUDA_MEM_LEAK_CHECK -e PYTORCH_TEST_RERUN_DISABLED_TESTS -e SKIP_SCCACHE_INITIALIZATION=1 -e HUGGING_FACE_HUB_TOKEN -e SCRIBE_GRAPHQL_ACCESS_TOKEN -e DASHBOARD_TAG -e IS_A100_RUNNER -e ARTIFACTS_FILE_SUFFIX --memory=122g --memory-swap=125g --env-file=/tmp/github_env_13661696663 --security-opt seccomp=unconfined --cap-add=SYS_PTRACE --ipc=host --shm-size=1g --tty --detach --name= --user jenkins -v /home/ec2-user/actions-runner/_work/pytorch/pytorch:/var/lib/jenkins/workspace -w /var/lib/jenkins/workspace 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e 2025-03-04T20:17:44.0283111Z + container_name=ad526e66cea466c172d0f0e6ea6665dd89838a93b43196189ae102ffb4d51ddd 2025-03-04T20:17:44.0289149Z + grep download.pytorch.org /etc/hosts 2025-03-04T20:17:44.0289693Z + docker exec -i ad526e66cea466c172d0f0e6ea6665dd89838a93b43196189ae102ffb4d51ddd sudo bash -c '/bin/cat >> /etc/hosts' 2025-03-04T20:17:44.1648509Z + echo DOCKER_CONTAINER_ID=ad526e66cea466c172d0f0e6ea6665dd89838a93b43196189ae102ffb4d51ddd 2025-03-04T20:17:44.1650095Z + [[ linux-jammy-py3.9-gcc11-build == *\s\3\9\0\x* ]] 2025-03-04T20:17:44.1650450Z ++ echo dist/torch-2.7.0a0+git1b74980-cp39-cp39-linux_x86_64.whl 2025-03-04T20:17:44.1659404Z + docker exec -t ad526e66cea466c172d0f0e6ea6665dd89838a93b43196189ae102ffb4d51ddd sh -c 'python3 -m pip install dist/torch-2.7.0a0+git1b74980-cp39-cp39-linux_x86_64.whl[opt-einsum] && .ci/pytorch/test.sh' 2025-03-04T20:17:44.4609516Z Processing ./dist/torch-2.7.0a0+git1b74980-cp39-cp39-linux_x86_64.whl (from torch==2.7.0a0+git1b74980) 2025-03-04T20:17:44.6377226Z Requirement already satisfied: filelock in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch==2.7.0a0+git1b74980->torch==2.7.0a0+git1b74980) (3.16.1) 2025-03-04T20:17:44.6378063Z Requirement already satisfied: typing-extensions>=4.10.0 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch==2.7.0a0+git1b74980->torch==2.7.0a0+git1b74980) (4.12.2) 2025-03-04T20:17:44.6560614Z Collecting sympy==1.13.3 (from torch==2.7.0a0+git1b74980->torch==2.7.0a0+git1b74980) 2025-03-04T20:17:44.6573075Z Using cached sympy-1.13.3-py3-none-any.whl.metadata (12 kB) 2025-03-04T20:17:44.6585250Z Requirement already satisfied: networkx in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch==2.7.0a0+git1b74980->torch==2.7.0a0+git1b74980) (2.8.8) 2025-03-04T20:17:44.6587926Z Requirement already satisfied: jinja2 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch==2.7.0a0+git1b74980->torch==2.7.0a0+git1b74980) (3.1.5) 2025-03-04T20:17:44.6588673Z Requirement already satisfied: fsspec in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch==2.7.0a0+git1b74980->torch==2.7.0a0+git1b74980) (2024.10.0) 2025-03-04T20:17:44.6596740Z Requirement already satisfied: mpmath<1.4,>=1.1.0 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from sympy==1.13.3->torch==2.7.0a0+git1b74980->torch==2.7.0a0+git1b74980) (1.3.0) 2025-03-04T20:17:44.6612103Z Requirement already satisfied: opt-einsum>=3.3 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch==2.7.0a0+git1b74980->torch==2.7.0a0+git1b74980) (3.3.0) 2025-03-04T20:17:44.6624703Z Requirement already satisfied: numpy>=1.7 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from opt-einsum>=3.3->torch==2.7.0a0+git1b74980->torch==2.7.0a0+git1b74980) (1.22.4) 2025-03-04T20:17:44.6887234Z Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from jinja2->torch==2.7.0a0+git1b74980->torch==2.7.0a0+git1b74980) (3.0.2) 2025-03-04T20:17:44.6947138Z Using cached sympy-1.13.3-py3-none-any.whl (6.2 MB) 2025-03-04T20:17:45.2023781Z Installing collected packages: sympy, torch 2025-03-04T20:17:45.2028572Z Attempting uninstall: sympy 2025-03-04T20:17:45.2032761Z Found existing installation: sympy 1.13.1 2025-03-04T20:17:45.2892296Z Uninstalling sympy-1.13.1: 2025-03-04T20:17:45.7640789Z Successfully uninstalled sympy-1.13.1 2025-03-04T20:17:54.6259817Z ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts. 2025-03-04T20:17:54.6260748Z timm 1.0.14 requires torchvision, which is not installed. 2025-03-04T20:17:54.6261303Z Successfully installed sympy-1.13.3 torch-2.7.0a0+git1b74980 2025-03-04T20:17:54.7055137Z + export TERM=vt100 2025-03-04T20:17:54.7055606Z + TERM=vt100 2025-03-04T20:17:54.7055817Z ++ dirname .ci/pytorch/test.sh 2025-03-04T20:17:54.7064535Z + source .ci/pytorch/common.sh 2025-03-04T20:17:54.7067588Z +++ dirname .ci/pytorch/common.sh 2025-03-04T20:17:54.7069124Z ++ source .ci/pytorch/common_utils.sh 2025-03-04T20:17:54.7071475Z +++ declare -f -t trap_add 2025-03-04T20:17:54.7071725Z ++ set -ex -o pipefail 2025-03-04T20:17:54.7071970Z ++ [[ linux-jammy-py3.9-gcc11-build == *rocm* ]] 2025-03-04T20:17:54.7072250Z ++ BUILD_TEST_LIBTORCH=0 2025-03-04T20:17:54.7072490Z + [[ linux-jammy-py3.9-gcc11-build != *rocm* ]] 2025-03-04T20:17:54.7072776Z + [[ linux-jammy-py3.9-gcc11-build != *s390x* ]] 2025-03-04T20:17:54.7073039Z + [[ -d /var/lib/jenkins/workspace ]] 2025-03-04T20:17:54.7077028Z ++ stat -c %u /var/lib/jenkins/workspace 2025-03-04T20:17:54.7087359Z + WORKSPACE_ORIGINAL_OWNER_ID=1000 2025-03-04T20:17:54.7087654Z + trap_add cleanup_workspace EXIT 2025-03-04T20:17:54.7087887Z + trap_add_cmd=cleanup_workspace 2025-03-04T20:17:54.7088092Z + shift 2025-03-04T20:17:54.7088267Z + for trap_add_name in "$@" 2025-03-04T20:17:54.7097841Z +++ trap -p EXIT 2025-03-04T20:17:54.7098072Z ++ eval 'extract_trap_cmd ' 2025-03-04T20:17:54.7098601Z +++ extract_trap_cmd 2025-03-04T20:17:54.7098778Z +++ printf '%s\n' '' 2025-03-04T20:17:54.7098997Z ++ printf '%s\n' cleanup_workspace 2025-03-04T20:17:54.7103028Z + trap -- ' 2025-03-04T20:17:54.7103707Z cleanup_workspace' EXIT 2025-03-04T20:17:54.7104036Z + sudo chown -R jenkins /var/lib/jenkins/workspace 2025-03-04T20:17:55.0545053Z + git config --global --add safe.directory /var/lib/jenkins/workspace 2025-03-04T20:17:55.0561082Z + echo 'Environment variables:' 2025-03-04T20:17:55.0561443Z Environment variables: 2025-03-04T20:17:55.0561681Z + env 2025-03-04T20:17:55.0568150Z INSTALLED_DB=yes 2025-03-04T20:17:55.0568733Z GITHUB_WORKSPACE=/home/ec2-user/actions-runner/_work/pytorch/pytorch 2025-03-04T20:17:55.0570057Z CONTINUE_THROUGH_ERROR=False 2025-03-04T20:17:55.0570542Z BUILD_ENVIRONMENT=linux-jammy-py3.9-gcc11-build 2025-03-04T20:17:55.0570845Z HOSTNAME=ad526e66cea4 2025-03-04T20:17:55.0571292Z GITHUB_PATH=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/add_path_acafbdf9-0203-49e1-b619-48c9c68412c1 2025-03-04T20:17:55.0572062Z GITHUB_ACTION=__self 2025-03-04T20:17:55.0572365Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK=0 2025-03-04T20:17:55.0572657Z GITHUB_RUN_NUMBER=120837 2025-03-04T20:17:55.0572902Z TEST_CONFIG=cpu_inductor_torchbench 2025-03-04T20:17:55.0573131Z GITHUB_REPOSITORY_OWNER_ID=21003710 2025-03-04T20:17:55.0573360Z TORCH_NVCC_FLAGS=-Xfatbin -compress-all 2025-03-04T20:17:55.0573579Z IS_A100_RUNNER=0 2025-03-04T20:17:55.0573973Z SCRIBE_GRAPHQL_ACCESS_TOKEN=*** 2025-03-04T20:17:55.0574199Z GITHUB_TRIGGERING_ACTOR=pytorch-bot[bot] 2025-03-04T20:17:55.0574415Z GITHUB_REF_TYPE=tag 2025-03-04T20:17:55.0574599Z TORCH_CUDA_ARCH_LIST=Maxwell 2025-03-04T20:17:55.0574826Z BASE_SHA=1b7498080987913ecb3aff6253c5e88f3540d911 2025-03-04T20:17:55.0575056Z XLA_CUDA= 2025-03-04T20:17:55.0575314Z HUGGING_FACE_HUB_TOKEN=*** 2025-03-04T20:17:55.0581226Z *** 2025-03-04T20:17:55.0581450Z GITHUB_REPOSITORY_ID=65600975 2025-03-04T20:17:55.0581661Z GITHUB_ACTIONS=true 2025-03-04T20:17:55.0581886Z SHA1=1b7498080987913ecb3aff6253c5e88f3540d911 2025-03-04T20:17:55.0582146Z GITHUB_SHA=1b7498080987913ecb3aff6253c5e88f3540d911 2025-03-04T20:17:55.0582529Z GITHUB_WORKFLOW_REF=pytorch/pytorch/.github/workflows/inductor.yml@refs/tags/ciflow/inductor/148205 2025-03-04T20:17:55.0582867Z UCC_HOME=/usr 2025-03-04T20:17:55.0583039Z VERBOSE_TEST_LOGS=False 2025-03-04T20:17:55.0583241Z GITHUB_REF=refs/tags/ciflow/inductor/148205 2025-03-04T20:17:55.0583467Z SHARD_NUMBER=1 2025-03-04T20:17:55.0583646Z GITHUB_REF_PROTECTED=false 2025-03-04T20:17:55.0583840Z HOME=/var/lib/jenkins 2025-03-04T20:17:55.0584049Z GITHUB_API_URL=https://api.github.com 2025-03-04T20:17:55.0584283Z PYTORCH_TEST_RERUN_DISABLED_TESTS=0 2025-03-04T20:17:55.0584487Z UCX_COMMIT= 2025-03-04T20:17:55.0584647Z NUM_TEST_SHARDS=2 2025-03-04T20:17:55.0584817Z UCX_HOME=/usr 2025-03-04T20:17:55.0585182Z GITHUB_STATE=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/save_state_acafbdf9-0203-49e1-b619-48c9c68412c1 2025-03-04T20:17:55.0585953Z JOB_NAME=linux-jammy-cpu-py3.9-gcc11-inductor / test (cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx) 2025-03-04T20:17:55.0586492Z GITHUB_ENV=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_env_acafbdf9-0203-49e1-b619-48c9c68412c1 2025-03-04T20:17:55.0586980Z GITHUB_EVENT_PATH=/home/ec2-user/actions-runner/_work/_temp/_github_workflow/event.json 2025-03-04T20:17:55.0587301Z GITHUB_EVENT_NAME=push 2025-03-04T20:17:55.0587486Z DASHBOARD_TAG= 2025-03-04T20:17:55.0587663Z GITHUB_RUN_ID=13661696663 2025-03-04T20:17:55.0588054Z GITHUB_STEP_SUMMARY=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/step_summary_acafbdf9-0203-49e1-b619-48c9c68412c1 2025-03-04T20:17:55.0588484Z GITHUB_ACTOR=pytorch-bot[bot] 2025-03-04T20:17:55.0588670Z PR_NUMBER= 2025-03-04T20:17:55.0588825Z DESIRED_CUDA= 2025-03-04T20:17:55.0588989Z GITHUB_RUN_ATTEMPT=1 2025-03-04T20:17:55.0589172Z ANACONDA_PYTHON_VERSION=3.9 2025-03-04T20:17:55.0589395Z GITHUB_GRAPHQL_URL=https://api.github.com/graphql 2025-03-04T20:17:55.0590643Z TERM=vt100 2025-03-04T20:17:55.0590804Z INSTALLED_VISION=yes 2025-03-04T20:17:55.0590984Z BRANCH= 2025-03-04T20:17:55.0591153Z SCCACHE_REGION=us-east-1 2025-03-04T20:17:55.0591358Z OPENSSL_ROOT_DIR=/opt/openssl 2025-03-04T20:17:55.0591561Z CUDA_PATH=/usr/local/cuda 2025-03-04T20:17:55.0591893Z GITHUB_ACTION_PATH=/home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/setup-linux 2025-03-04T20:17:55.0592255Z GITHUB_SERVER_URL=https://github.com 2025-03-04T20:17:55.0592468Z UCC_COMMIT= 2025-03-04T20:17:55.0592628Z REENABLED_ISSUES= 2025-03-04T20:17:55.0592787Z DOCS=yes 2025-03-04T20:17:55.0592943Z SHLVL=1 2025-03-04T20:17:55.0593095Z MAX_JOBS=30 2025-03-04T20:17:55.0593398Z GITHUB_ACTOR_ID=54816060 2025-03-04T20:17:55.0593649Z GITHUB_WORKFLOW_SHA=1b7498080987913ecb3aff6253c5e88f3540d911 2025-03-04T20:17:55.0593917Z GITHUB_REF_NAME=ciflow/inductor/148205 2025-03-04T20:17:55.0594207Z XLA_CLANG_CACHE_S3_BUCKET_NAME=ossci-compiler-clang-cache-circleci-xla 2025-03-04T20:17:55.0594492Z GITHUB_JOB=test 2025-03-04T20:17:55.0594665Z NO_TEST_TIMEOUT=False 2025-03-04T20:17:55.0594852Z TD_DISTRIBUTED=False 2025-03-04T20:17:55.0595046Z GITHUB_REPOSITORY=pytorch/pytorch 2025-03-04T20:17:55.0595256Z GITHUB_RETENTION_DAYS=90 2025-03-04T20:17:55.0595446Z OPENSSL_DIR=/opt/openssl 2025-03-04T20:17:55.0595639Z GITHUB_ACTION_REPOSITORY= 2025-03-04T20:17:55.0596108Z PATH=/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.9/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin 2025-03-04T20:17:55.0596588Z GITHUB_BASE_REF= 2025-03-04T20:17:55.0596760Z INSTALLED_ACL= 2025-03-04T20:17:55.0597039Z ARTIFACTS_FILE_SUFFIX=test-cpu_inductor_torchbench-1-2-linux.8xlarge.amx_38195233363 2025-03-04T20:17:55.0597350Z CI=true 2025-03-04T20:17:55.0597514Z GITHUB_REPOSITORY_OWNER=pytorch 2025-03-04T20:17:55.0597708Z JOB_ID=38195233363 2025-03-04T20:17:55.0597878Z INSTALLED_PROTOBUF=yes 2025-03-04T20:17:55.0598060Z GITHUB_HEAD_REF= 2025-03-04T20:17:55.0598232Z GITHUB_ACTION_REF= 2025-03-04T20:17:55.0598440Z SCCACHE_BUCKET=ossci-compiler-cache-circleci-v2 2025-03-04T20:17:55.0598681Z TEST_SHOWLOCALS=False 2025-03-04T20:17:55.0598864Z GITHUB_WORKFLOW=inductor 2025-03-04T20:17:55.0599057Z DEBIAN_FRONTEND=noninteractive 2025-03-04T20:17:55.0599444Z GITHUB_OUTPUT=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_output_acafbdf9-0203-49e1-b619-48c9c68412c1 2025-03-04T20:17:55.0599825Z NO_TD=False 2025-03-04T20:17:55.0599997Z SKIP_SCCACHE_INITIALIZATION=1 2025-03-04T20:17:55.0600185Z _=/usr/bin/env 2025-03-04T20:17:55.0600430Z ++ python -c 'import site; print(site.getsitepackages()[0])' 2025-03-04T20:17:55.0782950Z + TORCH_INSTALL_DIR=/opt/conda/envs/py_3.9/lib/python3.9/site-packages/torch 2025-03-04T20:17:55.0783468Z + TORCH_BIN_DIR=/opt/conda/envs/py_3.9/lib/python3.9/site-packages/torch/bin 2025-03-04T20:17:55.0783873Z + TORCH_LIB_DIR=/opt/conda/envs/py_3.9/lib/python3.9/site-packages/torch/lib 2025-03-04T20:17:55.0784292Z + TORCH_TEST_DIR=/opt/conda/envs/py_3.9/lib/python3.9/site-packages/torch/test 2025-03-04T20:17:55.0784894Z + BUILD_DIR=build 2025-03-04T20:17:55.0785340Z + BUILD_RENAMED_DIR=build_renamed 2025-03-04T20:17:55.0785600Z + BUILD_BIN_DIR=build/bin 2025-03-04T20:17:55.0785817Z + SHARD_NUMBER=1 2025-03-04T20:17:55.0786007Z + NUM_TEST_SHARDS=2 2025-03-04T20:17:55.0798811Z + export TORCH_SERIALIZATION_DEBUG=1 2025-03-04T20:17:55.0799138Z + TORCH_SERIALIZATION_DEBUG=1 2025-03-04T20:17:55.0799348Z + export VALGRIND=ON 2025-03-04T20:17:55.0799528Z + VALGRIND=ON 2025-03-04T20:17:55.0800388Z + [[ linux-jammy-py3.9-gcc11-build == *clang9* ]] 2025-03-04T20:17:55.0801145Z + [[ linux-jammy-py3.9-gcc11-build == *xpu* ]] 2025-03-04T20:17:55.0801423Z + [[ linux-jammy-py3.9-gcc11-build == *s390x* ]] 2025-03-04T20:17:55.0802697Z + [[ 0 == \1 ]] 2025-03-04T20:17:55.0802878Z + [[ False == \1 ]] 2025-03-04T20:17:55.0803079Z + [[ linux-jammy-py3.9-gcc11-build != *bazel* ]] 2025-03-04T20:17:55.0803366Z ++ realpath build/custom_test_artifacts 2025-03-04T20:17:55.0803874Z + CUSTOM_TEST_ARTIFACT_BUILD_DIR=/var/lib/jenkins/workspace/build/custom_test_artifacts 2025-03-04T20:17:55.0804193Z + [[ -n '' ]] 2025-03-04T20:17:55.0804374Z + echo 'Environment variables' 2025-03-04T20:17:55.0804576Z Environment variables 2025-03-04T20:17:55.0804749Z + env 2025-03-04T20:17:55.0804905Z INSTALLED_DB=yes 2025-03-04T20:17:55.0805152Z GITHUB_WORKSPACE=/home/ec2-user/actions-runner/_work/pytorch/pytorch 2025-03-04T20:17:55.0805427Z CONTINUE_THROUGH_ERROR=False 2025-03-04T20:17:55.0805647Z BUILD_ENVIRONMENT=linux-jammy-py3.9-gcc11-build 2025-03-04T20:17:55.0805878Z HOSTNAME=ad526e66cea4 2025-03-04T20:17:55.0806235Z GITHUB_PATH=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/add_path_acafbdf9-0203-49e1-b619-48c9c68412c1 2025-03-04T20:17:55.0806610Z GITHUB_ACTION=__self 2025-03-04T20:17:55.0806797Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK=0 2025-03-04T20:17:55.0807000Z GITHUB_RUN_NUMBER=120837 2025-03-04T20:17:55.0807196Z TEST_CONFIG=cpu_inductor_torchbench 2025-03-04T20:17:55.0807406Z GITHUB_REPOSITORY_OWNER_ID=21003710 2025-03-04T20:17:55.0807634Z TORCH_NVCC_FLAGS=-Xfatbin -compress-all 2025-03-04T20:17:55.0808751Z IS_A100_RUNNER=0 2025-03-04T20:17:55.0809120Z SCRIBE_GRAPHQL_ACCESS_TOKEN=*** 2025-03-04T20:17:55.0809347Z GITHUB_TRIGGERING_ACTOR=pytorch-bot[bot] 2025-03-04T20:17:55.0809565Z GITHUB_REF_TYPE=tag 2025-03-04T20:17:55.0809743Z TORCH_CUDA_ARCH_LIST=Maxwell 2025-03-04T20:17:55.0809961Z BASE_SHA=1b7498080987913ecb3aff6253c5e88f3540d911 2025-03-04T20:17:55.0810182Z XLA_CUDA= 2025-03-04T20:17:55.0810410Z HUGGING_FACE_HUB_TOKEN=*** 2025-03-04T20:17:55.0810762Z *** 2025-03-04T20:17:55.0810930Z GITHUB_REPOSITORY_ID=65600975 2025-03-04T20:17:55.0811126Z GITHUB_ACTIONS=true 2025-03-04T20:17:55.0811323Z SHA1=1b7498080987913ecb3aff6253c5e88f3540d911 2025-03-04T20:17:55.0811561Z GITHUB_SHA=1b7498080987913ecb3aff6253c5e88f3540d911 2025-03-04T20:17:55.0811931Z GITHUB_WORKFLOW_REF=pytorch/pytorch/.github/workflows/inductor.yml@refs/tags/ciflow/inductor/148205 2025-03-04T20:17:55.0812275Z UCC_HOME=/usr 2025-03-04T20:17:55.0812450Z TORCH_SERIALIZATION_DEBUG=1 2025-03-04T20:17:55.0812648Z VERBOSE_TEST_LOGS=False 2025-03-04T20:17:55.0812844Z GITHUB_REF=refs/tags/ciflow/inductor/148205 2025-03-04T20:17:55.0813058Z SHARD_NUMBER=1 2025-03-04T20:17:55.0813222Z GITHUB_REF_PROTECTED=false 2025-03-04T20:17:55.0813408Z HOME=/var/lib/jenkins 2025-03-04T20:17:55.0813611Z GITHUB_API_URL=https://api.github.com 2025-03-04T20:17:55.0813837Z PYTORCH_TEST_RERUN_DISABLED_TESTS=0 2025-03-04T20:17:55.0814036Z UCX_COMMIT= 2025-03-04T20:17:55.0814191Z NUM_TEST_SHARDS=2 2025-03-04T20:17:55.0814356Z UCX_HOME=/usr 2025-03-04T20:17:55.0814712Z GITHUB_STATE=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/save_state_acafbdf9-0203-49e1-b619-48c9c68412c1 2025-03-04T20:17:55.0815242Z JOB_NAME=linux-jammy-cpu-py3.9-gcc11-inductor / test (cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx) 2025-03-04T20:17:55.0815758Z GITHUB_ENV=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_env_acafbdf9-0203-49e1-b619-48c9c68412c1 2025-03-04T20:17:55.0816335Z GITHUB_EVENT_PATH=/home/ec2-user/actions-runner/_work/_temp/_github_workflow/event.json 2025-03-04T20:17:55.0816653Z GITHUB_EVENT_NAME=push 2025-03-04T20:17:55.0816842Z DASHBOARD_TAG= 2025-03-04T20:17:55.0817020Z GITHUB_RUN_ID=13661696663 2025-03-04T20:17:55.0817403Z GITHUB_STEP_SUMMARY=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/step_summary_acafbdf9-0203-49e1-b619-48c9c68412c1 2025-03-04T20:17:55.0817807Z GITHUB_ACTOR=pytorch-bot[bot] 2025-03-04T20:17:55.0817997Z PR_NUMBER= 2025-03-04T20:17:55.0818152Z DESIRED_CUDA= 2025-03-04T20:17:55.0818306Z GITHUB_RUN_ATTEMPT=1 2025-03-04T20:17:55.0818477Z VALGRIND=ON 2025-03-04T20:17:55.0818643Z ANACONDA_PYTHON_VERSION=3.9 2025-03-04T20:17:55.0818868Z GITHUB_GRAPHQL_URL=https://api.github.com/graphql 2025-03-04T20:17:55.0819092Z TERM=vt100 2025-03-04T20:17:55.0819247Z INSTALLED_VISION=yes 2025-03-04T20:17:55.0819416Z BRANCH= 2025-03-04T20:17:55.0819577Z SCCACHE_REGION=us-east-1 2025-03-04T20:17:55.0819827Z OPENSSL_ROOT_DIR=/opt/openssl 2025-03-04T20:17:55.0820022Z CUDA_PATH=/usr/local/cuda 2025-03-04T20:17:55.0820346Z GITHUB_ACTION_PATH=/home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/setup-linux 2025-03-04T20:17:55.0820696Z GITHUB_SERVER_URL=https://github.com 2025-03-04T20:17:55.0820901Z UCC_COMMIT= 2025-03-04T20:17:55.0821057Z REENABLED_ISSUES= 2025-03-04T20:17:55.0821219Z DOCS=yes 2025-03-04T20:17:55.0821358Z SHLVL=1 2025-03-04T20:17:55.0821507Z MAX_JOBS=30 2025-03-04T20:17:55.0821669Z GITHUB_ACTOR_ID=54816060 2025-03-04T20:17:55.0821900Z GITHUB_WORKFLOW_SHA=1b7498080987913ecb3aff6253c5e88f3540d911 2025-03-04T20:17:55.0822155Z GITHUB_REF_NAME=ciflow/inductor/148205 2025-03-04T20:17:55.0822447Z XLA_CLANG_CACHE_S3_BUCKET_NAME=ossci-compiler-clang-cache-circleci-xla 2025-03-04T20:17:55.0822716Z GITHUB_JOB=test 2025-03-04T20:17:55.0822879Z NO_TEST_TIMEOUT=False 2025-03-04T20:17:55.0823053Z TD_DISTRIBUTED=False 2025-03-04T20:17:55.0823239Z GITHUB_REPOSITORY=pytorch/pytorch 2025-03-04T20:17:55.0823447Z GITHUB_RETENTION_DAYS=90 2025-03-04T20:17:55.0823628Z OPENSSL_DIR=/opt/openssl 2025-03-04T20:17:55.0823825Z GITHUB_ACTION_REPOSITORY= 2025-03-04T20:17:55.0824274Z PATH=/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.9/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin 2025-03-04T20:17:55.0824719Z GITHUB_BASE_REF= 2025-03-04T20:17:55.0824880Z INSTALLED_ACL= 2025-03-04T20:17:55.0825137Z ARTIFACTS_FILE_SUFFIX=test-cpu_inductor_torchbench-1-2-linux.8xlarge.amx_38195233363 2025-03-04T20:17:55.0825426Z CI=true 2025-03-04T20:17:55.0825589Z GITHUB_REPOSITORY_OWNER=pytorch 2025-03-04T20:17:55.0825784Z JOB_ID=38195233363 2025-03-04T20:17:55.0825955Z INSTALLED_PROTOBUF=yes 2025-03-04T20:17:55.0826126Z GITHUB_HEAD_REF= 2025-03-04T20:17:55.0826290Z GITHUB_ACTION_REF= 2025-03-04T20:17:55.0826492Z SCCACHE_BUCKET=ossci-compiler-cache-circleci-v2 2025-03-04T20:17:55.0826724Z TEST_SHOWLOCALS=False 2025-03-04T20:17:55.0826900Z GITHUB_WORKFLOW=inductor 2025-03-04T20:17:55.0827089Z DEBIAN_FRONTEND=noninteractive 2025-03-04T20:17:55.0827464Z GITHUB_OUTPUT=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_output_acafbdf9-0203-49e1-b619-48c9c68412c1 2025-03-04T20:17:55.0827836Z NO_TD=False 2025-03-04T20:17:55.0828001Z SKIP_SCCACHE_INITIALIZATION=1 2025-03-04T20:17:55.0828196Z _=/usr/bin/env 2025-03-04T20:17:55.0828352Z + echo 'Testing pytorch' 2025-03-04T20:17:55.0828531Z Testing pytorch 2025-03-04T20:17:55.0828716Z + export LANG=C.UTF-8 2025-03-04T20:17:55.0828888Z + LANG=C.UTF-8 2025-03-04T20:17:55.0829064Z + PR_NUMBER= 2025-03-04T20:17:55.0829251Z + [[ cpu_inductor_torchbench == \d\e\f\a\u\l\t ]] 2025-03-04T20:17:55.0829500Z + [[ cpu_inductor_torchbench == \d\i\s\t\r\i\b\u\t\e\d ]] 2025-03-04T20:17:55.0829738Z + [[ cpu_inductor_torchbench == \s\l\o\w ]] 2025-03-04T20:17:55.0829989Z + [[ linux-jammy-py3.9-gcc11-build == *slow-gradcheck* ]] 2025-03-04T20:17:55.0830247Z + [[ linux-jammy-py3.9-gcc11-build == *cuda* ]] 2025-03-04T20:17:55.0830491Z + [[ linux-jammy-py3.9-gcc11-build == *rocm* ]] 2025-03-04T20:17:55.0830795Z + [[ linux-jammy-py3.9-gcc11-build == *xpu* ]] 2025-03-04T20:17:55.0831029Z + [[ cpu_inductor_torchbench == *crossref* ]] 2025-03-04T20:17:55.0831257Z + [[ linux-jammy-py3.9-gcc11-build == *rocm* ]] 2025-03-04T20:17:55.0831485Z + [[ linux-jammy-py3.9-gcc11-build == *xpu* ]] 2025-03-04T20:17:55.0831728Z + [[ linux-jammy-py3.9-gcc11-build != *-bazel-* ]] 2025-03-04T20:17:55.0831965Z + pip_install --user ninja==1.10.2 2025-03-04T20:17:55.0832294Z + pip_install_pkg='python3 -m pip install --progress-bar off' 2025-03-04T20:17:55.0832702Z + python3 -m pip install --progress-bar off --user ninja==1.10.2 2025-03-04T20:17:55.3773867Z Collecting ninja==1.10.2 2025-03-04T20:17:55.3865932Z Downloading ninja-1.10.2-py2.py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.whl.metadata (5.0 kB) 2025-03-04T20:17:55.3960936Z Downloading ninja-1.10.2-py2.py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.whl (108 kB) 2025-03-04T20:17:55.8674890Z Installing collected packages: ninja 2025-03-04T20:17:55.8745096Z  WARNING: The script ninja is installed in '/var/lib/jenkins/.local/bin' which is not on PATH. 2025-03-04T20:17:55.8747297Z Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location. 2025-03-04T20:17:55.8796863Z Successfully installed ninja-1.10.2 2025-03-04T20:17:55.9514831Z + export PATH=/var/lib/jenkins/.local/bin:/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.9/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin 2025-03-04T20:17:55.9517737Z + PATH=/var/lib/jenkins/.local/bin:/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.9/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin 2025-03-04T20:17:55.9518349Z + [[ linux-jammy-py3.9-gcc11-build == *aarch64* ]] 2025-03-04T20:17:55.9518596Z + install_tlparse 2025-03-04T20:17:55.9518798Z + pip_install --user tlparse==0.3.30 2025-03-04T20:17:55.9519116Z + pip_install_pkg='python3 -m pip install --progress-bar off' 2025-03-04T20:17:55.9519456Z + python3 -m pip install --progress-bar off --user tlparse==0.3.30 2025-03-04T20:17:56.2419159Z Collecting tlparse==0.3.30 2025-03-04T20:17:56.2497491Z Downloading tlparse-0.3.30-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (1.9 kB) 2025-03-04T20:17:56.2610842Z Downloading tlparse-0.3.30-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.3 MB) 2025-03-04T20:17:56.7482025Z Installing collected packages: tlparse 2025-03-04T20:17:56.7809497Z Successfully installed tlparse-0.3.30 2025-03-04T20:17:56.8506891Z ++ python -m site --user-base 2025-03-04T20:17:56.8728135Z + PATH=/var/lib/jenkins/.local/bin:/var/lib/jenkins/.local/bin:/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.9/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin 2025-03-04T20:17:56.8728835Z + [[ linux-jammy-py3.9-gcc11-build == *asan* ]] 2025-03-04T20:17:56.8729710Z + [[ linux-jammy-py3.9-gcc11-build == *-debug* ]] 2025-03-04T20:17:56.8730121Z + [[ linux-jammy-py3.9-gcc11-build != *-bazel-* ]] 2025-03-04T20:17:56.8730539Z + echo 'We are not in debug mode: linux-jammy-py3.9-gcc11-build. Expect the assertion to pass' 2025-03-04T20:17:56.8730938Z We are not in debug mode: linux-jammy-py3.9-gcc11-build. Expect the assertion to pass 2025-03-04T20:17:56.8731227Z + cd test 2025-03-04T20:17:56.8731466Z + python -c 'import torch; torch._C._crash_if_debug_asserts_fail(424242)' 2025-03-04T20:17:57.9508579Z + [[ cpu_inductor_torchbench == \n\o\g\p\u\_\N\O\_\A\V\X\2 ]] 2025-03-04T20:17:57.9508970Z + [[ cpu_inductor_torchbench == \n\o\g\p\u\_\A\V\X\5\1\2 ]] 2025-03-04T20:17:57.9509253Z + DYNAMO_BENCHMARK_FLAGS=() 2025-03-04T20:17:57.9509476Z + [[ cpu_inductor_torchbench == *pr_time_benchmarks* ]] 2025-03-04T20:17:57.9509736Z + [[ cpu_inductor_torchbench == *dynamo_eager* ]] 2025-03-04T20:17:57.9509983Z + [[ cpu_inductor_torchbench == *aot_eager* ]] 2025-03-04T20:17:57.9510319Z + [[ cpu_inductor_torchbench == *aot_inductor* ]] 2025-03-04T20:17:57.9510953Z + [[ cpu_inductor_torchbench == *inductor* ]] 2025-03-04T20:17:57.9511190Z + [[ cpu_inductor_torchbench != *perf* ]] 2025-03-04T20:17:57.9511448Z + DYNAMO_BENCHMARK_FLAGS+=(--inductor) 2025-03-04T20:17:57.9511677Z + [[ cpu_inductor_torchbench == *dynamic* ]] 2025-03-04T20:17:57.9511901Z + [[ cpu_inductor_torchbench == *cpu* ]] 2025-03-04T20:17:57.9512125Z + DYNAMO_BENCHMARK_FLAGS+=(--device cpu) 2025-03-04T20:17:57.9522573Z + [[ linux-jammy-py3.9-gcc11-build == *libtorch* ]] 2025-03-04T20:17:57.9522984Z + [[ linux-jammy-py3.9-gcc11-build == *-bazel-* ]] 2025-03-04T20:17:57.9523305Z + cd test 2025-03-04T20:17:57.9523544Z + python -c 'import torch; print(torch.__config__.show())' 2025-03-04T20:17:58.8379800Z PyTorch built with: 2025-03-04T20:17:58.8381473Z - GCC 11.4 2025-03-04T20:17:58.8381804Z - C++ Version: 201703 2025-03-04T20:17:58.8388077Z - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications 2025-03-04T20:17:58.8388988Z - Intel(R) MKL-DNN v3.5.3 (Git Hash 66f0cb9eb66affd2da3bf5f8d897376f04aae6af) 2025-03-04T20:17:58.8389282Z - OpenMP 201511 (a.k.a. OpenMP 4.5) 2025-03-04T20:17:58.8389520Z - LAPACK is enabled (usually provided by MKL) 2025-03-04T20:17:58.8389743Z - NNPACK is enabled 2025-03-04T20:17:58.8389932Z - CPU capability usage: AVX512 2025-03-04T20:17:58.8392613Z - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, COMMIT_SHA=1b7498080987913ecb3aff6253c5e88f3540d911, CXX_COMPILER=/opt/cache/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=1 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOCUPTI -DLIBKINETO_NOROCTRACER -DLIBKINETO_NOXPUPTI=ON -DUSE_FBGEMM -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=range-loop-construct -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-unknown-pragmas -Wno-unused-parameter -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wsuggest-override -Wno-psabi -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Werror -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, TORCH_VERSION=2.7.0, USE_CUDA=OFF, USE_CUDNN=OFF, USE_CUSPARSELT=OFF, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=OFF, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, 2025-03-04T20:17:58.8395723Z 2025-03-04T20:17:59.0207033Z + cd test 2025-03-04T20:17:59.0207625Z + python -c 'import torch; print(torch.__config__.parallel_info())' 2025-03-04T20:17:59.9448407Z ATen/Parallel: 2025-03-04T20:17:59.9451380Z at::get_num_threads() : 16 2025-03-04T20:17:59.9451875Z at::get_num_interop_threads() : 16 2025-03-04T20:17:59.9452238Z OpenMP 201511 (a.k.a. OpenMP 4.5) 2025-03-04T20:17:59.9452616Z omp_get_max_threads() : 16 2025-03-04T20:17:59.9453116Z Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications 2025-03-04T20:17:59.9453520Z mkl_get_max_threads() : 16 2025-03-04T20:17:59.9453815Z Intel(R) MKL-DNN v3.5.3 (Git Hash 66f0cb9eb66affd2da3bf5f8d897376f04aae6af) 2025-03-04T20:17:59.9454161Z std::thread::hardware_concurrency() : 32 2025-03-04T20:17:59.9454403Z Environment variables: 2025-03-04T20:17:59.9454613Z OMP_NUM_THREADS : [not set] 2025-03-04T20:17:59.9454819Z MKL_NUM_THREADS : [not set] 2025-03-04T20:17:59.9455014Z ATen parallel backend: OpenMP 2025-03-04T20:17:59.9455138Z 2025-03-04T20:18:00.1478409Z + [[ cpu_inductor_torchbench == *numpy_2* ]] 2025-03-04T20:18:00.1478989Z + [[ linux-jammy-py3.9-gcc11-build == *aarch64* ]] 2025-03-04T20:18:00.1479407Z + [[ cpu_inductor_torchbench == *backward* ]] 2025-03-04T20:18:00.1479752Z + [[ cpu_inductor_torchbench == *xla* ]] 2025-03-04T20:18:00.1480195Z + [[ cpu_inductor_torchbench == *executorch* ]] 2025-03-04T20:18:00.1480951Z + [[ cpu_inductor_torchbench == \j\i\t\_\l\e\g\a\c\y ]] 2025-03-04T20:18:00.1481261Z + [[ linux-jammy-py3.9-gcc11-build == *libtorch* ]] 2025-03-04T20:18:00.1481568Z + [[ cpu_inductor_torchbench == distributed ]] 2025-03-04T20:18:00.1481839Z + [[ cpu_inductor_torchbench == *inductor_distributed* ]] 2025-03-04T20:18:00.1482147Z + [[ cpu_inductor_torchbench == *inductor-halide* ]] 2025-03-04T20:18:00.1482427Z + [[ cpu_inductor_torchbench == *inductor-triton-cpu* ]] 2025-03-04T20:18:00.1482723Z + [[ cpu_inductor_torchbench == *inductor-micro-benchmark* ]] 2025-03-04T20:18:00.1483006Z + [[ cpu_inductor_torchbench == *huggingface* ]] 2025-03-04T20:18:00.1483249Z + [[ cpu_inductor_torchbench == *timm* ]] 2025-03-04T20:18:00.1483489Z + [[ cpu_inductor_torchbench == cachebench ]] 2025-03-04T20:18:00.1483744Z + [[ cpu_inductor_torchbench == verify_cachebench ]] 2025-03-04T20:18:00.1483993Z + [[ cpu_inductor_torchbench == *torchbench* ]] 2025-03-04T20:18:00.1484350Z + [[ cpu_inductor_torchbench == *cpu* ]] 2025-03-04T20:18:00.1484583Z + install_torchaudio cpu 2025-03-04T20:18:00.1484788Z + local commit 2025-03-04T20:18:00.1484974Z ++ get_pinned_commit audio 2025-03-04T20:18:00.1485190Z ++ cat .github/ci_commit_pins/audio.txt 2025-03-04T20:18:00.1487349Z + commit=c670ad81fda266b6598aeeef434583eb98197ae8 2025-03-04T20:18:00.1487737Z + [[ cpu == \c\u\d\a ]] 2025-03-04T20:18:00.1488298Z + pip_install --no-use-pep517 --user git+https://github.com/pytorch/audio.git@c670ad81fda266b6598aeeef434583eb98197ae8 2025-03-04T20:18:00.1488834Z + pip_install_pkg='python3 -m pip install --progress-bar off' 2025-03-04T20:18:00.1489364Z + python3 -m pip install --progress-bar off --no-use-pep517 --user git+https://github.com/pytorch/audio.git@c670ad81fda266b6598aeeef434583eb98197ae8 2025-03-04T20:18:00.4101741Z Collecting git+https://github.com/pytorch/audio.git@c670ad81fda266b6598aeeef434583eb98197ae8 2025-03-04T20:18:00.4102747Z Cloning https://github.com/pytorch/audio.git (to revision c670ad81fda266b6598aeeef434583eb98197ae8) to /tmp/pip-req-build-615obvdw 2025-03-04T20:18:00.4131051Z Running command git clone --filter=blob:none --quiet https://github.com/pytorch/audio.git /tmp/pip-req-build-615obvdw 2025-03-04T20:18:01.1079940Z Running command git rev-parse -q --verify 'sha^c670ad81fda266b6598aeeef434583eb98197ae8' 2025-03-04T20:18:01.1113847Z Running command git fetch -q https://github.com/pytorch/audio.git c670ad81fda266b6598aeeef434583eb98197ae8 2025-03-04T20:18:01.1874810Z Resolved https://github.com/pytorch/audio.git to commit c670ad81fda266b6598aeeef434583eb98197ae8 2025-03-04T20:18:01.1875488Z Running command git submodule update --init --recursive -q 2025-03-04T20:18:02.8376955Z Preparing metadata (setup.py) ... [?25l- done 2025-03-04T20:18:02.8414530Z [?25hRequirement already satisfied: torch in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torchaudio==2.6.0a0+c670ad8) (2.7.0a0+git1b74980) 2025-03-04T20:18:02.8432814Z Requirement already satisfied: filelock in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch->torchaudio==2.6.0a0+c670ad8) (3.16.1) 2025-03-04T20:18:02.8440290Z Requirement already satisfied: typing-extensions>=4.10.0 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch->torchaudio==2.6.0a0+c670ad8) (4.12.2) 2025-03-04T20:18:02.8445024Z Requirement already satisfied: sympy==1.13.3 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch->torchaudio==2.6.0a0+c670ad8) (1.13.3) 2025-03-04T20:18:02.8446934Z Requirement already satisfied: networkx in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch->torchaudio==2.6.0a0+c670ad8) (2.8.8) 2025-03-04T20:18:02.8447636Z Requirement already satisfied: jinja2 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch->torchaudio==2.6.0a0+c670ad8) (3.1.5) 2025-03-04T20:18:02.8448466Z Requirement already satisfied: fsspec in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch->torchaudio==2.6.0a0+c670ad8) (2024.10.0) 2025-03-04T20:18:02.8457937Z Requirement already satisfied: mpmath<1.4,>=1.1.0 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from sympy==1.13.3->torch->torchaudio==2.6.0a0+c670ad8) (1.3.0) 2025-03-04T20:18:02.8872734Z Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from jinja2->torch->torchaudio==2.6.0a0+c670ad8) (3.0.2) 2025-03-04T20:18:02.8968652Z Building wheels for collected packages: torchaudio 2025-03-04T20:18:51.9606124Z Building wheel for torchaudio (setup.py) ... [?25l- \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | done 2025-03-04T20:18:51.9630771Z [?25h Created wheel for torchaudio: filename=torchaudio-2.6.0a0+c670ad8-cp39-cp39-linux_x86_64.whl size=1820525 sha256=8d459130bb372c9c419cfec408c887ef7a449b48fdb00549d74683654add5448 2025-03-04T20:18:51.9631937Z Stored in directory: /var/lib/jenkins/.cache/pip/wheels/1c/6f/84/4c8de1f050144e10889ee8fe7b3a86ac99233001c78656be9d 2025-03-04T20:18:51.9665391Z Successfully built torchaudio 2025-03-04T20:18:52.3906883Z Installing collected packages: torchaudio 2025-03-04T20:18:52.5395196Z Successfully installed torchaudio-2.6.0a0+c670ad8 2025-03-04T20:18:52.6524271Z + install_torchvision 2025-03-04T20:18:52.6524781Z + local orig_preload 2025-03-04T20:18:52.6525123Z + local commit 2025-03-04T20:18:52.6530076Z ++ get_pinned_commit vision 2025-03-04T20:18:52.6530356Z ++ cat .github/ci_commit_pins/vision.txt 2025-03-04T20:18:52.6540112Z + commit=d23a6e1664d20707c11781299611436e1f0c104f 2025-03-04T20:18:52.6540410Z + orig_preload= 2025-03-04T20:18:52.6540610Z + '[' -n '' ']' 2025-03-04T20:18:52.6541038Z + pip_install --no-use-pep517 --user git+https://github.com/pytorch/vision.git@d23a6e1664d20707c11781299611436e1f0c104f 2025-03-04T20:18:52.6541546Z + pip_install_pkg='python3 -m pip install --progress-bar off' 2025-03-04T20:18:52.6542138Z + python3 -m pip install --progress-bar off --no-use-pep517 --user git+https://github.com/pytorch/vision.git@d23a6e1664d20707c11781299611436e1f0c104f 2025-03-04T20:18:52.9189722Z Collecting git+https://github.com/pytorch/vision.git@d23a6e1664d20707c11781299611436e1f0c104f 2025-03-04T20:18:52.9190345Z Cloning https://github.com/pytorch/vision.git (to revision d23a6e1664d20707c11781299611436e1f0c104f) to /tmp/pip-req-build-om_erjg3 2025-03-04T20:18:52.9228432Z Running command git clone --filter=blob:none --quiet https://github.com/pytorch/vision.git /tmp/pip-req-build-om_erjg3 2025-03-04T20:18:54.2469764Z Running command git rev-parse -q --verify 'sha^d23a6e1664d20707c11781299611436e1f0c104f' 2025-03-04T20:18:54.2499719Z Running command git fetch -q https://github.com/pytorch/vision.git d23a6e1664d20707c11781299611436e1f0c104f 2025-03-04T20:18:54.3286124Z Running command git checkout -q d23a6e1664d20707c11781299611436e1f0c104f 2025-03-04T20:18:54.6178327Z Resolved https://github.com/pytorch/vision.git to commit d23a6e1664d20707c11781299611436e1f0c104f 2025-03-04T20:18:56.4162690Z Preparing metadata (setup.py) ... [?25l- \ done 2025-03-04T20:18:56.4202539Z [?25hRequirement already satisfied: numpy in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torchvision==0.19.0a0+d23a6e1) (1.22.4) 2025-03-04T20:18:56.4203720Z Requirement already satisfied: torch in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torchvision==0.19.0a0+d23a6e1) (2.7.0a0+git1b74980) 2025-03-04T20:18:56.4212095Z Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torchvision==0.19.0a0+d23a6e1) (11.0.0) 2025-03-04T20:18:56.4273386Z Requirement already satisfied: filelock in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (3.16.1) 2025-03-04T20:18:56.4274345Z Requirement already satisfied: typing-extensions>=4.10.0 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (4.12.2) 2025-03-04T20:18:56.4285032Z Requirement already satisfied: sympy==1.13.3 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (1.13.3) 2025-03-04T20:18:56.4290279Z Requirement already satisfied: networkx in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (2.8.8) 2025-03-04T20:18:56.4354009Z Requirement already satisfied: jinja2 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (3.1.5) 2025-03-04T20:18:56.4357089Z Requirement already satisfied: fsspec in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (2024.10.0) 2025-03-04T20:18:56.4362915Z Requirement already satisfied: mpmath<1.4,>=1.1.0 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from sympy==1.13.3->torch->torchvision==0.19.0a0+d23a6e1) (1.3.0) 2025-03-04T20:18:56.4717113Z Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from jinja2->torch->torchvision==0.19.0a0+d23a6e1) (3.0.2) 2025-03-04T20:18:56.4772389Z Building wheels for collected packages: torchvision 2025-03-04T20:19:18.1065882Z Building wheel for torchvision (setup.py) ... [?25l- \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - done 2025-03-04T20:19:18.1085036Z [?25h Created wheel for torchvision: filename=torchvision-0.19.0a0+d23a6e1-cp39-cp39-linux_x86_64.whl size=1218930 sha256=7cddaea183caf8536fa65ad213a4d0f240f0d828f44f7a687312a1ad9316c8d8 2025-03-04T20:19:18.1085943Z Stored in directory: /var/lib/jenkins/.cache/pip/wheels/76/25/46/00629fe1ec5f276eb28faecc4ae7c48c9ef9e6bbaa0691ad87 2025-03-04T20:19:18.1117439Z Successfully built torchvision 2025-03-04T20:19:18.5418539Z Installing collected packages: torchvision 2025-03-04T20:19:18.8412358Z Successfully installed torchvision-0.19.0a0+d23a6e1 2025-03-04T20:19:18.9437069Z + '[' -n '' ']' 2025-03-04T20:19:18.9437750Z + TORCH_CUDA_ARCH_LIST='8.0;8.6' 2025-03-04T20:19:18.9443988Z + pip_install git+https://github.com/pytorch/ao.git 2025-03-04T20:19:18.9449635Z + pip_install_pkg='python3 -m pip install --progress-bar off' 2025-03-04T20:19:18.9450209Z + python3 -m pip install --progress-bar off git+https://github.com/pytorch/ao.git 2025-03-04T20:19:19.2007758Z Collecting git+https://github.com/pytorch/ao.git 2025-03-04T20:19:19.2013392Z Cloning https://github.com/pytorch/ao.git to /tmp/pip-req-build-vl4i91ls 2025-03-04T20:19:19.2048871Z Running command git clone --filter=blob:none --quiet https://github.com/pytorch/ao.git /tmp/pip-req-build-vl4i91ls 2025-03-04T20:19:19.9435697Z Resolved https://github.com/pytorch/ao.git to commit 9bcd73be6fb60cc169deeaf5b5508cb4fdaefcb5 2025-03-04T20:19:19.9436237Z Running command git submodule update --init --recursive -q 2025-03-04T20:19:23.5494631Z Preparing metadata (setup.py) ... [?25l- done 2025-03-04T20:19:23.5540274Z [?25hBuilding wheels for collected packages: torchao 2025-03-04T20:19:25.5235304Z Building wheel for torchao (setup.py) ... [?25l- \ | done 2025-03-04T20:19:25.5256773Z [?25h Created wheel for torchao: filename=torchao-0.10.0+git9bcd73be-py3-none-any.whl size=703040 sha256=fa45224cf56306a94939ca9eaac71f96f2093ed72f4928e5fc60cd5ca1c77ded 2025-03-04T20:19:25.5262543Z Stored in directory: /tmp/pip-ephem-wheel-cache-tog3js33/wheels/7d/5c/37/b607f0d104c0d0de07b506bc734c280ece23dce90e0c5cddc7 2025-03-04T20:19:25.5299556Z Successfully built torchao 2025-03-04T20:19:26.0290536Z Installing collected packages: torchao 2025-03-04T20:19:26.4768006Z Successfully installed torchao-0.10.0+git9bcd73be 2025-03-04T20:19:26.6636193Z + id=0 2025-03-04T20:19:26.6636470Z + pip_install opencv-python==4.8.0.74 2025-03-04T20:19:26.6636795Z + pip_install_pkg='python3 -m pip install --progress-bar off' 2025-03-04T20:19:26.6637127Z + python3 -m pip install --progress-bar off opencv-python==4.8.0.74 2025-03-04T20:19:27.0221610Z Collecting opencv-python==4.8.0.74 2025-03-04T20:19:27.0344145Z Downloading opencv_python-4.8.0.74-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (19 kB) 2025-03-04T20:19:27.0427393Z Requirement already satisfied: numpy>=1.17.0 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from opencv-python==4.8.0.74) (1.22.4) 2025-03-04T20:19:27.0508395Z Downloading opencv_python-4.8.0.74-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (61.7 MB) 2025-03-04T20:19:28.0492896Z Installing collected packages: opencv-python 2025-03-04T20:19:28.8124172Z Successfully installed opencv-python-4.8.0.74 2025-03-04T20:19:28.8966918Z + [[ cpu_inductor_torchbench == *inductor_torchbench_smoketest_perf* ]] 2025-03-04T20:19:28.8969398Z + [[ cpu_inductor_torchbench == *inductor_torchbench_cpu_smoketest_perf* ]] 2025-03-04T20:19:28.8969835Z + [[ cpu_inductor_torchbench == *torchbench_gcp_smoketest* ]] 2025-03-04T20:19:28.8970146Z + checkout_install_torchbench 2025-03-04T20:19:28.8970388Z + local commit 2025-03-04T20:19:28.8970964Z ++ get_pinned_commit 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2025-03-04T20:19:29.0774868Z remote: Counting objects: 65% (3544/5451) 2025-03-04T20:19:29.0775130Z remote: Counting objects: 66% (3598/5451) 2025-03-04T20:19:29.0775387Z remote: Counting objects: 67% (3653/5451) 2025-03-04T20:19:29.0775731Z remote: Counting objects: 68% (3707/5451) 2025-03-04T20:19:29.0775986Z remote: Counting objects: 69% (3762/5451) 2025-03-04T20:19:29.0776282Z remote: Counting objects: 70% (3816/5451) 2025-03-04T20:19:29.0776588Z remote: Counting objects: 71% (3871/5451) 2025-03-04T20:19:29.0779342Z remote: Counting objects: 72% (3925/5451) 2025-03-04T20:19:29.0779790Z remote: Counting objects: 73% (3980/5451) 2025-03-04T20:19:29.0780211Z remote: Counting objects: 74% (4034/5451) 2025-03-04T20:19:29.0780581Z remote: Counting objects: 75% (4089/5451) 2025-03-04T20:19:29.0780877Z remote: Counting objects: 76% (4143/5451) 2025-03-04T20:19:29.0783660Z remote: Counting objects: 77% (4198/5451) 2025-03-04T20:19:29.0784072Z remote: Counting objects: 78% (4252/5451) 2025-03-04T20:19:29.0789540Z remote: Counting objects: 79% (4307/5451) 2025-03-04T20:19:29.0790917Z remote: Counting objects: 80% (4361/5451) 2025-03-04T20:19:29.0791256Z remote: Counting objects: 81% (4416/5451) 2025-03-04T20:19:29.0791551Z remote: Counting objects: 82% (4470/5451) 2025-03-04T20:19:29.0791817Z remote: Counting objects: 83% (4525/5451) 2025-03-04T20:19:29.0792086Z remote: Counting objects: 84% (4579/5451) 2025-03-04T20:19:29.0792359Z remote: Counting objects: 85% (4634/5451) 2025-03-04T20:19:29.0792630Z remote: Counting objects: 86% (4688/5451) 2025-03-04T20:19:29.0792899Z remote: Counting objects: 87% (4743/5451) 2025-03-04T20:19:29.0793191Z remote: Counting objects: 88% (4797/5451) 2025-03-04T20:19:29.0793505Z remote: Counting objects: 89% (4852/5451) 2025-03-04T20:19:29.0793810Z remote: Counting objects: 90% (4906/5451) 2025-03-04T20:19:29.0794344Z remote: Counting objects: 91% (4961/5451) 2025-03-04T20:19:29.0794689Z remote: Counting objects: 92% (5015/5451) 2025-03-04T20:19:29.0794989Z remote: Counting objects: 93% (5070/5451) 2025-03-04T20:19:29.0795283Z remote: Counting objects: 94% (5124/5451) 2025-03-04T20:19:29.0795669Z remote: Counting objects: 95% (5179/5451) 2025-03-04T20:19:29.0795965Z remote: Counting objects: 96% (5233/5451) 2025-03-04T20:19:29.0796231Z remote: Counting objects: 97% (5288/5451) 2025-03-04T20:19:29.0796495Z remote: Counting objects: 98% (5342/5451) 2025-03-04T20:19:29.0796797Z remote: Counting objects: 99% (5397/5451) 2025-03-04T20:19:29.0797101Z remote: Counting objects: 100% (5451/5451) 2025-03-04T20:19:29.0797434Z remote: Counting objects: 100% (5451/5451), done. 2025-03-04T20:19:29.0809991Z remote: Compressing objects: 0% (1/654) 2025-03-04T20:19:29.0871193Z remote: Compressing objects: 1% (7/654) 2025-03-04T20:19:29.0894504Z remote: Compressing objects: 2% (14/654) 2025-03-04T20:19:29.0925018Z remote: Compressing objects: 3% (20/654) 2025-03-04T20:19:29.0967371Z remote: Compressing objects: 4% (27/654) 2025-03-04T20:19:29.1037752Z remote: Compressing objects: 5% (33/654) 2025-03-04T20:19:29.1060146Z remote: Compressing objects: 6% (40/654) 2025-03-04T20:19:29.1082094Z remote: Compressing objects: 7% (46/654) 2025-03-04T20:19:29.1106000Z remote: Compressing objects: 8% (53/654) 2025-03-04T20:19:29.1123379Z remote: Compressing objects: 9% (59/654) 2025-03-04T20:19:29.1126655Z remote: Compressing objects: 10% (66/654) 2025-03-04T20:19:29.1256362Z remote: Compressing objects: 11% (72/654) 2025-03-04T20:19:29.1395832Z remote: Compressing objects: 12% (79/654) 2025-03-04T20:19:29.1430133Z remote: Compressing objects: 13% (86/654) 2025-03-04T20:19:29.1612541Z remote: Compressing objects: 14% (92/654) 2025-03-04T20:19:29.1670329Z remote: Compressing objects: 15% (99/654) 2025-03-04T20:19:29.1712750Z remote: Compressing objects: 16% (105/654) 2025-03-04T20:19:29.1781293Z remote: Compressing objects: 17% (112/654) 2025-03-04T20:19:29.1837859Z remote: Compressing objects: 18% (118/654) 2025-03-04T20:19:29.1865037Z remote: Compressing objects: 19% (125/654) 2025-03-04T20:19:29.1890730Z remote: Compressing objects: 20% (131/654) 2025-03-04T20:19:29.1928766Z remote: Compressing objects: 21% (138/654) 2025-03-04T20:19:29.1969444Z remote: Compressing objects: 22% (144/654) 2025-03-04T20:19:29.1993712Z remote: Compressing objects: 23% (151/654) 2025-03-04T20:19:29.2024888Z remote: Compressing objects: 24% (157/654) 2025-03-04T20:19:29.2045081Z remote: Compressing objects: 25% (164/654) 2025-03-04T20:19:29.2062329Z remote: Compressing objects: 26% (171/654) 2025-03-04T20:19:29.2079524Z remote: Compressing objects: 27% (177/654) 2025-03-04T20:19:29.2094819Z remote: Compressing objects: 28% (184/654) 2025-03-04T20:19:29.2115774Z remote: Compressing objects: 29% (190/654) 2025-03-04T20:19:29.2135102Z remote: Compressing objects: 30% (197/654) 2025-03-04T20:19:29.2148952Z remote: Compressing objects: 31% (203/654) 2025-03-04T20:19:29.2156131Z remote: Compressing objects: 32% (210/654) 2025-03-04T20:19:29.2176003Z remote: Compressing objects: 33% (216/654) 2025-03-04T20:19:29.2184101Z remote: Compressing objects: 34% (223/654) 2025-03-04T20:19:29.2192529Z remote: Compressing objects: 35% (229/654) 2025-03-04T20:19:29.2195460Z remote: Compressing objects: 36% (236/654) 2025-03-04T20:19:29.2205293Z remote: Compressing objects: 37% (242/654) 2025-03-04T20:19:29.2214145Z remote: Compressing objects: 38% (249/654) 2025-03-04T20:19:29.2218637Z remote: Compressing objects: 39% (256/654) 2025-03-04T20:19:29.2223092Z remote: Compressing objects: 40% (262/654) 2025-03-04T20:19:29.2227383Z remote: Compressing objects: 41% (269/654) 2025-03-04T20:19:29.2229171Z remote: Compressing objects: 42% (275/654) 2025-03-04T20:19:29.2229492Z remote: Compressing objects: 43% (282/654) 2025-03-04T20:19:29.2230152Z remote: Compressing objects: 44% (288/654) 2025-03-04T20:19:29.2230441Z remote: Compressing objects: 45% (295/654) 2025-03-04T20:19:29.2230717Z remote: Compressing objects: 46% (301/654) 2025-03-04T20:19:29.2230989Z remote: Compressing objects: 47% (308/654) 2025-03-04T20:19:29.2231263Z remote: Compressing objects: 48% (314/654) 2025-03-04T20:19:29.2231533Z remote: Compressing objects: 49% (321/654) 2025-03-04T20:19:29.2243929Z remote: Compressing objects: 50% (327/654) 2025-03-04T20:19:29.2248922Z remote: Compressing objects: 51% (334/654) 2025-03-04T20:19:29.2252026Z remote: Compressing objects: 52% (341/654) 2025-03-04T20:19:29.2252446Z remote: Compressing objects: 53% (347/654) 2025-03-04T20:19:29.2252813Z remote: Compressing objects: 54% (354/654) 2025-03-04T20:19:29.2253099Z remote: Compressing objects: 55% (360/654) 2025-03-04T20:19:29.2254039Z remote: Compressing objects: 56% (367/654) 2025-03-04T20:19:29.2254356Z remote: Compressing objects: 57% (373/654) 2025-03-04T20:19:29.2260415Z remote: Compressing objects: 58% (380/654) 2025-03-04T20:19:29.2261718Z remote: Compressing objects: 59% (386/654) 2025-03-04T20:19:29.2266237Z remote: Compressing objects: 60% (393/654) 2025-03-04T20:19:29.2270407Z remote: Compressing objects: 61% (399/654) 2025-03-04T20:19:29.2270688Z remote: Compressing objects: 62% (406/654) 2025-03-04T20:19:29.2270951Z remote: Compressing objects: 63% (413/654) 2025-03-04T20:19:29.2272781Z remote: Compressing objects: 64% (419/654) 2025-03-04T20:19:29.2275322Z remote: Compressing objects: 65% (426/654) 2025-03-04T20:19:29.2283578Z remote: Compressing objects: 66% (432/654) 2025-03-04T20:19:29.2283970Z remote: Compressing objects: 67% (439/654) 2025-03-04T20:19:29.2284310Z remote: Compressing objects: 68% (445/654) 2025-03-04T20:19:29.2290877Z remote: Compressing objects: 69% (452/654) 2025-03-04T20:19:29.2292241Z remote: Compressing objects: 70% (458/654) 2025-03-04T20:19:29.2292536Z remote: Compressing objects: 71% (465/654) 2025-03-04T20:19:29.2292810Z remote: Compressing objects: 72% (471/654) 2025-03-04T20:19:29.2293078Z remote: Compressing objects: 73% (478/654) 2025-03-04T20:19:29.2300627Z remote: Compressing objects: 74% (484/654) 2025-03-04T20:19:29.2302670Z remote: Compressing objects: 75% (491/654) 2025-03-04T20:19:29.2303165Z remote: Compressing objects: 76% (498/654) 2025-03-04T20:19:29.2306195Z remote: Compressing objects: 77% (504/654) 2025-03-04T20:19:29.2306628Z remote: Compressing objects: 78% (511/654) 2025-03-04T20:19:29.2310370Z remote: Compressing objects: 79% (517/654) 2025-03-04T20:19:29.2310848Z remote: Compressing objects: 80% (524/654) 2025-03-04T20:19:29.2311249Z remote: Compressing objects: 81% (530/654) 2025-03-04T20:19:29.2311528Z remote: Compressing objects: 82% (537/654) 2025-03-04T20:19:29.2311824Z remote: Compressing objects: 83% (543/654) 2025-03-04T20:19:29.2312093Z remote: Compressing objects: 84% (550/654) 2025-03-04T20:19:29.2312365Z remote: Compressing objects: 85% (556/654) 2025-03-04T20:19:29.2312616Z remote: Compressing objects: 86% (563/654) 2025-03-04T20:19:29.2312880Z remote: Compressing objects: 87% (569/654) 2025-03-04T20:19:29.2313137Z remote: Compressing objects: 88% (576/654) 2025-03-04T20:19:29.2313397Z remote: Compressing objects: 89% (583/654) 2025-03-04T20:19:29.2327984Z remote: Compressing objects: 90% (589/654) 2025-03-04T20:19:29.2330115Z remote: Compressing objects: 91% (596/654) 2025-03-04T20:19:29.2335894Z remote: Compressing objects: 92% (602/654) 2025-03-04T20:19:29.2340762Z remote: Compressing objects: 93% (609/654) 2025-03-04T20:19:29.2346124Z remote: Compressing objects: 94% (615/654) 2025-03-04T20:19:29.2351344Z remote: Compressing objects: 95% (622/654) 2025-03-04T20:19:29.2352387Z remote: Compressing objects: 96% (628/654) 2025-03-04T20:19:29.2353109Z remote: Compressing objects: 97% (635/654) 2025-03-04T20:19:29.2353433Z remote: Compressing objects: 98% (641/654) 2025-03-04T20:19:29.2354030Z remote: Compressing objects: 99% (648/654) 2025-03-04T20:19:29.2354297Z remote: Compressing objects: 100% (654/654) 2025-03-04T20:19:29.2354587Z remote: Compressing objects: 100% (654/654), done. 2025-03-04T20:19:29.2416949Z Receiving objects: 0% (1/35224) 2025-03-04T20:19:29.2454297Z Receiving objects: 1% (353/35224) 2025-03-04T20:19:29.2493924Z Receiving objects: 2% (705/35224) 2025-03-04T20:19:29.2529831Z Receiving objects: 3% (1057/35224) 2025-03-04T20:19:29.2556314Z Receiving objects: 4% (1409/35224) 2025-03-04T20:19:29.2588681Z Receiving objects: 5% (1762/35224) 2025-03-04T20:19:29.2621487Z Receiving objects: 6% (2114/35224) 2025-03-04T20:19:29.2660437Z Receiving objects: 7% (2466/35224) 2025-03-04T20:19:29.2688071Z Receiving objects: 8% (2818/35224) 2025-03-04T20:19:29.2725921Z Receiving objects: 9% (3171/35224) 2025-03-04T20:19:29.2778935Z Receiving objects: 10% (3523/35224) 2025-03-04T20:19:29.2844694Z Receiving objects: 11% (3875/35224) 2025-03-04T20:19:29.2954599Z Receiving objects: 12% (4227/35224) 2025-03-04T20:19:29.3089040Z Receiving objects: 13% (4580/35224) 2025-03-04T20:19:29.3150455Z Receiving objects: 14% (4932/35224) 2025-03-04T20:19:29.3193219Z Receiving objects: 15% (5284/35224) 2025-03-04T20:19:29.3221047Z Receiving objects: 16% (5636/35224) 2025-03-04T20:19:29.3259579Z Receiving objects: 17% (5989/35224) 2025-03-04T20:19:29.3292004Z Receiving objects: 18% (6341/35224) 2025-03-04T20:19:29.3310928Z Receiving objects: 19% (6693/35224) 2025-03-04T20:19:29.5117416Z Receiving objects: 20% (7045/35224) 2025-03-04T20:19:30.2357801Z Receiving objects: 21% (7398/35224) 2025-03-04T20:19:30.5787793Z Receiving objects: 21% (7674/35224), 96.15 MiB | 96.14 MiB/s 2025-03-04T20:19:30.6484119Z Receiving objects: 22% (7750/35224), 96.15 MiB | 96.14 MiB/s 2025-03-04T20:19:30.7139976Z Receiving objects: 23% (8102/35224), 96.15 MiB | 96.14 MiB/s 2025-03-04T20:19:30.7796097Z Receiving objects: 24% (8454/35224), 96.15 MiB | 96.14 MiB/s 2025-03-04T20:19:30.8450020Z Receiving objects: 25% (8806/35224), 137.47 MiB | 91.70 MiB/s 2025-03-04T20:19:30.9112235Z Receiving objects: 26% (9159/35224), 137.47 MiB | 91.70 MiB/s 2025-03-04T20:19:30.9781010Z Receiving objects: 27% (9511/35224), 137.47 MiB | 91.70 MiB/s 2025-03-04T20:19:31.0472820Z Receiving objects: 28% (9863/35224), 137.47 MiB | 91.70 MiB/s 2025-03-04T20:19:31.2317803Z Receiving objects: 29% (10215/35224), 137.47 MiB | 91.70 MiB/s 2025-03-04T20:19:31.2351642Z Receiving objects: 30% (10568/35224), 137.47 MiB | 91.70 MiB/s 2025-03-04T20:19:31.2818744Z Receiving objects: 30% (10577/35224), 182.62 MiB | 91.35 MiB/s 2025-03-04T20:19:31.4968376Z Receiving objects: 31% (10920/35224), 182.62 MiB | 91.35 MiB/s 2025-03-04T20:19:31.6864225Z Receiving objects: 32% (11272/35224), 182.62 MiB | 91.35 MiB/s 2025-03-04T20:19:31.6882606Z Receiving objects: 33% (11624/35224), 182.62 MiB | 91.35 MiB/s 2025-03-04T20:19:31.6898302Z Receiving objects: 34% (11977/35224), 182.62 MiB | 91.35 MiB/s 2025-03-04T20:19:31.6904150Z Receiving objects: 35% (12329/35224), 182.62 MiB | 91.35 MiB/s 2025-03-04T20:19:31.6912596Z Receiving objects: 36% (12681/35224), 182.62 MiB | 91.35 MiB/s 2025-03-04T20:19:31.6927207Z Receiving objects: 37% (13033/35224), 182.62 MiB | 91.35 MiB/s 2025-03-04T20:19:31.6941501Z Receiving objects: 38% (13386/35224), 182.62 MiB | 91.35 MiB/s 2025-03-04T20:19:31.6941824Z Receiving objects: 39% (13738/35224), 182.62 MiB | 91.35 MiB/s 2025-03-04T20:19:31.6965497Z Receiving objects: 40% (14090/35224), 182.62 MiB | 91.35 MiB/s 2025-03-04T20:19:31.6974324Z Receiving objects: 41% (14442/35224), 182.62 MiB | 91.35 MiB/s 2025-03-04T20:19:31.7150904Z Receiving objects: 42% (14795/35224), 182.62 MiB | 91.35 MiB/s 2025-03-04T20:19:31.7155630Z Receiving objects: 43% (15147/35224), 182.62 MiB | 91.35 MiB/s 2025-03-04T20:19:31.7156245Z Receiving objects: 44% (15499/35224), 182.62 MiB | 91.35 MiB/s 2025-03-04T20:19:31.7172443Z Receiving objects: 45% (15851/35224), 182.62 MiB | 91.35 MiB/s 2025-03-04T20:19:31.7185387Z Receiving objects: 46% (16204/35224), 182.62 MiB | 91.35 MiB/s 2025-03-04T20:19:31.7195033Z Receiving objects: 47% (16556/35224), 182.62 MiB | 91.35 MiB/s 2025-03-04T20:19:31.7208649Z Receiving objects: 48% (16908/35224), 182.62 MiB | 91.35 MiB/s 2025-03-04T20:19:31.7311926Z Receiving objects: 49% (17260/35224), 182.62 MiB | 91.35 MiB/s 2025-03-04T20:19:31.7312402Z Receiving objects: 50% (17612/35224), 182.62 MiB | 91.35 MiB/s 2025-03-04T20:19:31.7324393Z Receiving objects: 51% (17965/35224), 182.62 MiB | 91.35 MiB/s 2025-03-04T20:19:31.7333866Z Receiving objects: 52% (18317/35224), 182.62 MiB | 91.35 MiB/s 2025-03-04T20:19:31.7549497Z Receiving objects: 53% (18669/35224), 182.62 MiB | 91.35 MiB/s 2025-03-04T20:19:31.7891220Z Receiving objects: 54% (19021/35224), 224.95 MiB | 90.01 MiB/s 2025-03-04T20:19:31.7903543Z Receiving objects: 55% (19374/35224), 224.95 MiB | 90.01 MiB/s 2025-03-04T20:19:31.7913223Z Receiving objects: 56% (19726/35224), 224.95 MiB | 90.01 MiB/s 2025-03-04T20:19:31.7927814Z Receiving objects: 57% (20078/35224), 224.95 MiB | 90.01 MiB/s 2025-03-04T20:19:31.7946015Z Receiving objects: 58% (20430/35224), 224.95 MiB | 90.01 MiB/s 2025-03-04T20:19:31.7951633Z Receiving objects: 59% (20783/35224), 224.95 MiB | 90.01 MiB/s 2025-03-04T20:19:31.7952036Z Receiving objects: 60% (21135/35224), 224.95 MiB | 90.01 MiB/s 2025-03-04T20:19:31.8128316Z Receiving objects: 61% (21487/35224), 224.95 MiB | 90.01 MiB/s 2025-03-04T20:19:31.8154076Z Receiving objects: 62% (21839/35224), 224.95 MiB | 90.01 MiB/s 2025-03-04T20:19:31.8166309Z Receiving objects: 63% (22192/35224), 224.95 MiB | 90.01 MiB/s 2025-03-04T20:19:31.8176653Z Receiving objects: 64% (22544/35224), 224.95 MiB | 90.01 MiB/s 2025-03-04T20:19:31.8215185Z Receiving objects: 65% (22896/35224), 224.95 MiB | 90.01 MiB/s 2025-03-04T20:19:31.8359480Z Receiving objects: 66% (23248/35224), 224.95 MiB | 90.01 MiB/s 2025-03-04T20:19:31.8369464Z Receiving objects: 67% (23601/35224), 224.95 MiB | 90.01 MiB/s 2025-03-04T20:19:31.8371194Z Receiving objects: 68% (23953/35224), 224.95 MiB | 90.01 MiB/s 2025-03-04T20:19:31.8380029Z Receiving objects: 69% (24305/35224), 224.95 MiB | 90.01 MiB/s 2025-03-04T20:19:31.8384787Z Receiving objects: 70% (24657/35224), 224.95 MiB | 90.01 MiB/s 2025-03-04T20:19:31.8392269Z Receiving objects: 71% (25010/35224), 224.95 MiB | 90.01 MiB/s 2025-03-04T20:19:31.8491996Z Receiving objects: 72% (25362/35224), 224.95 MiB | 90.01 MiB/s 2025-03-04T20:19:31.8502172Z Receiving objects: 73% (25714/35224), 224.95 MiB | 90.01 MiB/s 2025-03-04T20:19:31.8511144Z Receiving objects: 74% (26066/35224), 224.95 MiB | 90.01 MiB/s 2025-03-04T20:19:31.8538881Z Receiving objects: 75% (26418/35224), 224.95 MiB | 90.01 MiB/s 2025-03-04T20:19:31.8552763Z Receiving objects: 76% (26771/35224), 224.95 MiB | 90.01 MiB/s 2025-03-04T20:19:31.8575560Z Receiving objects: 77% (27123/35224), 224.95 MiB | 90.01 MiB/s 2025-03-04T20:19:31.8588631Z Receiving objects: 78% (27475/35224), 224.95 MiB | 90.01 MiB/s 2025-03-04T20:19:31.8612347Z Receiving objects: 79% (27827/35224), 224.95 MiB | 90.01 MiB/s 2025-03-04T20:19:31.8687966Z Receiving objects: 80% (28180/35224), 224.95 MiB | 90.01 MiB/s 2025-03-04T20:19:31.8701471Z Receiving objects: 81% (28532/35224), 224.95 MiB | 90.01 MiB/s 2025-03-04T20:19:31.8722245Z Receiving objects: 82% (28884/35224), 224.95 MiB | 90.01 MiB/s 2025-03-04T20:19:31.8733175Z Receiving objects: 83% (29236/35224), 224.95 MiB | 90.01 MiB/s 2025-03-04T20:19:31.8772721Z Receiving objects: 84% (29589/35224), 224.95 MiB | 90.01 MiB/s 2025-03-04T20:19:31.8782086Z Receiving objects: 85% (29941/35224), 224.95 MiB | 90.01 MiB/s 2025-03-04T20:19:31.8782671Z Receiving objects: 86% (30293/35224), 224.95 MiB | 90.01 MiB/s 2025-03-04T20:19:31.8783437Z Receiving objects: 87% (30645/35224), 224.95 MiB | 90.01 MiB/s 2025-03-04T20:19:31.8783904Z Receiving objects: 88% (30998/35224), 224.95 MiB | 90.01 MiB/s 2025-03-04T20:19:31.8784448Z Receiving objects: 89% (31350/35224), 224.95 MiB | 90.01 MiB/s 2025-03-04T20:19:31.8784736Z Receiving objects: 90% (31702/35224), 224.95 MiB | 90.01 MiB/s 2025-03-04T20:19:31.8790761Z Receiving objects: 91% (32054/35224), 224.95 MiB | 90.01 MiB/s 2025-03-04T20:19:31.8794631Z Receiving objects: 92% (32407/35224), 224.95 MiB | 90.01 MiB/s 2025-03-04T20:19:31.8794976Z Receiving objects: 93% (32759/35224), 224.95 MiB | 90.01 MiB/s 2025-03-04T20:19:31.8810905Z Receiving objects: 94% (33111/35224), 224.95 MiB | 90.01 MiB/s 2025-03-04T20:19:31.8841077Z Receiving objects: 95% (33463/35224), 224.95 MiB | 90.01 MiB/s 2025-03-04T20:19:31.8882054Z Receiving objects: 96% (33816/35224), 224.95 MiB | 90.01 MiB/s 2025-03-04T20:19:31.8895027Z Receiving objects: 97% (34168/35224), 224.95 MiB | 90.01 MiB/s 2025-03-04T20:19:31.8929640Z Receiving objects: 98% (34520/35224), 224.95 MiB | 90.01 MiB/s 2025-03-04T20:19:31.8973183Z Receiving objects: 99% (34872/35224), 224.95 MiB | 90.01 MiB/s 2025-03-04T20:19:31.8974002Z remote: Total 35224 (delta 5111), reused 4839 (delta 4797), pack-reused 29773 (from 2) 2025-03-04T20:19:31.8984706Z Receiving objects: 100% (35224/35224), 224.95 MiB | 90.01 MiB/s 2025-03-04T20:19:31.8985127Z Receiving objects: 100% (35224/35224), 233.51 MiB | 87.68 MiB/s, done. 2025-03-04T20:19:31.9024989Z Resolving deltas: 0% (0/19882) 2025-03-04T20:19:31.9140212Z Resolving deltas: 1% (199/19882) 2025-03-04T20:19:31.9214579Z Resolving deltas: 2% (398/19882) 2025-03-04T20:19:31.9271988Z Resolving deltas: 3% (597/19882) 2025-03-04T20:19:31.9289117Z Resolving deltas: 4% (796/19882) 2025-03-04T20:19:31.9294264Z Resolving deltas: 5% (995/19882) 2025-03-04T20:19:31.9305243Z Resolving deltas: 6% (1193/19882) 2025-03-04T20:19:31.9312030Z Resolving deltas: 7% (1392/19882) 2025-03-04T20:19:31.9319399Z Resolving deltas: 8% (1591/19882) 2025-03-04T20:19:31.9321315Z Resolving deltas: 9% (1790/19882) 2025-03-04T20:19:31.9331854Z Resolving deltas: 10% (1989/19882) 2025-03-04T20:19:31.9337078Z Resolving deltas: 11% (2188/19882) 2025-03-04T20:19:31.9341442Z Resolving deltas: 12% (2387/19882) 2025-03-04T20:19:31.9349001Z 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2025-03-04T20:19:32.0058318Z Resolving deltas: 90% (17894/19882) 2025-03-04T20:19:32.0071132Z Resolving deltas: 91% (18093/19882) 2025-03-04T20:19:32.0076185Z Resolving deltas: 92% (18292/19882) 2025-03-04T20:19:32.0087870Z Resolving deltas: 93% (18491/19882) 2025-03-04T20:19:32.0092924Z Resolving deltas: 94% (18690/19882) 2025-03-04T20:19:32.0098373Z Resolving deltas: 95% (18888/19882) 2025-03-04T20:19:32.0103261Z Resolving deltas: 96% (19087/19882) 2025-03-04T20:19:32.0112312Z Resolving deltas: 97% (19286/19882) 2025-03-04T20:19:32.0145597Z Resolving deltas: 98% (19485/19882) 2025-03-04T20:19:32.0174340Z Resolving deltas: 99% (19684/19882) 2025-03-04T20:19:32.0174638Z Resolving deltas: 100% (19882/19882) 2025-03-04T20:19:32.0174910Z Resolving deltas: 100% (19882/19882), done. 2025-03-04T20:19:32.9658329Z + pushd torchbench 2025-03-04T20:19:32.9662107Z ~/workspace/torchbench ~/workspace 2025-03-04T20:19:32.9665252Z + git checkout 373ffb19dc470f4423a3176a4133f8f4b3cdb5bd 2025-03-04T20:19:32.9921047Z Note: switching to '373ffb19dc470f4423a3176a4133f8f4b3cdb5bd'. 2025-03-04T20:19:32.9921326Z 2025-03-04T20:19:32.9921491Z You are in 'detached HEAD' state. You can look around, make experimental 2025-03-04T20:19:32.9921842Z changes and commit them, and you can discard any commits you make in this 2025-03-04T20:19:32.9922195Z state without impacting any branches by switching back to a branch. 2025-03-04T20:19:32.9922389Z 2025-03-04T20:19:32.9922533Z If you want to create a new branch to retain commits you create, you may 2025-03-04T20:19:32.9922866Z do so (now or later) by using -c with the switch command. Example: 2025-03-04T20:19:32.9923048Z 2025-03-04T20:19:32.9923149Z git switch -c 2025-03-04T20:19:32.9923300Z 2025-03-04T20:19:32.9923392Z Or undo this operation with: 2025-03-04T20:19:32.9923513Z 2025-03-04T20:19:32.9923588Z git switch - 2025-03-04T20:19:32.9923788Z 2025-03-04T20:19:32.9923951Z Turn off this advice by setting config variable advice.detachedHead to false 2025-03-04T20:19:32.9924160Z 2025-03-04T20:19:32.9924303Z HEAD is now at 373ffb19 Copy model before benchmark warmup runs (#145858) 2025-03-04T20:19:32.9944597Z + '[' '' ']' 2025-03-04T20:19:32.9945012Z + python install.py --continue_on_fail 2025-03-04T20:19:37.5136064Z checking packages numpy, torch, torchvision, torchaudio are installed, generating constaints...OK 2025-03-04T20:19:59.7639610Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/BERT_pytorch...OK 2025-03-04T20:20:09.9548574Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/Background_Matting...OK 2025-03-04T20:20:20.3946459Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/LearningToPaint...OK 2025-03-04T20:20:30.5628752Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/Super_SloMo...OK 2025-03-04T20:20:39.4179791Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/alexnet...OK 2025-03-04T20:20:53.6781829Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/basic_gnn_edgecnn...OK 2025-03-04T20:21:04.6085573Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/basic_gnn_gcn...OK 2025-03-04T20:21:15.2931033Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/basic_gnn_gin...OK 2025-03-04T20:21:25.9378435Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/basic_gnn_sage...OK 2025-03-04T20:21:25.9382545Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/cm3leon_generate...SKIP - No install.py is found 2025-03-04T20:21:35.4770424Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/dcgan...OK 2025-03-04T20:21:46.0439094Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/demucs...OK 2025-03-04T20:21:54.6862113Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/densenet121...OK 2025-03-04T20:22:31.4838680Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/detectron2_fasterrcnn_r_101_c4...OK 2025-03-04T20:22:48.1983337Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/detectron2_fasterrcnn_r_101_dc5...OK 2025-03-04T20:23:03.9861905Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/detectron2_fasterrcnn_r_101_fpn...OK 2025-03-04T20:23:19.4589177Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/detectron2_fasterrcnn_r_50_c4...OK 2025-03-04T20:23:36.2797670Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/detectron2_fasterrcnn_r_50_dc5...OK 2025-03-04T20:23:51.4078658Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/detectron2_fasterrcnn_r_50_fpn...OK 2025-03-04T20:24:06.3946198Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/detectron2_fcos_r_50_fpn...OK 2025-03-04T20:24:21.8280186Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/detectron2_maskrcnn...OK 2025-03-04T20:24:37.1468405Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/detectron2_maskrcnn_r_101_c4...OK 2025-03-04T20:24:52.8581551Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/detectron2_maskrcnn_r_101_fpn...OK 2025-03-04T20:25:08.1198689Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/detectron2_maskrcnn_r_50_c4...OK 2025-03-04T20:25:23.5614912Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/detectron2_maskrcnn_r_50_fpn...OK 2025-03-04T20:25:33.4765309Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/dlrm...OK 2025-03-04T20:25:58.8545744Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/doctr_det_predictor...OK 2025-03-04T20:26:13.4420146Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/doctr_reco_predictor...OK 2025-03-04T20:26:26.3777742Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/drq...OK 2025-03-04T20:26:42.9862370Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/fastNLP_Bert...OK 2025-03-04T20:26:52.9894581Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/functorch_dp_cifar10...OK 2025-03-04T20:27:03.4002694Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/functorch_maml_omniglot...OK 2025-03-04T20:27:18.2928549Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_Albert...OK 2025-03-04T20:27:33.0808779Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_Bart...OK 2025-03-04T20:27:46.9618678Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_Bert...OK 2025-03-04T20:28:01.9957831Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_Bert_large...OK 2025-03-04T20:28:16.2143733Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_BigBird...OK 2025-03-04T20:28:29.7871465Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_DistilBert...OK 2025-03-04T20:28:43.9687590Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_GPT2...OK 2025-03-04T20:29:04.2379684Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_GPT2_large...OK 2025-03-04T20:29:18.5189024Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_Longformer...OK 2025-03-04T20:29:31.2261801Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_Reformer...OK 2025-03-04T20:29:47.4464709Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_Roberta_base...OK 2025-03-04T20:30:00.7459537Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_T5...OK 2025-03-04T20:30:16.4535450Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_T5_base...OK 2025-03-04T20:30:16.4540175Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_T5_generate...SKIP - No install.py is found 2025-03-04T20:30:35.0131249Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_T5_large...OK 2025-03-04T20:30:46.8542451Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_Whisper...OK 2025-03-04T20:30:46.8543073Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_clip...SKIP - No install.py is found 2025-03-04T20:31:02.2725949Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_distil_whisper...OK 2025-03-04T20:31:12.4230044Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/lennard_jones...OK 2025-03-04T20:31:22.6682029Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/llama...OK 2025-03-04T20:32:00.4537180Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/llama_v2_7b_16h...OK 2025-03-04T20:33:05.8634922Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/llava...OK 2025-03-04T20:33:15.1039165Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/maml...OK 2025-03-04T20:33:25.7376498Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/maml_omniglot...OK 2025-03-04T20:33:25.7377266Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/microbench_unbacked_tolist_sum...SKIP - No install.py is found 2025-03-04T20:33:34.9173255Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/mnasnet1_0...OK 2025-03-04T20:33:44.3251914Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/mobilenet_v2...OK 2025-03-04T20:33:53.7854879Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/mobilenet_v2_quantized_qat...OK 2025-03-04T20:34:03.1192635Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/mobilenet_v3_large...OK 2025-03-04T20:34:12.4748901Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/moco...OK 2025-03-04T20:34:36.6278526Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/moondream...OK 2025-03-04T20:34:36.6279123Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/nanogpt...SKIP - No install.py is found 2025-03-04T20:34:47.1819662Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/nvidia_deeprecommender...OK 2025-03-04T20:34:58.7293875Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/opacus_cifar10...OK 2025-03-04T20:35:08.4400968Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/phlippe_densenet...OK 2025-03-04T20:35:17.6542894Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/phlippe_resnet...OK 2025-03-04T20:35:26.8137097Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/pyhpc_equation_of_state...OK 2025-03-04T20:35:35.9559902Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/pyhpc_isoneutral_mixing...OK 2025-03-04T20:35:45.1355818Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/pyhpc_turbulent_kinetic_energy...OK 2025-03-04T20:35:58.9794628Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/pytorch_CycleGAN_and_pix2pix...OK 2025-03-04T20:36:09.4663399Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/pytorch_stargan...OK 2025-03-04T20:36:21.6288746Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/pytorch_unet...OK 2025-03-04T20:36:30.8294328Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/resnet152...OK 2025-03-04T20:36:40.0612465Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/resnet18...OK 2025-03-04T20:36:49.4938122Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/resnet50...OK 2025-03-04T20:36:58.8453385Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/resnet50_quantized_qat...OK 2025-03-04T20:37:08.3062020Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/resnext50_32x4d...OK 2025-03-04T20:37:26.4135249Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/sam...OK 2025-03-04T20:37:46.7174696Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/sam_fast...OK 2025-03-04T20:37:56.1364495Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/shufflenet_v2_x1_0...OK 2025-03-04T20:37:56.1370165Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/simple_gpt...SKIP - No install.py is found 2025-03-04T20:37:56.1375931Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/simple_gpt_tp_manual...SKIP - No install.py is found 2025-03-04T20:38:07.9694566Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/soft_actor_critic...OK 2025-03-04T20:38:18.7506598Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/speech_transformer...OK 2025-03-04T20:38:27.8370763Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/squeezenet1_1...OK 2025-03-04T20:38:59.2403068Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/stable_diffusion_text_encoder...OK 2025-03-04T20:39:13.3733475Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/stable_diffusion_unet...OK 2025-03-04T20:39:27.5903127Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/tacotron2...OK 2025-03-04T20:39:42.9889081Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/timm_efficientdet...OK 2025-03-04T20:39:51.9499108Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/timm_efficientnet...OK 2025-03-04T20:40:00.8953253Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/timm_nfnet...OK 2025-03-04T20:40:09.7948682Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/timm_regnet...OK 2025-03-04T20:40:18.7266198Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/timm_resnest...OK 2025-03-04T20:40:27.7030734Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/timm_vision_transformer...OK 2025-03-04T20:40:36.6921645Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/timm_vision_transformer_large...OK 2025-03-04T20:40:45.6114681Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/timm_vovnet...OK 2025-03-04T20:40:59.2837198Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/torch_multimodal_clip...OK 2025-03-04T20:41:12.2369946Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/tts_angular...OK 2025-03-04T20:41:21.1090663Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/vgg16...OK 2025-03-04T20:41:31.3460994Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/vision_maskrcnn...OK 2025-03-04T20:41:41.7655120Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/yolov3...OK 2025-03-04T20:41:46.2057768Z installed torchbench with package constraints: {'numpy': '1.22.4', 'torch': '2.7.0a0+git1b74980', 'torchvision': '0.19.0a0+d23a6e1', 'torchaudio': '2.6.0a0+c670ad8'} 2025-03-04T20:41:46.4158248Z + pip install transformers==4.38.1 2025-03-04T20:41:46.7486304Z Collecting transformers==4.38.1 2025-03-04T20:41:46.7602760Z Downloading transformers-4.38.1-py3-none-any.whl.metadata (131 kB) 2025-03-04T20:41:46.8947033Z Requirement already satisfied: filelock in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from transformers==4.38.1) (3.16.1) 2025-03-04T20:41:46.8947688Z Requirement already satisfied: huggingface-hub<1.0,>=0.19.3 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from transformers==4.38.1) (0.29.1) 2025-03-04T20:41:46.8960026Z Requirement already satisfied: numpy>=1.17 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from transformers==4.38.1) (1.22.4) 2025-03-04T20:41:46.8965145Z Requirement already satisfied: packaging>=20.0 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from transformers==4.38.1) (24.2) 2025-03-04T20:41:46.8969940Z Requirement already satisfied: pyyaml>=5.1 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from transformers==4.38.1) (6.0.2) 2025-03-04T20:41:46.8974472Z Requirement already satisfied: regex!=2019.12.17 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from transformers==4.38.1) (2024.11.6) 2025-03-04T20:41:46.8979026Z Requirement already satisfied: requests in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from transformers==4.38.1) (2.32.3) 2025-03-04T20:41:46.9892535Z Collecting tokenizers<0.19,>=0.14 (from transformers==4.38.1) 2025-03-04T20:41:46.9902756Z Using cached tokenizers-0.15.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.7 kB) 2025-03-04T20:41:46.9926550Z Requirement already satisfied: safetensors>=0.4.1 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from transformers==4.38.1) (0.5.3) 2025-03-04T20:41:46.9932029Z Requirement already satisfied: tqdm>=4.27 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from transformers==4.38.1) (4.67.1) 2025-03-04T20:41:47.0080291Z Requirement already satisfied: fsspec>=2023.5.0 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.19.3->transformers==4.38.1) (2024.10.0) 2025-03-04T20:41:47.0083039Z Requirement already satisfied: typing-extensions>=3.7.4.3 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.19.3->transformers==4.38.1) (4.12.2) 2025-03-04T20:41:47.0210776Z Requirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from requests->transformers==4.38.1) (3.4.1) 2025-03-04T20:41:47.0216107Z Requirement already satisfied: idna<4,>=2.5 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from requests->transformers==4.38.1) (3.10) 2025-03-04T20:41:47.0217737Z Requirement already satisfied: urllib3<3,>=1.21.1 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from requests->transformers==4.38.1) (1.26.20) 2025-03-04T20:41:47.0218396Z Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from requests->transformers==4.38.1) (2025.1.31) 2025-03-04T20:41:47.0564359Z Downloading transformers-4.38.1-py3-none-any.whl (8.5 MB) 2025-03-04T20:41:47.1225781Z [?25l ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0/8.5 MB ? eta -:--:-- 2025-03-04T20:41:47.1226616Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 8.5/8.5 MB 134.5 MB/s eta 0:00:00 2025-03-04T20:41:47.1235619Z [?25hUsing cached tokenizers-0.15.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.6 MB) 2025-03-04T20:41:47.8458216Z Installing collected packages: tokenizers, transformers 2025-03-04T20:41:47.8458702Z Attempting uninstall: tokenizers 2025-03-04T20:41:47.8474334Z Found existing installation: tokenizers 0.19.1 2025-03-04T20:41:47.8500267Z Uninstalling tokenizers-0.19.1: 2025-03-04T20:41:47.8509732Z Successfully uninstalled tokenizers-0.19.1 2025-03-04T20:41:47.9123841Z Attempting uninstall: transformers 2025-03-04T20:41:47.9140319Z Found existing installation: transformers 4.44.2 2025-03-04T20:41:48.0175130Z Uninstalling transformers-4.44.2: 2025-03-04T20:41:48.0357342Z Successfully uninstalled transformers-4.44.2 2025-03-04T20:41:51.0581381Z Successfully installed tokenizers-0.15.2 transformers-4.38.1 2025-03-04T20:41:51.1618668Z + echo 'Print all dependencies after TorchBench is installed' 2025-03-04T20:41:51.1619077Z Print all dependencies after TorchBench is installed 2025-03-04T20:41:51.1619342Z + python -mpip freeze 2025-03-04T20:41:51.4722832Z absl-py==2.1.0 2025-03-04T20:41:51.4723154Z accelerate==1.4.0 2025-03-04T20:41:51.4723338Z aiohappyeyeballs==2.4.6 2025-03-04T20:41:51.4724195Z aiohttp==3.11.13 2025-03-04T20:41:51.4725298Z aiosignal==1.3.2 2025-03-04T20:41:51.4725504Z alabaster==0.7.16 2025-03-04T20:41:51.4725688Z annotated-types==0.7.0 2025-03-04T20:41:51.4725891Z antlr4-python3-runtime==4.9.3 2025-03-04T20:41:51.4726095Z anyascii==0.3.2 2025-03-04T20:41:51.4726259Z astroid==3.3.8 2025-03-04T20:41:51.4726423Z asttokens==3.0.0 2025-03-04T20:41:51.4726591Z astunparse==1.6.3 2025-03-04T20:41:51.4726756Z async-timeout==5.0.1 2025-03-04T20:41:51.4726923Z attrs==23.1.0 2025-03-04T20:41:51.4727079Z audioread==3.0.1 2025-03-04T20:41:51.4727233Z babel==2.17.0 2025-03-04T20:41:51.4727786Z backcall==0.2.0 2025-03-04T20:41:51.4728013Z beautifulsoup4==4.13.3 2025-03-04T20:41:51.4728782Z -e git+https://github.com/pytorch/benchmark@373ffb19dc470f4423a3176a4133f8f4b3cdb5bd#egg=bert_pytorch&subdirectory=torchbenchmark/models/BERT_pytorch 2025-03-04T20:41:51.4729786Z black==25.1.0 2025-03-04T20:41:51.4729962Z blinker==1.9.0 2025-03-04T20:41:51.4730125Z blis==1.2.0 2025-03-04T20:41:51.4730281Z blobfile==3.0.0 2025-03-04T20:41:51.4730438Z bokeh==3.4.3 2025-03-04T20:41:51.4730590Z boto3==1.35.42 2025-03-04T20:41:51.4730744Z botocore==1.35.99 2025-03-04T20:41:51.4730901Z breathe==4.34.0 2025-03-04T20:41:51.4731057Z bs4==0.0.1 2025-03-04T20:41:51.4731204Z cachetools==5.5.2 2025-03-04T20:41:51.4731365Z cardboardlint==1.3.1 2025-03-04T20:41:51.4731529Z catalogue==2.0.10 2025-03-04T20:41:51.4731687Z certifi==2025.1.31 2025-03-04T20:41:51.4731836Z cffi==1.17.1 2025-03-04T20:41:51.4731994Z charset-normalizer==3.4.1 2025-03-04T20:41:51.4733148Z click==8.1.8 2025-03-04T20:41:51.4733336Z cloudpathlib==0.21.0 2025-03-04T20:41:51.4733638Z cloudpickle==3.1.1 2025-03-04T20:41:51.4733803Z colorama==0.4.6 2025-03-04T20:41:51.4733956Z comm==0.2.2 2025-03-04T20:41:51.4734110Z confection==0.1.5 2025-03-04T20:41:51.4734272Z contourpy==1.2.1 2025-03-04T20:41:51.4734434Z coremltools==5.0b5 2025-03-04T20:41:51.4734598Z cryptography==44.0.1 2025-03-04T20:41:51.4734760Z csvw==3.5.1 2025-03-04T20:41:51.4734910Z cycler==0.12.1 2025-03-04T20:41:51.4735055Z cymem==2.0.11 2025-03-04T20:41:51.4735206Z Cython==3.0.12 2025-03-04T20:41:51.4735360Z DALL-E==0.1 2025-03-04T20:41:51.4735520Z dataclasses-json==0.6.7 2025-03-04T20:41:51.4735695Z datasets==3.3.2 2025-03-04T20:41:51.4735848Z debugpy==1.8.12 2025-03-04T20:41:51.4736000Z decorator==5.2.1 2025-03-04T20:41:51.4736174Z defusedxml==0.7.1 2025-03-04T20:41:51.4736335Z Deprecated==1.2.18 2025-03-04T20:41:51.4736671Z detectron2 @ git+https://github.com/facebookresearch/detectron2.git@0df2d73d0013db7de629602c23cc120219b4f2b8 2025-03-04T20:41:51.4737022Z diffusers==0.30.3 2025-03-04T20:41:51.4737178Z dill==0.3.8 2025-03-04T20:41:51.4737330Z diskcache==5.6.3 2025-03-04T20:41:51.4737483Z distro==1.9.0 2025-03-04T20:41:51.4737635Z dlinfo==2.0.0 2025-03-04T20:41:51.4737788Z dnspython==2.7.0 2025-03-04T20:41:51.4737953Z docker-pycreds==0.4.0 2025-03-04T20:41:51.4738119Z docutils==0.16 2025-03-04T20:41:51.4738271Z dominate==2.9.1 2025-03-04T20:41:51.4738592Z effdet @ git+https://github.com/rwightman/efficientdet-pytorch.git@d43c9e34cd62d22b4205831bb735f6dd83b8e881 2025-03-04T20:41:51.4738938Z einops==0.8.1 2025-03-04T20:41:51.4739101Z eval_type_backport==0.2.2 2025-03-04T20:41:51.4739279Z exceptiongroup==1.2.2 2025-03-04T20:41:51.4739445Z execnet==2.1.1 2025-03-04T20:41:51.4739599Z executing==2.2.0 2025-03-04T20:41:51.4739754Z exhale==0.2.3 2025-03-04T20:41:51.4739906Z expecttest==0.3.0 2025-03-04T20:41:51.4740070Z fastjsonschema==2.21.1 2025-03-04T20:41:51.4740241Z FastNLP==0.6.0 2025-03-04T20:41:51.4740402Z fbscribelogger==0.1.7 2025-03-04T20:41:51.4740578Z ffmpeg-python==0.2.0 2025-03-04T20:41:51.4740747Z filelock==3.16.1 2025-03-04T20:41:51.4740905Z Flask==3.1.0 2025-03-04T20:41:51.4741059Z flatbuffers==2.0 2025-03-04T20:41:51.4741224Z fonttools==4.56.0 2025-03-04T20:41:51.4741384Z frozenlist==1.5.0 2025-03-04T20:41:51.4741538Z fsspec==2024.10.0 2025-03-04T20:41:51.4741695Z ftfy==6.3.1 2025-03-04T20:41:51.4741987Z functorch @ git+https://github.com/pytorch/functorch.git@b71aa0b4387b86c278132209b99538be48ef4c74 2025-03-04T20:41:51.4742309Z future==1.0.0 2025-03-04T20:41:51.4742476Z fvcore==0.1.5.post20221221 2025-03-04T20:41:51.4742657Z gdown==5.2.0 2025-03-04T20:41:51.4742814Z ghstack==0.8.0 2025-03-04T20:41:51.4742971Z gitdb==4.0.12 2025-03-04T20:41:51.4743128Z GitPython==3.1.44 2025-03-04T20:41:51.4743294Z google-auth==2.38.0 2025-03-04T20:41:51.4743473Z google-auth-oauthlib==1.0.0 2025-03-04T20:41:51.4743660Z greenlet==3.1.1 2025-03-04T20:41:51.4743819Z grpcio==1.70.0 2025-03-04T20:41:51.4743969Z gym==0.26.2 2025-03-04T20:41:51.4744118Z gym-notices==0.0.8 2025-03-04T20:41:51.4744279Z h5py==3.13.0 2025-03-04T20:41:51.4744435Z higher==0.2.1 2025-03-04T20:41:51.4744596Z huggingface-hub==0.29.1 2025-03-04T20:41:51.4744836Z hydra-core==1.3.2 2025-03-04T20:41:51.4745003Z hypothesis==5.35.1 2025-03-04T20:41:51.4745165Z idna==3.10 2025-03-04T20:41:51.4745484Z imageio==2.37.0 2025-03-04T20:41:51.4745651Z imagesize==1.4.1 2025-03-04T20:41:51.4745823Z importlib_metadata==8.6.1 2025-03-04T20:41:51.4746003Z inflect==7.5.0 2025-03-04T20:41:51.4746164Z iniconfig==2.0.0 2025-03-04T20:41:51.4746324Z iopath==0.1.9 2025-03-04T20:41:51.4746474Z ipykernel==6.29.5 2025-03-04T20:41:51.4746643Z ipython==8.12.0 2025-03-04T20:41:51.4746800Z isodate==0.7.2 2025-03-04T20:41:51.4746959Z isort==6.0.1 2025-03-04T20:41:51.4747118Z itsdangerous==2.2.0 2025-03-04T20:41:51.4747280Z jedi==0.19.2 2025-03-04T20:41:51.4747431Z Jinja2==3.1.5 2025-03-04T20:41:51.4747583Z jmespath==1.0.1 2025-03-04T20:41:51.4747738Z joblib==1.4.2 2025-03-04T20:41:51.4747890Z jsonpatch==1.33 2025-03-04T20:41:51.4748049Z jsonpointer==3.0.0 2025-03-04T20:41:51.4748212Z jsonschema==4.23.0 2025-03-04T20:41:51.4748461Z jsonschema-specifications==2024.10.1 2025-03-04T20:41:51.4748668Z junitparser==2.1.1 2025-03-04T20:41:51.4748832Z jupyter-cache==0.6.1 2025-03-04T20:41:51.4749008Z jupyter_client==8.6.3 2025-03-04T20:41:51.4749179Z jupyter_core==5.7.2 2025-03-04T20:41:51.4749343Z kaldi-io==0.9.8 2025-03-04T20:41:51.4749500Z kiwisolver==1.4.7 2025-03-04T20:41:51.4749658Z kornia==0.8.0 2025-03-04T20:41:51.4749814Z kornia_rs==0.1.8 2025-03-04T20:41:51.4749973Z lameenc==1.8.1 2025-03-04T20:41:51.4750134Z langcodes==3.5.0 2025-03-04T20:41:51.4750292Z langdetect==1.0.9 2025-03-04T20:41:51.4750455Z language-tags==1.2.0 2025-03-04T20:41:51.4750626Z language_data==1.3.0 2025-03-04T20:41:51.4750790Z lark==0.12.0 2025-03-04T20:41:51.4750939Z lazy_loader==0.4 2025-03-04T20:41:51.4751095Z libcst==1.6.0 2025-03-04T20:41:51.4751250Z librosa==0.9.2 2025-03-04T20:41:51.4751404Z lintrunner==0.12.7 2025-03-04T20:41:51.4751567Z llvmlite==0.38.1 2025-03-04T20:41:51.4751722Z lxml==5.3.0 2025-03-04T20:41:51.4751879Z marisa-trie==1.2.1 2025-03-04T20:41:51.4752039Z Markdown==3.7 2025-03-04T20:41:51.4752201Z markdown-it-py==2.2.0 2025-03-04T20:41:51.4752375Z MarkupSafe==3.0.2 2025-03-04T20:41:51.4752538Z marshmallow==3.26.1 2025-03-04T20:41:51.4752701Z matplotlib==3.5.3 2025-03-04T20:41:51.4752870Z matplotlib-inline==0.1.7 2025-03-04T20:41:51.4753043Z mccabe==0.7.0 2025-03-04T20:41:51.4753205Z mdit-py-plugins==0.3.5 2025-03-04T20:41:51.4753375Z mdurl==0.1.2 2025-03-04T20:41:51.4753533Z ml_dtypes==0.5.1 2025-03-04T20:41:51.4753692Z MonkeyType==23.3.0 2025-03-04T20:41:51.4753858Z more-itertools==10.6.0 2025-03-04T20:41:51.4754030Z mpmath==1.3.0 2025-03-04T20:41:51.4754182Z msgpack==1.1.0 2025-03-04T20:41:51.4754338Z multidict==6.1.0 2025-03-04T20:41:51.4754498Z multiprocess==0.70.16 2025-03-04T20:41:51.4754665Z murmurhash==1.0.12 2025-03-04T20:41:51.4754820Z musdb==0.4.2 2025-03-04T20:41:51.4754975Z museval==0.4.1 2025-03-04T20:41:51.4755122Z mypy==1.13.0 2025-03-04T20:41:51.4755280Z mypy-extensions==1.0.0 2025-03-04T20:41:51.4755454Z myst-nb==0.17.2 2025-03-04T20:41:51.4755615Z myst-parser==0.18.1 2025-03-04T20:41:51.4755785Z nbclient==0.7.4 2025-03-04T20:41:51.4755941Z nbformat==5.10.4 2025-03-04T20:41:51.4756102Z nest-asyncio==1.6.0 2025-03-04T20:41:51.4756262Z networkx==2.8.8 2025-03-04T20:41:51.4756417Z ninja==1.10.2 2025-03-04T20:41:51.4756569Z nose==1.3.7 2025-03-04T20:41:51.4756732Z numba==0.55.2 2025-03-04T20:41:51.4756881Z numpy==1.22.4 2025-03-04T20:41:51.4757041Z nvidia-cublas-cu12==12.4.5.8 2025-03-04T20:41:51.4757240Z nvidia-cuda-cupti-cu12==12.4.127 2025-03-04T20:41:51.4757436Z nvidia-cuda-nvrtc-cu12==12.4.127 2025-03-04T20:41:51.4757639Z nvidia-cuda-runtime-cu12==12.4.127 2025-03-04T20:41:51.4757846Z nvidia-cudnn-cu12==9.1.0.70 2025-03-04T20:41:51.4758034Z nvidia-cufft-cu12==11.2.1.3 2025-03-04T20:41:51.4758221Z nvidia-curand-cu12==10.3.5.147 2025-03-04T20:41:51.4758416Z nvidia-cusolver-cu12==11.6.1.9 2025-03-04T20:41:51.4758603Z nvidia-cusparse-cu12==12.3.1.170 2025-03-04T20:41:51.4758798Z nvidia-cusparselt-cu12==0.6.2 2025-03-04T20:41:51.4759031Z nvidia-ml-py==12.570.86 2025-03-04T20:41:51.4759265Z nvidia-nccl-cu12==2.21.5 2025-03-04T20:41:51.4759460Z nvidia-nvjitlink-cu12==12.4.127 2025-03-04T20:41:51.4759654Z nvidia-nvtx-cu12==12.4.127 2025-03-04T20:41:51.4759831Z oauthlib==3.2.2 2025-03-04T20:41:51.4759991Z omegaconf==2.3.0 2025-03-04T20:41:51.4760146Z onnx==1.17.0 2025-03-04T20:41:51.4760299Z onnxscript==0.1.0 2025-03-04T20:41:51.4760460Z opacus==1.5.3 2025-03-04T20:41:51.4760621Z opencv-python==4.8.0.74 2025-03-04T20:41:51.4760794Z opt-einsum==3.3.0 2025-03-04T20:41:51.4760954Z optree==0.13.0 2025-03-04T20:41:51.4761116Z packaging==24.2 2025-03-04T20:41:51.4761280Z pandas==2.0.3 2025-03-04T20:41:51.4761430Z parameterized==0.8.1 2025-03-04T20:41:51.4761595Z parso==0.8.4 2025-03-04T20:41:51.4761751Z patch==1.16 2025-03-04T20:41:51.4761900Z pathspec==0.12.1 2025-03-04T20:41:51.4762061Z pexpect==4.9.0 2025-03-04T20:41:51.4762217Z phonemizer==3.3.0 2025-03-04T20:41:51.4762376Z pickleshare==0.7.5 2025-03-04T20:41:51.4762585Z pillow==11.0.0 2025-03-04T20:41:51.4762741Z platformdirs==4.3.6 2025-03-04T20:41:51.4762901Z pluggy==1.5.0 2025-03-04T20:41:51.4763054Z ply==3.11 2025-03-04T20:41:51.4763199Z pooch==1.8.2 2025-03-04T20:41:51.4763352Z portalocker==3.1.1 2025-03-04T20:41:51.4763504Z preshed==3.0.9 2025-03-04T20:41:51.4763660Z prettytable==3.15.1 2025-03-04T20:41:51.4763828Z prompt_toolkit==3.0.50 2025-03-04T20:41:51.4764000Z propcache==0.3.0 2025-03-04T20:41:51.4764158Z protobuf==3.20.2 2025-03-04T20:41:51.4764313Z psutil==7.0.0 2025-03-04T20:41:51.4764468Z ptyprocess==0.7.0 2025-03-04T20:41:51.4764633Z PuLP==2.9.0 2025-03-04T20:41:51.4765920Z pure_eval==0.2.3 2025-03-04T20:41:51.4766143Z pwlf==2.2.1 2025-03-04T20:41:51.4766306Z py-cpuinfo==9.0.0 2025-03-04T20:41:51.4766469Z pyaml==25.1.0 2025-03-04T20:41:51.4766627Z pyarrow==19.0.1 2025-03-04T20:41:51.4766777Z pyasn1==0.6.1 2025-03-04T20:41:51.4766936Z pyasn1_modules==0.4.1 2025-03-04T20:41:51.4767113Z pyclipper==1.3.0.post6 2025-03-04T20:41:51.4767304Z pycocotools==2.0.8 2025-03-04T20:41:51.4767479Z pycparser==2.22 2025-03-04T20:41:51.4767644Z pycryptodomex==3.21.0 2025-03-04T20:41:51.4767816Z pydantic==2.10.6 2025-03-04T20:41:51.4767980Z pydantic_core==2.27.2 2025-03-04T20:41:51.4768149Z pyDOE==0.3.8 2025-03-04T20:41:51.4768312Z pydot==3.0.4 2025-03-04T20:41:51.4768462Z pygame==2.6.1 2025-03-04T20:41:51.4768613Z PyGithub==2.3.0 2025-03-04T20:41:51.4768768Z Pygments==2.15.0 2025-03-04T20:41:51.4768924Z PyJWT==2.10.1 2025-03-04T20:41:51.4769068Z pylint==3.3.4 2025-03-04T20:41:51.4769218Z PyNaCl==1.5.0 2025-03-04T20:41:51.4769370Z pynvml==12.0.0 2025-03-04T20:41:51.4769530Z pyparsing==3.2.1 2025-03-04T20:41:51.4769690Z pypdfium2==4.30.1 2025-03-04T20:41:51.4769847Z pysbd==0.3.4 2025-03-04T20:41:51.4769995Z PySocks==1.7.1 2025-03-04T20:41:51.4770145Z pytest==8.3.5 2025-03-04T20:41:51.4770301Z pytest-benchmark==5.1.0 2025-03-04T20:41:51.4770471Z pytest-cpp==2.3.0 2025-03-04T20:41:51.4770641Z pytest-flakefinder==1.1.0 2025-03-04T20:41:51.4770829Z pytest-rerunfailures==14.0 2025-03-04T20:41:51.4771018Z pytest-subtests==0.13.1 2025-03-04T20:41:51.4771182Z pytest-xdist==3.3.1 2025-03-04T20:41:51.4771360Z python-dateutil==2.9.0.post0 2025-03-04T20:41:51.4771550Z python-doctr==0.10.0 2025-03-04T20:41:51.4771718Z python-etcd==0.4.5 2025-03-04T20:41:51.4772155Z pytorch-labs-segment-anything-fast @ git+https://github.com/pytorch-labs/segment-anything-fast.git@e6aadeb86f3ae1f58c3f98e2a91e251716e0f2aa 2025-03-04T20:41:51.4772788Z -e git+https://github.com/pytorch/pytorch_sphinx_theme.git@4125c834e1aa0945fde6ef58ff2f77f7abedc460#egg=pytorch_sphinx_theme 2025-03-04T20:41:51.4773174Z pytz==2025.1 2025-03-04T20:41:51.4773330Z PyWavelets==1.4.1 2025-03-04T20:41:51.4773489Z PyYAML==6.0.2 2025-03-04T20:41:51.4773640Z pyzmq==26.2.1 2025-03-04T20:41:51.4773791Z pyzstd==0.16.2 2025-03-04T20:41:51.4773947Z RapidFuzz==3.12.2 2025-03-04T20:41:51.4774108Z rdflib==7.1.3 2025-03-04T20:41:51.4774258Z redis==5.2.1 2025-03-04T20:41:51.4774412Z referencing==0.36.2 2025-03-04T20:41:51.4774572Z regex==2024.11.6 2025-03-04T20:41:51.4774728Z requests==2.32.3 2025-03-04T20:41:51.4774893Z requests-oauthlib==2.0.0 2025-03-04T20:41:51.4775165Z resampy==0.4.3 2025-03-04T20:41:51.4775324Z rfc3986==1.5.0 2025-03-04T20:41:51.4775477Z rich==13.9.4 2025-03-04T20:41:51.4775632Z rpds-py==0.23.1 2025-03-04T20:41:51.4775786Z rsa==4.9 2025-03-04T20:41:51.4775941Z s3transfer==0.10.4 2025-03-04T20:41:51.4776104Z safetensors==0.5.3 2025-03-04T20:41:51.4776266Z scikit-image==0.19.3 2025-03-04T20:41:51.4776431Z scikit-learn==1.6.1 2025-03-04T20:41:51.4776590Z scipy==1.10.1 2025-03-04T20:41:51.4776739Z segments==2.3.0 2025-03-04T20:41:51.4776890Z sentencepiece==0.2.0 2025-03-04T20:41:51.4777055Z sentry-sdk==2.22.0 2025-03-04T20:41:51.4777217Z setproctitle==1.3.5 2025-03-04T20:41:51.4777380Z shapely==2.0.7 2025-03-04T20:41:51.4777536Z shellingham==1.5.4 2025-03-04T20:41:51.4777696Z simplejson==3.20.1 2025-03-04T20:41:51.4777849Z six==1.17.0 2025-03-04T20:41:51.4777999Z smart-open==7.1.0 2025-03-04T20:41:51.4778155Z smmap==5.0.2 2025-03-04T20:41:51.4778372Z snowballstemmer==2.2.0 2025-03-04T20:41:51.4778544Z sortedcontainers==2.4.0 2025-03-04T20:41:51.4778713Z soundfile==0.13.1 2025-03-04T20:41:51.4778868Z soupsieve==2.6 2025-03-04T20:41:51.4779016Z soxr==0.5.0.post1 2025-03-04T20:41:51.4779171Z spacy==3.8.3 2025-03-04T20:41:51.4779324Z spacy-legacy==3.0.12 2025-03-04T20:41:51.4779493Z spacy-loggers==1.0.5 2025-03-04T20:41:51.4779655Z Sphinx==5.3.0 2025-03-04T20:41:51.4779819Z sphinx-copybutton==0.5.0 2025-03-04T20:41:51.4779999Z sphinx-panels==0.4.1 2025-03-04T20:41:51.4780179Z sphinxcontrib-applehelp==2.0.0 2025-03-04T20:41:51.4780380Z sphinxcontrib-devhelp==2.0.0 2025-03-04T20:41:51.4780577Z sphinxcontrib-htmlhelp==2.1.0 2025-03-04T20:41:51.4780776Z sphinxcontrib-jsmath==1.0.1 2025-03-04T20:41:51.4780967Z sphinxcontrib-katex==0.8.6 2025-03-04T20:41:51.4781161Z sphinxcontrib-qthelp==2.0.0 2025-03-04T20:41:51.4781371Z sphinxcontrib-serializinghtml==2.0.0 2025-03-04T20:41:51.4781584Z SQLAlchemy==2.0.38 2025-03-04T20:41:51.4781743Z srsly==2.5.1 2025-03-04T20:41:51.4781905Z stack-data==0.6.3 2025-03-04T20:41:51.4782069Z stempeg==0.2.3 2025-03-04T20:41:51.4782233Z submitit==1.5.2 2025-03-04T20:41:51.4782391Z sympy==1.13.3 2025-03-04T20:41:51.4782546Z tabulate==0.9.0 2025-03-04T20:41:51.4782709Z tb-nightly==2.13.0a20230426 2025-03-04T20:41:51.4782894Z tensorboard==2.13.0 2025-03-04T20:41:51.4783073Z tensorboard-data-server==0.7.2 2025-03-04T20:41:51.4783267Z tensorboardX==2.6.2.2 2025-03-04T20:41:51.4783435Z termcolor==2.5.0 2025-03-04T20:41:51.4783594Z thinc==8.3.4 2025-03-04T20:41:51.4783751Z threadpoolctl==3.5.0 2025-03-04T20:41:51.4783914Z thriftpy2==0.5.2 2025-03-04T20:41:51.4784075Z tifffile==2024.8.30 2025-03-04T20:41:51.4784414Z timm @ git+https://github.com/huggingface/pytorch-image-models.git@730b907b4d45a4713cbc425cbf224c46089fd514 2025-03-04T20:41:51.4784767Z tlparse==0.3.30 2025-03-04T20:41:51.4784929Z tokenizers==0.15.2 2025-03-04T20:41:51.4785090Z tomli==2.2.1 2025-03-04T20:41:51.4785340Z tomlkit==0.13.2 2025-03-04T20:41:51.4785871Z torch @ file:///var/lib/jenkins/workspace/dist/torch-2.7.0a0%2Bgit1b74980-cp39-cp39-linux_x86_64.whl#sha256=dc833e86ed3bb70ba05ba214f76e8bf0f1fc74304f0d390f2a2591a3606fe7c6 2025-03-04T20:41:51.4786564Z torch_geometric @ git+https://github.com/pyg-team/pytorch_geometric.git@cabcd4097442ba60aa1efa11e1619dd9bb8fb527 2025-03-04T20:41:51.4787058Z torchao @ git+https://github.com/pytorch/ao.git@9bcd73be6fb60cc169deeaf5b5508cb4fdaefcb5 2025-03-04T20:41:51.4787494Z torchaudio @ git+https://github.com/pytorch/audio.git@c670ad81fda266b6598aeeef434583eb98197ae8 2025-03-04T20:41:51.4788036Z torchmultimodal @ git+https://github.com/facebookresearch/multimodal.git@6569fcc03450c2360b50d772bf9b18ec3487fcf4 2025-03-04T20:41:51.4788533Z torchvision @ git+https://github.com/pytorch/vision.git@d23a6e1664d20707c11781299611436e1f0c104f 2025-03-04T20:41:51.4788852Z tornado==6.4.2 2025-03-04T20:41:51.4789015Z tqdm==4.67.1 2025-03-04T20:41:51.4789172Z traitlets==5.14.3 2025-03-04T20:41:51.4789338Z transformers==4.38.1 2025-03-04T20:41:51.4789509Z treetable==0.2.5 2025-03-04T20:41:51.4789698Z triton @ file:///var/lib/jenkins/triton/python 2025-03-04T20:41:51.4789965Z typeguard==4.4.2 2025-03-04T20:41:51.4790121Z typer==0.15.2 2025-03-04T20:41:51.4790285Z typing-inspect==0.9.0 2025-03-04T20:41:51.4790466Z typing_extensions==4.12.2 2025-03-04T20:41:51.4790647Z tzdata==2025.1 2025-03-04T20:41:51.4790803Z Unidecode==1.3.8 2025-03-04T20:41:51.4790980Z unittest-xml-reporting==3.2.0 2025-03-04T20:41:51.4791177Z uritemplate==4.1.1 2025-03-04T20:41:51.4791343Z urllib3==1.26.20 2025-03-04T20:41:51.4791505Z visdom==0.2.4 2025-03-04T20:41:51.4791663Z wandb==0.19.7 2025-03-04T20:41:51.4791818Z wasabi==1.1.3 2025-03-04T20:41:51.4791973Z wcwidth==0.2.13 2025-03-04T20:41:51.4792130Z weasel==0.4.1 2025-03-04T20:41:51.4792293Z websocket-client==1.8.0 2025-03-04T20:41:51.4792466Z Werkzeug==3.1.3 2025-03-04T20:41:51.4792623Z wrapt==1.17.2 2025-03-04T20:41:51.4792777Z xdoctest==1.1.0 2025-03-04T20:41:51.4792931Z xxhash==3.5.0 2025-03-04T20:41:51.4793123Z xyzservices==2025.1.0 2025-03-04T20:41:51.4793288Z yacs==0.1.8 2025-03-04T20:41:51.4793438Z yarl==1.18.3 2025-03-04T20:41:51.4793595Z z3-solver==4.12.6.0 2025-03-04T20:41:51.4793757Z zipp==3.21.0 2025-03-04T20:41:51.5083236Z + popd 2025-03-04T20:41:51.5083480Z ~/workspace 2025-03-04T20:41:51.5083682Z + [[ cpu_inductor_torchbench != *cpu* ]] 2025-03-04T20:41:51.5083923Z ++ pwd 2025-03-04T20:41:51.5087138Z + PYTHONPATH=/var/lib/jenkins/workspace/torchbench 2025-03-04T20:41:51.5087489Z + test_dynamo_benchmark torchbench 0 2025-03-04T20:41:51.5090335Z ++ pwd 2025-03-04T20:41:51.5091763Z + TEST_REPORTS_DIR=/var/lib/jenkins/workspace/test/test-reports 2025-03-04T20:41:51.5092055Z + local suite=torchbench 2025-03-04T20:41:51.5092233Z + shift 2025-03-04T20:41:51.5092388Z + local shard_id=0 2025-03-04T20:41:51.5092551Z + shift 2025-03-04T20:41:51.5092732Z + [[ cpu_inductor_torchbench == *perf_compare* ]] 2025-03-04T20:41:51.5092969Z + [[ cpu_inductor_torchbench == *perf* ]] 2025-03-04T20:41:51.5093187Z + [[ cpu_inductor_torchbench == *cpu* ]] 2025-03-04T20:41:51.5093420Z + local dt=float32 2025-03-04T20:41:51.5093606Z + [[ cpu_inductor_torchbench == *amp* ]] 2025-03-04T20:41:51.5093827Z + [[ cpu_inductor_torchbench == *freezing* ]] 2025-03-04T20:41:51.5094125Z + test_single_dynamo_benchmark inference torchbench 0 --inference --float32 2025-03-04T20:41:51.5098948Z ++ pwd 2025-03-04T20:41:51.5099176Z + TEST_REPORTS_DIR=/var/lib/jenkins/workspace/test/test-reports 2025-03-04T20:41:51.5099482Z + mkdir -p /var/lib/jenkins/workspace/test/test-reports 2025-03-04T20:41:51.5120856Z + local name=inference 2025-03-04T20:41:51.5124992Z + shift 2025-03-04T20:41:51.5126935Z + local suite=torchbench 2025-03-04T20:41:51.5130494Z + shift 2025-03-04T20:41:51.5130709Z + local shard_id=0 2025-03-04T20:41:51.5130890Z + shift 2025-03-04T20:41:51.5131061Z + partition_flags=() 2025-03-04T20:41:51.5131254Z + local partition_flags 2025-03-04T20:41:51.5131436Z + [[ -n 2 ]] 2025-03-04T20:41:51.5131592Z + [[ -n 0 ]] 2025-03-04T20:41:51.5131860Z + partition_flags=(--total-partitions "$NUM_TEST_SHARDS" --partition-id "$shard_id") 2025-03-04T20:41:51.5132225Z + [[ cpu_inductor_torchbench == *perf_compare* ]] 2025-03-04T20:41:51.5132471Z + [[ cpu_inductor_torchbench == *perf* ]] 2025-03-04T20:41:51.5132704Z + [[ cpu_inductor_torchbench == *_avx2* ]] 2025-03-04T20:41:51.5132950Z + [[ cpu_inductor_torchbench == *_avx512* ]] 2025-03-04T20:41:51.5133682Z + python benchmarks/dynamo/torchbench.py --ci --accuracy --timing --explain --print-compilation-time --inductor --device cpu --inference --float32 --total-partitions 2 --partition-id 0 --output /var/lib/jenkins/workspace/test/test-reports/inference_torchbench.csv 2025-03-04T20:41:55.9990622Z 2025-03-04T20:41:57.4416806Z loading model: 0it [00:00, ?it/s] 2025-03-04T20:41:57.4422092Z loading model: 0it [00:01, ?it/s] 2025-03-04T20:41:57.4428345Z cpu eval BERT_pytorch 2025-03-04T20:41:57.9259623Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T20:41:58.1372884Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T20:41:58.3704308Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T20:42:18.0197265Z Compilation time (from dynamo_timed): 18.296754534 2025-03-04T20:42:18.0246584Z pass 2025-03-04T20:42:18.0250861Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T20:42:18.0256178Z TIMING: _recursive_pre_grad_passes:0.00761 _recursive_joint_graph_passes:0.3534 _recursive_post_grad_passes:0.04651 async_compile.wait:1.34153 code_gen:12.0867 inductor_compile:13.21408 backend_compile:16.11648 entire_frame_compile:18.29675 gc:0.00071 total_wall_time:18.29675 2025-03-04T20:42:18.0257497Z STATS: call_* op count: 543 | FakeTensor.__torch_dispatch__:1424 | FakeTensorMode.__torch_dispatch__:15521 | ProxyTorchDispatchMode.__torch_dispatch__:6258 2025-03-04T20:42:18.0258339Z Dynamo produced 1 graphs covering 543 ops with 0 graph breaks (0 unique) 2025-03-04T20:42:22.0615431Z 2025-03-04T20:42:24.7763389Z loading model: 0it [00:00, ?it/s] 2025-03-04T20:42:24.7763693Z loading model: 0it [00:02, ?it/s] 2025-03-04T20:42:24.7777413Z cpu eval Background_Matting 2025-03-04T20:42:24.8333668Z Compilation time (from dynamo_timed): 0 2025-03-04T20:42:24.8338263Z pass_due_to_skip 2025-03-04T20:42:24.8342583Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T20:42:24.8346546Z TIMING: total_wall_time:0 2025-03-04T20:42:24.8348435Z STATS: call_* op count: 0 2025-03-04T20:42:24.8348721Z Dynamo produced 0 graphs covering 0 ops with 0 graph breaks (0 unique) 2025-03-04T20:42:27.8722635Z 2025-03-04T20:42:29.4778741Z loading model: 0it [00:00, ?it/s] 2025-03-04T20:42:29.4781048Z loading model: 0it [00:01, ?it/s] 2025-03-04T20:42:29.4781384Z cpu eval LearningToPaint 2025-03-04T20:42:29.5560054Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T20:42:29.5858613Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T20:42:29.6136000Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T20:42:40.6800289Z Compilation time (from dynamo_timed): 10.243614266 2025-03-04T20:42:40.6800725Z pass 2025-03-04T20:42:40.6801050Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T20:42:40.6801867Z TIMING: _recursive_pre_grad_passes:0.00506 _recursive_joint_graph_passes:0.31482 _recursive_post_grad_passes:0.02591 async_compile.wait:1.21373 code_gen:7.38897 inductor_compile:7.83544 backend_compile:9.23187 entire_frame_compile:10.24361 gc:0.00024 total_wall_time:10.24361 2025-03-04T20:42:40.6803032Z STATS: call_* op count: 71 | FakeTensorMode.__torch_dispatch__:4371 | ProxyTorchDispatchMode.__torch_dispatch__:1902 | FakeTensor.__torch_dispatch__:590 2025-03-04T20:42:40.6803608Z Dynamo produced 1 graphs covering 71 ops with 0 graph breaks (0 unique) 2025-03-04T20:42:44.8439975Z 2025-03-04T20:42:45.6855541Z loading model: 0it [00:00, ?it/s]Downloading: "https://download.pytorch.org/models/vgg16-397923af.pth" to /var/lib/jenkins/.cache/torch/hub/checkpoints/vgg16-397923af.pth 2025-03-04T20:42:45.7135308Z 2025-03-04T20:42:45.7137126Z 2025-03-04T20:42:45.8133415Z 0% 0.00/528M [00:00, code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T20:47:36.9066751Z permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T20:47:36.9066887Z logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T20:47:36.9066950Z 2025-03-04T20:47:36.9067363Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T20:47:36.9067514Z view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_193.view(4, -1, 4, 73, 75); x_193 = None 2025-03-04T20:47:36.9067688Z permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2); view_2 = None 2025-03-04T20:47:36.9067861Z pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2); permute_1 = None 2025-03-04T20:47:36.9067930Z 2025-03-04T20:47:36.9068321Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:47:36.9068564Z pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T20:47:36.9068625Z 2025-03-04T20:47:36.9069054Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:47:36.9069198Z unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T20:47:36.9069347Z expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T20:47:36.9069485Z anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T20:47:36.9069546Z 2025-03-04T20:47:36.9069919Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:47:36.9070117Z deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T20:47:36.9070182Z 2025-03-04T20:47:36.9070489Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:47:36.9070629Z boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T20:47:36.9070687Z 2025-03-04T20:47:36.9071004Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:47:36.9071135Z getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:47:36.9071271Z getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:47:36.9071422Z widths: "f32[328500][1]cpu" = getitem_2 - getitem_3; getitem_2 = getitem_3 = None 2025-03-04T20:47:36.9071494Z 2025-03-04T20:47:36.9071828Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:47:36.9071959Z getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:47:36.9072083Z getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:47:36.9072238Z heights: "f32[328500][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T20:47:36.9072306Z 2025-03-04T20:47:36.9072640Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:47:36.9072763Z getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:47:36.9072862Z mul: "f32[328500][1]cpu" = 0.5 * widths 2025-03-04T20:47:36.9072992Z ctr_x: "f32[328500][1]cpu" = getitem_6 + mul; getitem_6 = mul = None 2025-03-04T20:47:36.9073061Z 2025-03-04T20:47:36.9073388Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:47:36.9073533Z getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:47:36.9073621Z mul_1: "f32[328500][1]cpu" = 0.5 * heights 2025-03-04T20:47:36.9073749Z ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1; getitem_7 = mul_1 = None 2025-03-04T20:47:36.9073811Z 2025-03-04T20:47:36.9074184Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:47:36.9074338Z getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:47:36.9074490Z dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0; getitem_8 = None 2025-03-04T20:47:36.9074555Z 2025-03-04T20:47:36.9074888Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:47:36.9075044Z getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:47:36.9075165Z dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0; getitem_9 = None 2025-03-04T20:47:36.9075229Z 2025-03-04T20:47:36.9075559Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:47:36.9075717Z getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:47:36.9075841Z dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0; getitem_10 = None 2025-03-04T20:47:36.9075935Z 2025-03-04T20:47:36.9076272Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:47:36.9076455Z getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:47:36.9076574Z dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0; getitem_11 = None 2025-03-04T20:47:36.9076636Z 2025-03-04T20:47:36.9076997Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:47:36.9077146Z dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:47:36.9077210Z 2025-03-04T20:47:36.9077565Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:47:36.9077705Z dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:47:36.9077774Z 2025-03-04T20:47:36.9078132Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:47:36.9078277Z getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:47:36.9078400Z mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12; dx = getitem_12 = None 2025-03-04T20:47:36.9078559Z getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:47:36.9078778Z pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13; mul_2 = getitem_13 = None 2025-03-04T20:47:36.9078856Z 2025-03-04T20:47:36.9079220Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:47:36.9079368Z getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:47:36.9079493Z mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14; dy = getitem_14 = None 2025-03-04T20:47:36.9079654Z getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:47:36.9079801Z pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15; mul_3 = getitem_15 = None 2025-03-04T20:47:36.9079873Z 2025-03-04T20:47:36.9080227Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:47:36.9080356Z exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:47:36.9080560Z getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:47:36.9080715Z pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16; exp = getitem_16 = None 2025-03-04T20:47:36.9080779Z 2025-03-04T20:47:36.9081129Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:47:36.9081243Z exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:47:36.9081416Z getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:47:36.9081548Z pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17; exp_1 = getitem_17 = None 2025-03-04T20:47:36.9081619Z 2025-03-04T20:47:36.9081937Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:47:36.9082075Z mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T20:47:36.9082190Z x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:47:36.9082260Z 2025-03-04T20:47:36.9082575Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:47:36.9082671Z mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T20:47:36.9082784Z y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:47:36.9082854Z 2025-03-04T20:47:36.9083163Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:47:36.9083283Z mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:47:36.9083412Z x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:47:36.9083485Z 2025-03-04T20:47:36.9083796Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:47:36.9083915Z mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:47:36.9084041Z y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:47:36.9084110Z 2025-03-04T20:47:36.9084466Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:47:36.9084667Z pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:47:36.9084727Z 2025-03-04T20:47:36.9085060Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:47:36.9085218Z proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4)); pred_boxes = None 2025-03-04T20:47:36.9085277Z 2025-03-04T20:47:36.9085651Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:47:36.9085816Z proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T20:47:36.9085880Z 2025-03-04T20:47:36.9086351Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T20:47:36.9086483Z arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T20:47:36.9086545Z 2025-03-04T20:47:36.9086867Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:47:36.9086998Z batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T20:47:36.9087064Z 2025-03-04T20:47:36.9087486Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:47:36.9087603Z topk = logits_i.topk(6000, dim = 1); logits_i = None 2025-03-04T20:47:36.9087698Z topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0] 2025-03-04T20:47:36.9087809Z topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1]; topk = None 2025-03-04T20:47:36.9087867Z 2025-03-04T20:47:36.9088364Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:47:36.9088523Z getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T20:47:36.9088756Z topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)]; proposals_i_1 = getitem_20 = topk_idx = None 2025-03-04T20:47:36.9088815Z 2025-03-04T20:47:36.9089267Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:47:36.9089426Z full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:47:36.9089496Z 2025-03-04T20:47:36.9089788Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:47:36.9089938Z level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu')); full = level_ids = None 2025-03-04T20:47:36.9089998Z 2025-03-04T20:47:36.9090380Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T20:47:36.9090517Z getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0]; topk_proposals_i = None 2025-03-04T20:47:36.9090584Z 2025-03-04T20:47:36.9090880Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:47:36.9091029Z tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32); getitem_22 = None 2025-03-04T20:47:36.9091092Z 2025-03-04T20:47:36.9091489Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T20:47:36.9091618Z scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0]; topk_scores_i = None 2025-03-04T20:47:36.9091684Z 2025-03-04T20:47:36.9092162Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T20:47:36.9092292Z isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1); tensor_1 = None 2025-03-04T20:47:36.9092415Z all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:47:36.9092563Z isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T20:47:36.9092729Z valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T20:47:36.9092790Z 2025-03-04T20:47:36.9093158Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T20:47:36.9093268Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T20:47:36.9093334Z 2025-03-04T20:47:36.9093838Z 2025-03-04T20:47:36.9093930Z class GraphModule(torch.nn.Module): 2025-03-04T20:47:36.9184960Z def forward(self, L_stack0_tensor: "f32[4, 3, 1156, 1199][4158132, 1386044, 1199, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", 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"f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[1024, 1024, 3, 3][9216, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[1024][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"): 2025-03-04T20:47:36.9185702Z l_stack0_tensor = L_stack0_tensor 2025-03-04T20:47:36.9186087Z l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ 2025-03-04T20:47:36.9186495Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9186895Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9187261Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9187576Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9187893Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:47:36.9188241Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9188576Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9188905Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9189227Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9189518Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:47:36.9189862Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9190197Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9190518Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9190830Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9191127Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:47:36.9191463Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9191805Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9192194Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9192596Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9192993Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:47:36.9193437Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9193851Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9194272Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9194728Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9195081Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:47:36.9195495Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9195884Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9196257Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9196632Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9197027Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:47:36.9197445Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9197847Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9198230Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9198610Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9199032Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:47:36.9199459Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9199871Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9200256Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9200659Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9201003Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:47:36.9201398Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9201815Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9202266Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9202694Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9203056Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:47:36.9203451Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9203849Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9204184Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9204559Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9204913Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:47:36.9205292Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9205666Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9206039Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9206383Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9206691Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:47:36.9207075Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9207445Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9207857Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9208208Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9208528Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:47:36.9208897Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9209285Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9209689Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9210028Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9210350Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:47:36.9210726Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9211078Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9211397Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9211716Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9212011Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:47:36.9212367Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9212717Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9213055Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9213385Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9213662Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:47:36.9214004Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9214366Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9214695Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9215007Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9215287Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:47:36.9215624Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9215958Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9216307Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9216608Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9216895Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:47:36.9217227Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9217566Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9217884Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9218188Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9218470Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:47:36.9218801Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9219139Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9219449Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9219761Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9220037Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:47:36.9220375Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9220739Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9221094Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9221447Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9221767Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:47:36.9222174Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9222573Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9222909Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9223220Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9223512Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ 2025-03-04T20:47:36.9223863Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9224208Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9224546Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9224847Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9225142Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ 2025-03-04T20:47:36.9225487Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9225839Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9226160Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9226480Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9226773Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ 2025-03-04T20:47:36.9227146Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9227497Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9227819Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9228139Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9228425Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:47:36.9228778Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9229146Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9229471Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9229787Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9230073Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:47:36.9230425Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9230760Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9231090Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9231399Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9231693Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:47:36.9232039Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9232381Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9232709Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9233019Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9233327Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:47:36.9233714Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9234075Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9234414Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9234744Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9235029Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:47:36.9235416Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9235764Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9236084Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9236406Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9236695Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:47:36.9237049Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9237385Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9237712Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9238025Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9238325Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:47:36.9238721Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9239082Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9239441Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9239793Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9240149Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:47:36.9240494Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9240843Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9241166Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9241487Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9241814Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:47:36.9242156Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9242515Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9242852Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9243164Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9243446Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:47:36.9243785Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9244118Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9244439Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9244747Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9245032Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ 2025-03-04T20:47:36.9245369Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9245701Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9246018Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9246324Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9246653Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ 2025-03-04T20:47:36.9246989Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9247322Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9247642Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9247946Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9248265Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ 2025-03-04T20:47:36.9248600Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9248934Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9249250Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9249568Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9249847Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ 2025-03-04T20:47:36.9250190Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9250528Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9250838Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9251153Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9251436Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ 2025-03-04T20:47:36.9251773Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9252104Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9252426Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9252763Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9253055Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ 2025-03-04T20:47:36.9253396Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9253721Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9254040Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9254393Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9254676Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ 2025-03-04T20:47:36.9255005Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9255343Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9255659Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9255970Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9256255Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ 2025-03-04T20:47:36.9256586Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9256924Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9257243Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9257558Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9257839Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ 2025-03-04T20:47:36.9258181Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9258512Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9258864Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9259180Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9259458Z l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ 2025-03-04T20:47:36.9259798Z l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9260130Z l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9260454Z l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9260784Z l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9261068Z l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ 2025-03-04T20:47:36.9261399Z l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9261734Z l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9262058Z l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9262364Z l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9262649Z l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ 2025-03-04T20:47:36.9262984Z l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9263322Z l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9263639Z l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9263952Z l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9264231Z l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ 2025-03-04T20:47:36.9264573Z l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9264910Z l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9265256Z l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9265571Z l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9265847Z l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ 2025-03-04T20:47:36.9266188Z l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9266521Z l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9266874Z l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9267181Z l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9267467Z l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ 2025-03-04T20:47:36.9267805Z l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9268131Z l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9268456Z l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9268758Z l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9269042Z l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ 2025-03-04T20:47:36.9269379Z l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9269716Z l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9270033Z l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9270344Z l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9270626Z l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ 2025-03-04T20:47:36.9270957Z l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9271328Z l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9271645Z l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9271956Z l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9272232Z l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ 2025-03-04T20:47:36.9272572Z l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9272934Z l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9273253Z l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9273562Z l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9273843Z l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ 2025-03-04T20:47:36.9274194Z l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9274544Z l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9274863Z l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9275170Z l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9275463Z l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ 2025-03-04T20:47:36.9275806Z l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9276156Z l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9276483Z l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9276796Z l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9277087Z l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ 2025-03-04T20:47:36.9277431Z l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9277812Z l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9278140Z l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9278469Z l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9278827Z l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ 2025-03-04T20:47:36.9279217Z l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9279651Z l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9280025Z l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9280393Z l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9280724Z l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ 2025-03-04T20:47:36.9281119Z l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9281495Z l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9281872Z l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9282221Z l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9282537Z l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ 2025-03-04T20:47:36.9282923Z l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9283300Z l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9283678Z l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9284025Z l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9284342Z l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ 2025-03-04T20:47:36.9284753Z l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9285145Z l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9285499Z l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9285851Z l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9286168Z l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ 2025-03-04T20:47:36.9286576Z l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9286960Z l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9287319Z l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9287647Z l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9287935Z l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ 2025-03-04T20:47:36.9288288Z l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9288640Z l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9288969Z l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9289300Z l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9289597Z l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ 2025-03-04T20:47:36.9289967Z l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9290311Z l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9290631Z l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9290935Z l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9291254Z l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ 2025-03-04T20:47:36.9291595Z l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9291935Z l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9292255Z l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9292564Z l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9292891Z l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ 2025-03-04T20:47:36.9293226Z l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9293566Z l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9293880Z l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9294193Z l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9294479Z l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ 2025-03-04T20:47:36.9294823Z l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9295163Z l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9295476Z l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9295789Z l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9296076Z l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ 2025-03-04T20:47:36.9296415Z l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9296746Z l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9297066Z l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9297370Z l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9297688Z l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ 2025-03-04T20:47:36.9298037Z l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9298373Z l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9298693Z l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9299003Z l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9299321Z l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ 2025-03-04T20:47:36.9299657Z l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9299996Z l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9300311Z l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9300627Z l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9300917Z l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ 2025-03-04T20:47:36.9301252Z l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9301594Z l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9302011Z l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9302336Z l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9302617Z l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ 2025-03-04T20:47:36.9302965Z l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9303300Z l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9303623Z l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9303988Z l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9304275Z l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ 2025-03-04T20:47:36.9304621Z l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9304954Z l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9305277Z l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9305625Z l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9305913Z l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ 2025-03-04T20:47:36.9306250Z l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9306596Z l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9306922Z l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9307227Z l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9307513Z l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ 2025-03-04T20:47:36.9307847Z l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9308185Z l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9308503Z l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9308820Z l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9309099Z l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ 2025-03-04T20:47:36.9309442Z l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9309776Z l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9310117Z l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9310431Z l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9310710Z l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ 2025-03-04T20:47:36.9311048Z l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9311374Z l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9311729Z l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9312040Z l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9312322Z l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ 2025-03-04T20:47:36.9312663Z l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9312992Z l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9313316Z l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9313624Z l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9313913Z l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ 2025-03-04T20:47:36.9314249Z l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9314593Z l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9314921Z l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9315239Z l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9315545Z l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ 2025-03-04T20:47:36.9315891Z l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9316269Z l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9316598Z l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9316919Z l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9317208Z l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ 2025-03-04T20:47:36.9317565Z l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9317917Z l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9318277Z l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9318598Z l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9318938Z l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ 2025-03-04T20:47:36.9319291Z l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9319642Z l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9319973Z l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9320283Z l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9320579Z l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ 2025-03-04T20:47:36.9320948Z l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9321293Z l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9321626Z l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9321938Z l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9322231Z l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ 2025-03-04T20:47:36.9322586Z l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9322971Z l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9323297Z l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9323619Z l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9323920Z l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ 2025-03-04T20:47:36.9324279Z l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9324660Z l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9324979Z l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9325295Z l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9325580Z l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ 2025-03-04T20:47:36.9325943Z l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9326288Z l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9326615Z l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9326936Z l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9327220Z l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ 2025-03-04T20:47:36.9327574Z l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9327911Z l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9328239Z l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9328552Z l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9328848Z l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ 2025-03-04T20:47:36.9329221Z l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9329583Z l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9329922Z l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9330233Z l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9330525Z l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ 2025-03-04T20:47:36.9330908Z l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9331260Z l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9331576Z l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9331893Z l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9332171Z l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ 2025-03-04T20:47:36.9332518Z l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9332859Z l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9333172Z l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9333486Z l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9333769Z l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ 2025-03-04T20:47:36.9334115Z l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9334449Z l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9334770Z l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9335077Z l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9335404Z l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ 2025-03-04T20:47:36.9335746Z l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9336077Z l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9336398Z l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9336698Z l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9337018Z l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ 2025-03-04T20:47:36.9337351Z l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9337689Z l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9338005Z l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9338323Z l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9338614Z l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ 2025-03-04T20:47:36.9338953Z l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9339294Z l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9339609Z l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9339918Z l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9340204Z l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ 2025-03-04T20:47:36.9340549Z l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9340880Z l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9341203Z l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9341552Z l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9341837Z l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ 2025-03-04T20:47:36.9342184Z l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9342519Z l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9342845Z l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9343153Z l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9343534Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T20:47:36.9343847Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T20:47:36.9344163Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T20:47:36.9344532Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T20:47:36.9344889Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T20:47:36.9345238Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T20:47:36.9345571Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T20:47:36.9345643Z 2025-03-04T20:47:36.9345921Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9346391Z x: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1); l_stack0_tensor = l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = None 2025-03-04T20:47:36.9346458Z 2025-03-04T20:47:36.9346737Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9348185Z x_1: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9348251Z 2025-03-04T20:47:36.9348538Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x) 2025-03-04T20:47:36.9348673Z x_2: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.relu_(x_1); x_1 = None 2025-03-04T20:47:36.9348740Z 2025-03-04T20:47:36.9349094Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1) 2025-03-04T20:47:36.9349329Z x_3: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1); x_2 = None 2025-03-04T20:47:36.9349388Z 2025-03-04T20:47:36.9349691Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9350105Z x_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:47:36.9350172Z 2025-03-04T20:47:36.9350435Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9351964Z x_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_4 = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9352034Z 2025-03-04T20:47:36.9352318Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9352460Z out: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_5); x_5 = None 2025-03-04T20:47:36.9352520Z 2025-03-04T20:47:36.9352774Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9353204Z x_6: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out = l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:47:36.9353273Z 2025-03-04T20:47:36.9353539Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9355118Z x_7: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_6 = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9355192Z 2025-03-04T20:47:36.9355479Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9355625Z out_1: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_7); x_7 = None 2025-03-04T20:47:36.9355685Z 2025-03-04T20:47:36.9355942Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9356385Z x_8: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_1 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:47:36.9356506Z 2025-03-04T20:47:36.9356772Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9358330Z x_9: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_8 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9358401Z 2025-03-04T20:47:36.9358698Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9359156Z x_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); x_3 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:47:36.9359218Z 2025-03-04T20:47:36.9359490Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9361092Z x_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_10 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9361156Z 2025-03-04T20:47:36.9361501Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:47:36.9361647Z x_9 += x_11; out_2: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_9; x_9 = x_11 = None 2025-03-04T20:47:36.9361718Z 2025-03-04T20:47:36.9362005Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9362163Z out_3: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_2); out_2 = None 2025-03-04T20:47:36.9362223Z 2025-03-04T20:47:36.9362484Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9362916Z x_12: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:47:36.9363010Z 2025-03-04T20:47:36.9363287Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9364844Z x_13: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_12 = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9364913Z 2025-03-04T20:47:36.9365200Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9365350Z out_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_13); x_13 = None 2025-03-04T20:47:36.9365411Z 2025-03-04T20:47:36.9365668Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9366115Z x_14: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_4 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:47:36.9366179Z 2025-03-04T20:47:36.9366455Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9368056Z x_15: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_14 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9368130Z 2025-03-04T20:47:36.9368412Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9368556Z out_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_15); x_15 = None 2025-03-04T20:47:36.9368624Z 2025-03-04T20:47:36.9368873Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9369322Z x_16: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_5 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:47:36.9369416Z 2025-03-04T20:47:36.9369691Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9371250Z x_17: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_16 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9371323Z 2025-03-04T20:47:36.9371632Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:47:36.9371793Z x_17 += out_3; out_6: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_17; x_17 = out_3 = None 2025-03-04T20:47:36.9371868Z 2025-03-04T20:47:36.9372184Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9372355Z out_7: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_6); out_6 = None 2025-03-04T20:47:36.9372425Z 2025-03-04T20:47:36.9372712Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9373196Z x_18: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:47:36.9373275Z 2025-03-04T20:47:36.9373576Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9375235Z x_19: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_18 = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9375308Z 2025-03-04T20:47:36.9375585Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9375723Z out_8: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_19); x_19 = None 2025-03-04T20:47:36.9375783Z 2025-03-04T20:47:36.9376029Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9376445Z x_20: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_8 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:47:36.9376541Z 2025-03-04T20:47:36.9376799Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9378303Z x_21: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_20 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9378371Z 2025-03-04T20:47:36.9378646Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9378782Z out_9: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_21); x_21 = None 2025-03-04T20:47:36.9378841Z 2025-03-04T20:47:36.9379097Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9379515Z x_22: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_9 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:47:36.9379586Z 2025-03-04T20:47:36.9379844Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9381392Z x_23: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_22 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9381463Z 2025-03-04T20:47:36.9381751Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:47:36.9381907Z x_23 += out_7; out_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_23; x_23 = out_7 = None 2025-03-04T20:47:36.9381967Z 2025-03-04T20:47:36.9382245Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9382392Z out_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_10); out_10 = None 2025-03-04T20:47:36.9382458Z 2025-03-04T20:47:36.9382698Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9383149Z x_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:47:36.9383210Z 2025-03-04T20:47:36.9383474Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9384966Z x_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_24 = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9385029Z 2025-03-04T20:47:36.9385313Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9385453Z out_12: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_25); x_25 = None 2025-03-04T20:47:36.9385519Z 2025-03-04T20:47:36.9385765Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9386202Z x_26: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_12 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:47:36.9386263Z 2025-03-04T20:47:36.9386528Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9388059Z x_27: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_26 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9388122Z 2025-03-04T20:47:36.9388400Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9388537Z out_13: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_27); x_27 = None 2025-03-04T20:47:36.9388604Z 2025-03-04T20:47:36.9388844Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9389280Z x_28: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_13 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:47:36.9389403Z 2025-03-04T20:47:36.9389669Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9391181Z x_29: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_28 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9391242Z 2025-03-04T20:47:36.9391491Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9391924Z x_30: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); out_11 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:47:36.9391990Z 2025-03-04T20:47:36.9392251Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9393834Z x_31: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_30 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9393899Z 2025-03-04T20:47:36.9394211Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:47:36.9394367Z x_29 += x_31; out_14: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_29; x_29 = x_31 = None 2025-03-04T20:47:36.9394427Z 2025-03-04T20:47:36.9394716Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9394863Z out_15: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_14); out_14 = None 2025-03-04T20:47:36.9394931Z 2025-03-04T20:47:36.9395186Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9395623Z x_32: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:47:36.9395715Z 2025-03-04T20:47:36.9395989Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9397539Z x_33: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_32 = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9397609Z 2025-03-04T20:47:36.9397917Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9398064Z out_16: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_33); x_33 = None 2025-03-04T20:47:36.9398140Z 2025-03-04T20:47:36.9398407Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9398944Z x_34: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_16 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:47:36.9399023Z 2025-03-04T20:47:36.9399329Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9400941Z x_35: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_34 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9401037Z 2025-03-04T20:47:36.9401332Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9401469Z out_17: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_35); x_35 = None 2025-03-04T20:47:36.9401540Z 2025-03-04T20:47:36.9401792Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9402329Z x_36: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_17 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:47:36.9402404Z 2025-03-04T20:47:36.9402730Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9404280Z x_37: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_36 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9404347Z 2025-03-04T20:47:36.9404637Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:47:36.9404791Z x_37 += out_15; out_18: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_37; x_37 = out_15 = None 2025-03-04T20:47:36.9404860Z 2025-03-04T20:47:36.9405142Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9405295Z out_19: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_18); out_18 = None 2025-03-04T20:47:36.9405356Z 2025-03-04T20:47:36.9405613Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9406046Z x_38: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:47:36.9406110Z 2025-03-04T20:47:36.9406380Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9407963Z x_39: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_38 = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9408041Z 2025-03-04T20:47:36.9408326Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9408472Z out_20: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_39); x_39 = None 2025-03-04T20:47:36.9408532Z 2025-03-04T20:47:36.9408786Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9409226Z x_40: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_20 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:47:36.9409317Z 2025-03-04T20:47:36.9409585Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9411116Z x_41: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_40 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9411188Z 2025-03-04T20:47:36.9411476Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9411611Z out_21: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_41); x_41 = None 2025-03-04T20:47:36.9411679Z 2025-03-04T20:47:36.9411925Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9412361Z x_42: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_21 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:47:36.9412423Z 2025-03-04T20:47:36.9412687Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9414196Z x_43: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_42 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9414267Z 2025-03-04T20:47:36.9414541Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:47:36.9414688Z x_43 += out_19; out_22: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_43; x_43 = out_19 = None 2025-03-04T20:47:36.9414753Z 2025-03-04T20:47:36.9415023Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9415170Z out_23: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_22); out_22 = None 2025-03-04T20:47:36.9415230Z 2025-03-04T20:47:36.9415477Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9415924Z x_44: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T20:47:36.9415991Z 2025-03-04T20:47:36.9416251Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9417742Z x_45: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_44 = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9417811Z 2025-03-04T20:47:36.9418085Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9418223Z out_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_45); x_45 = None 2025-03-04T20:47:36.9418283Z 2025-03-04T20:47:36.9418532Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9418948Z x_46: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_24 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T20:47:36.9419014Z 2025-03-04T20:47:36.9419272Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9420802Z x_47: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_46 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9420874Z 2025-03-04T20:47:36.9421154Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9421298Z out_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_47); x_47 = None 2025-03-04T20:47:36.9421356Z 2025-03-04T20:47:36.9421605Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9422026Z x_48: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_25 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T20:47:36.9422121Z 2025-03-04T20:47:36.9422378Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9423881Z x_49: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_48 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9423950Z 2025-03-04T20:47:36.9424220Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:47:36.9424374Z x_49 += out_23; out_26: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_49; x_49 = out_23 = None 2025-03-04T20:47:36.9424432Z 2025-03-04T20:47:36.9424714Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9424856Z out_27: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_26); out_26 = None 2025-03-04T20:47:36.9424922Z 2025-03-04T20:47:36.9425169Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9425581Z x_50: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:47:36.9425641Z 2025-03-04T20:47:36.9425909Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9427445Z x_51: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_50 = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9427511Z 2025-03-04T20:47:36.9427795Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9427925Z out_28: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_51); x_51 = None 2025-03-04T20:47:36.9427990Z 2025-03-04T20:47:36.9428233Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9428684Z x_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_28 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:47:36.9428744Z 2025-03-04T20:47:36.9429008Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9430497Z x_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_52 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9430561Z 2025-03-04T20:47:36.9430843Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9430972Z out_29: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_53); x_53 = None 2025-03-04T20:47:36.9431038Z 2025-03-04T20:47:36.9431283Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9431705Z x_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_29 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:47:36.9431763Z 2025-03-04T20:47:36.9432028Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9433550Z x_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_54 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9433615Z 2025-03-04T20:47:36.9433865Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9434285Z x_56: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); out_27 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:47:36.9434352Z 2025-03-04T20:47:36.9434609Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9436182Z x_57: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_56 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9436251Z 2025-03-04T20:47:36.9436533Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:47:36.9436678Z x_55 += x_57; out_30: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_55; x_55 = x_57 = None 2025-03-04T20:47:36.9436738Z 2025-03-04T20:47:36.9437029Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9437170Z out_31: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_30); out_30 = None 2025-03-04T20:47:36.9437238Z 2025-03-04T20:47:36.9437489Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9437914Z x_58: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:47:36.9437976Z 2025-03-04T20:47:36.9438250Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9439909Z x_59: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_58 = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9439984Z 2025-03-04T20:47:36.9440296Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9440440Z out_32: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_59); x_59 = None 2025-03-04T20:47:36.9440508Z 2025-03-04T20:47:36.9440760Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9441193Z x_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_32 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:47:36.9441283Z 2025-03-04T20:47:36.9441567Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9443112Z x_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_60 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9443175Z 2025-03-04T20:47:36.9443476Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9443605Z out_33: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_61); x_61 = None 2025-03-04T20:47:36.9443674Z 2025-03-04T20:47:36.9443931Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9444365Z x_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_33 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:47:36.9444434Z 2025-03-04T20:47:36.9444708Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9446243Z x_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_62 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9446307Z 2025-03-04T20:47:36.9446636Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:47:36.9446781Z x_63 += out_31; out_34: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_63; x_63 = out_31 = None 2025-03-04T20:47:36.9446848Z 2025-03-04T20:47:36.9447130Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9447276Z out_35: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_34); out_34 = None 2025-03-04T20:47:36.9447335Z 2025-03-04T20:47:36.9447592Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9448020Z x_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:47:36.9448108Z 2025-03-04T20:47:36.9448379Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9449917Z x_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_64 = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9449987Z 2025-03-04T20:47:36.9450269Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9450405Z out_36: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_65); x_65 = None 2025-03-04T20:47:36.9450464Z 2025-03-04T20:47:36.9450718Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9451148Z x_66: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_36 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:47:36.9451211Z 2025-03-04T20:47:36.9451483Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9453024Z x_67: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_66 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9453094Z 2025-03-04T20:47:36.9453371Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9453506Z out_37: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_67); x_67 = None 2025-03-04T20:47:36.9453565Z 2025-03-04T20:47:36.9453816Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9454238Z x_68: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_37 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:47:36.9454334Z 2025-03-04T20:47:36.9454603Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9456125Z x_69: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_68 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9456194Z 2025-03-04T20:47:36.9456469Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:47:36.9456608Z x_69 += out_35; out_38: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_69; x_69 = out_35 = None 2025-03-04T20:47:36.9456674Z 2025-03-04T20:47:36.9456946Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9457087Z out_39: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_38); out_38 = None 2025-03-04T20:47:36.9457145Z 2025-03-04T20:47:36.9457392Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9457798Z x_70: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T20:47:36.9457864Z 2025-03-04T20:47:36.9458121Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9459679Z x_71: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_70 = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9459750Z 2025-03-04T20:47:36.9460026Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9460159Z out_40: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_71); x_71 = None 2025-03-04T20:47:36.9460217Z 2025-03-04T20:47:36.9460466Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9460879Z x_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_40 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T20:47:36.9460975Z 2025-03-04T20:47:36.9461232Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9462724Z x_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_72 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9462791Z 2025-03-04T20:47:36.9463068Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9463201Z out_41: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_73); x_73 = None 2025-03-04T20:47:36.9463260Z 2025-03-04T20:47:36.9463509Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9463932Z x_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_41 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T20:47:36.9464004Z 2025-03-04T20:47:36.9464272Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9465800Z x_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_74 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9465871Z 2025-03-04T20:47:36.9466144Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:47:36.9466295Z x_75 += out_39; out_42: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_75; x_75 = out_39 = None 2025-03-04T20:47:36.9466353Z 2025-03-04T20:47:36.9466638Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9466776Z out_43: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_42); out_42 = None 2025-03-04T20:47:36.9466842Z 2025-03-04T20:47:36.9467091Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9467533Z x_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = None 2025-03-04T20:47:36.9467593Z 2025-03-04T20:47:36.9467860Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9469341Z x_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_76 = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9469409Z 2025-03-04T20:47:36.9469692Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9469818Z out_44: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_77); x_77 = None 2025-03-04T20:47:36.9469884Z 2025-03-04T20:47:36.9470131Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9470548Z x_78: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_44 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = None 2025-03-04T20:47:36.9470609Z 2025-03-04T20:47:36.9470880Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9472438Z x_79: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_78 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9472503Z 2025-03-04T20:47:36.9472795Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9472924Z out_45: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_79); x_79 = None 2025-03-04T20:47:36.9472991Z 2025-03-04T20:47:36.9473237Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9473670Z x_80: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_45 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = None 2025-03-04T20:47:36.9473759Z 2025-03-04T20:47:36.9474031Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9475553Z x_81: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_80 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9475614Z 2025-03-04T20:47:36.9475898Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:47:36.9476036Z x_81 += out_43; out_46: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_81; x_81 = out_43 = None 2025-03-04T20:47:36.9476100Z 2025-03-04T20:47:36.9476381Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9476524Z out_47: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_46); out_46 = None 2025-03-04T20:47:36.9476588Z 2025-03-04T20:47:36.9476847Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9477260Z x_82: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = None 2025-03-04T20:47:36.9477329Z 2025-03-04T20:47:36.9477594Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9479270Z x_83: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_82 = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9479352Z 2025-03-04T20:47:36.9479661Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9479810Z out_48: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_83); x_83 = None 2025-03-04T20:47:36.9479875Z 2025-03-04T20:47:36.9480149Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9480606Z x_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_48 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = None 2025-03-04T20:47:36.9480676Z 2025-03-04T20:47:36.9480944Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9482484Z x_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_84 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9482557Z 2025-03-04T20:47:36.9482847Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9482990Z out_49: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_85); x_85 = None 2025-03-04T20:47:36.9483051Z 2025-03-04T20:47:36.9483313Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9483743Z x_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_49 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = None 2025-03-04T20:47:36.9483813Z 2025-03-04T20:47:36.9484083Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9485644Z x_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_86 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9485716Z 2025-03-04T20:47:36.9485996Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:47:36.9486146Z x_87 += out_47; out_50: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_87; x_87 = out_47 = None 2025-03-04T20:47:36.9486208Z 2025-03-04T20:47:36.9486501Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9486639Z out_51: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_50); out_50 = None 2025-03-04T20:47:36.9486742Z 2025-03-04T20:47:36.9486993Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9487414Z x_88: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ = None 2025-03-04T20:47:36.9487474Z 2025-03-04T20:47:36.9487747Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9489275Z x_89: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_88 = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9489336Z 2025-03-04T20:47:36.9489618Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9489742Z out_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_89); x_89 = None 2025-03-04T20:47:36.9489808Z 2025-03-04T20:47:36.9490050Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9490464Z x_90: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_52 = l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ = None 2025-03-04T20:47:36.9490529Z 2025-03-04T20:47:36.9490784Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9492298Z x_91: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_90 = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9492361Z 2025-03-04T20:47:36.9492645Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9492771Z out_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_91); x_91 = None 2025-03-04T20:47:36.9492838Z 2025-03-04T20:47:36.9493078Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9493526Z x_92: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_53 = l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ = None 2025-03-04T20:47:36.9493593Z 2025-03-04T20:47:36.9493851Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9495338Z x_93: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_92 = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9495398Z 2025-03-04T20:47:36.9495676Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:47:36.9495814Z x_93 += out_51; out_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_93; x_93 = out_51 = None 2025-03-04T20:47:36.9495881Z 2025-03-04T20:47:36.9496154Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9496300Z out_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_54); out_54 = None 2025-03-04T20:47:36.9496358Z 2025-03-04T20:47:36.9496607Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9497012Z x_94: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ = None 2025-03-04T20:47:36.9497071Z 2025-03-04T20:47:36.9497333Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9498849Z x_95: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_94 = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9498918Z 2025-03-04T20:47:36.9499194Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9499326Z out_56: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_95); x_95 = None 2025-03-04T20:47:36.9499421Z 2025-03-04T20:47:36.9499669Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9500088Z x_96: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_56 = l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ = None 2025-03-04T20:47:36.9500148Z 2025-03-04T20:47:36.9500412Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9502052Z x_97: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_96 = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9502130Z 2025-03-04T20:47:36.9502418Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9502543Z out_57: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_97); x_97 = None 2025-03-04T20:47:36.9502612Z 2025-03-04T20:47:36.9502856Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9503270Z x_98: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_57 = l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ = None 2025-03-04T20:47:36.9503331Z 2025-03-04T20:47:36.9503595Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9505141Z x_99: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_98 = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9505215Z 2025-03-04T20:47:36.9505489Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:47:36.9505626Z x_99 += out_55; out_58: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_99; x_99 = out_55 = None 2025-03-04T20:47:36.9505693Z 2025-03-04T20:47:36.9505969Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9506152Z out_59: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_58); out_58 = None 2025-03-04T20:47:36.9506216Z 2025-03-04T20:47:36.9506475Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9506890Z x_100: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ = None 2025-03-04T20:47:36.9506962Z 2025-03-04T20:47:36.9507225Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9508764Z x_101: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_100 = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9508838Z 2025-03-04T20:47:36.9509123Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9509275Z out_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_101); x_101 = None 2025-03-04T20:47:36.9509338Z 2025-03-04T20:47:36.9509594Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9510019Z x_102: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_60 = l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ = None 2025-03-04T20:47:36.9510090Z 2025-03-04T20:47:36.9510356Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9511913Z x_103: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_102 = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9511983Z 2025-03-04T20:47:36.9512260Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9512402Z out_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_103); x_103 = None 2025-03-04T20:47:36.9512499Z 2025-03-04T20:47:36.9512751Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9513171Z x_104: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_61 = l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ = None 2025-03-04T20:47:36.9513238Z 2025-03-04T20:47:36.9513495Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9515019Z x_105: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_104 = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9515088Z 2025-03-04T20:47:36.9515360Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:47:36.9515515Z x_105 += out_59; out_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_105; x_105 = out_59 = None 2025-03-04T20:47:36.9515579Z 2025-03-04T20:47:36.9515871Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9516010Z out_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_62); out_62 = None 2025-03-04T20:47:36.9516079Z 2025-03-04T20:47:36.9516329Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9516755Z x_106: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_63, l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ = None 2025-03-04T20:47:36.9516815Z 2025-03-04T20:47:36.9517092Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9518727Z x_107: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_106 = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9518801Z 2025-03-04T20:47:36.9519110Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9519286Z out_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_107); x_107 = None 2025-03-04T20:47:36.9519359Z 2025-03-04T20:47:36.9519630Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9520084Z x_108: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_64, l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_64 = l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ = None 2025-03-04T20:47:36.9520147Z 2025-03-04T20:47:36.9520423Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9521974Z x_109: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_108, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_108 = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9522038Z 2025-03-04T20:47:36.9522336Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9522474Z out_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_109); x_109 = None 2025-03-04T20:47:36.9522542Z 2025-03-04T20:47:36.9522797Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9523253Z x_110: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_65, l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_65 = l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ = None 2025-03-04T20:47:36.9523319Z 2025-03-04T20:47:36.9523616Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9525275Z x_111: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_110, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_110 = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9525342Z 2025-03-04T20:47:36.9525648Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:47:36.9525826Z x_111 += out_63; out_66: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_111; x_111 = out_63 = None 2025-03-04T20:47:36.9525897Z 2025-03-04T20:47:36.9526184Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9526330Z out_67: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_66); out_66 = None 2025-03-04T20:47:36.9526391Z 2025-03-04T20:47:36.9526650Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9527073Z x_112: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_67, l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ = None 2025-03-04T20:47:36.9527142Z 2025-03-04T20:47:36.9527413Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9528962Z x_113: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_112, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_112 = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9529033Z 2025-03-04T20:47:36.9529322Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9529465Z out_68: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_113); x_113 = None 2025-03-04T20:47:36.9529526Z 2025-03-04T20:47:36.9529786Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9530212Z x_114: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_68, l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_68 = l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ = None 2025-03-04T20:47:36.9530281Z 2025-03-04T20:47:36.9530549Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9532148Z x_115: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_114, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_114 = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9532219Z 2025-03-04T20:47:36.9532538Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9532679Z out_69: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_115); x_115 = None 2025-03-04T20:47:36.9532740Z 2025-03-04T20:47:36.9532996Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9533425Z x_116: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_69, l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_69 = l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ = None 2025-03-04T20:47:36.9533494Z 2025-03-04T20:47:36.9533767Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9535307Z x_117: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_116, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_116 = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9535376Z 2025-03-04T20:47:36.9535648Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:47:36.9535803Z x_117 += out_67; out_70: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_117; x_117 = out_67 = None 2025-03-04T20:47:36.9535864Z 2025-03-04T20:47:36.9536149Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9536282Z out_71: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_70); out_70 = None 2025-03-04T20:47:36.9536348Z 2025-03-04T20:47:36.9536594Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9537010Z x_118: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_71, l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ = None 2025-03-04T20:47:36.9537116Z 2025-03-04T20:47:36.9537375Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9538884Z x_119: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_118, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_118 = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9538976Z 2025-03-04T20:47:36.9539263Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9539404Z out_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_119); x_119 = None 2025-03-04T20:47:36.9539465Z 2025-03-04T20:47:36.9539715Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9540130Z x_120: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_72, l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_72 = l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ = None 2025-03-04T20:47:36.9540198Z 2025-03-04T20:47:36.9540459Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9541969Z x_121: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_120, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_120 = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9542040Z 2025-03-04T20:47:36.9542320Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9542456Z out_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_121); x_121 = None 2025-03-04T20:47:36.9542517Z 2025-03-04T20:47:36.9542765Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9543182Z x_122: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_73, l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_73 = l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ = None 2025-03-04T20:47:36.9543247Z 2025-03-04T20:47:36.9543537Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9545042Z x_123: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_122, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_122 = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9545137Z 2025-03-04T20:47:36.9545409Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:47:36.9545557Z x_123 += out_71; out_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_123; x_123 = out_71 = None 2025-03-04T20:47:36.9545616Z 2025-03-04T20:47:36.9545897Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9546030Z out_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_74); out_74 = None 2025-03-04T20:47:36.9546095Z 2025-03-04T20:47:36.9546335Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9546750Z x_124: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_75, l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ = None 2025-03-04T20:47:36.9546811Z 2025-03-04T20:47:36.9547073Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9548577Z x_125: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_124, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_124 = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9548647Z 2025-03-04T20:47:36.9548931Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9549061Z out_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_125); x_125 = None 2025-03-04T20:47:36.9549127Z 2025-03-04T20:47:36.9549369Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9549820Z x_126: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_76, l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_76 = l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ = None 2025-03-04T20:47:36.9549883Z 2025-03-04T20:47:36.9550143Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9551625Z x_127: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_126, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_126 = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9551723Z 2025-03-04T20:47:36.9552006Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9552132Z out_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_127); x_127 = None 2025-03-04T20:47:36.9552198Z 2025-03-04T20:47:36.9552440Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9552863Z x_128: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_77, l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_77 = l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ = None 2025-03-04T20:47:36.9552925Z 2025-03-04T20:47:36.9553192Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9554718Z x_129: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_128, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_128 = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9554781Z 2025-03-04T20:47:36.9555064Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:47:36.9555209Z x_129 += out_75; out_78: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_129; x_129 = out_75 = None 2025-03-04T20:47:36.9555275Z 2025-03-04T20:47:36.9555558Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9555701Z out_79: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_78); out_78 = None 2025-03-04T20:47:36.9555760Z 2025-03-04T20:47:36.9556015Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9556472Z x_130: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_79, l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ = None 2025-03-04T20:47:36.9556542Z 2025-03-04T20:47:36.9556804Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9558358Z x_131: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_130, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_130 = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9558456Z 2025-03-04T20:47:36.9558909Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9559078Z out_80: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_131); x_131 = None 2025-03-04T20:47:36.9559147Z 2025-03-04T20:47:36.9559446Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9559944Z x_132: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_80, l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_80 = l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ = None 2025-03-04T20:47:36.9560029Z 2025-03-04T20:47:36.9560297Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9561848Z x_133: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_132, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_132 = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9561918Z 2025-03-04T20:47:36.9562197Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9562351Z out_81: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_133); x_133 = None 2025-03-04T20:47:36.9562412Z 2025-03-04T20:47:36.9562674Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9563176Z x_134: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_81, l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_81 = l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ = None 2025-03-04T20:47:36.9563251Z 2025-03-04T20:47:36.9563515Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9565084Z x_135: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_134, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_134 = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9565183Z 2025-03-04T20:47:36.9565469Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:47:36.9565625Z x_135 += out_79; out_82: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_135; x_135 = out_79 = None 2025-03-04T20:47:36.9565687Z 2025-03-04T20:47:36.9565980Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9566117Z out_83: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_82); out_82 = None 2025-03-04T20:47:36.9566190Z 2025-03-04T20:47:36.9566449Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9566883Z x_136: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_83, l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ = None 2025-03-04T20:47:36.9566951Z 2025-03-04T20:47:36.9567219Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9568784Z x_137: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_136, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_136 = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9568851Z 2025-03-04T20:47:36.9569147Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9569280Z out_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_137); x_137 = None 2025-03-04T20:47:36.9569348Z 2025-03-04T20:47:36.9569599Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9570063Z x_138: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_84, l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_84 = l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ = None 2025-03-04T20:47:36.9570133Z 2025-03-04T20:47:36.9570408Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9571987Z x_139: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_138, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_138 = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9572074Z 2025-03-04T20:47:36.9572356Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9572486Z out_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_139); x_139 = None 2025-03-04T20:47:36.9572554Z 2025-03-04T20:47:36.9572795Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9573223Z x_140: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_85, l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_85 = l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ = None 2025-03-04T20:47:36.9573290Z 2025-03-04T20:47:36.9573549Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9575063Z x_141: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_140, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_140 = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9575126Z 2025-03-04T20:47:36.9575405Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:47:36.9575553Z x_141 += out_83; out_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_141; x_141 = out_83 = None 2025-03-04T20:47:36.9575614Z 2025-03-04T20:47:36.9575897Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9576033Z out_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_86); out_86 = None 2025-03-04T20:47:36.9576129Z 2025-03-04T20:47:36.9576372Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9576787Z x_142: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_87, l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ = None 2025-03-04T20:47:36.9576847Z 2025-03-04T20:47:36.9577114Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9578611Z x_143: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_142, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_142 = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9578706Z 2025-03-04T20:47:36.9578992Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9579122Z out_88: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_143); x_143 = None 2025-03-04T20:47:36.9579190Z 2025-03-04T20:47:36.9579438Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9579861Z x_144: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_88, l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_88 = l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ = None 2025-03-04T20:47:36.9579921Z 2025-03-04T20:47:36.9580187Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9581704Z x_145: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_144, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_144 = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9581775Z 2025-03-04T20:47:36.9582057Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9582187Z out_89: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_145); x_145 = None 2025-03-04T20:47:36.9582250Z 2025-03-04T20:47:36.9582523Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9582947Z x_146: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_89, l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_89 = l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ = None 2025-03-04T20:47:36.9583006Z 2025-03-04T20:47:36.9583267Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9584769Z x_147: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_146, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_146 = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9584868Z 2025-03-04T20:47:36.9585152Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:47:36.9585295Z x_147 += out_87; out_90: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_147; x_147 = out_87 = None 2025-03-04T20:47:36.9585361Z 2025-03-04T20:47:36.9585645Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9585793Z out_91: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_90); out_90 = None 2025-03-04T20:47:36.9585852Z 2025-03-04T20:47:36.9586108Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9586525Z x_148: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_91, l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ = None 2025-03-04T20:47:36.9586593Z 2025-03-04T20:47:36.9586857Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9588399Z x_149: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_148, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_148 = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9588469Z 2025-03-04T20:47:36.9588754Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9588921Z out_92: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_149); x_149 = None 2025-03-04T20:47:36.9588985Z 2025-03-04T20:47:36.9589237Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9589653Z x_150: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_92, l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_92 = l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ = None 2025-03-04T20:47:36.9589723Z 2025-03-04T20:47:36.9589982Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9591504Z x_151: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_150, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_150 = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9591600Z 2025-03-04T20:47:36.9591877Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9592017Z out_93: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_151); x_151 = None 2025-03-04T20:47:36.9592078Z 2025-03-04T20:47:36.9592329Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9592745Z x_152: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_93, l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_93 = l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ = None 2025-03-04T20:47:36.9592812Z 2025-03-04T20:47:36.9593070Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9594581Z x_153: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_152, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_152 = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9594651Z 2025-03-04T20:47:36.9594923Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:47:36.9595074Z x_153 += out_91; out_94: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_153; x_153 = out_91 = None 2025-03-04T20:47:36.9595135Z 2025-03-04T20:47:36.9595442Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9595578Z out_95: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_94); out_94 = None 2025-03-04T20:47:36.9595645Z 2025-03-04T20:47:36.9595889Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9596314Z x_154: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_95, l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ = None 2025-03-04T20:47:36.9596374Z 2025-03-04T20:47:36.9596640Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9598171Z x_155: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_154, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_154 = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9598231Z 2025-03-04T20:47:36.9598590Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9598769Z out_96: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_155); x_155 = None 2025-03-04T20:47:36.9598842Z 2025-03-04T20:47:36.9599087Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9599534Z x_156: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_96, l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_96 = l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ = None 2025-03-04T20:47:36.9599599Z 2025-03-04T20:47:36.9599899Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9601507Z x_157: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_156, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_156 = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9601572Z 2025-03-04T20:47:36.9602021Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9602235Z out_97: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_157); x_157 = None 2025-03-04T20:47:36.9602312Z 2025-03-04T20:47:36.9602582Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9603051Z x_158: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_97, l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_97 = l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ = None 2025-03-04T20:47:36.9603115Z 2025-03-04T20:47:36.9603406Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9605019Z x_159: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_158, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_158 = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9605123Z 2025-03-04T20:47:36.9605424Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:47:36.9605580Z x_159 += out_95; out_98: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_159; x_159 = out_95 = None 2025-03-04T20:47:36.9605651Z 2025-03-04T20:47:36.9605946Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9606095Z out_99: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_98); out_98 = None 2025-03-04T20:47:36.9606158Z 2025-03-04T20:47:36.9606426Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9606873Z x_160: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_99, l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ = None 2025-03-04T20:47:36.9606937Z 2025-03-04T20:47:36.9607218Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9608818Z x_161: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_160, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_160 = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9608915Z 2025-03-04T20:47:36.9609196Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9609343Z out_100: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_161); x_161 = None 2025-03-04T20:47:36.9609402Z 2025-03-04T20:47:36.9609653Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9610079Z x_162: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_100, l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_100 = l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ = None 2025-03-04T20:47:36.9610139Z 2025-03-04T20:47:36.9610659Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9612177Z x_163: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_162, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_162 = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9612246Z 2025-03-04T20:47:36.9612529Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9612673Z out_101: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_163); x_163 = None 2025-03-04T20:47:36.9612742Z 2025-03-04T20:47:36.9612992Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9613437Z x_164: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_101, l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_101 = l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ = None 2025-03-04T20:47:36.9613497Z 2025-03-04T20:47:36.9613770Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9615329Z x_165: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_164, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_164 = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9615397Z 2025-03-04T20:47:36.9615724Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:47:36.9615875Z x_165 += out_99; out_102: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_165; x_165 = out_99 = None 2025-03-04T20:47:36.9615942Z 2025-03-04T20:47:36.9616226Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9616378Z out_103: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_102); out_102 = None 2025-03-04T20:47:36.9616438Z 2025-03-04T20:47:36.9616694Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9617117Z x_166: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_103, l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ = None 2025-03-04T20:47:36.9617214Z 2025-03-04T20:47:36.9617479Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9619030Z x_167: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_166, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_166 = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9619101Z 2025-03-04T20:47:36.9619382Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9619524Z out_104: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_167); x_167 = None 2025-03-04T20:47:36.9619584Z 2025-03-04T20:47:36.9619839Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9620263Z x_168: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_104, l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_104 = l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ = None 2025-03-04T20:47:36.9620338Z 2025-03-04T20:47:36.9620599Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9622182Z x_169: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_168, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_168 = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9622253Z 2025-03-04T20:47:36.9622539Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9622684Z out_105: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_169); x_169 = None 2025-03-04T20:47:36.9622746Z 2025-03-04T20:47:36.9623003Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9623438Z x_170: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_105, l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_105 = l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ = None 2025-03-04T20:47:36.9623534Z 2025-03-04T20:47:36.9623793Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9625308Z x_171: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_170, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_170 = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9625378Z 2025-03-04T20:47:36.9625649Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:47:36.9625807Z x_171 += out_103; out_106: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_171; x_171 = out_103 = None 2025-03-04T20:47:36.9625867Z 2025-03-04T20:47:36.9626150Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9626287Z out_107: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_106); out_106 = None 2025-03-04T20:47:36.9626353Z 2025-03-04T20:47:36.9626595Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9627014Z x_172: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_107, l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ = None 2025-03-04T20:47:36.9627074Z 2025-03-04T20:47:36.9627339Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9628873Z x_173: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_172, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_172 = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9628937Z 2025-03-04T20:47:36.9629221Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9629355Z out_108: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_173); x_173 = None 2025-03-04T20:47:36.9629421Z 2025-03-04T20:47:36.9629668Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9630103Z x_174: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_108, l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_108 = l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ = None 2025-03-04T20:47:36.9630192Z 2025-03-04T20:47:36.9630456Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9631969Z x_175: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_174, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_174 = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9632031Z 2025-03-04T20:47:36.9632314Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9632443Z out_109: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_175); x_175 = None 2025-03-04T20:47:36.9632508Z 2025-03-04T20:47:36.9632750Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9633178Z x_176: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_109, l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_109 = l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ = None 2025-03-04T20:47:36.9633239Z 2025-03-04T20:47:36.9633504Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9635133Z x_177: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_176, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_176 = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9635195Z 2025-03-04T20:47:36.9635471Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:47:36.9635626Z x_177 += out_107; out_110: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_177; x_177 = out_107 = None 2025-03-04T20:47:36.9635694Z 2025-03-04T20:47:36.9635969Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9636113Z out_111: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_110); out_110 = None 2025-03-04T20:47:36.9636173Z 2025-03-04T20:47:36.9636459Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9636873Z x_178: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_111, l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ = None 2025-03-04T20:47:36.9636940Z 2025-03-04T20:47:36.9637203Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9638789Z x_179: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_178, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_178 = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9638867Z 2025-03-04T20:47:36.9639152Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9639296Z out_112: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_179); x_179 = None 2025-03-04T20:47:36.9639357Z 2025-03-04T20:47:36.9639625Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9640081Z x_180: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_112, l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_112 = l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ = None 2025-03-04T20:47:36.9640152Z 2025-03-04T20:47:36.9640436Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9642046Z x_181: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_180, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_180 = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9642121Z 2025-03-04T20:47:36.9642405Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9642547Z out_113: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_181); x_181 = None 2025-03-04T20:47:36.9642608Z 2025-03-04T20:47:36.9642863Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9643327Z x_182: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_113, l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_113 = l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ = None 2025-03-04T20:47:36.9643386Z 2025-03-04T20:47:36.9643656Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9645200Z x_183: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_182, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_182 = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9645272Z 2025-03-04T20:47:36.9645553Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:47:36.9645714Z x_183 += out_111; out_114: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_183; x_183 = out_111 = None 2025-03-04T20:47:36.9645774Z 2025-03-04T20:47:36.9646062Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9646211Z out_115: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_114); out_114 = None 2025-03-04T20:47:36.9646277Z 2025-03-04T20:47:36.9646532Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9646950Z x_184: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_115, l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ = None 2025-03-04T20:47:36.9647017Z 2025-03-04T20:47:36.9647284Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9648847Z x_185: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_184, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_184 = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9648922Z 2025-03-04T20:47:36.9649205Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9649346Z out_116: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_185); x_185 = None 2025-03-04T20:47:36.9649436Z 2025-03-04T20:47:36.9649694Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9650122Z x_186: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_116, l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_116 = l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ = None 2025-03-04T20:47:36.9650189Z 2025-03-04T20:47:36.9650452Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9652004Z x_187: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_186, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_186 = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9652074Z 2025-03-04T20:47:36.9652358Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9652508Z out_117: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_187); x_187 = None 2025-03-04T20:47:36.9652567Z 2025-03-04T20:47:36.9652818Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9653235Z x_188: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_117, l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_117 = l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ = None 2025-03-04T20:47:36.9653303Z 2025-03-04T20:47:36.9653558Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9655099Z x_189: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_188, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_188 = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9655171Z 2025-03-04T20:47:36.9655439Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:47:36.9655591Z x_189 += out_115; out_118: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_189; x_189 = out_115 = None 2025-03-04T20:47:36.9655650Z 2025-03-04T20:47:36.9655929Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9656105Z out_119: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_118); out_118 = None 2025-03-04T20:47:36.9656172Z 2025-03-04T20:47:36.9656599Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:47:36.9656753Z arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32) 2025-03-04T20:47:36.9656813Z 2025-03-04T20:47:36.9657107Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:47:36.9657239Z shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T20:47:36.9657304Z 2025-03-04T20:47:36.9657735Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:47:36.9657889Z arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32) 2025-03-04T20:47:36.9657948Z 2025-03-04T20:47:36.9658243Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:47:36.9658374Z shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T20:47:36.9658441Z 2025-03-04T20:47:36.9658808Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:47:36.9658991Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T20:47:36.9659088Z shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0] 2025-03-04T20:47:36.9659213Z shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T20:47:36.9659274Z 2025-03-04T20:47:36.9659602Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:47:36.9659724Z shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T20:47:36.9659790Z 2025-03-04T20:47:36.9660108Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:47:36.9660228Z shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T20:47:36.9660286Z 2025-03-04T20:47:36.9660707Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:47:36.9660920Z shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T20:47:36.9660986Z 2025-03-04T20:47:36.9661397Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:47:36.9661525Z view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T20:47:36.9661949Z view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T20:47:36.9662127Z add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T20:47:36.9662243Z x_190: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4); add = None 2025-03-04T20:47:36.9662301Z 2025-03-04T20:47:36.9662600Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:47:36.9662721Z tensor: "f32[82125, 4][4, 1]cpu" = x_190.to(torch.float32); x_190 = None 2025-03-04T20:47:36.9662787Z 2025-03-04T20:47:36.9663031Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9663807Z x_191: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_119, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); out_119 = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T20:47:36.9663869Z 2025-03-04T20:47:36.9664143Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:47:36.9664324Z x_192: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_191, inplace = False); x_191 = None 2025-03-04T20:47:36.9664391Z 2025-03-04T20:47:36.9664762Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:47:36.9665608Z score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_192, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T20:47:36.9665677Z 2025-03-04T20:47:36.9666030Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:47:36.9666870Z x_193: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_192, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_192 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T20:47:36.9666934Z 2025-03-04T20:47:36.9667273Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T20:47:36.9667420Z permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T20:47:36.9667559Z logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T20:47:36.9667620Z 2025-03-04T20:47:36.9668041Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T20:47:36.9668196Z view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_193.view(4, -1, 4, 73, 75); x_193 = None 2025-03-04T20:47:36.9668408Z permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2); view_2 = None 2025-03-04T20:47:36.9668591Z pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2); permute_1 = None 2025-03-04T20:47:36.9668652Z 2025-03-04T20:47:36.9669091Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:47:36.9669289Z pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T20:47:36.9669355Z 2025-03-04T20:47:36.9669773Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:47:36.9669930Z unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T20:47:36.9670071Z expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T20:47:36.9670213Z anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T20:47:36.9670274Z 2025-03-04T20:47:36.9670655Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:47:36.9670826Z deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T20:47:36.9670896Z 2025-03-04T20:47:36.9671226Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:47:36.9671383Z boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T20:47:36.9671456Z 2025-03-04T20:47:36.9671774Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:47:36.9671902Z getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:47:36.9672032Z getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:47:36.9672177Z widths: "f32[328500][1]cpu" = getitem_2 - getitem_3; getitem_2 = getitem_3 = None 2025-03-04T20:47:36.9672250Z 2025-03-04T20:47:36.9672607Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:47:36.9672758Z getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:47:36.9672884Z getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:47:36.9673074Z heights: "f32[328500][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T20:47:36.9673139Z 2025-03-04T20:47:36.9673471Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:47:36.9673594Z getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:47:36.9673692Z mul: "f32[328500][1]cpu" = 0.5 * widths 2025-03-04T20:47:36.9673819Z ctr_x: "f32[328500][1]cpu" = getitem_6 + mul; getitem_6 = mul = None 2025-03-04T20:47:36.9673890Z 2025-03-04T20:47:36.9674220Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:47:36.9674374Z getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:47:36.9674498Z mul_1: "f32[328500][1]cpu" = 0.5 * heights 2025-03-04T20:47:36.9674637Z ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1; getitem_7 = mul_1 = None 2025-03-04T20:47:36.9674711Z 2025-03-04T20:47:36.9675085Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:47:36.9675233Z getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:47:36.9675353Z dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0; getitem_8 = None 2025-03-04T20:47:36.9675413Z 2025-03-04T20:47:36.9675720Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:47:36.9675868Z getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:47:36.9675992Z dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0; getitem_9 = None 2025-03-04T20:47:36.9676055Z 2025-03-04T20:47:36.9676369Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:47:36.9676530Z getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:47:36.9676639Z dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0; getitem_10 = None 2025-03-04T20:47:36.9676706Z 2025-03-04T20:47:36.9677015Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:47:36.9677204Z getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:47:36.9677313Z dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0; getitem_11 = None 2025-03-04T20:47:36.9677385Z 2025-03-04T20:47:36.9677734Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:47:36.9677887Z dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:47:36.9677948Z 2025-03-04T20:47:36.9678288Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:47:36.9678417Z dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:47:36.9678483Z 2025-03-04T20:47:36.9678894Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:47:36.9679080Z getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:47:36.9679210Z mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12; dx = getitem_12 = None 2025-03-04T20:47:36.9679370Z getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:47:36.9679511Z pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13; mul_2 = getitem_13 = None 2025-03-04T20:47:36.9679583Z 2025-03-04T20:47:36.9679942Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:47:36.9680086Z getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:47:36.9680207Z mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14; dy = getitem_14 = None 2025-03-04T20:47:36.9680364Z getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:47:36.9680537Z pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15; mul_3 = getitem_15 = None 2025-03-04T20:47:36.9680608Z 2025-03-04T20:47:36.9680935Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:47:36.9681058Z exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:47:36.9681211Z getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:47:36.9681347Z pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16; exp = getitem_16 = None 2025-03-04T20:47:36.9681412Z 2025-03-04T20:47:36.9681746Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:47:36.9681858Z exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:47:36.9682023Z getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:47:36.9682146Z pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17; exp_1 = getitem_17 = None 2025-03-04T20:47:36.9682211Z 2025-03-04T20:47:36.9682511Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:47:36.9682609Z mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T20:47:36.9682718Z x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:47:36.9682785Z 2025-03-04T20:47:36.9683083Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:47:36.9683179Z mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T20:47:36.9683296Z y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:47:36.9683356Z 2025-03-04T20:47:36.9683652Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:47:36.9683767Z mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:47:36.9683895Z x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:47:36.9683953Z 2025-03-04T20:47:36.9684251Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:47:36.9684355Z mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:47:36.9684480Z y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:47:36.9684542Z 2025-03-04T20:47:36.9684912Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:47:36.9685087Z pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:47:36.9685161Z 2025-03-04T20:47:36.9685482Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:47:36.9685639Z proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4)); pred_boxes = None 2025-03-04T20:47:36.9685698Z 2025-03-04T20:47:36.9686073Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:47:36.9686275Z proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T20:47:36.9686341Z 2025-03-04T20:47:36.9686818Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T20:47:36.9686953Z arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T20:47:36.9687012Z 2025-03-04T20:47:36.9687308Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:47:36.9687440Z batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T20:47:36.9687505Z 2025-03-04T20:47:36.9687938Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:47:36.9688053Z topk = logits_i.topk(6000, dim = 1); logits_i = None 2025-03-04T20:47:36.9688150Z topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0] 2025-03-04T20:47:36.9688268Z topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1]; topk = None 2025-03-04T20:47:36.9688329Z 2025-03-04T20:47:36.9688786Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:47:36.9688944Z getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T20:47:36.9689183Z topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)]; proposals_i_1 = getitem_20 = topk_idx = None 2025-03-04T20:47:36.9689249Z 2025-03-04T20:47:36.9689703Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:47:36.9689864Z full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:47:36.9689932Z 2025-03-04T20:47:36.9690219Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:47:36.9690372Z level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu')); full = level_ids = None 2025-03-04T20:47:36.9690439Z 2025-03-04T20:47:36.9690844Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T20:47:36.9690996Z getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0]; topk_proposals_i = None 2025-03-04T20:47:36.9691054Z 2025-03-04T20:47:36.9691343Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:47:36.9691479Z tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32); getitem_22 = None 2025-03-04T20:47:36.9691543Z 2025-03-04T20:47:36.9691907Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T20:47:36.9692041Z scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0]; topk_scores_i = None 2025-03-04T20:47:36.9692099Z 2025-03-04T20:47:36.9692605Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T20:47:36.9692736Z isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1); tensor_1 = None 2025-03-04T20:47:36.9692855Z all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:47:36.9693002Z isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T20:47:36.9693130Z valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T20:47:36.9693188Z 2025-03-04T20:47:36.9710729Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T20:47:36.9710965Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T20:47:36.9711059Z 2025-03-04T20:47:36.9711067Z 2025-03-04T20:47:36.9711162Z class GraphModule(torch.nn.Module): 2025-03-04T20:47:36.9800676Z def forward(self, L_stack0_tensor: "f32[4, 3, 1156, 1199][4158132, 1386044, 1199, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_: "f32[512, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_: 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L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_: "f32[1024, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 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"f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[1024, 1024, 3, 3][9216, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[1024][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"): 2025-03-04T20:47:36.9801362Z l_stack0_tensor = L_stack0_tensor 2025-03-04T20:47:36.9801649Z l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ 2025-03-04T20:47:36.9802132Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9802471Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9802772Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9803073Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9803366Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:47:36.9803703Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9804053Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9804369Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9804681Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9804962Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:47:36.9805309Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9805694Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9806019Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9806333Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9806616Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:47:36.9806959Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9807326Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9807646Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9807953Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9808257Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:47:36.9808603Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9808955Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9809292Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9809664Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9809982Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:47:36.9810377Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9810777Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9811149Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9811483Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9811762Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:47:36.9812135Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9812475Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9812787Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9813104Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9813385Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:47:36.9813754Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9814081Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9814401Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9814704Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9814988Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:47:36.9815336Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9815667Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9815986Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9816289Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9816576Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:47:36.9816913Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9817255Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9817568Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9817880Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9818166Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:47:36.9818531Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9818872Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9819190Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9819505Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9819783Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:47:36.9820161Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9820492Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9820815Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9821171Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9821500Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:47:36.9821906Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9822323Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9822733Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9823081Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9823432Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:47:36.9823823Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9824210Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9824569Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9824895Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9825232Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:47:36.9825638Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9826036Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9826421Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9826796Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9827188Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:47:36.9827571Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9827970Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9828338Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9828701Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9829028Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:47:36.9829416Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9829801Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9830180Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9830550Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9830873Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:47:36.9831270Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9831659Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9832028Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9832399Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9832723Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:47:36.9833107Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9833493Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9833860Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9834231Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9834550Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:47:36.9834929Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9835305Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9835666Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9836021Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9836331Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:47:36.9836711Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9837086Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9837436Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9837786Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9838094Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ 2025-03-04T20:47:36.9838471Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9838904Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9839272Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9839647Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9839957Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ 2025-03-04T20:47:36.9840307Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9840644Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9840973Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9841321Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9841618Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ 2025-03-04T20:47:36.9841959Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9842318Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9842635Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9842955Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9843248Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:47:36.9843599Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9843936Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9844258Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9844577Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9844870Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:47:36.9845214Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9845545Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9845898Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9846209Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9846488Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:47:36.9846827Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9847154Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9847508Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9847816Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9848123Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:47:36.9848472Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9848827Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9849173Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9849495Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9849785Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:47:36.9850124Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9850462Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9850783Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9851100Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9851381Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:47:36.9851724Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9852072Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9852444Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9852760Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9853036Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:47:36.9853377Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9853704Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9854053Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9854355Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9854640Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:47:36.9854977Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9855311Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9855634Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9855940Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9856227Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:47:36.9856558Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9856900Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9857214Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9857522Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9857806Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:47:36.9858139Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9858506Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9858824Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9859138Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9859417Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ 2025-03-04T20:47:36.9859760Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9860124Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9860449Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9860761Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9861039Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ 2025-03-04T20:47:36.9861377Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9861709Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9862030Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9862336Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9862625Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ 2025-03-04T20:47:36.9862959Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9863301Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9863621Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9863929Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9864218Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ 2025-03-04T20:47:36.9864577Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9864919Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9865234Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9865549Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9865829Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ 2025-03-04T20:47:36.9866171Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9866534Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9866847Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9867162Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9867438Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ 2025-03-04T20:47:36.9867782Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9868116Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9868441Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9868743Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9869027Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ 2025-03-04T20:47:36.9869370Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9869704Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9870023Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9870328Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9870620Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ 2025-03-04T20:47:36.9870981Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9871319Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9871630Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9871943Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9872219Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ 2025-03-04T20:47:36.9872593Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9872934Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9873248Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9873561Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9873839Z l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ 2025-03-04T20:47:36.9874183Z l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9874513Z l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9874834Z l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9875140Z l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9875429Z l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ 2025-03-04T20:47:36.9875775Z l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9876107Z l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9876428Z l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9876733Z l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9877064Z l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ 2025-03-04T20:47:36.9877409Z l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9877749Z l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9878070Z l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9878385Z l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9878787Z l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ 2025-03-04T20:47:36.9879140Z l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9879488Z l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9879809Z l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9880133Z l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9880416Z l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ 2025-03-04T20:47:36.9880763Z l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9881115Z l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9881455Z l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9881782Z l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9882071Z l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ 2025-03-04T20:47:36.9882430Z l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9882777Z l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9883117Z l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9883438Z l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9883767Z l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ 2025-03-04T20:47:36.9884121Z l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9884483Z l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9884822Z l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9885146Z l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9885715Z l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ 2025-03-04T20:47:36.9886055Z l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9886408Z l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9886741Z l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9887078Z l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9887369Z l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ 2025-03-04T20:47:36.9887734Z l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9888077Z l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9888398Z l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9888718Z l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9889009Z l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ 2025-03-04T20:47:36.9889371Z l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9889721Z l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9890052Z l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9890423Z l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9890716Z l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ 2025-03-04T20:47:36.9891074Z l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9891424Z l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9891751Z l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9892096Z l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9892388Z l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ 2025-03-04T20:47:36.9892730Z l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9893074Z l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9893392Z l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9893724Z l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9894025Z l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ 2025-03-04T20:47:36.9894374Z l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9894723Z l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9895050Z l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9895378Z l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9895668Z l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ 2025-03-04T20:47:36.9896023Z l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9896378Z l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9896709Z l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9897068Z l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9897361Z l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ 2025-03-04T20:47:36.9897729Z l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9898085Z l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9898422Z l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9898771Z l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9899071Z l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ 2025-03-04T20:47:36.9899405Z l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9899745Z l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9900073Z l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9900383Z l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9900672Z l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ 2025-03-04T20:47:36.9901008Z l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9901342Z l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9901663Z l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9902074Z l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9902364Z l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ 2025-03-04T20:47:36.9902709Z l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9903045Z l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9903415Z l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9903733Z l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9904012Z l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ 2025-03-04T20:47:36.9904356Z l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9904688Z l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9905048Z l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9905360Z l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9905656Z l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ 2025-03-04T20:47:36.9906006Z l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9906347Z l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9906686Z l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9906992Z l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9907285Z l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ 2025-03-04T20:47:36.9907620Z l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9907964Z l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9908280Z l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9908592Z l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9908880Z l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ 2025-03-04T20:47:36.9909210Z l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9909586Z l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9909900Z l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9910213Z l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9910495Z l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ 2025-03-04T20:47:36.9910837Z l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9911206Z l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9911521Z l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9911833Z l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9912113Z l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ 2025-03-04T20:47:36.9912453Z l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9912790Z l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9913113Z l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9913417Z l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9913705Z l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ 2025-03-04T20:47:36.9914044Z l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9914390Z l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9914723Z l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9915040Z l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9915338Z l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ 2025-03-04T20:47:36.9915708Z l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9916057Z l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9916389Z l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9916699Z l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9916992Z l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ 2025-03-04T20:47:36.9917341Z l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9917713Z l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9918035Z l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9918354Z l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9918641Z l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ 2025-03-04T20:47:36.9919048Z l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9919400Z l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9919729Z l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9920058Z l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9920358Z l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ 2025-03-04T20:47:36.9920717Z l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9921062Z l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9921394Z l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9921711Z l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9922008Z l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ 2025-03-04T20:47:36.9922395Z l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9922736Z l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9923065Z l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9923378Z l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9923674Z l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ 2025-03-04T20:47:36.9924058Z l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9924406Z l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9924728Z l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9925049Z l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9925341Z l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ 2025-03-04T20:47:36.9925689Z l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9926034Z l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9926355Z l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9926672Z l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9926963Z l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ 2025-03-04T20:47:36.9927314Z l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9927653Z l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9927981Z l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9928302Z l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9928623Z l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ 2025-03-04T20:47:36.9928976Z l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9929319Z l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9929645Z l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9929959Z l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9930289Z l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ 2025-03-04T20:47:36.9930623Z l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9930960Z l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9931281Z l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9931587Z l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9931878Z l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ 2025-03-04T20:47:36.9932213Z l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9932551Z l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9932866Z l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9933180Z l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9933464Z l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ 2025-03-04T20:47:36.9933806Z l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9934141Z l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9934454Z l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9934793Z l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9935078Z l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ 2025-03-04T20:47:36.9935424Z l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9935759Z l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9936083Z l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9936424Z l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9936718Z l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ 2025-03-04T20:47:36.9937066Z l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9937408Z l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9937739Z l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9938067Z l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9938350Z l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ 2025-03-04T20:47:36.9938685Z l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9939025Z l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9939346Z l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9939669Z l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9939971Z l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ 2025-03-04T20:47:36.9940304Z l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9940640Z l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9940980Z l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9941296Z l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9941574Z l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ 2025-03-04T20:47:36.9941912Z l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9942242Z l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9942565Z l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9942908Z l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9943190Z l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ 2025-03-04T20:47:36.9943533Z l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9943862Z l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9944188Z l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9944494Z l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9944778Z l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ 2025-03-04T20:47:36.9945112Z l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9945448Z l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9945773Z l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9946078Z l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9946363Z l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ 2025-03-04T20:47:36.9946696Z l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9947034Z l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9947384Z l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9947698Z l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9947980Z l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ 2025-03-04T20:47:36.9948322Z l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9948662Z l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9949005Z l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9949319Z l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9949600Z l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ 2025-03-04T20:47:36.9949939Z l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9950275Z l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9950598Z l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9950903Z l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9951190Z l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ 2025-03-04T20:47:36.9951532Z l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9951869Z l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9952192Z l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9952504Z l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9952798Z l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ 2025-03-04T20:47:36.9953144Z l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9953533Z l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9953850Z l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9954169Z l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9954461Z l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ 2025-03-04T20:47:36.9954805Z l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9955185Z l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9955508Z l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9955826Z l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9956114Z l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ 2025-03-04T20:47:36.9956462Z l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:47:36.9956807Z l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:47:36.9957140Z l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:47:36.9957465Z l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:47:36.9957818Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T20:47:36.9958156Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T20:47:36.9958477Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T20:47:36.9958920Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T20:47:36.9959300Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T20:47:36.9959673Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T20:47:36.9960075Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T20:47:36.9960155Z 2025-03-04T20:47:36.9960459Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9960953Z x: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1); l_stack0_tensor = l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = None 2025-03-04T20:47:36.9961024Z 2025-03-04T20:47:36.9961311Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9962807Z x_1: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9962901Z 2025-03-04T20:47:36.9963199Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x) 2025-03-04T20:47:36.9963344Z x_2: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.relu_(x_1); x_1 = None 2025-03-04T20:47:36.9963407Z 2025-03-04T20:47:36.9963782Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1) 2025-03-04T20:47:36.9964017Z x_3: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1); x_2 = None 2025-03-04T20:47:36.9964084Z 2025-03-04T20:47:36.9964341Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9964771Z x_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:47:36.9964835Z 2025-03-04T20:47:36.9965113Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9966671Z x_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_4 = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9966742Z 2025-03-04T20:47:36.9967116Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9967260Z out: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_5); x_5 = None 2025-03-04T20:47:36.9967330Z 2025-03-04T20:47:36.9967593Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9968041Z x_6: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out = l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:47:36.9968103Z 2025-03-04T20:47:36.9968386Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9969990Z x_7: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_6 = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9970058Z 2025-03-04T20:47:36.9970349Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9970483Z out_1: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_7); x_7 = None 2025-03-04T20:47:36.9970549Z 2025-03-04T20:47:36.9970795Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9971241Z x_8: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_1 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:47:36.9971301Z 2025-03-04T20:47:36.9971575Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9973169Z x_9: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_8 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9973242Z 2025-03-04T20:47:36.9973517Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9974019Z x_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); x_3 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:47:36.9974091Z 2025-03-04T20:47:36.9974384Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9976014Z x_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_10 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9976112Z 2025-03-04T20:47:36.9976390Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:47:36.9976540Z x_9 += x_11; out_2: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_9; x_9 = x_11 = None 2025-03-04T20:47:36.9976599Z 2025-03-04T20:47:36.9976890Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9977042Z out_3: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_2); out_2 = None 2025-03-04T20:47:36.9977107Z 2025-03-04T20:47:36.9977354Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9977781Z x_12: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:47:36.9977841Z 2025-03-04T20:47:36.9978114Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9979661Z x_13: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_12 = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9979723Z 2025-03-04T20:47:36.9980015Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9980157Z out_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_13); x_13 = None 2025-03-04T20:47:36.9980256Z 2025-03-04T20:47:36.9980507Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9980940Z x_14: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_4 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:47:36.9980999Z 2025-03-04T20:47:36.9981273Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9982841Z x_15: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_14 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9982932Z 2025-03-04T20:47:36.9983232Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9983379Z out_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_15); x_15 = None 2025-03-04T20:47:36.9983452Z 2025-03-04T20:47:36.9983756Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9984199Z x_16: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_5 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:47:36.9984258Z 2025-03-04T20:47:36.9984539Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9986147Z x_17: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_16 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9986211Z 2025-03-04T20:47:36.9986520Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:47:36.9986680Z x_17 += out_3; out_6: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_17; x_17 = out_3 = None 2025-03-04T20:47:36.9986753Z 2025-03-04T20:47:36.9987091Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9987246Z out_7: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_6); out_6 = None 2025-03-04T20:47:36.9987308Z 2025-03-04T20:47:36.9987565Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9987991Z x_18: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:47:36.9988062Z 2025-03-04T20:47:36.9988330Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9989899Z x_19: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_18 = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9989966Z 2025-03-04T20:47:36.9990262Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9990419Z out_8: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_19); x_19 = None 2025-03-04T20:47:36.9990481Z 2025-03-04T20:47:36.9990750Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9991195Z x_20: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_8 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:47:36.9991266Z 2025-03-04T20:47:36.9991544Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9993178Z x_21: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_20 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9993252Z 2025-03-04T20:47:36.9993547Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9993759Z out_9: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_21); x_21 = None 2025-03-04T20:47:36.9993823Z 2025-03-04T20:47:36.9994095Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9994523Z x_22: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_9 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:47:36.9994591Z 2025-03-04T20:47:36.9994853Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:36.9996482Z x_23: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_22 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:47:36.9996595Z 2025-03-04T20:47:36.9996890Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:47:36.9997066Z x_23 += out_7; out_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_23; x_23 = out_7 = None 2025-03-04T20:47:36.9997135Z 2025-03-04T20:47:36.9997465Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:47:36.9997633Z out_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_10); out_10 = None 2025-03-04T20:47:36.9997708Z 2025-03-04T20:47:36.9997988Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:36.9998474Z x_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:47:36.9998549Z 2025-03-04T20:47:36.9998907Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0000780Z x_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_24 = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0000845Z 2025-03-04T20:47:37.0001167Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0001311Z out_12: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_25); x_25 = None 2025-03-04T20:47:37.0001380Z 2025-03-04T20:47:37.0001632Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0002187Z x_26: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_12 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:47:37.0002261Z 2025-03-04T20:47:37.0002525Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0004125Z x_27: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_26 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0004186Z 2025-03-04T20:47:37.0004484Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0004635Z out_13: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_27); x_27 = None 2025-03-04T20:47:37.0004695Z 2025-03-04T20:47:37.0004946Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0005389Z x_28: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_13 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:47:37.0005454Z 2025-03-04T20:47:37.0005720Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0007280Z x_29: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_28 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0007349Z 2025-03-04T20:47:37.0007601Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0008093Z x_30: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); out_11 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:47:37.0008155Z 2025-03-04T20:47:37.0008425Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0010030Z x_31: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_30 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0010132Z 2025-03-04T20:47:37.0010420Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:47:37.0010569Z x_29 += x_31; out_14: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_29; x_29 = x_31 = None 2025-03-04T20:47:37.0010636Z 2025-03-04T20:47:37.0010920Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0011081Z out_15: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_14); out_14 = None 2025-03-04T20:47:37.0011141Z 2025-03-04T20:47:37.0011397Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0011819Z x_32: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:47:37.0011885Z 2025-03-04T20:47:37.0012148Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0013686Z x_33: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_32 = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0013756Z 2025-03-04T20:47:37.0014042Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0014187Z out_16: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_33); x_33 = None 2025-03-04T20:47:37.0014251Z 2025-03-04T20:47:37.0014534Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0014964Z x_34: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_16 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:47:37.0015030Z 2025-03-04T20:47:37.0015296Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0016854Z x_35: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_34 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0016952Z 2025-03-04T20:47:37.0017237Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0017381Z out_17: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_35); x_35 = None 2025-03-04T20:47:37.0017444Z 2025-03-04T20:47:37.0017709Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0018126Z x_36: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_17 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:47:37.0018193Z 2025-03-04T20:47:37.0018449Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0019995Z x_37: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_36 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0020065Z 2025-03-04T20:47:37.0020342Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:47:37.0020500Z x_37 += out_15; out_18: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_37; x_37 = out_15 = None 2025-03-04T20:47:37.0020560Z 2025-03-04T20:47:37.0020889Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0021035Z out_19: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_18); out_18 = None 2025-03-04T20:47:37.0021101Z 2025-03-04T20:47:37.0021342Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0021761Z x_38: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:47:37.0021820Z 2025-03-04T20:47:37.0022084Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0023633Z x_39: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_38 = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0023692Z 2025-03-04T20:47:37.0023974Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0024110Z out_20: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_39); x_39 = None 2025-03-04T20:47:37.0024174Z 2025-03-04T20:47:37.0024415Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0024839Z x_40: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_20 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:47:37.0024896Z 2025-03-04T20:47:37.0025159Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0026656Z x_41: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_40 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0026718Z 2025-03-04T20:47:37.0027002Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0027139Z out_21: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_41); x_41 = None 2025-03-04T20:47:37.0027232Z 2025-03-04T20:47:37.0027482Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0027921Z x_42: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_21 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:47:37.0027984Z 2025-03-04T20:47:37.0028256Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0029810Z x_43: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_42 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0029902Z 2025-03-04T20:47:37.0030185Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:47:37.0030336Z x_43 += out_19; out_22: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_43; x_43 = out_19 = None 2025-03-04T20:47:37.0030404Z 2025-03-04T20:47:37.0030690Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0030843Z out_23: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_22); out_22 = None 2025-03-04T20:47:37.0030902Z 2025-03-04T20:47:37.0031157Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0031581Z x_44: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T20:47:37.0031650Z 2025-03-04T20:47:37.0031913Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0033471Z x_45: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_44 = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0033541Z 2025-03-04T20:47:37.0033853Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0034001Z out_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_45); x_45 = None 2025-03-04T20:47:37.0034060Z 2025-03-04T20:47:37.0034315Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0034747Z x_46: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_24 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T20:47:37.0034815Z 2025-03-04T20:47:37.0035079Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0036664Z x_47: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_46 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0036733Z 2025-03-04T20:47:37.0037018Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0037167Z out_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_47); x_47 = None 2025-03-04T20:47:37.0037227Z 2025-03-04T20:47:37.0037481Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0037938Z x_48: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_25 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T20:47:37.0038009Z 2025-03-04T20:47:37.0038297Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0040105Z x_49: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_48 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0040179Z 2025-03-04T20:47:37.0040458Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:47:37.0040649Z x_49 += out_23; out_26: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_49; x_49 = out_23 = None 2025-03-04T20:47:37.0040712Z 2025-03-04T20:47:37.0041002Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0041150Z out_27: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_26); out_26 = None 2025-03-04T20:47:37.0041219Z 2025-03-04T20:47:37.0041470Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0041899Z x_50: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:47:37.0042003Z 2025-03-04T20:47:37.0042288Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0043915Z x_51: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_50 = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0043984Z 2025-03-04T20:47:37.0044297Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0044439Z out_28: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_51); x_51 = None 2025-03-04T20:47:37.0044512Z 2025-03-04T20:47:37.0044778Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0045246Z x_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_28 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:47:37.0045315Z 2025-03-04T20:47:37.0045583Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0047202Z x_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_52 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0047269Z 2025-03-04T20:47:37.0047618Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0047766Z out_29: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_53); x_53 = None 2025-03-04T20:47:37.0047829Z 2025-03-04T20:47:37.0048100Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0048549Z x_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_29 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:47:37.0048619Z 2025-03-04T20:47:37.0048899Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0050554Z x_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_54 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0050625Z 2025-03-04T20:47:37.0050889Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0051360Z x_56: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); out_27 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:47:37.0051424Z 2025-03-04T20:47:37.0051710Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0053390Z x_57: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_56 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0053465Z 2025-03-04T20:47:37.0053767Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:47:37.0053912Z x_55 += x_57; out_30: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_55; x_55 = x_57 = None 2025-03-04T20:47:37.0053981Z 2025-03-04T20:47:37.0054282Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0054467Z out_31: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_30); out_30 = None 2025-03-04T20:47:37.0054531Z 2025-03-04T20:47:37.0054797Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0055216Z x_58: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:47:37.0055283Z 2025-03-04T20:47:37.0055547Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0057147Z x_59: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_58 = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0057249Z 2025-03-04T20:47:37.0057543Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0057690Z out_32: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_59); x_59 = None 2025-03-04T20:47:37.0057755Z 2025-03-04T20:47:37.0058019Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0058430Z x_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_32 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:47:37.0058494Z 2025-03-04T20:47:37.0058751Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0060241Z x_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_60 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0060311Z 2025-03-04T20:47:37.0060594Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0060727Z out_33: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_61); x_61 = None 2025-03-04T20:47:37.0060793Z 2025-03-04T20:47:37.0061072Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0061490Z x_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_33 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:47:37.0061557Z 2025-03-04T20:47:37.0061825Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0063342Z x_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_62 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0063437Z 2025-03-04T20:47:37.0063709Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:47:37.0063857Z x_63 += out_31; out_34: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_63; x_63 = out_31 = None 2025-03-04T20:47:37.0063916Z 2025-03-04T20:47:37.0064203Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0064338Z out_35: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_34); out_34 = None 2025-03-04T20:47:37.0064403Z 2025-03-04T20:47:37.0064645Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0065051Z x_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:47:37.0065111Z 2025-03-04T20:47:37.0065374Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0066917Z x_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_64 = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0066978Z 2025-03-04T20:47:37.0067275Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0067434Z out_36: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_65); x_65 = None 2025-03-04T20:47:37.0067501Z 2025-03-04T20:47:37.0067744Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0068158Z x_66: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_36 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:47:37.0068217Z 2025-03-04T20:47:37.0068482Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0069972Z x_67: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_66 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0070064Z 2025-03-04T20:47:37.0070351Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0070479Z out_37: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_67); x_67 = None 2025-03-04T20:47:37.0070546Z 2025-03-04T20:47:37.0070787Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0071205Z x_68: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_37 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:47:37.0071266Z 2025-03-04T20:47:37.0071530Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0073060Z x_69: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_68 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0073121Z 2025-03-04T20:47:37.0073399Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:47:37.0073539Z x_69 += out_35; out_38: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_69; x_69 = out_35 = None 2025-03-04T20:47:37.0073608Z 2025-03-04T20:47:37.0073915Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0074060Z out_39: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_38); out_38 = None 2025-03-04T20:47:37.0074121Z 2025-03-04T20:47:37.0074377Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0074795Z x_70: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T20:47:37.0074864Z 2025-03-04T20:47:37.0075132Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0076713Z x_71: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_70 = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0076783Z 2025-03-04T20:47:37.0077128Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0077268Z out_40: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_71); x_71 = None 2025-03-04T20:47:37.0077330Z 2025-03-04T20:47:37.0077593Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0078035Z x_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_40 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T20:47:37.0078106Z 2025-03-04T20:47:37.0078412Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0080230Z x_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_72 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0080311Z 2025-03-04T20:47:37.0080618Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0080791Z out_41: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_73); x_73 = None 2025-03-04T20:47:37.0080857Z 2025-03-04T20:47:37.0081119Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0081551Z x_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_41 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T20:47:37.0081623Z 2025-03-04T20:47:37.0081895Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0083481Z x_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_74 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0083584Z 2025-03-04T20:47:37.0083874Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:47:37.0084036Z x_75 += out_39; out_42: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_75; x_75 = out_39 = None 2025-03-04T20:47:37.0084098Z 2025-03-04T20:47:37.0084399Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0084541Z out_43: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_42); out_42 = None 2025-03-04T20:47:37.0084609Z 2025-03-04T20:47:37.0084864Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0085297Z x_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = None 2025-03-04T20:47:37.0085364Z 2025-03-04T20:47:37.0085641Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0087240Z x_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_76 = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0087303Z 2025-03-04T20:47:37.0087638Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0087773Z out_44: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_77); x_77 = None 2025-03-04T20:47:37.0087842Z 2025-03-04T20:47:37.0088095Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0088540Z x_78: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_44 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = None 2025-03-04T20:47:37.0088608Z 2025-03-04T20:47:37.0088883Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0090500Z x_79: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_78 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0090561Z 2025-03-04T20:47:37.0090869Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0090998Z out_45: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_79); x_79 = None 2025-03-04T20:47:37.0091065Z 2025-03-04T20:47:37.0091318Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0091765Z x_80: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_45 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = None 2025-03-04T20:47:37.0091830Z 2025-03-04T20:47:37.0092096Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0093645Z x_81: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_80 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0093705Z 2025-03-04T20:47:37.0093988Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:47:37.0094162Z x_81 += out_43; out_46: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_81; x_81 = out_43 = None 2025-03-04T20:47:37.0094230Z 2025-03-04T20:47:37.0094510Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0094651Z out_47: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_46); out_46 = None 2025-03-04T20:47:37.0094718Z 2025-03-04T20:47:37.0094965Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0095386Z x_82: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = None 2025-03-04T20:47:37.0095511Z 2025-03-04T20:47:37.0095799Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0097348Z x_83: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_82 = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0097419Z 2025-03-04T20:47:37.0097709Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0097838Z out_48: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_83); x_83 = None 2025-03-04T20:47:37.0097905Z 2025-03-04T20:47:37.0098152Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0098576Z x_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_48 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = None 2025-03-04T20:47:37.0098637Z 2025-03-04T20:47:37.0098913Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0100470Z x_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_84 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0100546Z 2025-03-04T20:47:37.0100881Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0101017Z out_49: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_85); x_85 = None 2025-03-04T20:47:37.0101089Z 2025-03-04T20:47:37.0101351Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0101809Z x_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_49 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = None 2025-03-04T20:47:37.0101955Z 2025-03-04T20:47:37.0102239Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0103830Z x_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_86 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0103901Z 2025-03-04T20:47:37.0104196Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:47:37.0104337Z x_87 += out_47; out_50: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_87; x_87 = out_47 = None 2025-03-04T20:47:37.0104405Z 2025-03-04T20:47:37.0104688Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0104831Z out_51: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_50); out_50 = None 2025-03-04T20:47:37.0104892Z 2025-03-04T20:47:37.0105148Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0105579Z x_88: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ = None 2025-03-04T20:47:37.0105655Z 2025-03-04T20:47:37.0105934Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0107585Z x_89: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_88 = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0107663Z 2025-03-04T20:47:37.0108013Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0108156Z out_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_89); x_89 = None 2025-03-04T20:47:37.0108220Z 2025-03-04T20:47:37.0108488Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0108941Z x_90: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_52 = l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ = None 2025-03-04T20:47:37.0109042Z 2025-03-04T20:47:37.0109328Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0110952Z x_91: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_90 = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0111025Z 2025-03-04T20:47:37.0111326Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0111471Z out_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_91); x_91 = None 2025-03-04T20:47:37.0111534Z 2025-03-04T20:47:37.0111806Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0112252Z x_92: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_53 = l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ = None 2025-03-04T20:47:37.0112324Z 2025-03-04T20:47:37.0112613Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0114242Z x_93: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_92 = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0114315Z 2025-03-04T20:47:37.0114646Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:47:37.0114802Z x_93 += out_51; out_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_93; x_93 = out_51 = None 2025-03-04T20:47:37.0114864Z 2025-03-04T20:47:37.0115173Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0115317Z out_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_54); out_54 = None 2025-03-04T20:47:37.0115388Z 2025-03-04T20:47:37.0115648Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0116092Z x_94: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ = None 2025-03-04T20:47:37.0116186Z 2025-03-04T20:47:37.0116475Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0118064Z x_95: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_94 = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0118126Z 2025-03-04T20:47:37.0118417Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0118562Z out_56: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_95); x_95 = None 2025-03-04T20:47:37.0118636Z 2025-03-04T20:47:37.0118972Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0119472Z x_96: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_56 = l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ = None 2025-03-04T20:47:37.0119545Z 2025-03-04T20:47:37.0119854Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0121454Z x_97: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_96 = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0121519Z 2025-03-04T20:47:37.0121808Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0121936Z out_57: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_97); x_97 = None 2025-03-04T20:47:37.0122004Z 2025-03-04T20:47:37.0122251Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0122683Z x_98: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_57 = l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ = None 2025-03-04T20:47:37.0122785Z 2025-03-04T20:47:37.0123058Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0124636Z x_99: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_98 = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0124706Z 2025-03-04T20:47:37.0125006Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:47:37.0125155Z x_99 += out_55; out_58: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_99; x_99 = out_55 = None 2025-03-04T20:47:37.0125226Z 2025-03-04T20:47:37.0125522Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0125681Z out_59: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_58); out_58 = None 2025-03-04T20:47:37.0125748Z 2025-03-04T20:47:37.0126031Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0126506Z x_100: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ = None 2025-03-04T20:47:37.0126582Z 2025-03-04T20:47:37.0126882Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0128519Z x_101: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_100 = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0128592Z 2025-03-04T20:47:37.0128878Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0129026Z out_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_101); x_101 = None 2025-03-04T20:47:37.0129086Z 2025-03-04T20:47:37.0129340Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0129761Z x_102: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_60 = l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ = None 2025-03-04T20:47:37.0129860Z 2025-03-04T20:47:37.0130127Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0131662Z x_103: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_102 = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0131734Z 2025-03-04T20:47:37.0132016Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0132158Z out_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_103); x_103 = None 2025-03-04T20:47:37.0132217Z 2025-03-04T20:47:37.0132473Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0132902Z x_104: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_61 = l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ = None 2025-03-04T20:47:37.0132977Z 2025-03-04T20:47:37.0133243Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0134770Z x_105: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_104 = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0134844Z 2025-03-04T20:47:37.0135131Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:47:37.0135290Z x_105 += out_59; out_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_105; x_105 = out_59 = None 2025-03-04T20:47:37.0135350Z 2025-03-04T20:47:37.0135638Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0135773Z out_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_62); out_62 = None 2025-03-04T20:47:37.0135841Z 2025-03-04T20:47:37.0136086Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0136538Z x_106: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_63, l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ = None 2025-03-04T20:47:37.0136607Z 2025-03-04T20:47:37.0136875Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0138410Z x_107: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_106 = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0138471Z 2025-03-04T20:47:37.0138755Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0138894Z out_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_107); x_107 = None 2025-03-04T20:47:37.0138953Z 2025-03-04T20:47:37.0139203Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0139615Z x_108: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_64, l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_64 = l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ = None 2025-03-04T20:47:37.0139681Z 2025-03-04T20:47:37.0139938Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0141495Z x_109: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_108, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_108 = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0141567Z 2025-03-04T20:47:37.0141865Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0142005Z out_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_109); x_109 = None 2025-03-04T20:47:37.0142065Z 2025-03-04T20:47:37.0142321Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0142772Z x_110: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_65, l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_65 = l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ = None 2025-03-04T20:47:37.0142867Z 2025-03-04T20:47:37.0143123Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0144627Z x_111: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_110, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_110 = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0144692Z 2025-03-04T20:47:37.0144965Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:47:37.0145112Z x_111 += out_63; out_66: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_111; x_111 = out_63 = None 2025-03-04T20:47:37.0145170Z 2025-03-04T20:47:37.0145452Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0145587Z out_67: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_66); out_66 = None 2025-03-04T20:47:37.0145656Z 2025-03-04T20:47:37.0145905Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0146334Z x_112: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_67, l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ = None 2025-03-04T20:47:37.0146392Z 2025-03-04T20:47:37.0146653Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0148184Z x_113: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_112, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_112 = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0148255Z 2025-03-04T20:47:37.0148538Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0148669Z out_68: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_113); x_113 = None 2025-03-04T20:47:37.0148732Z 2025-03-04T20:47:37.0148977Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0149431Z x_114: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_68, l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_68 = l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ = None 2025-03-04T20:47:37.0149490Z 2025-03-04T20:47:37.0149754Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0151277Z x_115: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_114, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_114 = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0151346Z 2025-03-04T20:47:37.0151649Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0151777Z out_69: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_115); x_115 = None 2025-03-04T20:47:37.0151843Z 2025-03-04T20:47:37.0152088Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0152529Z x_116: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_69, l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_69 = l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ = None 2025-03-04T20:47:37.0152589Z 2025-03-04T20:47:37.0152859Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0154436Z x_117: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_116, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_116 = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0154501Z 2025-03-04T20:47:37.0154787Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:47:37.0154939Z x_117 += out_67; out_70: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_117; x_117 = out_67 = None 2025-03-04T20:47:37.0155007Z 2025-03-04T20:47:37.0155290Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0155469Z out_71: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_70); out_70 = None 2025-03-04T20:47:37.0155530Z 2025-03-04T20:47:37.0155787Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0156202Z x_118: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_71, l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ = None 2025-03-04T20:47:37.0156273Z 2025-03-04T20:47:37.0156537Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0158080Z x_119: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_118, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_118 = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0158151Z 2025-03-04T20:47:37.0158433Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0158573Z out_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_119); x_119 = None 2025-03-04T20:47:37.0158636Z 2025-03-04T20:47:37.0158957Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0159388Z x_120: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_72, l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_72 = l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ = None 2025-03-04T20:47:37.0159460Z 2025-03-04T20:47:37.0159727Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0161327Z x_121: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_120, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_120 = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0161402Z 2025-03-04T20:47:37.0161686Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0161830Z out_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_121); x_121 = None 2025-03-04T20:47:37.0161920Z 2025-03-04T20:47:37.0162180Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0162611Z x_122: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_73, l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_73 = l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ = None 2025-03-04T20:47:37.0162682Z 2025-03-04T20:47:37.0162946Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0164498Z x_123: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_122, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_122 = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0164570Z 2025-03-04T20:47:37.0164850Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:47:37.0165006Z x_123 += out_71; out_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_123; x_123 = out_71 = None 2025-03-04T20:47:37.0165068Z 2025-03-04T20:47:37.0165363Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0165502Z out_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_74); out_74 = None 2025-03-04T20:47:37.0165570Z 2025-03-04T20:47:37.0165821Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0166250Z x_124: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_75, l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ = None 2025-03-04T20:47:37.0166313Z 2025-03-04T20:47:37.0166584Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0168175Z x_125: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_124, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_124 = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0168237Z 2025-03-04T20:47:37.0168529Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0168697Z out_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_125); x_125 = None 2025-03-04T20:47:37.0168766Z 2025-03-04T20:47:37.0169014Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0169447Z x_126: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_76, l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_76 = l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ = None 2025-03-04T20:47:37.0169515Z 2025-03-04T20:47:37.0169784Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0171315Z x_127: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_126, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_126 = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0171378Z 2025-03-04T20:47:37.0171679Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0171809Z out_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_127); x_127 = None 2025-03-04T20:47:37.0171879Z 2025-03-04T20:47:37.0172117Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0172535Z x_128: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_77, l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_77 = l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ = None 2025-03-04T20:47:37.0172598Z 2025-03-04T20:47:37.0172853Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0174386Z x_129: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_128, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_128 = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0174447Z 2025-03-04T20:47:37.0174724Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:47:37.0174866Z x_129 += out_75; out_78: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_129; x_129 = out_75 = None 2025-03-04T20:47:37.0174960Z 2025-03-04T20:47:37.0175240Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0175384Z out_79: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_78); out_78 = None 2025-03-04T20:47:37.0175450Z 2025-03-04T20:47:37.0175692Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0176108Z x_130: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_79, l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ = None 2025-03-04T20:47:37.0176167Z 2025-03-04T20:47:37.0176432Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0177965Z x_131: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_130, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_130 = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0178034Z 2025-03-04T20:47:37.0178322Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0178453Z out_80: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_131); x_131 = None 2025-03-04T20:47:37.0178519Z 2025-03-04T20:47:37.0178761Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0179187Z x_132: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_80, l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_80 = l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ = None 2025-03-04T20:47:37.0179245Z 2025-03-04T20:47:37.0179513Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0181067Z x_133: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_132, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_132 = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0181136Z 2025-03-04T20:47:37.0181426Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0181590Z out_81: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_133); x_133 = None 2025-03-04T20:47:37.0181655Z 2025-03-04T20:47:37.0181898Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0182321Z x_134: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_81, l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_81 = l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ = None 2025-03-04T20:47:37.0182380Z 2025-03-04T20:47:37.0182645Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0184151Z x_135: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_134, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_134 = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0184219Z 2025-03-04T20:47:37.0184497Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:47:37.0184646Z x_135 += out_79; out_82: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_135; x_135 = out_79 = None 2025-03-04T20:47:37.0184711Z 2025-03-04T20:47:37.0184985Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0185127Z out_83: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_82); out_82 = None 2025-03-04T20:47:37.0185187Z 2025-03-04T20:47:37.0185436Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0185841Z x_136: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_83, l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ = None 2025-03-04T20:47:37.0185910Z 2025-03-04T20:47:37.0186199Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0187718Z x_137: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_136, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_136 = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0187816Z 2025-03-04T20:47:37.0188092Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0188226Z out_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_137); x_137 = None 2025-03-04T20:47:37.0188286Z 2025-03-04T20:47:37.0188534Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0188945Z x_138: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_84, l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_84 = l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ = None 2025-03-04T20:47:37.0189010Z 2025-03-04T20:47:37.0189271Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0190775Z x_139: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_138, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_138 = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0190844Z 2025-03-04T20:47:37.0191123Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0191257Z out_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_139); x_139 = None 2025-03-04T20:47:37.0191316Z 2025-03-04T20:47:37.0191562Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0191972Z x_140: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_85, l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_85 = l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ = None 2025-03-04T20:47:37.0192040Z 2025-03-04T20:47:37.0192295Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0193830Z x_141: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_140, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_140 = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0193932Z 2025-03-04T20:47:37.0194206Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:47:37.0194355Z x_141 += out_83; out_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_141; x_141 = out_83 = None 2025-03-04T20:47:37.0194415Z 2025-03-04T20:47:37.0194696Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0194831Z out_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_86); out_86 = None 2025-03-04T20:47:37.0194900Z 2025-03-04T20:47:37.0195143Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0195562Z x_142: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_87, l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ = None 2025-03-04T20:47:37.0195626Z 2025-03-04T20:47:37.0195891Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0197447Z x_143: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_142, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_142 = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0197510Z 2025-03-04T20:47:37.0197804Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0197935Z out_88: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_143); x_143 = None 2025-03-04T20:47:37.0198002Z 2025-03-04T20:47:37.0198254Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0198785Z x_144: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_88, l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_88 = l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ = None 2025-03-04T20:47:37.0198894Z 2025-03-04T20:47:37.0199176Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0200736Z x_145: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_144, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_144 = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0200853Z 2025-03-04T20:47:37.0201150Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0201284Z out_89: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_145); x_145 = None 2025-03-04T20:47:37.0201352Z 2025-03-04T20:47:37.0201604Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0202158Z x_146: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_89, l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_89 = l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ = None 2025-03-04T20:47:37.0202228Z 2025-03-04T20:47:37.0202510Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0204091Z x_147: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_146, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_146 = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0204157Z 2025-03-04T20:47:37.0204448Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:47:37.0204597Z x_147 += out_87; out_90: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_147; x_147 = out_87 = None 2025-03-04T20:47:37.0204666Z 2025-03-04T20:47:37.0204954Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0205102Z out_91: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_90); out_90 = None 2025-03-04T20:47:37.0205163Z 2025-03-04T20:47:37.0205428Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0205911Z x_148: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_91, l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ = None 2025-03-04T20:47:37.0205984Z 2025-03-04T20:47:37.0206257Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0207828Z x_149: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_148, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_148 = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0207937Z 2025-03-04T20:47:37.0208219Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0208358Z out_92: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_149); x_149 = None 2025-03-04T20:47:37.0208418Z 2025-03-04T20:47:37.0208675Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0209108Z x_150: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_92, l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_92 = l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ = None 2025-03-04T20:47:37.0209172Z 2025-03-04T20:47:37.0209442Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0210985Z x_151: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_150, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_150 = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0211057Z 2025-03-04T20:47:37.0211342Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0211479Z out_93: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_151); x_151 = None 2025-03-04T20:47:37.0211538Z 2025-03-04T20:47:37.0211793Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0212251Z x_152: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_93, l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_93 = l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ = None 2025-03-04T20:47:37.0212315Z 2025-03-04T20:47:37.0212584Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0214095Z x_153: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_152, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_152 = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0214191Z 2025-03-04T20:47:37.0214467Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:47:37.0214609Z x_153 += out_91; out_94: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_153; x_153 = out_91 = None 2025-03-04T20:47:37.0214675Z 2025-03-04T20:47:37.0214953Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0215093Z out_95: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_94); out_94 = None 2025-03-04T20:47:37.0215150Z 2025-03-04T20:47:37.0215402Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0215813Z x_154: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_95, l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ = None 2025-03-04T20:47:37.0215879Z 2025-03-04T20:47:37.0216138Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0217654Z x_155: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_154, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_154 = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0217722Z 2025-03-04T20:47:37.0217997Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0218134Z out_96: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_155); x_155 = None 2025-03-04T20:47:37.0218193Z 2025-03-04T20:47:37.0218440Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0218885Z x_156: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_96, l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_96 = l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ = None 2025-03-04T20:47:37.0218954Z 2025-03-04T20:47:37.0219210Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0220717Z x_157: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_156, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_156 = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0220825Z 2025-03-04T20:47:37.0221106Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0221243Z out_97: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_157); x_157 = None 2025-03-04T20:47:37.0221301Z 2025-03-04T20:47:37.0221553Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0221972Z x_158: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_97, l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_97 = l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ = None 2025-03-04T20:47:37.0222039Z 2025-03-04T20:47:37.0222297Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0223828Z x_159: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_158, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_158 = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0223897Z 2025-03-04T20:47:37.0224170Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:47:37.0224320Z x_159 += out_95; out_98: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_159; x_159 = out_95 = None 2025-03-04T20:47:37.0224377Z 2025-03-04T20:47:37.0224658Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0224790Z out_99: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_98); out_98 = None 2025-03-04T20:47:37.0224857Z 2025-03-04T20:47:37.0225127Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0225542Z x_160: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_99, l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ = None 2025-03-04T20:47:37.0225600Z 2025-03-04T20:47:37.0225864Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0227383Z x_161: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_160, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_160 = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0227470Z 2025-03-04T20:47:37.0227749Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0227887Z out_100: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_161); x_161 = None 2025-03-04T20:47:37.0227953Z 2025-03-04T20:47:37.0228195Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0228620Z x_162: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_100, l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_100 = l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ = None 2025-03-04T20:47:37.0228681Z 2025-03-04T20:47:37.0228943Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0230453Z x_163: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_162, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_162 = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0230515Z 2025-03-04T20:47:37.0230798Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0230933Z out_101: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_163); x_163 = None 2025-03-04T20:47:37.0230998Z 2025-03-04T20:47:37.0231240Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0231703Z x_164: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_101, l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_101 = l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ = None 2025-03-04T20:47:37.0231763Z 2025-03-04T20:47:37.0232032Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0233565Z x_165: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_164, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_164 = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0233654Z 2025-03-04T20:47:37.0233945Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:47:37.0234099Z x_165 += out_99; out_102: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_165; x_165 = out_99 = None 2025-03-04T20:47:37.0234169Z 2025-03-04T20:47:37.0234460Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0234619Z out_103: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_102); out_102 = None 2025-03-04T20:47:37.0234680Z 2025-03-04T20:47:37.0234953Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0235375Z x_166: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_103, l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ = None 2025-03-04T20:47:37.0235444Z 2025-03-04T20:47:37.0235719Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0237286Z x_167: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_166, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_166 = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0237358Z 2025-03-04T20:47:37.0237650Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0237828Z out_104: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_167); x_167 = None 2025-03-04T20:47:37.0237888Z 2025-03-04T20:47:37.0238146Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0238574Z x_168: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_104, l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_104 = l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ = None 2025-03-04T20:47:37.0238642Z 2025-03-04T20:47:37.0238966Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0240537Z x_169: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_168, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_168 = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0240637Z 2025-03-04T20:47:37.0240921Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0241064Z out_105: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_169); x_169 = None 2025-03-04T20:47:37.0241127Z 2025-03-04T20:47:37.0241393Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0241830Z x_170: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_105, l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_105 = l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ = None 2025-03-04T20:47:37.0241900Z 2025-03-04T20:47:37.0242172Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0243739Z x_171: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_170, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_170 = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0243811Z 2025-03-04T20:47:37.0244115Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:47:37.0244283Z x_171 += out_103; out_106: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_171; x_171 = out_103 = None 2025-03-04T20:47:37.0244348Z 2025-03-04T20:47:37.0244735Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0244881Z out_107: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_106); out_106 = None 2025-03-04T20:47:37.0244948Z 2025-03-04T20:47:37.0245199Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0245633Z x_172: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_107, l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ = None 2025-03-04T20:47:37.0245702Z 2025-03-04T20:47:37.0245969Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0247557Z x_173: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_172, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_172 = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0247619Z 2025-03-04T20:47:37.0247914Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0248058Z out_108: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_173); x_173 = None 2025-03-04T20:47:37.0248119Z 2025-03-04T20:47:37.0248377Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0248804Z x_174: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_108, l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_108 = l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ = None 2025-03-04T20:47:37.0248871Z 2025-03-04T20:47:37.0249139Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0250688Z x_175: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_174, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_174 = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0250755Z 2025-03-04T20:47:37.0251040Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0251216Z out_109: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_175); x_175 = None 2025-03-04T20:47:37.0251275Z 2025-03-04T20:47:37.0251520Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0251931Z x_176: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_109, l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_109 = l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ = None 2025-03-04T20:47:37.0251994Z 2025-03-04T20:47:37.0252249Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0253745Z x_177: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_176, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_176 = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0253839Z 2025-03-04T20:47:37.0254113Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:47:37.0254276Z x_177 += out_107; out_110: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_177; x_177 = out_107 = None 2025-03-04T20:47:37.0254334Z 2025-03-04T20:47:37.0254621Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0254763Z out_111: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_110); out_110 = None 2025-03-04T20:47:37.0254828Z 2025-03-04T20:47:37.0255079Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0255512Z x_178: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_111, l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ = None 2025-03-04T20:47:37.0255577Z 2025-03-04T20:47:37.0255853Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0257428Z x_179: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_178, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_178 = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0257497Z 2025-03-04T20:47:37.0257816Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0257948Z out_112: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_179); x_179 = None 2025-03-04T20:47:37.0258014Z 2025-03-04T20:47:37.0258256Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0258690Z x_180: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_112, l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_112 = l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ = None 2025-03-04T20:47:37.0258750Z 2025-03-04T20:47:37.0259024Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0260603Z x_181: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_180, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_180 = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0260674Z 2025-03-04T20:47:37.0260971Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0261104Z out_113: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_181); x_181 = None 2025-03-04T20:47:37.0261171Z 2025-03-04T20:47:37.0261418Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0261853Z x_182: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_113, l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_113 = l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ = None 2025-03-04T20:47:37.0261913Z 2025-03-04T20:47:37.0262185Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0263720Z x_183: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_182, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_182 = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0266535Z 2025-03-04T20:47:37.0266889Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:47:37.0267050Z x_183 += out_111; out_114: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_183; x_183 = out_111 = None 2025-03-04T20:47:37.0267121Z 2025-03-04T20:47:37.0267411Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0267561Z out_115: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_114); out_114 = None 2025-03-04T20:47:37.0267621Z 2025-03-04T20:47:37.0267886Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0268314Z x_184: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_115, l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ = None 2025-03-04T20:47:37.0268440Z 2025-03-04T20:47:37.0268706Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0270265Z x_185: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_184, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_184 = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0270327Z 2025-03-04T20:47:37.0270614Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0270748Z out_116: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_185); x_185 = None 2025-03-04T20:47:37.0270816Z 2025-03-04T20:47:37.0271066Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0271506Z x_186: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_116, l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_116 = l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ = None 2025-03-04T20:47:37.0271569Z 2025-03-04T20:47:37.0271846Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0273439Z x_187: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_186, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_186 = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0273567Z 2025-03-04T20:47:37.0273863Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0273999Z out_117: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_187); x_187 = None 2025-03-04T20:47:37.0274067Z 2025-03-04T20:47:37.0274318Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0274780Z x_188: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_117, l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_117 = l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ = None 2025-03-04T20:47:37.0274869Z 2025-03-04T20:47:37.0275185Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:47:37.0276944Z x_189: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_188, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_188 = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:47:37.0277017Z 2025-03-04T20:47:37.0277339Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:47:37.0277510Z x_189 += out_115; out_118: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_189; x_189 = out_115 = None 2025-03-04T20:47:37.0277585Z 2025-03-04T20:47:37.0277897Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:47:37.0278063Z out_119: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_118); out_118 = None 2025-03-04T20:47:37.0278128Z 2025-03-04T20:47:37.0278640Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:47:37.0278867Z arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32) 2025-03-04T20:47:37.0278946Z 2025-03-04T20:47:37.0279258Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:47:37.0279411Z shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T20:47:37.0279474Z 2025-03-04T20:47:37.0279940Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:47:37.0280112Z arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32) 2025-03-04T20:47:37.0280172Z 2025-03-04T20:47:37.0280537Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:47:37.0280674Z shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T20:47:37.0280743Z 2025-03-04T20:47:37.0281121Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:47:37.0281307Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T20:47:37.0281403Z shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0] 2025-03-04T20:47:37.0281526Z shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T20:47:37.0281589Z 2025-03-04T20:47:37.0281932Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:47:37.0282074Z shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T20:47:37.0282141Z 2025-03-04T20:47:37.0282468Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:47:37.0282589Z shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T20:47:37.0282648Z 2025-03-04T20:47:37.0283042Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:47:37.0283254Z shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T20:47:37.0283321Z 2025-03-04T20:47:37.0283743Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:47:37.0283874Z view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T20:47:37.0284302Z view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T20:47:37.0284434Z add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T20:47:37.0284557Z x_190: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4); add = None 2025-03-04T20:47:37.0284625Z 2025-03-04T20:47:37.0284918Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:47:37.0285049Z tensor: "f32[82125, 4][4, 1]cpu" = x_190.to(torch.float32); x_190 = None 2025-03-04T20:47:37.0285111Z 2025-03-04T20:47:37.0285367Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:37.0286125Z x_191: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_119, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); out_119 = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T20:47:37.0286193Z 2025-03-04T20:47:37.0286466Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:47:37.0286694Z x_192: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_191, inplace = False); x_191 = None 2025-03-04T20:47:37.0286760Z 2025-03-04T20:47:37.0287130Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:47:37.0287963Z score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_192, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T20:47:37.0288022Z 2025-03-04T20:47:37.0288379Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:47:37.0289189Z x_193: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_192, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_192 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T20:47:37.0289253Z 2025-03-04T20:47:37.0289586Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T20:47:37.0289728Z permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T20:47:37.0289870Z logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T20:47:37.0289929Z 2025-03-04T20:47:37.0290342Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T20:47:37.0290494Z view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_193.view(4, -1, 4, 73, 75); x_193 = None 2025-03-04T20:47:37.0290665Z permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2); view_2 = None 2025-03-04T20:47:37.0290835Z pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2); permute_1 = None 2025-03-04T20:47:37.0290900Z 2025-03-04T20:47:37.0291292Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:47:37.0291501Z pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T20:47:37.0291560Z 2025-03-04T20:47:37.0291992Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:47:37.0292136Z unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T20:47:37.0292288Z expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T20:47:37.0292423Z anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T20:47:37.0292492Z 2025-03-04T20:47:37.0292916Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:47:37.0293104Z deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T20:47:37.0293164Z 2025-03-04T20:47:37.0293477Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:47:37.0293612Z boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T20:47:37.0293679Z 2025-03-04T20:47:37.0293988Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:47:37.0294122Z getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:47:37.0294266Z getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:47:37.0294415Z widths: "f32[328500][1]cpu" = getitem_2 - getitem_3; getitem_2 = getitem_3 = None 2025-03-04T20:47:37.0294475Z 2025-03-04T20:47:37.0294796Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:47:37.0294920Z getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:47:37.0295034Z getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:47:37.0295181Z heights: "f32[328500][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T20:47:37.0295242Z 2025-03-04T20:47:37.0295555Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:47:37.0295673Z getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:47:37.0295765Z mul: "f32[328500][1]cpu" = 0.5 * widths 2025-03-04T20:47:37.0295883Z ctr_x: "f32[328500][1]cpu" = getitem_6 + mul; getitem_6 = mul = None 2025-03-04T20:47:37.0295951Z 2025-03-04T20:47:37.0296263Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:47:37.0296411Z getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:47:37.0296497Z mul_1: "f32[328500][1]cpu" = 0.5 * heights 2025-03-04T20:47:37.0296627Z ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1; getitem_7 = mul_1 = None 2025-03-04T20:47:37.0296687Z 2025-03-04T20:47:37.0297049Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:47:37.0297214Z getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:47:37.0297328Z dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0; getitem_8 = None 2025-03-04T20:47:37.0297386Z 2025-03-04T20:47:37.0297687Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:47:37.0297830Z getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:47:37.0297941Z dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0; getitem_9 = None 2025-03-04T20:47:37.0297999Z 2025-03-04T20:47:37.0298294Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:47:37.0298441Z getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:47:37.0298603Z dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0; getitem_10 = None 2025-03-04T20:47:37.0298664Z 2025-03-04T20:47:37.0298967Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:47:37.0299143Z getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:47:37.0299259Z dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0; getitem_11 = None 2025-03-04T20:47:37.0299319Z 2025-03-04T20:47:37.0299656Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:47:37.0299790Z dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:47:37.0299859Z 2025-03-04T20:47:37.0300205Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:47:37.0300339Z dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:47:37.0300398Z 2025-03-04T20:47:37.0300744Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:47:37.0300879Z getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:47:37.0301009Z mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12; dx = getitem_12 = None 2025-03-04T20:47:37.0301159Z getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:47:37.0301301Z pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13; mul_2 = getitem_13 = None 2025-03-04T20:47:37.0301366Z 2025-03-04T20:47:37.0301727Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:47:37.0301864Z getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:47:37.0302135Z mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14; dy = getitem_14 = None 2025-03-04T20:47:37.0302293Z getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:47:37.0302421Z pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15; mul_3 = getitem_15 = None 2025-03-04T20:47:37.0302492Z 2025-03-04T20:47:37.0302815Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:47:37.0302936Z exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:47:37.0303092Z getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:47:37.0303224Z pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16; exp = getitem_16 = None 2025-03-04T20:47:37.0303286Z 2025-03-04T20:47:37.0303627Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:47:37.0303736Z exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:47:37.0303900Z getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:47:37.0304024Z pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17; exp_1 = getitem_17 = None 2025-03-04T20:47:37.0304091Z 2025-03-04T20:47:37.0304507Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:47:37.0304606Z mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T20:47:37.0304715Z x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:47:37.0304783Z 2025-03-04T20:47:37.0305084Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:47:37.0305179Z mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T20:47:37.0305287Z y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:47:37.0305356Z 2025-03-04T20:47:37.0305658Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:47:37.0305774Z mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:47:37.0305920Z x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:47:37.0305985Z 2025-03-04T20:47:37.0306278Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:47:37.0306389Z mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:47:37.0306508Z y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:47:37.0306576Z 2025-03-04T20:47:37.0306913Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:47:37.0307092Z pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:47:37.0307154Z 2025-03-04T20:47:37.0307486Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:47:37.0307639Z proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4)); pred_boxes = None 2025-03-04T20:47:37.0307708Z 2025-03-04T20:47:37.0308083Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:47:37.0308255Z proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T20:47:37.0308317Z 2025-03-04T20:47:37.0308794Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T20:47:37.0308926Z arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T20:47:37.0308992Z 2025-03-04T20:47:37.0309283Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:47:37.0309423Z batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T20:47:37.0309484Z 2025-03-04T20:47:37.0309916Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:47:37.0310026Z topk = logits_i.topk(6000, dim = 1); logits_i = None 2025-03-04T20:47:37.0310125Z topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0] 2025-03-04T20:47:37.0310269Z topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1]; topk = None 2025-03-04T20:47:37.0310330Z 2025-03-04T20:47:37.0310816Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:47:37.0310974Z getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T20:47:37.0311210Z topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)]; proposals_i_1 = getitem_20 = topk_idx = None 2025-03-04T20:47:37.0311268Z 2025-03-04T20:47:37.0311716Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:47:37.0311875Z full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:47:37.0311960Z 2025-03-04T20:47:37.0312263Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:47:37.0312415Z level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu')); full = level_ids = None 2025-03-04T20:47:37.0312472Z 2025-03-04T20:47:37.0312849Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T20:47:37.0312987Z getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0]; topk_proposals_i = None 2025-03-04T20:47:37.0313051Z 2025-03-04T20:47:37.0313339Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:47:37.0313489Z tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32); getitem_22 = None 2025-03-04T20:47:37.0313547Z 2025-03-04T20:47:37.0313920Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T20:47:37.0314051Z scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0]; topk_scores_i = None 2025-03-04T20:47:37.0314116Z 2025-03-04T20:47:37.0314585Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T20:47:37.0314723Z isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1); tensor_1 = None 2025-03-04T20:47:37.0314840Z all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:47:37.0315005Z isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T20:47:37.0315135Z valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T20:47:37.0315206Z 2025-03-04T20:47:37.0315572Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T20:47:37.0315698Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T20:47:37.0315757Z 2025-03-04T20:47:52.9891621Z 2025-03-04T20:47:52.9892167Z class GraphModule(torch.nn.Module): 2025-03-04T20:47:52.9894846Z def forward(self, L_features_res4_: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_weight_: "f32[1024, 1024, 3, 3][9216, 9, 3, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_bias_: "f32[1024][1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"): 2025-03-04T20:47:52.9897064Z l_features_res4_ = L_features_res4_ 2025-03-04T20:47:52.9897457Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T20:47:52.9897973Z l_self_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T20:47:52.9898458Z l_self_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T20:47:52.9899071Z l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T20:47:52.9899637Z l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T20:47:52.9900203Z l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T20:47:52.9901463Z l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T20:47:52.9901833Z 2025-03-04T20:47:52.9902554Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:47:52.9903226Z arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32) 2025-03-04T20:47:52.9903507Z 2025-03-04T20:47:52.9903895Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:47:52.9904395Z shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T20:47:52.9904647Z 2025-03-04T20:47:52.9905181Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:47:52.9905861Z arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32) 2025-03-04T20:47:52.9906130Z 2025-03-04T20:47:52.9906509Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:47:52.9907426Z shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T20:47:52.9907737Z 2025-03-04T20:47:52.9908222Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:47:52.9908841Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T20:47:52.9909174Z shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0] 2025-03-04T20:47:52.9909441Z shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T20:47:52.9909673Z 2025-03-04T20:47:52.9910098Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:47:52.9910610Z shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T20:47:52.9910907Z 2025-03-04T20:47:52.9911387Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:47:52.9911894Z shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T20:47:52.9912130Z 2025-03-04T20:47:52.9912593Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:47:52.9913240Z shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T20:47:52.9913559Z 2025-03-04T20:47:52.9914063Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:47:52.9914687Z view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T20:47:52.9915182Z view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T20:47:52.9915761Z add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T20:47:52.9916051Z x: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4); add = None 2025-03-04T20:47:52.9916279Z 2025-03-04T20:47:52.9916673Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:47:52.9917160Z tensor: "f32[82125, 4][4, 1]cpu" = x.to(torch.float32); x = None 2025-03-04T20:47:52.9917395Z 2025-03-04T20:47:52.9917740Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:52.9918650Z x_1: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(l_features_res4_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_res4_ = l_self_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T20:47:52.9919360Z 2025-03-04T20:47:52.9919720Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:47:52.9920223Z x_2: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False); x_1 = None 2025-03-04T20:47:52.9920514Z 2025-03-04T20:47:52.9920981Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:47:52.9922037Z score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T20:47:52.9922770Z 2025-03-04T20:47:52.9923201Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:47:52.9924735Z x_3: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_2 = l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T20:47:52.9925470Z 2025-03-04T20:47:52.9925883Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T20:47:52.9926414Z permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T20:47:52.9926751Z logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T20:47:52.9927032Z 2025-03-04T20:47:52.9927525Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T20:47:52.9928124Z view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_3.view(4, -1, 4, 73, 75); x_3 = None 2025-03-04T20:47:52.9928515Z permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2); view_2 = None 2025-03-04T20:47:52.9928900Z pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2); permute_1 = None 2025-03-04T20:47:52.9929183Z 2025-03-04T20:47:52.9929696Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:47:52.9930339Z pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T20:47:52.9930645Z 2025-03-04T20:47:52.9931150Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:47:52.9931771Z unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T20:47:52.9932106Z expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T20:47:52.9932436Z anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T20:47:52.9932681Z 2025-03-04T20:47:52.9933132Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:47:52.9933710Z deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T20:47:52.9933987Z 2025-03-04T20:47:52.9934374Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:47:52.9934870Z boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T20:47:52.9935119Z 2025-03-04T20:47:52.9935506Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:47:52.9935989Z getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:47:52.9936284Z getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:47:52.9936596Z widths: "f32[328500][1]cpu" = getitem_2 - getitem_3; getitem_2 = getitem_3 = None 2025-03-04T20:47:52.9936844Z 2025-03-04T20:47:52.9937235Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:47:52.9937716Z getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:47:52.9938089Z getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:47:52.9938403Z heights: "f32[328500][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T20:47:52.9938657Z 2025-03-04T20:47:52.9939044Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:47:52.9939519Z getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:47:52.9939774Z mul: "f32[328500][1]cpu" = 0.5 * widths 2025-03-04T20:47:52.9940028Z ctr_x: "f32[328500][1]cpu" = getitem_6 + mul; getitem_6 = mul = None 2025-03-04T20:47:52.9940262Z 2025-03-04T20:47:52.9940649Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:47:52.9941167Z getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:47:52.9941453Z mul_1: "f32[328500][1]cpu" = 0.5 * heights 2025-03-04T20:47:52.9941718Z ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1; getitem_7 = mul_1 = None 2025-03-04T20:47:52.9941956Z 2025-03-04T20:47:52.9942408Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:47:52.9942908Z getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:47:52.9943228Z dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0; getitem_8 = None 2025-03-04T20:47:52.9943455Z 2025-03-04T20:47:52.9943837Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:47:52.9944336Z getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:47:52.9944655Z dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0; getitem_9 = None 2025-03-04T20:47:52.9944883Z 2025-03-04T20:47:52.9945262Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:47:52.9945758Z getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:47:52.9946074Z dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0; getitem_10 = None 2025-03-04T20:47:52.9946295Z 2025-03-04T20:47:52.9946680Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:47:52.9947212Z getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:47:52.9947545Z dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0; getitem_11 = None 2025-03-04T20:47:52.9947774Z 2025-03-04T20:47:52.9948194Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:47:52.9948724Z dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:47:52.9949066Z 2025-03-04T20:47:52.9950847Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:47:52.9951493Z dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:47:52.9951745Z 2025-03-04T20:47:52.9952171Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:47:52.9953108Z getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:47:52.9953992Z mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12; dx = getitem_12 = None 2025-03-04T20:47:52.9954854Z getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:47:52.9955215Z pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13; mul_2 = getitem_13 = None 2025-03-04T20:47:52.9955542Z 2025-03-04T20:47:52.9955979Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:47:52.9956505Z getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:47:52.9956815Z mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14; dy = getitem_14 = None 2025-03-04T20:47:52.9957171Z getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:47:52.9957506Z pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15; mul_3 = getitem_15 = None 2025-03-04T20:47:52.9957759Z 2025-03-04T20:47:52.9958245Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:47:52.9958767Z exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:47:52.9959084Z getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:47:52.9959419Z pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16; exp = getitem_16 = None 2025-03-04T20:47:52.9959661Z 2025-03-04T20:47:52.9960072Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:47:52.9960566Z exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:47:52.9960891Z getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:47:52.9961233Z pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17; exp_1 = getitem_17 = None 2025-03-04T20:47:52.9961477Z 2025-03-04T20:47:52.9961876Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:47:52.9962325Z mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T20:47:52.9962580Z x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:47:52.9962800Z 2025-03-04T20:47:52.9963181Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:47:52.9963629Z mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T20:47:52.9963877Z y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:47:52.9964098Z 2025-03-04T20:47:52.9964473Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:47:52.9964934Z mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:47:52.9965216Z x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:47:52.9965451Z 2025-03-04T20:47:52.9965822Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:47:52.9966277Z mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:47:52.9966581Z y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:47:52.9966857Z 2025-03-04T20:47:52.9967286Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:47:52.9967856Z pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:47:52.9968139Z 2025-03-04T20:47:52.9968550Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:47:52.9969086Z proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4)); pred_boxes = None 2025-03-04T20:47:52.9969354Z 2025-03-04T20:47:52.9969820Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:47:52.9970434Z proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T20:47:52.9970709Z 2025-03-04T20:47:52.9971263Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T20:47:52.9971926Z arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T20:47:52.9972169Z 2025-03-04T20:47:52.9972539Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:47:52.9973013Z batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T20:47:52.9973266Z 2025-03-04T20:47:52.9973776Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:47:52.9974362Z topk = logits_i.topk(6000, dim = 1); logits_i = None 2025-03-04T20:47:52.9974619Z topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0] 2025-03-04T20:47:52.9974878Z topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1]; topk = None 2025-03-04T20:47:52.9975096Z 2025-03-04T20:47:52.9975629Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:47:52.9976290Z getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T20:47:52.9976730Z topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)]; proposals_i_1 = getitem_20 = topk_idx = None 2025-03-04T20:47:52.9977063Z 2025-03-04T20:47:52.9977595Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:47:52.9978253Z full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:47:52.9978525Z 2025-03-04T20:47:52.9978893Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:47:52.9979378Z level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu')); full = level_ids = None 2025-03-04T20:47:52.9979634Z 2025-03-04T20:47:52.9980153Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T20:47:52.9980716Z getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0]; topk_proposals_i = None 2025-03-04T20:47:52.9980968Z 2025-03-04T20:47:52.9981337Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:47:52.9981817Z tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32); getitem_22 = None 2025-03-04T20:47:52.9982071Z 2025-03-04T20:47:52.9982521Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T20:47:52.9983075Z scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0]; topk_scores_i = None 2025-03-04T20:47:52.9983342Z 2025-03-04T20:47:52.9983897Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T20:47:52.9984552Z isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1); tensor_1 = None 2025-03-04T20:47:52.9984852Z all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:47:52.9985168Z isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T20:47:52.9985498Z valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T20:47:52.9985738Z 2025-03-04T20:47:52.9986179Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T20:47:52.9986707Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T20:47:52.9986933Z 2025-03-04T20:47:52.9987070Z 2025-03-04T20:47:52.9987155Z class GraphModule(torch.nn.Module): 2025-03-04T20:47:52.9988447Z def forward(self, L_features_res4_: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_weight_: "f32[1024, 1024, 3, 3][9216, 9, 3, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_bias_: "f32[1024][1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"): 2025-03-04T20:47:52.9989720Z l_features_res4_ = L_features_res4_ 2025-03-04T20:47:52.9990105Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T20:47:52.9990614Z l_self_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T20:47:52.9991081Z l_self_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T20:47:52.9991595Z l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T20:47:52.9992163Z l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T20:47:52.9992712Z l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T20:47:52.9993624Z l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T20:47:52.9994588Z 2025-03-04T20:47:52.9995135Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:47:52.9996669Z arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32) 2025-03-04T20:47:52.9997331Z 2025-03-04T20:47:52.9997770Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:47:52.9998279Z shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T20:47:52.9998525Z 2025-03-04T20:47:52.9999069Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:47:52.9999753Z arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32) 2025-03-04T20:47:53.0000017Z 2025-03-04T20:47:53.0000417Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:47:53.0000942Z shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T20:47:53.0001198Z 2025-03-04T20:47:53.0001661Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:47:53.0003201Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T20:47:53.0003558Z shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0] 2025-03-04T20:47:53.0003832Z shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T20:47:53.0004066Z 2025-03-04T20:47:53.0004499Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:47:53.0005017Z shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T20:47:53.0005253Z 2025-03-04T20:47:53.0005670Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:47:53.0006178Z shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T20:47:53.0006412Z 2025-03-04T20:47:53.0006887Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:47:53.0007567Z shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T20:47:53.0007885Z 2025-03-04T20:47:53.0008383Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:47:53.0009051Z view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T20:47:53.0009554Z view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T20:47:53.0010044Z add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T20:47:53.0010424Z x: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4); add = None 2025-03-04T20:47:53.0010701Z 2025-03-04T20:47:53.0011085Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:47:53.0011550Z tensor: "f32[82125, 4][4, 1]cpu" = x.to(torch.float32); x = None 2025-03-04T20:47:53.0011780Z 2025-03-04T20:47:53.0012115Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:47:53.0013022Z x_1: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(l_features_res4_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_res4_ = l_self_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T20:47:53.0013758Z 2025-03-04T20:47:53.0014116Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:47:53.0014615Z x_2: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False); x_1 = None 2025-03-04T20:47:53.0014900Z 2025-03-04T20:47:53.0015357Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:47:53.0016399Z score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T20:47:53.0017129Z 2025-03-04T20:47:53.0017565Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:47:53.0018551Z x_3: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_2 = l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T20:47:53.0019244Z 2025-03-04T20:47:53.0019658Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T20:47:53.0020187Z permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T20:47:53.0020528Z logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T20:47:53.0020775Z 2025-03-04T20:47:53.0021262Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T20:47:53.0021862Z view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_3.view(4, -1, 4, 73, 75); x_3 = None 2025-03-04T20:47:53.0022221Z permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2); view_2 = None 2025-03-04T20:47:53.0022601Z pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2); permute_1 = None 2025-03-04T20:47:53.0022880Z 2025-03-04T20:47:53.0023386Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:47:53.0024072Z pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T20:47:53.0024380Z 2025-03-04T20:47:53.0024885Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:47:53.0025499Z unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T20:47:53.0025838Z expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T20:47:53.0026166Z anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T20:47:53.0026410Z 2025-03-04T20:47:53.0026869Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:47:53.0027469Z deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T20:47:53.0027746Z 2025-03-04T20:47:53.0028132Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:47:53.0028625Z boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T20:47:53.0028871Z 2025-03-04T20:47:53.0029260Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:47:53.0029748Z getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:47:53.0030047Z getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:47:53.0030361Z widths: "f32[328500][1]cpu" = getitem_2 - getitem_3; getitem_2 = getitem_3 = None 2025-03-04T20:47:53.0030612Z 2025-03-04T20:47:53.0031001Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:47:53.0031485Z getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:47:53.0031773Z getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:47:53.0032085Z heights: "f32[328500][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T20:47:53.0032338Z 2025-03-04T20:47:53.0032723Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:47:53.0033206Z getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:47:53.0033463Z mul: "f32[328500][1]cpu" = 0.5 * widths 2025-03-04T20:47:53.0033716Z ctr_x: "f32[328500][1]cpu" = getitem_6 + mul; getitem_6 = mul = None 2025-03-04T20:47:53.0033945Z 2025-03-04T20:47:53.0034336Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:47:53.0034835Z getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:47:53.0035115Z mul_1: "f32[328500][1]cpu" = 0.5 * heights 2025-03-04T20:47:53.0035456Z ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1; getitem_7 = mul_1 = None 2025-03-04T20:47:53.0035697Z 2025-03-04T20:47:53.0036152Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:47:53.0036735Z getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:47:53.0037064Z dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0; getitem_8 = None 2025-03-04T20:47:53.0037291Z 2025-03-04T20:47:53.0037668Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:47:53.0038157Z getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:47:53.0038466Z dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0; getitem_9 = None 2025-03-04T20:47:53.0038690Z 2025-03-04T20:47:53.0039064Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:47:53.0039551Z getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:47:53.0039887Z dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0; getitem_10 = None 2025-03-04T20:47:53.0040110Z 2025-03-04T20:47:53.0040497Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:47:53.0041039Z getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:47:53.0041385Z dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0; getitem_11 = None 2025-03-04T20:47:53.0041620Z 2025-03-04T20:47:53.0042045Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:47:53.0042575Z dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:47:53.0042829Z 2025-03-04T20:47:53.0043252Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:47:53.0043774Z dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:47:53.0044025Z 2025-03-04T20:47:53.0044454Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:47:53.0044992Z getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:47:53.0045309Z mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12; dx = getitem_12 = None 2025-03-04T20:47:53.0045638Z getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:47:53.0045981Z pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13; mul_2 = getitem_13 = None 2025-03-04T20:47:53.0046238Z 2025-03-04T20:47:53.0046674Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:47:53.0047214Z getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:47:53.0047524Z mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14; dy = getitem_14 = None 2025-03-04T20:47:53.0047849Z getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:47:53.0048190Z pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15; mul_3 = getitem_15 = None 2025-03-04T20:47:53.0048443Z 2025-03-04T20:47:53.0048860Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:47:53.0049399Z exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:47:53.0049795Z getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:47:53.0050144Z pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16; exp = getitem_16 = None 2025-03-04T20:47:53.0050390Z 2025-03-04T20:47:53.0050808Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:47:53.0051314Z exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:47:53.0051648Z getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:47:53.0051997Z pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17; exp_1 = getitem_17 = None 2025-03-04T20:47:53.0052246Z 2025-03-04T20:47:53.0052648Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:47:53.0053137Z mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T20:47:53.0053395Z x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:47:53.0053625Z 2025-03-04T20:47:53.0054013Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:47:53.0054469Z mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T20:47:53.0054722Z y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:47:53.0054950Z 2025-03-04T20:47:53.0055342Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:47:53.0055822Z mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:47:53.0056113Z x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:47:53.0056355Z 2025-03-04T20:47:53.0056746Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:47:53.0057224Z mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:47:53.0057501Z y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:47:53.0057736Z 2025-03-04T20:47:53.0058161Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:47:53.0058723Z pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:47:53.0059006Z 2025-03-04T20:47:53.0059417Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:47:53.0059950Z proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4)); pred_boxes = None 2025-03-04T20:47:53.0060218Z 2025-03-04T20:47:53.0060672Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:47:53.0061258Z proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T20:47:53.0061535Z 2025-03-04T20:47:53.0062089Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T20:47:53.0062803Z arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T20:47:53.0063045Z 2025-03-04T20:47:53.0063412Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:47:53.0063884Z batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T20:47:53.0064127Z 2025-03-04T20:47:53.0064633Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:47:53.0065215Z topk = logits_i.topk(6000, dim = 1); logits_i = None 2025-03-04T20:47:53.0065475Z topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0] 2025-03-04T20:47:53.0065737Z topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1]; topk = None 2025-03-04T20:47:53.0065975Z 2025-03-04T20:47:53.0066507Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:47:53.0067163Z getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T20:47:53.0067603Z topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)]; proposals_i_1 = getitem_20 = topk_idx = None 2025-03-04T20:47:53.0067934Z 2025-03-04T20:47:53.0068466Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:47:53.0069123Z full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:47:53.0069397Z 2025-03-04T20:47:53.0069764Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:47:53.0070250Z level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu')); full = level_ids = None 2025-03-04T20:47:53.0070505Z 2025-03-04T20:47:53.0070970Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T20:47:53.0071535Z getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0]; topk_proposals_i = None 2025-03-04T20:47:53.0071787Z 2025-03-04T20:47:53.0072155Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:47:53.0072637Z tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32); getitem_22 = None 2025-03-04T20:47:53.0072891Z 2025-03-04T20:47:53.0073340Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T20:47:53.0073888Z scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0]; topk_scores_i = None 2025-03-04T20:47:53.0074136Z 2025-03-04T20:47:53.0074685Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T20:47:53.0075416Z isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1); tensor_1 = None 2025-03-04T20:47:53.0075733Z all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:47:53.0076119Z isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T20:47:53.0076460Z valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T20:47:53.0076710Z 2025-03-04T20:47:53.0077158Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T20:47:53.0077692Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T20:47:53.0077915Z 2025-03-04T20:47:55.4159912Z 2025-03-04T20:47:55.4165520Z class GraphModule(torch.nn.Module): 2025-03-04T20:47:55.4168036Z def forward(self, L_pred_anchor_deltas_0_: "f32[4, 82125, 4][328500, 4, 1]cpu", L_anchors_0_tensor: "f32[82125, 4][4, 1]cpu", L_pred_objectness_logits_0_: "f32[4, 82125][82125, 1]cpu"): 2025-03-04T20:47:55.4168622Z l_pred_anchor_deltas_0_ = L_pred_anchor_deltas_0_ 2025-03-04T20:47:55.4173550Z l_anchors_0_tensor = L_anchors_0_tensor 2025-03-04T20:47:55.4178028Z l_pred_objectness_logits_0_ = L_pred_objectness_logits_0_ 2025-03-04T20:47:55.4179973Z 2025-03-04T20:47:55.4180687Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:47:55.4181457Z pred_anchor_deltas_i: "f32[328500, 4][4, 1]cpu" = l_pred_anchor_deltas_0_.reshape(-1, 4); l_pred_anchor_deltas_0_ = None 2025-03-04T20:47:55.4186061Z 2025-03-04T20:47:55.4186864Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:47:55.4187976Z unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = l_anchors_0_tensor.unsqueeze(0); l_anchors_0_tensor = None 2025-03-04T20:47:55.4188494Z expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T20:47:55.4188835Z anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T20:47:55.4189091Z 2025-03-04T20:47:55.4189592Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:47:55.4190204Z deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.float(); pred_anchor_deltas_i = None 2025-03-04T20:47:55.4190494Z 2025-03-04T20:47:55.4190913Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:47:55.4191434Z boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T20:47:55.4191702Z 2025-03-04T20:47:55.4192114Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:47:55.4192620Z getitem: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:47:55.4192933Z getitem_1: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:47:55.4193252Z widths: "f32[328500][1]cpu" = getitem - getitem_1; getitem = getitem_1 = None 2025-03-04T20:47:55.4193518Z 2025-03-04T20:47:55.4193929Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:47:55.4194434Z getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:47:55.4194736Z getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:47:55.4195231Z heights: "f32[328500][1]cpu" = getitem_2 - getitem_3; getitem_2 = getitem_3 = None 2025-03-04T20:47:55.4195736Z 2025-03-04T20:47:55.4196157Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:47:55.4196661Z getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:47:55.4196939Z mul: "f32[328500][1]cpu" = 0.5 * widths 2025-03-04T20:47:55.4197197Z ctr_x: "f32[328500][1]cpu" = getitem_4 + mul; getitem_4 = mul = None 2025-03-04T20:47:55.4197444Z 2025-03-04T20:47:55.4197832Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:47:55.4198330Z getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:47:55.4198612Z mul_1: "f32[328500][1]cpu" = 0.5 * heights 2025-03-04T20:47:55.4198901Z ctr_y: "f32[328500][1]cpu" = getitem_5 + mul_1; getitem_5 = mul_1 = None 2025-03-04T20:47:55.4199137Z 2025-03-04T20:47:55.4199575Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:47:55.4200070Z getitem_6: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:47:55.4200386Z dx: "f32[328500, 1][1, 1]cpu" = getitem_6 / 1.0; getitem_6 = None 2025-03-04T20:47:55.4200614Z 2025-03-04T20:47:55.4200994Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:47:55.4201488Z getitem_7: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:47:55.4201795Z dy: "f32[328500, 1][1, 1]cpu" = getitem_7 / 1.0; getitem_7 = None 2025-03-04T20:47:55.4202227Z 2025-03-04T20:47:55.4202616Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:47:55.4203108Z getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:47:55.4203413Z dw: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0; getitem_8 = None 2025-03-04T20:47:55.4203637Z 2025-03-04T20:47:55.4204022Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:47:55.4204545Z getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:47:55.4204881Z dh: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0; getitem_9 = None 2025-03-04T20:47:55.4205106Z 2025-03-04T20:47:55.4205522Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:47:55.4206040Z dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:47:55.4206286Z 2025-03-04T20:47:55.4206691Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:47:55.4207198Z dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:47:55.4207440Z 2025-03-04T20:47:55.4207855Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:47:55.4208379Z getitem_10: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:47:55.4208775Z mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_10; dx = getitem_10 = None 2025-03-04T20:47:55.4209101Z getitem_11: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:47:55.4209440Z pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_11; mul_2 = getitem_11 = None 2025-03-04T20:47:55.4209689Z 2025-03-04T20:47:55.4210115Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:47:55.4210643Z getitem_12: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:47:55.4210950Z mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_12; dy = getitem_12 = None 2025-03-04T20:47:55.4211263Z getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:47:55.4211618Z pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_13; mul_3 = getitem_13 = None 2025-03-04T20:47:55.4211868Z 2025-03-04T20:47:55.4212277Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:47:55.4212770Z exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:47:55.4213087Z getitem_14: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:47:55.4213423Z pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_14; exp = getitem_14 = None 2025-03-04T20:47:55.4213664Z 2025-03-04T20:47:55.4214068Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:47:55.4214556Z exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:47:55.4214886Z getitem_15: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:47:55.4215227Z pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_15; exp_1 = getitem_15 = None 2025-03-04T20:47:55.4215466Z 2025-03-04T20:47:55.4215855Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:47:55.4216305Z mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T20:47:55.4216557Z x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:47:55.4216784Z 2025-03-04T20:47:55.4217165Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:47:55.4217608Z mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T20:47:55.4217858Z y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:47:55.4218083Z 2025-03-04T20:47:55.4218458Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:47:55.4218919Z mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:47:55.4219199Z x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:47:55.4219429Z 2025-03-04T20:47:55.4219805Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:47:55.4220262Z mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:47:55.4220538Z y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:47:55.4220794Z 2025-03-04T20:47:55.4221247Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:47:55.4221813Z pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:47:55.4222091Z 2025-03-04T20:47:55.4222499Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:47:55.4223030Z proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4)); pred_boxes = None 2025-03-04T20:47:55.4223299Z 2025-03-04T20:47:55.4223759Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:47:55.4224376Z proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T20:47:55.4224659Z 2025-03-04T20:47:55.4225218Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T20:47:55.4225899Z arange: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T20:47:55.4226141Z 2025-03-04T20:47:55.4226515Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:47:55.4226992Z batch_idx: "i64[4][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T20:47:55.4227229Z 2025-03-04T20:47:55.4227744Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:47:55.4228399Z topk = l_pred_objectness_logits_0_.topk(6000, dim = 1); l_pred_objectness_logits_0_ = None 2025-03-04T20:47:55.4228717Z topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0] 2025-03-04T20:47:55.4228979Z topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1]; topk = None 2025-03-04T20:47:55.4229201Z 2025-03-04T20:47:55.4229741Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:47:55.4230409Z getitem_18: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T20:47:55.4230849Z topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_18, topk_idx)]; proposals_i_1 = getitem_18 = topk_idx = None 2025-03-04T20:47:55.4231189Z 2025-03-04T20:47:55.4231727Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:47:55.4232393Z full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:47:55.4232670Z 2025-03-04T20:47:55.4233048Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:47:55.4233546Z level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu')); full = level_ids = None 2025-03-04T20:47:55.4233810Z 2025-03-04T20:47:55.4234272Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T20:47:55.4234911Z getitem_20: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0]; topk_proposals_i = None 2025-03-04T20:47:55.4235180Z 2025-03-04T20:47:55.4235641Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:47:55.4236179Z tensor: "f32[6000, 4][4, 1]cpu" = getitem_20.to(torch.float32); getitem_20 = None 2025-03-04T20:47:55.4236448Z 2025-03-04T20:47:55.4236914Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T20:47:55.4237491Z scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0]; topk_scores_i = None 2025-03-04T20:47:55.4237753Z 2025-03-04T20:47:55.4238329Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T20:47:55.4239035Z isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor); tensor = None 2025-03-04T20:47:55.4239351Z all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:47:55.4239686Z isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T20:47:55.4240035Z valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T20:47:55.4240294Z 2025-03-04T20:47:55.4240761Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T20:47:55.4241319Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T20:47:55.4241563Z 2025-03-04T20:48:17.2624832Z 2025-03-04T20:48:17.2625505Z class GraphModule(torch.nn.Module): 2025-03-04T20:48:17.2631856Z def forward(self, L_stack0_: "f32[3261, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1261 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1261 - s0, 4][4, 1]cpu"): 2025-03-04T20:48:17.2633403Z l_stack0_ = L_stack0_ 2025-03-04T20:48:17.2633853Z l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_ 2025-03-04T20:48:17.2634518Z l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_ 2025-03-04T20:48:17.2635259Z l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ 2025-03-04T20:48:17.2635895Z l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ 2025-03-04T20:48:17.2636441Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T20:48:17.2636885Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T20:48:17.2637295Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T20:48:17.2637945Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T20:48:17.2638239Z 2025-03-04T20:48:17.2638905Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/roi_heads.py:480 in torch_dynamo_resume_in_forward_at_477, code: predictions = self.box_predictor(box_features.mean(dim=[2, 3])) 2025-03-04T20:48:17.2639558Z mean: "f32[3261, 2048][2048, 1]cpu" = l_stack0_.mean(dim = [2, 3]); l_stack0_ = None 2025-03-04T20:48:17.2639819Z 2025-03-04T20:48:17.2640256Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x) 2025-03-04T20:48:17.2641278Z scores: "f32[3261, 81][81, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_); l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None 2025-03-04T20:48:17.2642049Z 2025-03-04T20:48:17.2642467Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x) 2025-03-04T20:48:17.2643492Z proposal_deltas: "f32[3261, 320][320, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_); mean = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None 2025-03-04T20:48:17.2644240Z 2025-03-04T20:48:17.2644618Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:48:17.2645088Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T20:48:17.2645341Z getitem: "Sym(s0)" = size[0] 2025-03-04T20:48:17.2645573Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T20:48:17.2645852Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T20:48:17.2646106Z getitem_2: "Sym(1261 - s0)" = size_1[0] 2025-03-04T20:48:17.2646344Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T20:48:17.2646559Z 2025-03-04T20:48:17.2646932Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:48:17.2647876Z proposal_boxes: "f32[3261, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T20:48:17.2648592Z 2025-03-04T20:48:17.2649043Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:48:17.2649603Z deltas: "f32[3261, 320][320, 1]cpu" = proposal_deltas.float(); proposal_deltas = None 2025-03-04T20:48:17.2649860Z 2025-03-04T20:48:17.2650244Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:48:17.2650771Z boxes: "f32[3261, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T20:48:17.2651058Z 2025-03-04T20:48:17.2651503Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:48:17.2652060Z getitem_4: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:48:17.2652417Z getitem_5: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:48:17.2652798Z widths: "f32[3261][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T20:48:17.2653071Z 2025-03-04T20:48:17.2653498Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:48:17.2654029Z getitem_6: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:48:17.2654314Z getitem_7: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:48:17.2654632Z heights: "f32[3261][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T20:48:17.2654883Z 2025-03-04T20:48:17.2655274Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:48:17.2655782Z getitem_8: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:48:17.2656050Z mul: "f32[3261][1]cpu" = 0.5 * widths 2025-03-04T20:48:17.2656315Z ctr_x: "f32[3261][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T20:48:17.2656560Z 2025-03-04T20:48:17.2656982Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:48:17.2657507Z getitem_9: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:48:17.2657784Z mul_1: "f32[3261][1]cpu" = 0.5 * heights 2025-03-04T20:48:17.2658045Z ctr_y: "f32[3261][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T20:48:17.2658284Z 2025-03-04T20:48:17.2658743Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:48:17.2659263Z getitem_10: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:48:17.2659583Z dx: "f32[3261, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T20:48:17.2659809Z 2025-03-04T20:48:17.2660196Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:48:17.2660693Z getitem_11: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:48:17.2661006Z dy: "f32[3261, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T20:48:17.2661231Z 2025-03-04T20:48:17.2667130Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:48:17.2667711Z getitem_12: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:48:17.2668080Z dw: "f32[3261, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T20:48:17.2668324Z 2025-03-04T20:48:17.2668746Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:48:17.2669285Z getitem_13: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:48:17.2669628Z dh: "f32[3261, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T20:48:17.2669860Z 2025-03-04T20:48:17.2670294Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:48:17.2670824Z dw_1: "f32[3261, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:48:17.2671183Z 2025-03-04T20:48:17.2671663Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:48:17.2672191Z dh_1: "f32[3261, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:48:17.2672437Z 2025-03-04T20:48:17.2672866Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:48:17.2673452Z getitem_14: "f32[3261, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:48:17.2673770Z mul_2: "f32[3261, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T20:48:17.2674104Z getitem_15: "f32[3261, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:48:17.2674465Z pred_ctr_x: "f32[3261, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T20:48:17.2674781Z 2025-03-04T20:48:17.2675415Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:48:17.2676045Z getitem_16: "f32[3261, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:48:17.2676388Z mul_3: "f32[3261, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T20:48:17.2676724Z getitem_17: "f32[3261, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:48:17.2677060Z pred_ctr_y: "f32[3261, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T20:48:17.2677315Z 2025-03-04T20:48:17.2677737Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:48:17.2678247Z exp: "f32[3261, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:48:17.2678572Z getitem_18: "f32[3261, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:48:17.2678911Z pred_w: "f32[3261, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T20:48:17.2679151Z 2025-03-04T20:48:17.2679570Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:48:17.2680074Z exp_1: "f32[3261, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:48:17.2680400Z getitem_19: "f32[3261, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:48:17.2680746Z pred_h: "f32[3261, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T20:48:17.2680994Z 2025-03-04T20:48:17.2681399Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:48:17.2681865Z mul_6: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T20:48:17.2682125Z x1: "f32[3261, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:48:17.2682352Z 2025-03-04T20:48:17.2682745Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:48:17.2683205Z mul_7: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T20:48:17.2683458Z y1: "f32[3261, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:48:17.2683686Z 2025-03-04T20:48:17.2684075Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:48:17.2684595Z mul_8: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:48:17.2684936Z x2: "f32[3261, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:48:17.2685177Z 2025-03-04T20:48:17.2685560Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:48:17.2686035Z mul_9: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:48:17.2686318Z y2: "f32[3261, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:48:17.2686568Z 2025-03-04T20:48:17.2687028Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:48:17.2687653Z pred_boxes: "f32[3261, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:48:17.2687966Z 2025-03-04T20:48:17.2688441Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:48:17.2689000Z predict_boxes: "f32[3261, 320][320, 1]cpu" = pred_boxes.reshape((3261, 320)); pred_boxes = None 2025-03-04T20:48:17.2689284Z 2025-03-04T20:48:17.2689752Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T20:48:17.2690389Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T20:48:17.2690762Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T20:48:17.2691050Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T20:48:17.2691351Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T20:48:17.2691672Z getitem_23: "f32[1261 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T20:48:17.2691923Z 2025-03-04T20:48:17.2692303Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:48:17.2692864Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T20:48:17.2693211Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T20:48:17.2693451Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T20:48:17.2693814Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T20:48:17.2694166Z getitem_26: "Sym(1261 - s0)" = size_3[0] 2025-03-04T20:48:17.2694410Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T20:48:17.2694628Z 2025-03-04T20:48:17.2695098Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T20:48:17.2695673Z probs: "f32[3261, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1); scores = None 2025-03-04T20:48:17.2695959Z 2025-03-04T20:48:17.2696405Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T20:48:17.2697019Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T20:48:17.2697375Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T20:48:17.2697663Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T20:48:17.2697979Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T20:48:17.2698324Z getitem_31: "f32[1261 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T20:48:17.2698582Z 2025-03-04T20:48:17.2699137Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T20:48:17.2699834Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T20:48:17.2700165Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:48:17.2700493Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T20:48:17.2700826Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T20:48:17.2701111Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T20:48:17.2701359Z 2025-03-04T20:48:17.2701786Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T20:48:17.2702569Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T20:48:17.2702797Z 2025-03-04T20:48:17.2702936Z 2025-03-04T20:48:17.2703023Z class GraphModule(torch.nn.Module): 2025-03-04T20:48:17.2704362Z def forward(self, L_stack0_: "f32[3261, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1261 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1261 - s0, 4][4, 1]cpu"): 2025-03-04T20:48:17.2705666Z l_stack0_ = L_stack0_ 2025-03-04T20:48:17.2706040Z l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_ 2025-03-04T20:48:17.2706586Z l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_ 2025-03-04T20:48:17.2707134Z l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ 2025-03-04T20:48:17.2707708Z l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ 2025-03-04T20:48:17.2708194Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T20:48:17.2708595Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T20:48:17.2708988Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T20:48:17.2709378Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T20:48:17.2709660Z 2025-03-04T20:48:17.2710186Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/roi_heads.py:480 in torch_dynamo_resume_in_forward_at_477, code: predictions = self.box_predictor(box_features.mean(dim=[2, 3])) 2025-03-04T20:48:17.2710822Z mean: "f32[3261, 2048][2048, 1]cpu" = l_stack0_.mean(dim = [2, 3]); l_stack0_ = None 2025-03-04T20:48:17.2711073Z 2025-03-04T20:48:17.2711557Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x) 2025-03-04T20:48:17.2712602Z scores: "f32[3261, 81][81, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_); l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None 2025-03-04T20:48:17.2713351Z 2025-03-04T20:48:17.2713771Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x) 2025-03-04T20:48:17.2714845Z proposal_deltas: "f32[3261, 320][320, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_); mean = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None 2025-03-04T20:48:17.2715818Z 2025-03-04T20:48:17.2716234Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:48:17.2716710Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T20:48:17.2716972Z getitem: "Sym(s0)" = size[0] 2025-03-04T20:48:17.2717213Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T20:48:17.2717500Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T20:48:17.2717764Z getitem_2: "Sym(1261 - s0)" = size_1[0] 2025-03-04T20:48:17.2718014Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T20:48:17.2718244Z 2025-03-04T20:48:17.2718627Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:48:17.2719602Z proposal_boxes: "f32[3261, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T20:48:17.2720336Z 2025-03-04T20:48:17.2720807Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:48:17.2721399Z deltas: "f32[3261, 320][320, 1]cpu" = proposal_deltas.float(); proposal_deltas = None 2025-03-04T20:48:17.2721674Z 2025-03-04T20:48:17.2722080Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:48:17.2722622Z boxes: "f32[3261, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T20:48:17.2722907Z 2025-03-04T20:48:17.2723321Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:48:17.2723835Z getitem_4: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:48:17.2724144Z getitem_5: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:48:17.2724469Z widths: "f32[3261][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T20:48:17.2724733Z 2025-03-04T20:48:17.2725147Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:48:17.2725649Z getitem_6: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:48:17.2725970Z getitem_7: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:48:17.2726318Z heights: "f32[3261][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T20:48:17.2726584Z 2025-03-04T20:48:17.2726991Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:48:17.2727483Z getitem_8: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:48:17.2727747Z mul: "f32[3261][1]cpu" = 0.5 * widths 2025-03-04T20:48:17.2728002Z ctr_x: "f32[3261][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T20:48:17.2728240Z 2025-03-04T20:48:17.2728646Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:48:17.2729165Z getitem_9: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:48:17.2729469Z mul_1: "f32[3261][1]cpu" = 0.5 * heights 2025-03-04T20:48:17.2729740Z ctr_y: "f32[3261][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T20:48:17.2729984Z 2025-03-04T20:48:17.2730472Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:48:17.2730987Z getitem_10: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:48:17.2731312Z dx: "f32[3261, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T20:48:17.2731542Z 2025-03-04T20:48:17.2731929Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:48:17.2732442Z getitem_11: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:48:17.2732771Z dy: "f32[3261, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T20:48:17.2733005Z 2025-03-04T20:48:17.2733396Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:48:17.2733909Z getitem_12: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:48:17.2734240Z dw: "f32[3261, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T20:48:17.2734477Z 2025-03-04T20:48:17.2734897Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:48:17.2735448Z getitem_13: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:48:17.2735787Z dh: "f32[3261, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T20:48:17.2736018Z 2025-03-04T20:48:17.2736444Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:48:17.2736975Z dw_1: "f32[3261, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:48:17.2737220Z 2025-03-04T20:48:17.2737637Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:48:17.2738154Z dh_1: "f32[3261, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:48:17.2738402Z 2025-03-04T20:48:17.2738829Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:48:17.2739426Z getitem_14: "f32[3261, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:48:17.2739767Z mul_2: "f32[3261, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T20:48:17.2740095Z getitem_15: "f32[3261, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:48:17.2740437Z pred_ctr_x: "f32[3261, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T20:48:17.2740687Z 2025-03-04T20:48:17.2741130Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:48:17.2741667Z getitem_16: "f32[3261, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:48:17.2741979Z mul_3: "f32[3261, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T20:48:17.2742305Z getitem_17: "f32[3261, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:48:17.2742668Z pred_ctr_y: "f32[3261, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T20:48:17.2742921Z 2025-03-04T20:48:17.2743340Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:48:17.2743844Z exp: "f32[3261, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:48:17.2744165Z getitem_18: "f32[3261, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:48:17.2744503Z pred_w: "f32[3261, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T20:48:17.2744753Z 2025-03-04T20:48:17.2745185Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:48:17.2745704Z exp_1: "f32[3261, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:48:17.2746042Z getitem_19: "f32[3261, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:48:17.2746393Z pred_h: "f32[3261, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T20:48:17.2746659Z 2025-03-04T20:48:17.2747060Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:48:17.2747527Z mul_6: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T20:48:17.2747787Z x1: "f32[3261, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:48:17.2748020Z 2025-03-04T20:48:17.2748418Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:48:17.2748878Z mul_7: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T20:48:17.2749136Z y1: "f32[3261, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:48:17.2749365Z 2025-03-04T20:48:17.2749755Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:48:17.2750229Z mul_8: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:48:17.2750514Z x2: "f32[3261, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:48:17.2750756Z 2025-03-04T20:48:17.2751143Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:48:17.2751616Z mul_9: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:48:17.2751902Z y2: "f32[3261, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:48:17.2752167Z 2025-03-04T20:48:17.2752624Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:48:17.2753209Z pred_boxes: "f32[3261, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:48:17.2753489Z 2025-03-04T20:48:17.2753916Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:48:17.2754485Z predict_boxes: "f32[3261, 320][320, 1]cpu" = pred_boxes.reshape((3261, 320)); pred_boxes = None 2025-03-04T20:48:17.2754777Z 2025-03-04T20:48:17.2755346Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T20:48:17.2756065Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T20:48:17.2756473Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T20:48:17.2756764Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T20:48:17.2757071Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T20:48:17.2757388Z getitem_23: "f32[1261 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T20:48:17.2757665Z 2025-03-04T20:48:17.2758067Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:48:17.2758653Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T20:48:17.2759018Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T20:48:17.2759270Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T20:48:17.2759652Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T20:48:17.2760025Z getitem_26: "Sym(1261 - s0)" = size_3[0] 2025-03-04T20:48:17.2760280Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T20:48:17.2760506Z 2025-03-04T20:48:17.2760947Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T20:48:17.2761533Z probs: "f32[3261, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1); scores = None 2025-03-04T20:48:17.2761849Z 2025-03-04T20:48:17.2762324Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T20:48:17.2762985Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T20:48:17.2763377Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T20:48:17.2763682Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T20:48:17.2763994Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T20:48:17.2764324Z getitem_31: "f32[1261 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T20:48:17.2764593Z 2025-03-04T20:48:17.2765175Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T20:48:17.2765911Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T20:48:17.2766338Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:48:17.2766706Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T20:48:17.2767161Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T20:48:17.2767469Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T20:48:17.2767703Z 2025-03-04T20:48:17.2768142Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T20:48:17.2768657Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T20:48:17.2768889Z 2025-03-04T20:48:17.2769019Z 2025-03-04T20:48:17.2769118Z class GraphModule(torch.nn.Module): 2025-03-04T20:48:17.2770514Z def forward(self, L_stack0_: "f32[3261, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1261 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1261 - s0, 4][4, 1]cpu"): 2025-03-04T20:48:17.2771840Z l_stack0_ = L_stack0_ 2025-03-04T20:48:17.2772220Z l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_ 2025-03-04T20:48:17.2772772Z l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_ 2025-03-04T20:48:17.2773326Z l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ 2025-03-04T20:48:17.2773879Z l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ 2025-03-04T20:48:17.2774348Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T20:48:17.2774740Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T20:48:17.2775133Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T20:48:17.2775525Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T20:48:17.2775821Z 2025-03-04T20:48:17.2776377Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/roi_heads.py:480 in torch_dynamo_resume_in_forward_at_477, code: predictions = self.box_predictor(box_features.mean(dim=[2, 3])) 2025-03-04T20:48:17.2777020Z mean: "f32[3261, 2048][2048, 1]cpu" = l_stack0_.mean(dim = [2, 3]); l_stack0_ = None 2025-03-04T20:48:17.2777281Z 2025-03-04T20:48:17.2777681Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x) 2025-03-04T20:48:17.2778656Z scores: "f32[3261, 81][81, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_); l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None 2025-03-04T20:48:17.2779382Z 2025-03-04T20:48:17.2779842Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x) 2025-03-04T20:48:17.2780833Z proposal_deltas: "f32[3261, 320][320, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_); mean = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None 2025-03-04T20:48:17.2781582Z 2025-03-04T20:48:17.2781958Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:48:17.2782413Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T20:48:17.2782662Z getitem: "Sym(s0)" = size[0] 2025-03-04T20:48:17.2782888Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T20:48:17.2783156Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T20:48:17.2783448Z getitem_2: "Sym(1261 - s0)" = size_1[0] 2025-03-04T20:48:17.2783687Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T20:48:17.2783901Z 2025-03-04T20:48:17.2784267Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:48:17.2785212Z proposal_boxes: "f32[3261, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T20:48:17.2785952Z 2025-03-04T20:48:17.2786412Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:48:17.2786990Z deltas: "f32[3261, 320][320, 1]cpu" = proposal_deltas.float(); proposal_deltas = None 2025-03-04T20:48:17.2787259Z 2025-03-04T20:48:17.2787655Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:48:17.2788185Z boxes: "f32[3261, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T20:48:17.2788482Z 2025-03-04T20:48:17.2788879Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:48:17.2789376Z getitem_4: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:48:17.2789679Z getitem_5: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:48:17.2789997Z widths: "f32[3261][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T20:48:17.2790260Z 2025-03-04T20:48:17.2790668Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:48:17.2791166Z getitem_6: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:48:17.2791455Z getitem_7: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:48:17.2791767Z heights: "f32[3261][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T20:48:17.2792024Z 2025-03-04T20:48:17.2792416Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:48:17.2792896Z getitem_8: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:48:17.2793150Z mul: "f32[3261][1]cpu" = 0.5 * widths 2025-03-04T20:48:17.2793431Z ctr_x: "f32[3261][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T20:48:17.2793692Z 2025-03-04T20:48:17.2794108Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:48:17.2794634Z getitem_9: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:48:17.2795036Z mul_1: "f32[3261][1]cpu" = 0.5 * heights 2025-03-04T20:48:17.2795305Z ctr_y: "f32[3261][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T20:48:17.2795559Z 2025-03-04T20:48:17.2795984Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:48:17.2796525Z getitem_10: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:48:17.2796892Z dx: "f32[3261, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T20:48:17.2797135Z 2025-03-04T20:48:17.2797546Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:48:17.2798047Z getitem_11: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:48:17.2798366Z dy: "f32[3261, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T20:48:17.2798594Z 2025-03-04T20:48:17.2798971Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:48:17.2799470Z getitem_12: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:48:17.2799785Z dw: "f32[3261, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T20:48:17.2800014Z 2025-03-04T20:48:17.2800400Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:48:17.2800933Z getitem_13: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:48:17.2801276Z dh: "f32[3261, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T20:48:17.2801508Z 2025-03-04T20:48:17.2802092Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:48:17.2802664Z dw_1: "f32[3261, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:48:17.2802931Z 2025-03-04T20:48:17.2803497Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:48:17.2804057Z dh_1: "f32[3261, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:48:17.2804317Z 2025-03-04T20:48:17.2804769Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:48:17.2805335Z getitem_14: "f32[3261, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:48:17.2805665Z mul_2: "f32[3261, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T20:48:17.2806010Z getitem_15: "f32[3261, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:48:17.2806372Z pred_ctr_x: "f32[3261, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T20:48:17.2806641Z 2025-03-04T20:48:17.2807177Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:48:17.2807773Z getitem_16: "f32[3261, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:48:17.2808101Z mul_3: "f32[3261, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T20:48:17.2808445Z getitem_17: "f32[3261, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:48:17.2808804Z pred_ctr_y: "f32[3261, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T20:48:17.2809076Z 2025-03-04T20:48:17.2809520Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:48:17.2810054Z exp: "f32[3261, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:48:17.2810402Z getitem_18: "f32[3261, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:48:17.2810794Z pred_w: "f32[3261, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T20:48:17.2811052Z 2025-03-04T20:48:17.2811494Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:48:17.2812016Z exp_1: "f32[3261, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:48:17.2812357Z getitem_19: "f32[3261, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:48:17.2812719Z pred_h: "f32[3261, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T20:48:17.2812979Z 2025-03-04T20:48:17.2813397Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:48:17.2813889Z mul_6: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T20:48:17.2814163Z x1: "f32[3261, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:48:17.2814404Z 2025-03-04T20:48:17.2814809Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:48:17.2815290Z mul_7: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T20:48:17.2815556Z y1: "f32[3261, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:48:17.2815795Z 2025-03-04T20:48:17.2816204Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:48:17.2816701Z mul_8: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:48:17.2817007Z x2: "f32[3261, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:48:17.2817256Z 2025-03-04T20:48:17.2817661Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:48:17.2818130Z mul_9: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:48:17.2818410Z y2: "f32[3261, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:48:17.2818648Z 2025-03-04T20:48:17.2819083Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:48:17.2819665Z pred_boxes: "f32[3261, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:48:17.2819954Z 2025-03-04T20:48:17.2820369Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:48:17.2820979Z predict_boxes: "f32[3261, 320][320, 1]cpu" = pred_boxes.reshape((3261, 320)); pred_boxes = None 2025-03-04T20:48:17.2821266Z 2025-03-04T20:48:17.2821716Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T20:48:17.2822352Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T20:48:17.2822734Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T20:48:17.2823024Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T20:48:17.2823328Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T20:48:17.2823647Z getitem_23: "f32[1261 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T20:48:17.2823930Z 2025-03-04T20:48:17.2824313Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:48:17.2824876Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T20:48:17.2825222Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T20:48:17.2825462Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T20:48:17.2825838Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T20:48:17.2826208Z getitem_26: "Sym(1261 - s0)" = size_3[0] 2025-03-04T20:48:17.2826462Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T20:48:17.2826677Z 2025-03-04T20:48:17.2827097Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T20:48:17.2827666Z probs: "f32[3261, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1); scores = None 2025-03-04T20:48:17.2827950Z 2025-03-04T20:48:17.2828395Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T20:48:17.2829001Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T20:48:17.2829357Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T20:48:17.2829643Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T20:48:17.2829941Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T20:48:17.2830262Z getitem_31: "f32[1261 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T20:48:17.2830520Z 2025-03-04T20:48:17.2831079Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T20:48:17.2831771Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T20:48:17.2832110Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:48:17.2832449Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T20:48:17.2832789Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T20:48:17.2833088Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T20:48:17.2833329Z 2025-03-04T20:48:17.2833818Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T20:48:17.2834390Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T20:48:17.2834633Z 2025-03-04T20:48:17.2834771Z 2025-03-04T20:48:17.2834943Z class GraphModule(torch.nn.Module): 2025-03-04T20:48:17.2836446Z def forward(self, L_stack0_: "f32[3261, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1261 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1261 - s0, 4][4, 1]cpu"): 2025-03-04T20:48:17.2837851Z l_stack0_ = L_stack0_ 2025-03-04T20:48:17.2838257Z l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_ 2025-03-04T20:48:17.2838850Z l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_ 2025-03-04T20:48:17.2839438Z l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ 2025-03-04T20:48:17.2840027Z l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ 2025-03-04T20:48:17.2840529Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T20:48:17.2840958Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T20:48:17.2841372Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T20:48:17.2841779Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T20:48:17.2842081Z 2025-03-04T20:48:17.2842642Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/roi_heads.py:480 in torch_dynamo_resume_in_forward_at_477, code: predictions = self.box_predictor(box_features.mean(dim=[2, 3])) 2025-03-04T20:48:17.2843306Z mean: "f32[3261, 2048][2048, 1]cpu" = l_stack0_.mean(dim = [2, 3]); l_stack0_ = None 2025-03-04T20:48:17.2843577Z 2025-03-04T20:48:17.2843992Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x) 2025-03-04T20:48:17.2844973Z scores: "f32[3261, 81][81, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_); l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None 2025-03-04T20:48:17.2845669Z 2025-03-04T20:48:17.2846062Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x) 2025-03-04T20:48:17.2847067Z proposal_deltas: "f32[3261, 320][320, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_); mean = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None 2025-03-04T20:48:17.2847852Z 2025-03-04T20:48:17.2848258Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:48:17.2848707Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T20:48:17.2848943Z getitem: "Sym(s0)" = size[0] 2025-03-04T20:48:17.2849166Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T20:48:17.2849427Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T20:48:17.2849684Z getitem_2: "Sym(1261 - s0)" = size_1[0] 2025-03-04T20:48:17.2849920Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T20:48:17.2850129Z 2025-03-04T20:48:17.2850492Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:48:17.2851412Z proposal_boxes: "f32[3261, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T20:48:17.2852124Z 2025-03-04T20:48:17.2852580Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:48:17.2853151Z deltas: "f32[3261, 320][320, 1]cpu" = proposal_deltas.float(); proposal_deltas = None 2025-03-04T20:48:17.2853417Z 2025-03-04T20:48:17.2853806Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:48:17.2854325Z boxes: "f32[3261, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T20:48:17.2854605Z 2025-03-04T20:48:17.2855028Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:48:17.2855517Z getitem_4: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:48:17.2855814Z getitem_5: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:48:17.2856125Z widths: "f32[3261][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T20:48:17.2856390Z 2025-03-04T20:48:17.2856800Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:48:17.2857304Z getitem_6: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:48:17.2857601Z getitem_7: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:48:17.2857916Z heights: "f32[3261][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T20:48:17.2858181Z 2025-03-04T20:48:17.2858588Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:48:17.2859081Z getitem_8: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:48:17.2859350Z mul: "f32[3261][1]cpu" = 0.5 * widths 2025-03-04T20:48:17.2859600Z ctr_x: "f32[3261][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T20:48:17.2859834Z 2025-03-04T20:48:17.2860226Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:48:17.2860724Z getitem_9: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:48:17.2861003Z mul_1: "f32[3261][1]cpu" = 0.5 * heights 2025-03-04T20:48:17.2861276Z ctr_y: "f32[3261][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T20:48:17.2861507Z 2025-03-04T20:48:17.2861936Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:48:17.2862438Z getitem_10: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:48:17.2862752Z dx: "f32[3261, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T20:48:17.2862978Z 2025-03-04T20:48:17.2863353Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:48:17.2863845Z getitem_11: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:48:17.2864157Z dy: "f32[3261, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T20:48:17.2864395Z 2025-03-04T20:48:17.2864777Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:48:17.2865280Z getitem_12: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:48:17.2865586Z dw: "f32[3261, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T20:48:17.2865813Z 2025-03-04T20:48:17.2866204Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:48:17.2866738Z getitem_13: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:48:17.2867078Z dh: "f32[3261, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T20:48:17.2867308Z 2025-03-04T20:48:17.2867738Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:48:17.2868268Z dw_1: "f32[3261, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:48:17.2868515Z 2025-03-04T20:48:17.2868939Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:48:17.2869457Z dh_1: "f32[3261, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:48:17.2869703Z 2025-03-04T20:48:17.2870137Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:48:17.2870668Z getitem_14: "f32[3261, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:48:17.2870976Z mul_2: "f32[3261, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T20:48:17.2871304Z getitem_15: "f32[3261, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:48:17.2871640Z pred_ctr_x: "f32[3261, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T20:48:17.2871890Z 2025-03-04T20:48:17.2872331Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:48:17.2872871Z getitem_16: "f32[3261, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:48:17.2873197Z mul_3: "f32[3261, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T20:48:17.2873531Z getitem_17: "f32[3261, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:48:17.2873882Z pred_ctr_y: "f32[3261, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T20:48:17.2874169Z 2025-03-04T20:48:17.2874642Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:48:17.2875293Z exp: "f32[3261, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:48:17.2875650Z getitem_18: "f32[3261, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:48:17.2876027Z pred_w: "f32[3261, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T20:48:17.2876303Z 2025-03-04T20:48:17.2876781Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:48:17.2877284Z exp_1: "f32[3261, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:48:17.2877618Z getitem_19: "f32[3261, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:48:17.2877995Z pred_h: "f32[3261, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T20:48:17.2878245Z 2025-03-04T20:48:17.2878648Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:48:17.2879106Z mul_6: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T20:48:17.2879360Z x1: "f32[3261, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:48:17.2879585Z 2025-03-04T20:48:17.2879980Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:48:17.2880430Z mul_7: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T20:48:17.2880682Z y1: "f32[3261, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:48:17.2880911Z 2025-03-04T20:48:17.2881299Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:48:17.2881771Z mul_8: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:48:17.2882050Z x2: "f32[3261, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:48:17.2882294Z 2025-03-04T20:48:17.2882681Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:48:17.2883150Z mul_9: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:48:17.2883438Z y2: "f32[3261, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:48:17.2883683Z 2025-03-04T20:48:17.2884118Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:48:17.2884699Z pred_boxes: "f32[3261, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:48:17.2884995Z 2025-03-04T20:48:17.2885427Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:48:17.2885999Z predict_boxes: "f32[3261, 320][320, 1]cpu" = pred_boxes.reshape((3261, 320)); pred_boxes = None 2025-03-04T20:48:17.2886279Z 2025-03-04T20:48:17.2886721Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T20:48:17.2887337Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T20:48:17.2887764Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T20:48:17.2888052Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T20:48:17.2888351Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T20:48:17.2888664Z getitem_23: "f32[1261 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T20:48:17.2888923Z 2025-03-04T20:48:17.2889304Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:48:17.2889852Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T20:48:17.2890199Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T20:48:17.2890438Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T20:48:17.2890799Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T20:48:17.2891171Z getitem_26: "Sym(1261 - s0)" = size_3[0] 2025-03-04T20:48:17.2891415Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T20:48:17.2891632Z 2025-03-04T20:48:17.2892048Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T20:48:17.2892598Z probs: "f32[3261, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1); scores = None 2025-03-04T20:48:17.2892882Z 2025-03-04T20:48:17.2893317Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T20:48:17.2893916Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T20:48:17.2894274Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T20:48:17.2894565Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T20:48:17.2894864Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T20:48:17.2895175Z getitem_31: "f32[1261 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T20:48:17.2895427Z 2025-03-04T20:48:17.2895969Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T20:48:17.2896647Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T20:48:17.2896984Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:48:17.2897328Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T20:48:17.2897669Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T20:48:17.2897957Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T20:48:17.2898197Z 2025-03-04T20:48:17.2898630Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T20:48:17.2899147Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T20:48:17.2899381Z 2025-03-04T20:48:19.2569805Z 2025-03-04T20:48:19.2572559Z class GraphModule(torch.nn.Module): 2025-03-04T20:48:19.2574172Z def forward(self, L_predictions_0_: "f32[3261, 81][81, 1]cpu", L_predictions_1_: "f32[3261, 320][320, 1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1261 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1261 - s0, 4][4, 1]cpu"): 2025-03-04T20:48:19.2576534Z l_predictions_0_ = L_predictions_0_ 2025-03-04T20:48:19.2576822Z l_predictions_1_ = L_predictions_1_ 2025-03-04T20:48:19.2577717Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T20:48:19.2578172Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T20:48:19.2583066Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T20:48:19.2587367Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T20:48:19.2589458Z 2025-03-04T20:48:19.2608100Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:48:19.2609023Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T20:48:19.2609291Z getitem: "Sym(s0)" = size[0] 2025-03-04T20:48:19.2609525Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T20:48:19.2609812Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T20:48:19.2610085Z getitem_2: "Sym(1261 - s0)" = size_1[0] 2025-03-04T20:48:19.2610334Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T20:48:19.2610559Z 2025-03-04T20:48:19.2610956Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:48:19.2611930Z proposal_boxes: "f32[3261, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T20:48:19.2612687Z 2025-03-04T20:48:19.2613164Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:48:19.2613753Z deltas: "f32[3261, 320][320, 1]cpu" = l_predictions_1_.float(); l_predictions_1_ = None 2025-03-04T20:48:19.2614030Z 2025-03-04T20:48:19.2614459Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:48:19.2615075Z boxes: "f32[3261, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T20:48:19.2615505Z 2025-03-04T20:48:19.2615993Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:48:19.2616504Z getitem_4: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:48:19.2616799Z getitem_5: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:48:19.2617115Z widths: "f32[3261][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T20:48:19.2617373Z 2025-03-04T20:48:19.2617778Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:48:19.2618272Z getitem_6: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:48:19.2618562Z getitem_7: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:48:19.2618874Z heights: "f32[3261][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T20:48:19.2619226Z 2025-03-04T20:48:19.2619822Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:48:19.2620321Z getitem_8: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:48:19.2620572Z mul: "f32[3261][1]cpu" = 0.5 * widths 2025-03-04T20:48:19.2620820Z ctr_x: "f32[3261][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T20:48:19.2621056Z 2025-03-04T20:48:19.2621454Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:48:19.2621959Z getitem_9: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:48:19.2622233Z mul_1: "f32[3261][1]cpu" = 0.5 * heights 2025-03-04T20:48:19.2622492Z ctr_y: "f32[3261][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T20:48:19.2622746Z 2025-03-04T20:48:19.2623208Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:48:19.2623723Z getitem_10: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:48:19.2624046Z dx: "f32[3261, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T20:48:19.2624276Z 2025-03-04T20:48:19.2624660Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:48:19.2625163Z getitem_11: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:48:19.2625481Z dy: "f32[3261, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T20:48:19.2625709Z 2025-03-04T20:48:19.2626098Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:48:19.2626650Z getitem_12: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:48:19.2626963Z dw: "f32[3261, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T20:48:19.2627199Z 2025-03-04T20:48:19.2627580Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:48:19.2628105Z getitem_13: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:48:19.2628440Z dh: "f32[3261, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T20:48:19.2628662Z 2025-03-04T20:48:19.2629076Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:48:19.2629600Z dw_1: "f32[3261, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:48:19.2629850Z 2025-03-04T20:48:19.2630262Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:48:19.2630783Z dh_1: "f32[3261, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:48:19.2631032Z 2025-03-04T20:48:19.2631462Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:48:19.2632000Z getitem_14: "f32[3261, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:48:19.2632316Z mul_2: "f32[3261, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T20:48:19.2632715Z getitem_15: "f32[3261, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:48:19.2633069Z pred_ctr_x: "f32[3261, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T20:48:19.2633328Z 2025-03-04T20:48:19.2633782Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:48:19.2634335Z getitem_16: "f32[3261, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:48:19.2634652Z mul_3: "f32[3261, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T20:48:19.2635114Z getitem_17: "f32[3261, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:48:19.2635469Z pred_ctr_y: "f32[3261, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T20:48:19.2635732Z 2025-03-04T20:48:19.2636198Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:48:19.2636738Z exp: "f32[3261, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:48:19.2637072Z getitem_18: "f32[3261, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:48:19.2637418Z pred_w: "f32[3261, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T20:48:19.2637668Z 2025-03-04T20:48:19.2638095Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:48:19.2638611Z exp_1: "f32[3261, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:48:19.2638944Z getitem_19: "f32[3261, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:48:19.2639303Z pred_h: "f32[3261, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T20:48:19.2639558Z 2025-03-04T20:48:19.2639966Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:48:19.2640443Z mul_6: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T20:48:19.2640705Z x1: "f32[3261, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:48:19.2640941Z 2025-03-04T20:48:19.2641348Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:48:19.2641821Z mul_7: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T20:48:19.2642080Z y1: "f32[3261, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:48:19.2642317Z 2025-03-04T20:48:19.2642725Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:48:19.2643216Z mul_8: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:48:19.2643513Z x2: "f32[3261, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:48:19.2643754Z 2025-03-04T20:48:19.2644154Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:48:19.2644637Z mul_9: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:48:19.2644938Z y2: "f32[3261, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:48:19.2645173Z 2025-03-04T20:48:19.2645594Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:48:19.2646219Z pred_boxes: "f32[3261, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:48:19.2646507Z 2025-03-04T20:48:19.2646935Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:48:19.2647474Z predict_boxes: "f32[3261, 320][320, 1]cpu" = pred_boxes.reshape((3261, 320)); pred_boxes = None 2025-03-04T20:48:19.2647747Z 2025-03-04T20:48:19.2648179Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T20:48:19.2648791Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T20:48:19.2649162Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T20:48:19.2649478Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T20:48:19.2649794Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T20:48:19.2650119Z getitem_23: "f32[1261 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T20:48:19.2650387Z 2025-03-04T20:48:19.2650776Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:48:19.2651320Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T20:48:19.2651660Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T20:48:19.2651897Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T20:48:19.2652258Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T20:48:19.2652612Z getitem_26: "Sym(1261 - s0)" = size_3[0] 2025-03-04T20:48:19.2652856Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T20:48:19.2653064Z 2025-03-04T20:48:19.2653482Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T20:48:19.2654073Z probs: "f32[3261, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1); l_predictions_0_ = None 2025-03-04T20:48:19.2654395Z 2025-03-04T20:48:19.2654827Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T20:48:19.2655416Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T20:48:19.2655768Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T20:48:19.2656056Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T20:48:19.2656351Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T20:48:19.2656657Z getitem_31: "f32[1261 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T20:48:19.2656908Z 2025-03-04T20:48:19.2657448Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T20:48:19.2658128Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T20:48:19.2658462Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:48:19.2658792Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T20:48:19.2659208Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T20:48:19.2659491Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T20:48:19.2659718Z 2025-03-04T20:48:19.2660157Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T20:48:19.2660676Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T20:48:19.2660903Z 2025-03-04T20:48:19.2660993Z 2025-03-04T20:48:19.2661080Z class GraphModule(torch.nn.Module): 2025-03-04T20:48:19.2661946Z def forward(self, L_predictions_0_: "f32[3261, 81][81, 1]cpu", L_predictions_1_: "f32[3261, 320][320, 1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1261 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1261 - s0, 4][4, 1]cpu"): 2025-03-04T20:48:19.2663210Z l_predictions_0_ = L_predictions_0_ 2025-03-04T20:48:19.2663434Z l_predictions_1_ = L_predictions_1_ 2025-03-04T20:48:19.2663746Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T20:48:19.2664146Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T20:48:19.2664540Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T20:48:19.2664938Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T20:48:19.2665223Z 2025-03-04T20:48:19.2665604Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:48:19.2666071Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T20:48:19.2666314Z getitem: "Sym(s0)" = size[0] 2025-03-04T20:48:19.2666541Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T20:48:19.2666809Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T20:48:19.2667062Z getitem_2: "Sym(1261 - s0)" = size_1[0] 2025-03-04T20:48:19.2667297Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T20:48:19.2667510Z 2025-03-04T20:48:19.2667878Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:48:19.2668824Z proposal_boxes: "f32[3261, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T20:48:19.2669694Z 2025-03-04T20:48:19.2670201Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:48:19.2670817Z deltas: "f32[3261, 320][320, 1]cpu" = l_predictions_1_.float(); l_predictions_1_ = None 2025-03-04T20:48:19.2671096Z 2025-03-04T20:48:19.2671509Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:48:19.2672051Z boxes: "f32[3261, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T20:48:19.2672333Z 2025-03-04T20:48:19.2672744Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:48:19.2673311Z getitem_4: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:48:19.2673616Z getitem_5: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:48:19.2673940Z widths: "f32[3261][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T20:48:19.2674205Z 2025-03-04T20:48:19.2674626Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:48:19.2675216Z getitem_6: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:48:19.2675520Z getitem_7: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:48:19.2675848Z heights: "f32[3261][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T20:48:19.2676117Z 2025-03-04T20:48:19.2676514Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:48:19.2677018Z getitem_8: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:48:19.2677264Z mul: "f32[3261][1]cpu" = 0.5 * widths 2025-03-04T20:48:19.2677515Z ctr_x: "f32[3261][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T20:48:19.2677746Z 2025-03-04T20:48:19.2678137Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:48:19.2678637Z getitem_9: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:48:19.2678917Z mul_1: "f32[3261][1]cpu" = 0.5 * heights 2025-03-04T20:48:19.2679183Z ctr_y: "f32[3261][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T20:48:19.2679438Z 2025-03-04T20:48:19.2679893Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:48:19.2680426Z getitem_10: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:48:19.2680760Z dx: "f32[3261, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T20:48:19.2680997Z 2025-03-04T20:48:19.2681393Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:48:19.2681920Z getitem_11: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:48:19.2682260Z dy: "f32[3261, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T20:48:19.2682491Z 2025-03-04T20:48:19.2682881Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:48:19.2683407Z getitem_12: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:48:19.2683736Z dw: "f32[3261, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T20:48:19.2683969Z 2025-03-04T20:48:19.2684370Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:48:19.2684926Z getitem_13: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:48:19.2685278Z dh: "f32[3261, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T20:48:19.2685513Z 2025-03-04T20:48:19.2685949Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:48:19.2686541Z dw_1: "f32[3261, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:48:19.2686797Z 2025-03-04T20:48:19.2687220Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:48:19.2687744Z dh_1: "f32[3261, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:48:19.2687995Z 2025-03-04T20:48:19.2688429Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:48:19.2688971Z getitem_14: "f32[3261, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:48:19.2689284Z mul_2: "f32[3261, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T20:48:19.2689618Z getitem_15: "f32[3261, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:48:19.2689992Z pred_ctr_x: "f32[3261, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T20:48:19.2690252Z 2025-03-04T20:48:19.2690698Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:48:19.2691254Z getitem_16: "f32[3261, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:48:19.2691571Z mul_3: "f32[3261, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T20:48:19.2691902Z getitem_17: "f32[3261, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:48:19.2692245Z pred_ctr_y: "f32[3261, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T20:48:19.2692501Z 2025-03-04T20:48:19.2692932Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:48:19.2693449Z exp: "f32[3261, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:48:19.2693777Z getitem_18: "f32[3261, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:48:19.2694122Z pred_w: "f32[3261, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T20:48:19.2694372Z 2025-03-04T20:48:19.2694799Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:48:19.2695308Z exp_1: "f32[3261, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:48:19.2695643Z getitem_19: "f32[3261, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:48:19.2695993Z pred_h: "f32[3261, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T20:48:19.2696248Z 2025-03-04T20:48:19.2696657Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:48:19.2697133Z mul_6: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T20:48:19.2697398Z x1: "f32[3261, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:48:19.2697634Z 2025-03-04T20:48:19.2698036Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:48:19.2698503Z mul_7: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T20:48:19.2698772Z y1: "f32[3261, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:48:19.2699001Z 2025-03-04T20:48:19.2699387Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:48:19.2699917Z mul_8: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:48:19.2700202Z x2: "f32[3261, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:48:19.2700439Z 2025-03-04T20:48:19.2700827Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:48:19.2701297Z mul_9: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:48:19.2701577Z y2: "f32[3261, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:48:19.2701813Z 2025-03-04T20:48:19.2702505Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:48:19.2703152Z pred_boxes: "f32[3261, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:48:19.2703445Z 2025-03-04T20:48:19.2703871Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:48:19.2704450Z predict_boxes: "f32[3261, 320][320, 1]cpu" = pred_boxes.reshape((3261, 320)); pred_boxes = None 2025-03-04T20:48:19.2704743Z 2025-03-04T20:48:19.2705212Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T20:48:19.2705827Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T20:48:19.2706192Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T20:48:19.2706483Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T20:48:19.2706790Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T20:48:19.2707107Z getitem_23: "f32[1261 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T20:48:19.2707363Z 2025-03-04T20:48:19.2707741Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:48:19.2708307Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T20:48:19.2708664Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T20:48:19.2708908Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T20:48:19.2709277Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T20:48:19.2709644Z getitem_26: "Sym(1261 - s0)" = size_3[0] 2025-03-04T20:48:19.2709902Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T20:48:19.2710120Z 2025-03-04T20:48:19.2710561Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T20:48:19.2711193Z probs: "f32[3261, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1); l_predictions_0_ = None 2025-03-04T20:48:19.2711532Z 2025-03-04T20:48:19.2711996Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T20:48:19.2712625Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T20:48:19.2712997Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T20:48:19.2713398Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T20:48:19.2713718Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T20:48:19.2714066Z getitem_31: "f32[1261 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T20:48:19.2714352Z 2025-03-04T20:48:19.2715053Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T20:48:19.2715834Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T20:48:19.2716215Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:48:19.2716595Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T20:48:19.2717004Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T20:48:19.2717331Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T20:48:19.2717590Z 2025-03-04T20:48:19.2718076Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T20:48:19.2718650Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T20:48:19.2718905Z 2025-03-04T20:48:19.2719004Z 2025-03-04T20:48:19.2719099Z class GraphModule(torch.nn.Module): 2025-03-04T20:48:19.2720006Z def forward(self, L_predictions_0_: "f32[3261, 81][81, 1]cpu", L_predictions_1_: "f32[3261, 320][320, 1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1261 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1261 - s0, 4][4, 1]cpu"): 2025-03-04T20:48:19.2720891Z l_predictions_0_ = L_predictions_0_ 2025-03-04T20:48:19.2721138Z l_predictions_1_ = L_predictions_1_ 2025-03-04T20:48:19.2721487Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T20:48:19.2721939Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T20:48:19.2722379Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T20:48:19.2722820Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T20:48:19.2723137Z 2025-03-04T20:48:19.2723556Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:48:19.2724073Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T20:48:19.2724374Z getitem: "Sym(s0)" = size[0] 2025-03-04T20:48:19.2724615Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T20:48:19.2724886Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T20:48:19.2725146Z getitem_2: "Sym(1261 - s0)" = size_1[0] 2025-03-04T20:48:19.2725381Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T20:48:19.2725666Z 2025-03-04T20:48:19.2726057Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:48:19.2726999Z proposal_boxes: "f32[3261, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T20:48:19.2727737Z 2025-03-04T20:48:19.2728231Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:48:19.2728809Z deltas: "f32[3261, 320][320, 1]cpu" = l_predictions_1_.float(); l_predictions_1_ = None 2025-03-04T20:48:19.2729079Z 2025-03-04T20:48:19.2729477Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:48:19.2729998Z boxes: "f32[3261, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T20:48:19.2730275Z 2025-03-04T20:48:19.2730683Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:48:19.2731197Z getitem_4: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:48:19.2731497Z getitem_5: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:48:19.2731810Z widths: "f32[3261][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T20:48:19.2732068Z 2025-03-04T20:48:19.2732475Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:48:19.2732978Z getitem_6: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:48:19.2733261Z getitem_7: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:48:19.2733564Z heights: "f32[3261][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T20:48:19.2733814Z 2025-03-04T20:48:19.2734207Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:48:19.2734700Z getitem_8: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:48:19.2734954Z mul: "f32[3261][1]cpu" = 0.5 * widths 2025-03-04T20:48:19.2735209Z ctr_x: "f32[3261][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T20:48:19.2735443Z 2025-03-04T20:48:19.2735842Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:48:19.2736353Z getitem_9: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:48:19.2736639Z mul_1: "f32[3261][1]cpu" = 0.5 * heights 2025-03-04T20:48:19.2736889Z ctr_y: "f32[3261][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T20:48:19.2737117Z 2025-03-04T20:48:19.2737518Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:48:19.2738020Z getitem_10: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:48:19.2738335Z dx: "f32[3261, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T20:48:19.2738560Z 2025-03-04T20:48:19.2738936Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:48:19.2739431Z getitem_11: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:48:19.2739737Z dy: "f32[3261, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T20:48:19.2739958Z 2025-03-04T20:48:19.2740332Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:48:19.2740875Z getitem_12: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:48:19.2741186Z dw: "f32[3261, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T20:48:19.2741402Z 2025-03-04T20:48:19.2741778Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:48:19.2742299Z getitem_13: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:48:19.2742637Z dh: "f32[3261, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T20:48:19.2742865Z 2025-03-04T20:48:19.2743293Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:48:19.2743811Z dw_1: "f32[3261, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:48:19.2744074Z 2025-03-04T20:48:19.2744484Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:48:19.2744990Z dh_1: "f32[3261, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:48:19.2745233Z 2025-03-04T20:48:19.2745661Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:48:19.2746658Z getitem_14: "f32[3261, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:48:19.2746986Z mul_2: "f32[3261, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T20:48:19.2747326Z getitem_15: "f32[3261, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:48:19.2747685Z pred_ctr_x: "f32[3261, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T20:48:19.2747946Z 2025-03-04T20:48:19.2748390Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:48:19.2748935Z getitem_16: "f32[3261, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:48:19.2749250Z mul_3: "f32[3261, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T20:48:19.2749579Z getitem_17: "f32[3261, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:48:19.2749919Z pred_ctr_y: "f32[3261, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T20:48:19.2750172Z 2025-03-04T20:48:19.2750598Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:48:19.2751106Z exp: "f32[3261, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:48:19.2751430Z getitem_18: "f32[3261, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:48:19.2751770Z pred_w: "f32[3261, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T20:48:19.2752018Z 2025-03-04T20:48:19.2752442Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:48:19.2752947Z exp_1: "f32[3261, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:48:19.2753276Z getitem_19: "f32[3261, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:48:19.2753626Z pred_h: "f32[3261, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T20:48:19.2753909Z 2025-03-04T20:48:19.2754361Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:48:19.2754837Z mul_6: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T20:48:19.2755198Z x1: "f32[3261, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:48:19.2755460Z 2025-03-04T20:48:19.2755870Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:48:19.2756365Z mul_7: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T20:48:19.2756620Z y1: "f32[3261, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:48:19.2756847Z 2025-03-04T20:48:19.2757237Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:48:19.2757757Z mul_8: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:48:19.2758049Z x2: "f32[3261, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:48:19.2758285Z 2025-03-04T20:48:19.2758677Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:48:19.2759147Z mul_9: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:48:19.2759433Z y2: "f32[3261, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:48:19.2759678Z 2025-03-04T20:48:19.2760120Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:48:19.2760707Z pred_boxes: "f32[3261, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:48:19.2761002Z 2025-03-04T20:48:19.2761427Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:48:19.2761989Z predict_boxes: "f32[3261, 320][320, 1]cpu" = pred_boxes.reshape((3261, 320)); pred_boxes = None 2025-03-04T20:48:19.2762273Z 2025-03-04T20:48:19.2762721Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T20:48:19.2764105Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T20:48:19.2764493Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T20:48:19.2764783Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T20:48:19.2765083Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T20:48:19.2765394Z getitem_23: "f32[1261 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T20:48:19.2765644Z 2025-03-04T20:48:19.2766013Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:48:19.2766552Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T20:48:19.2766887Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T20:48:19.2767117Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T20:48:19.2767470Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T20:48:19.2767806Z getitem_26: "Sym(1261 - s0)" = size_3[0] 2025-03-04T20:48:19.2768083Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T20:48:19.2768334Z 2025-03-04T20:48:19.2768748Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T20:48:19.2769336Z probs: "f32[3261, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1); l_predictions_0_ = None 2025-03-04T20:48:19.2769650Z 2025-03-04T20:48:19.2770078Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T20:48:19.2770665Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T20:48:19.2771020Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T20:48:19.2771305Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T20:48:19.2771622Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T20:48:19.2771933Z getitem_31: "f32[1261 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T20:48:19.2772184Z 2025-03-04T20:48:19.2772746Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T20:48:19.2773439Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T20:48:19.2773777Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:48:19.2774113Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T20:48:19.2774459Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T20:48:19.2774749Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T20:48:19.2774981Z 2025-03-04T20:48:19.2775408Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T20:48:19.2775937Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T20:48:19.2776178Z 2025-03-04T20:48:21.4140664Z 2025-03-04T20:48:21.4141653Z class GraphModule(torch.nn.Module): 2025-03-04T20:48:21.4142235Z def forward(self, L_scores_0_: "f32[1000, 81][81, 1]cpu", L_boxes_0_: "f32[1000, 320][320, 1]cpu"): 2025-03-04T20:48:21.4142596Z l_scores_0_ = L_scores_0_ 2025-03-04T20:48:21.4142800Z l_boxes_0_ = L_boxes_0_ 2025-03-04T20:48:21.4142990Z 2025-03-04T20:48:21.4143631Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T20:48:21.4144832Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(l_boxes_0_); l_boxes_0_ = None 2025-03-04T20:48:21.4145208Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:48:21.4145528Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(l_scores_0_); l_scores_0_ = None 2025-03-04T20:48:21.4145851Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T20:48:21.4146142Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T20:48:21.4146381Z 2025-03-04T20:48:21.4146875Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T20:48:21.4148782Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T20:48:21.4149628Z 2025-03-04T20:48:21.4149742Z 2025-03-04T20:48:21.4149835Z class GraphModule(torch.nn.Module): 2025-03-04T20:48:21.4150160Z def forward(self, L_scores_0_: "f32[1000, 81][81, 1]cpu", L_boxes_0_: "f32[1000, 320][320, 1]cpu"): 2025-03-04T20:48:21.4150466Z l_scores_0_ = L_scores_0_ 2025-03-04T20:48:21.4150666Z l_boxes_0_ = L_boxes_0_ 2025-03-04T20:48:21.4150849Z 2025-03-04T20:48:21.4151449Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T20:48:21.4152188Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(l_boxes_0_); l_boxes_0_ = None 2025-03-04T20:48:21.4152516Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:48:21.4152881Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(l_scores_0_); l_scores_0_ = None 2025-03-04T20:48:21.4153201Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T20:48:21.4153497Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T20:48:21.4153736Z 2025-03-04T20:48:21.4155151Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T20:48:21.4155702Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T20:48:21.4155933Z 2025-03-04T20:48:53.4436103Z Compilation time (from dynamo_timed): 75.271293287 2025-03-04T20:48:53.4436399Z pass 2025-03-04T20:48:53.4441790Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T20:48:53.4442627Z TIMING: entire_frame_compile:75.27129 gc:0.04338 _recursive_pre_grad_passes:0.03292 _recursive_joint_graph_passes:0.25871 _recursive_post_grad_passes:0.26104 async_compile.wait:34.91021 code_gen:46.09189 inductor_compile:50.01853 backend_compile:60.11241 total_wall_time:75.27129 2025-03-04T20:48:53.4443637Z STATS: call_* op count: 777 | FakeTensorMode.__torch_dispatch__:33337 | FakeTensor.__torch_dispatch__:3860 | ProxyTorchDispatchMode.__torch_dispatch__:12067 | attempt fast:91 | slow no contiguity match:44 | fast is_contiguous:47 2025-03-04T20:48:53.4444251Z Dynamo produced 53 graphs covering 777 ops with 42 graph breaks (6 unique) 2025-03-04T20:48:59.6148921Z 2025-03-04T20:49:09.8405184Z loading model: 0it [00:00, ?it/s] 2025-03-04T20:49:09.8411125Z loading model: 0it [00:10, ?it/s] 2025-03-04T20:49:09.8419320Z cpu eval detectron2_fasterrcnn_r_101_dc5 2025-03-04T20:49:26.9173212Z WARNING:common:fp64 golden ref were not generated for detectron2_fasterrcnn_r_101_dc5. Setting accuracy check to cosine 2025-03-04T20:49:26.9275667Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T20:49:37.3905624Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T20:49:47.6119416Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T20:49:58.6634656Z 2025-03-04T20:49:58.6635299Z class GraphModule(torch.nn.Module): 2025-03-04T20:49:58.6760514Z def forward(self, L_stack0_tensor: "f32[4, 3, 1156, 1199][4158132, 1386044, 1199, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_: "f32[512, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", 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"f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_: "f32[512, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_: "f32[2048, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[2048, 2048, 3, 3][18432, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[2048][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"): 2025-03-04T20:49:58.6861279Z l_stack0_tensor = L_stack0_tensor 2025-03-04T20:49:58.6861704Z l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.6862349Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.6863045Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.6863714Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.6864430Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.6865059Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.6865735Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.6866577Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.6867396Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.6868084Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.6868734Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.6869541Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.6870420Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.6871282Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.6872081Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.6872878Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.6873724Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.6874668Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.6875645Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.6876378Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.6877039Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:49:58.6877745Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.6878508Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.6879254Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:49:58.6879967Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:49:58.6880621Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.6881292Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.6882013Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.6882725Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.6883407Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.6884052Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.6884733Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.6885462Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.6886167Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.6886850Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.6887492Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.6888159Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.6888881Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.6889630Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.6890307Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.6890951Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.6891628Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.6892353Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.6893069Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.6893744Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.6894410Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.6895168Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.6895938Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.6896636Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.6897312Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.6897955Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.6898630Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.6899417Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.6900217Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.6900974Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.6901707Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.6902583Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.6903466Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.6904225Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.6904955Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.6905660Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.6906404Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.6907199Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.6907929Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.6908619Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.6909278Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.6909966Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.6910712Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.6911468Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.6912193Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.6912903Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:49:58.6913658Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.6914530Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.6915332Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:49:58.6916073Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:49:58.6916755Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.6917438Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.6918242Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.6918973Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.6919666Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.6920322Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.6921007Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.6921756Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.6922473Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.6923161Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.6923810Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.6924499Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.6925239Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.6925953Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.6926644Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.6927303Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.6927993Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.6928735Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.6929452Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.6930142Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.6930797Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.6931533Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.6932271Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.6932997Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.6933692Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.6934376Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.6935081Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.6935818Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.6936536Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.6937225Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.6937858Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.6938543Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.6939278Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.6939994Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.6940690Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.6941331Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.6942008Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.6942729Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.6943426Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.6944099Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.6944736Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.6945468Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.6946198Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.6946911Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.6947605Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.6948260Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.6948962Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.6949708Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.6950426Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.6951112Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.6951775Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.6952502Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.6953284Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.6954133Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.6954894Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.6955581Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.6956275Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.6957020Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.6957747Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.6958448Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.6959175Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:49:58.6959900Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.6960680Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.6961435Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:49:58.6962161Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:49:58.6962846Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.6963536Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.6964278Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.6964997Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.6965689Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.6966342Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.6967030Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.6967762Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.6968476Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.6969171Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.6969825Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.6970516Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.6971241Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.6971957Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.6972701Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.6973337Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.6974002Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.6974720Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.6975415Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.6976110Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.6976749Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.6977412Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.6978132Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.6978830Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.6979505Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.6980139Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.6980813Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.6981549Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.6982248Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.6982927Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.6983566Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.6984237Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.6984954Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.6985652Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.6986377Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.6987022Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.6987695Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.6988420Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.6989128Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.6989821Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.6990457Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.6991148Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.6991894Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.6992616Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.6993316Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.6994032Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.6994737Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.6995489Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.6996217Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.6996993Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.6997653Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.6998347Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.6999098Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.6999909Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.7000606Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.7001278Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.7002104Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.7002855Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.7003630Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.7004393Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.7005070Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.7005776Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.7006531Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.7007260Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.7007971Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.7008635Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.7009333Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.7010076Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.7010802Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.7011481Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.7012137Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.7012818Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.7013562Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.7014326Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.7014998Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.7015635Z l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.7016302Z l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.7017030Z l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.7017748Z l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.7018420Z l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.7019061Z l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.7019729Z l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.7020451Z l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.7021151Z l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.7021821Z l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.7022461Z l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.7023131Z l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.7023874Z l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.7024591Z l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.7025290Z l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.7025974Z l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.7026647Z l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.7027443Z l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.7028162Z l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.7028853Z l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.7029507Z l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.7030190Z l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.7030948Z l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.7031665Z l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.7032355Z l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.7033007Z l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.7033695Z l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.7034550Z l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.7035332Z l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.7036031Z l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.7036689Z l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.7037381Z l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.7038134Z l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.7038859Z l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.7039554Z l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.7040217Z l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.7040944Z l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.7041704Z l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.7042440Z l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.7043115Z l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.7043760Z l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.7044433Z l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.7045173Z l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.7045875Z l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.7046553Z l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.7047195Z l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.7047867Z l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.7048595Z l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.7049300Z l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.7049976Z l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.7050620Z l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.7051304Z l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.7052025Z l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.7052721Z l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.7053406Z l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.7054067Z l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.7054848Z l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.7055611Z l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.7056329Z l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.7057023Z l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.7057696Z l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.7058418Z l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.7059173Z l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.7059898Z l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.7060614Z l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.7061332Z l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.7062042Z l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.7062794Z l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.7063539Z l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.7064241Z l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.7064908Z l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.7065604Z l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.7066348Z l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.7067073Z l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.7067801Z l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.7068532Z l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.7069249Z l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.7070052Z l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.7070866Z l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.7071648Z l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.7073153Z l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.7073950Z l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.7074737Z l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.7075518Z l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.7076233Z l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.7076925Z l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.7077635Z l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.7078401Z l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.7079146Z l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.7079864Z l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.7080548Z l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.7081259Z l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.7082024Z l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.7082763Z l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.7083516Z l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.7084209Z l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.7084918Z l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.7085679Z l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.7086383Z l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.7087064Z l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.7087757Z l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.7088446Z l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.7089197Z l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.7089947Z l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.7090662Z l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.7091326Z l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.7092013Z l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.7092739Z l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.7093448Z l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.7094134Z l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.7094786Z l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.7095463Z l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.7096191Z l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.7096910Z l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.7097653Z l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.7098320Z l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.7099009Z l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.7099754Z l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.7100498Z l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.7101199Z l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.7101846Z l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.7102619Z l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.7103370Z l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.7104100Z l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.7104791Z l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.7105472Z l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.7106198Z l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.7106962Z l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.7107709Z l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.7108432Z l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.7109110Z l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.7109813Z l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.7110580Z l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.7111409Z l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.7112134Z l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.7112829Z l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.7113544Z l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.7114369Z l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.7115178Z l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.7115898Z l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.7116574Z l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.7117274Z l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.7118023Z l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.7118757Z l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.7119464Z l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.7120134Z l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.7120841Z l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.7121593Z l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.7122324Z l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.7123024Z l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.7123690Z l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.7124384Z l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.7125184Z l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.7125913Z l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.7126593Z l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.7127239Z l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.7127927Z l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.7128685Z l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.7129406Z l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.7130097Z l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.7130756Z l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.7131453Z l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.7132202Z l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.7132939Z l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.7133650Z l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.7134324Z l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.7135008Z l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.7135761Z l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.7136485Z l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.7137183Z l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.7137843Z l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.7138534Z l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.7139350Z l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.7140097Z l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.7140819Z l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.7141477Z l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.7142187Z l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.7142967Z l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.7143713Z l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.7144404Z l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.7145069Z l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.7145778Z l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.7146516Z l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.7147239Z l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.7147930Z l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.7148596Z l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.7149314Z l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.7150102Z l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.7151051Z l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.7152137Z l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.7153215Z l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.7154097Z l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.7155093Z l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.7156229Z l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.7157298Z l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.7158355Z l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.7159119Z l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.7159899Z l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.7160625Z l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.7161326Z l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.7161994Z l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.7162695Z l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.7163446Z l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.7164458Z l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.7165459Z l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.7166486Z l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.7167346Z l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.7168169Z l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.7168922Z l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.7169651Z l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.7170386Z l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.7171104Z l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.7171887Z l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.7172718Z l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.7173466Z l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.7174150Z l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.7174847Z l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.7175606Z l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.7176343Z l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.7177049Z l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.7177803Z l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.7178692Z l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.7179618Z l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.7180373Z l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.7181082Z l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.7181755Z l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.7182440Z l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.7183193Z l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.7183933Z l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.7184718Z l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.7185395Z l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.7186103Z l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.7186869Z l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.7187615Z l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.7188338Z l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.7189020Z l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.7189735Z l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.7190512Z l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.7191260Z l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.7191989Z l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.7192677Z l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.7193397Z l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.7194252Z l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.7194996Z l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.7195722Z l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.7196404Z l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.7197119Z l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.7197891Z l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.7198639Z l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.7199423Z l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.7200118Z l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.7200897Z l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.7201668Z l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.7202564Z l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.7203332Z l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.7204020Z l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.7204742Z l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.7205489Z l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.7206221Z l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.7206925Z l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.7207593Z l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.7208291Z l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.7209041Z l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.7209772Z l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.7210475Z l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.7211141Z l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.7211840Z l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.7212590Z l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.7213381Z l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.7214082Z l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.7214767Z l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:49:58.7215498Z l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.7216277Z l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.7217050Z l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:49:58.7217773Z l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:49:58.7218442Z l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.7219124Z l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.7219865Z l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.7220590Z l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.7221295Z l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.7221938Z l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.7222609Z l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.7223335Z l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.7224035Z l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.7224715Z l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.7225372Z l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.7226060Z l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.7226862Z l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.7227568Z l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.7228246Z l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.7228905Z l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.7229602Z l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.7230366Z l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.7231087Z l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.7231789Z l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.7232443Z l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.7233130Z l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.7233951Z l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.7234677Z l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.7235369Z l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.7236023Z l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.7236713Z l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.7237463Z l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.7238190Z l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.7238882Z l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.7239609Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T20:49:58.7240346Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T20:49:58.7241103Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T20:49:58.7241848Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T20:49:58.7242634Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T20:49:58.7243402Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T20:49:58.7244157Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T20:49:58.7244650Z 2025-03-04T20:49:58.7245043Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7245862Z x: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1); l_stack0_tensor = l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.7246473Z 2025-03-04T20:49:58.7246835Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7248666Z x_1: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7250269Z 2025-03-04T20:49:58.7250655Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x) 2025-03-04T20:49:58.7251120Z x_2: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.relu_(x_1); x_1 = None 2025-03-04T20:49:58.7251371Z 2025-03-04T20:49:58.7251816Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1) 2025-03-04T20:49:58.7252454Z x_3: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1); x_2 = None 2025-03-04T20:49:58.7252801Z 2025-03-04T20:49:58.7253134Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7253860Z x_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.7254409Z 2025-03-04T20:49:58.7254792Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7256644Z x_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_4 = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7258311Z 2025-03-04T20:49:58.7258686Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7259167Z out: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_5); x_5 = None 2025-03-04T20:49:58.7259419Z 2025-03-04T20:49:58.7259753Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7260492Z x_6: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out = l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.7261033Z 2025-03-04T20:49:58.7261383Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7263242Z x_7: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_6 = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7264944Z 2025-03-04T20:49:58.7265321Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7265801Z out_1: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_7); x_7 = None 2025-03-04T20:49:58.7266055Z 2025-03-04T20:49:58.7266391Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7267142Z x_8: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_1 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.7267693Z 2025-03-04T20:49:58.7268036Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7269928Z x_9: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_8 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7271536Z 2025-03-04T20:49:58.7271855Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7272635Z x_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); x_3 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:49:58.7273193Z 2025-03-04T20:49:58.7273543Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7275579Z x_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_10 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7277274Z 2025-03-04T20:49:58.7277639Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.7278121Z x_9 += x_11; out_2: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_9; x_9 = x_11 = None 2025-03-04T20:49:58.7278380Z 2025-03-04T20:49:58.7278756Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7279265Z out_3: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_2); out_2 = None 2025-03-04T20:49:58.7279532Z 2025-03-04T20:49:58.7279869Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7280617Z x_12: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.7281146Z 2025-03-04T20:49:58.7281491Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7283357Z x_13: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_12 = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7284997Z 2025-03-04T20:49:58.7285358Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7285823Z out_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_13); x_13 = None 2025-03-04T20:49:58.7286098Z 2025-03-04T20:49:58.7286426Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7287147Z x_14: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_4 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.7287680Z 2025-03-04T20:49:58.7288020Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7289876Z x_15: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_14 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7291518Z 2025-03-04T20:49:58.7291892Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7292372Z out_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_15); x_15 = None 2025-03-04T20:49:58.7292633Z 2025-03-04T20:49:58.7292968Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7293713Z x_16: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_5 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.7294265Z 2025-03-04T20:49:58.7294617Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7296538Z x_17: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_16 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7298214Z 2025-03-04T20:49:58.7298578Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.7299064Z x_17 += out_3; out_6: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_17; x_17 = out_3 = None 2025-03-04T20:49:58.7299333Z 2025-03-04T20:49:58.7299702Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7300216Z out_7: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_6); out_6 = None 2025-03-04T20:49:58.7300485Z 2025-03-04T20:49:58.7300822Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7301561Z x_18: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.7302201Z 2025-03-04T20:49:58.7302560Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7304432Z x_19: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_18 = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7306074Z 2025-03-04T20:49:58.7306440Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7306916Z out_8: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_19); x_19 = None 2025-03-04T20:49:58.7307165Z 2025-03-04T20:49:58.7307489Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7308214Z x_20: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_8 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.7308751Z 2025-03-04T20:49:58.7309118Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7311165Z x_21: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_20 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7312868Z 2025-03-04T20:49:58.7313238Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7313723Z out_9: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_21); x_21 = None 2025-03-04T20:49:58.7314047Z 2025-03-04T20:49:58.7314386Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7315128Z x_22: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_9 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.7315674Z 2025-03-04T20:49:58.7316022Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7317877Z x_23: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_22 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7319488Z 2025-03-04T20:49:58.7319853Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.7320323Z x_23 += out_7; out_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_23; x_23 = out_7 = None 2025-03-04T20:49:58.7320586Z 2025-03-04T20:49:58.7320957Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7321430Z out_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_10); out_10 = None 2025-03-04T20:49:58.7321687Z 2025-03-04T20:49:58.7322010Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7322723Z x_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.7323254Z 2025-03-04T20:49:58.7323591Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7325450Z x_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_24 = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7327075Z 2025-03-04T20:49:58.7327478Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7327966Z out_12: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_25); x_25 = None 2025-03-04T20:49:58.7328228Z 2025-03-04T20:49:58.7328561Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7329313Z x_26: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_12 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.7329876Z 2025-03-04T20:49:58.7330262Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7332138Z x_27: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_26 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7333880Z 2025-03-04T20:49:58.7334243Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7334754Z out_13: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_27); x_27 = None 2025-03-04T20:49:58.7335006Z 2025-03-04T20:49:58.7335340Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7336090Z x_28: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_13 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.7336648Z 2025-03-04T20:49:58.7336993Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7338906Z x_29: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_28 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7340591Z 2025-03-04T20:49:58.7340925Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7341697Z x_30: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); out_11 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:49:58.7342264Z 2025-03-04T20:49:58.7342610Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7344530Z x_31: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_30 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7346226Z 2025-03-04T20:49:58.7346589Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.7347070Z x_29 += x_31; out_14: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_29; x_29 = x_31 = None 2025-03-04T20:49:58.7347321Z 2025-03-04T20:49:58.7347685Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7348180Z out_15: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_14); out_14 = None 2025-03-04T20:49:58.7348450Z 2025-03-04T20:49:58.7348784Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7349522Z x_32: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.7350066Z 2025-03-04T20:49:58.7350411Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7352329Z x_33: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_32 = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7354139Z 2025-03-04T20:49:58.7354566Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7355135Z out_16: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_33); x_33 = None 2025-03-04T20:49:58.7355451Z 2025-03-04T20:49:58.7355832Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7356665Z x_34: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_16 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.7357254Z 2025-03-04T20:49:58.7357626Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7359585Z x_35: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_34 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7361258Z 2025-03-04T20:49:58.7361631Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7362117Z out_17: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_35); x_35 = None 2025-03-04T20:49:58.7362376Z 2025-03-04T20:49:58.7362716Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7363465Z x_36: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_17 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.7364032Z 2025-03-04T20:49:58.7364405Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7366342Z x_37: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_36 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7368068Z 2025-03-04T20:49:58.7368437Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.7368936Z x_37 += out_15; out_18: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_37; x_37 = out_15 = None 2025-03-04T20:49:58.7369222Z 2025-03-04T20:49:58.7369607Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7370145Z out_19: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_18); out_18 = None 2025-03-04T20:49:58.7370428Z 2025-03-04T20:49:58.7370787Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7371527Z x_38: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.7372075Z 2025-03-04T20:49:58.7372426Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7374321Z x_39: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_38 = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7376014Z 2025-03-04T20:49:58.7376385Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7376877Z out_20: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_39); x_39 = None 2025-03-04T20:49:58.7377134Z 2025-03-04T20:49:58.7377469Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7378210Z x_40: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_20 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.7378762Z 2025-03-04T20:49:58.7379139Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7381167Z x_41: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_40 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7382898Z 2025-03-04T20:49:58.7383284Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7383787Z out_21: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_41); x_41 = None 2025-03-04T20:49:58.7384077Z 2025-03-04T20:49:58.7384428Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7385207Z x_42: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_21 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.7385786Z 2025-03-04T20:49:58.7386155Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7388125Z x_43: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_42 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7389873Z 2025-03-04T20:49:58.7390258Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.7390774Z x_43 += out_19; out_22: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_43; x_43 = out_19 = None 2025-03-04T20:49:58.7391058Z 2025-03-04T20:49:58.7391448Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7391966Z out_23: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_22); out_22 = None 2025-03-04T20:49:58.7392249Z 2025-03-04T20:49:58.7392598Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7393348Z x_44: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.7393971Z 2025-03-04T20:49:58.7394362Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7396397Z x_45: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_44 = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7398080Z 2025-03-04T20:49:58.7398453Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7398951Z out_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_45); x_45 = None 2025-03-04T20:49:58.7399206Z 2025-03-04T20:49:58.7399533Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7400335Z x_46: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_24 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.7400880Z 2025-03-04T20:49:58.7401225Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7403254Z x_47: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_46 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7404924Z 2025-03-04T20:49:58.7405286Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7405766Z out_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_47); x_47 = None 2025-03-04T20:49:58.7406017Z 2025-03-04T20:49:58.7406346Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7407071Z x_48: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_25 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.7407608Z 2025-03-04T20:49:58.7407951Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7409879Z x_49: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_48 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7411532Z 2025-03-04T20:49:58.7411881Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.7412376Z x_49 += out_23; out_26: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_49; x_49 = out_23 = None 2025-03-04T20:49:58.7412636Z 2025-03-04T20:49:58.7412992Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7413468Z out_27: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_26); out_26 = None 2025-03-04T20:49:58.7413728Z 2025-03-04T20:49:58.7414052Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7414758Z x_50: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.7415280Z 2025-03-04T20:49:58.7415646Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7417473Z x_51: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_50 = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7419091Z 2025-03-04T20:49:58.7419457Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7419929Z out_28: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_51); x_51 = None 2025-03-04T20:49:58.7420189Z 2025-03-04T20:49:58.7420513Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7421222Z x_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_28 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.7421749Z 2025-03-04T20:49:58.7422085Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7423956Z x_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_52 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7425557Z 2025-03-04T20:49:58.7425948Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7426411Z out_29: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_53); x_53 = None 2025-03-04T20:49:58.7426655Z 2025-03-04T20:49:58.7426981Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7427700Z x_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_29 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.7428233Z 2025-03-04T20:49:58.7428574Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7430452Z x_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_54 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7432097Z 2025-03-04T20:49:58.7432432Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7433183Z x_56: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); out_27 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:49:58.7433742Z 2025-03-04T20:49:58.7434160Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7436169Z x_57: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_56 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7437886Z 2025-03-04T20:49:58.7438253Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.7438729Z x_55 += x_57; out_30: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_55; x_55 = x_57 = None 2025-03-04T20:49:58.7438985Z 2025-03-04T20:49:58.7439353Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7439861Z out_31: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_30); out_30 = None 2025-03-04T20:49:58.7440118Z 2025-03-04T20:49:58.7440447Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7441171Z x_58: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.7441704Z 2025-03-04T20:49:58.7442054Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7443912Z x_59: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_58 = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7445561Z 2025-03-04T20:49:58.7445919Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7446383Z out_32: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_59); x_59 = None 2025-03-04T20:49:58.7446628Z 2025-03-04T20:49:58.7446952Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7447666Z x_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_32 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.7448194Z 2025-03-04T20:49:58.7448531Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7450379Z x_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_60 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7452004Z 2025-03-04T20:49:58.7452361Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7452824Z out_33: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_61); x_61 = None 2025-03-04T20:49:58.7453090Z 2025-03-04T20:49:58.7453418Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7454133Z x_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_33 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.7454664Z 2025-03-04T20:49:58.7455004Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7456814Z x_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_62 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7458420Z 2025-03-04T20:49:58.7458783Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.7459260Z x_63 += out_31; out_34: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_63; x_63 = out_31 = None 2025-03-04T20:49:58.7459521Z 2025-03-04T20:49:58.7459884Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7460377Z out_35: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_34); out_34 = None 2025-03-04T20:49:58.7460621Z 2025-03-04T20:49:58.7460947Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7461658Z x_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.7462187Z 2025-03-04T20:49:58.7462521Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7464361Z x_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_64 = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7465957Z 2025-03-04T20:49:58.7466317Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7466802Z out_36: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_65); x_65 = None 2025-03-04T20:49:58.7467046Z 2025-03-04T20:49:58.7467370Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7468090Z x_66: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_36 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.7468628Z 2025-03-04T20:49:58.7468981Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7470832Z x_67: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_66 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7472460Z 2025-03-04T20:49:58.7472832Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7473309Z out_37: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_67); x_67 = None 2025-03-04T20:49:58.7473558Z 2025-03-04T20:49:58.7473949Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7474740Z x_68: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_37 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.7475321Z 2025-03-04T20:49:58.7475673Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7477597Z x_69: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_68 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7479257Z 2025-03-04T20:49:58.7479621Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.7480099Z x_69 += out_35; out_38: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_69; x_69 = out_35 = None 2025-03-04T20:49:58.7480378Z 2025-03-04T20:49:58.7480759Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7481245Z out_39: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_38); out_38 = None 2025-03-04T20:49:58.7481507Z 2025-03-04T20:49:58.7481847Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7482578Z x_70: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.7483118Z 2025-03-04T20:49:58.7483473Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7485348Z x_71: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_70 = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7487000Z 2025-03-04T20:49:58.7487379Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7487864Z out_40: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_71); x_71 = None 2025-03-04T20:49:58.7488118Z 2025-03-04T20:49:58.7488457Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7489197Z x_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_40 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.7489742Z 2025-03-04T20:49:58.7490097Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7491987Z x_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_72 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7493630Z 2025-03-04T20:49:58.7493995Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7494496Z out_41: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_73); x_73 = None 2025-03-04T20:49:58.7494744Z 2025-03-04T20:49:58.7495076Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7495813Z x_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_41 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.7496356Z 2025-03-04T20:49:58.7496696Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7498540Z x_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_74 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7500170Z 2025-03-04T20:49:58.7500526Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.7500989Z x_75 += out_39; out_42: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_75; x_75 = out_39 = None 2025-03-04T20:49:58.7501243Z 2025-03-04T20:49:58.7501602Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7502223Z out_43: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_42); out_42 = None 2025-03-04T20:49:58.7502487Z 2025-03-04T20:49:58.7502826Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7503585Z x_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.7504126Z 2025-03-04T20:49:58.7504480Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7506446Z x_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_76 = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7508101Z 2025-03-04T20:49:58.7508473Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7508946Z out_44: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_77); x_77 = None 2025-03-04T20:49:58.7509205Z 2025-03-04T20:49:58.7509548Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7510325Z x_78: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_44 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.7510899Z 2025-03-04T20:49:58.7511272Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7513205Z x_79: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_78 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7514898Z 2025-03-04T20:49:58.7515267Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7515756Z out_45: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_79); x_79 = None 2025-03-04T20:49:58.7516004Z 2025-03-04T20:49:58.7516336Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7517066Z x_80: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_45 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.7517606Z 2025-03-04T20:49:58.7517955Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7519851Z x_81: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_80 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7521484Z 2025-03-04T20:49:58.7521840Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.7522329Z x_81 += out_43; out_46: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_81; x_81 = out_43 = None 2025-03-04T20:49:58.7522584Z 2025-03-04T20:49:58.7522941Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7523407Z out_47: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_46); out_46 = None 2025-03-04T20:49:58.7523656Z 2025-03-04T20:49:58.7523977Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7524676Z x_82: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.7525189Z 2025-03-04T20:49:58.7525529Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7527330Z x_83: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_82 = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7528921Z 2025-03-04T20:49:58.7529280Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7529738Z out_48: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_83); x_83 = None 2025-03-04T20:49:58.7529981Z 2025-03-04T20:49:58.7530302Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7531008Z x_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_48 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.7531530Z 2025-03-04T20:49:58.7531867Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7533750Z x_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_84 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7535357Z 2025-03-04T20:49:58.7535729Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7536186Z out_49: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_85); x_85 = None 2025-03-04T20:49:58.7536434Z 2025-03-04T20:49:58.7536758Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7537469Z x_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_49 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.7538002Z 2025-03-04T20:49:58.7538340Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7540152Z x_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_86 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7541750Z 2025-03-04T20:49:58.7542104Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.7542573Z x_87 += out_47; out_50: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_87; x_87 = out_47 = None 2025-03-04T20:49:58.7542825Z 2025-03-04T20:49:58.7543180Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7543648Z out_51: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_50); out_50 = None 2025-03-04T20:49:58.7543898Z 2025-03-04T20:49:58.7544219Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7544926Z x_88: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.7545470Z 2025-03-04T20:49:58.7545848Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7547675Z x_89: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_88 = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7549320Z 2025-03-04T20:49:58.7549756Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7550218Z out_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_89); x_89 = None 2025-03-04T20:49:58.7550462Z 2025-03-04T20:49:58.7550784Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7551521Z x_90: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_52 = l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.7552070Z 2025-03-04T20:49:58.7552424Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7554386Z x_91: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_90 = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7556031Z 2025-03-04T20:49:58.7556402Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7556871Z out_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_91); x_91 = None 2025-03-04T20:49:58.7557116Z 2025-03-04T20:49:58.7557444Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7558185Z x_92: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_53 = l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.7558727Z 2025-03-04T20:49:58.7559076Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7561010Z x_93: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_92 = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7562658Z 2025-03-04T20:49:58.7563021Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.7563496Z x_93 += out_51; out_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_93; x_93 = out_51 = None 2025-03-04T20:49:58.7563754Z 2025-03-04T20:49:58.7564126Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7564612Z out_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_54); out_54 = None 2025-03-04T20:49:58.7564867Z 2025-03-04T20:49:58.7565201Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7565934Z x_94: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.7566461Z 2025-03-04T20:49:58.7566806Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7568614Z x_95: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_94 = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7570207Z 2025-03-04T20:49:58.7570567Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7571032Z out_56: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_95); x_95 = None 2025-03-04T20:49:58.7571277Z 2025-03-04T20:49:58.7571601Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7572308Z x_96: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_56 = l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.7572847Z 2025-03-04T20:49:58.7573217Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7575047Z x_97: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_96 = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7576671Z 2025-03-04T20:49:58.7577032Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7577493Z out_57: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_97); x_97 = None 2025-03-04T20:49:58.7577737Z 2025-03-04T20:49:58.7578065Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7578786Z x_98: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_57 = l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.7579318Z 2025-03-04T20:49:58.7579661Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7581484Z x_99: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_98 = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7583087Z 2025-03-04T20:49:58.7583448Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.7583911Z x_99 += out_55; out_58: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_99; x_99 = out_55 = None 2025-03-04T20:49:58.7584161Z 2025-03-04T20:49:58.7584516Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7584989Z out_59: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_58); out_58 = None 2025-03-04T20:49:58.7585238Z 2025-03-04T20:49:58.7585561Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7586307Z x_100: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.7586848Z 2025-03-04T20:49:58.7587186Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7589023Z x_101: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_100 = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7590746Z 2025-03-04T20:49:58.7591120Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7591604Z out_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_101); x_101 = None 2025-03-04T20:49:58.7591865Z 2025-03-04T20:49:58.7592205Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7592957Z x_102: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_60 = l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.7593509Z 2025-03-04T20:49:58.7593915Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7595876Z x_103: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_102 = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7595947Z 2025-03-04T20:49:58.7596232Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7596377Z out_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_103); x_103 = None 2025-03-04T20:49:58.7596437Z 2025-03-04T20:49:58.7596696Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7597128Z x_104: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_61 = l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.7597222Z 2025-03-04T20:49:58.7597526Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7599075Z x_105: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_104 = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7599162Z 2025-03-04T20:49:58.7599444Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.7599600Z x_105 += out_59; out_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_105; x_105 = out_59 = None 2025-03-04T20:49:58.7599661Z 2025-03-04T20:49:58.7599952Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7600091Z out_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_62); out_62 = None 2025-03-04T20:49:58.7600157Z 2025-03-04T20:49:58.7600405Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7600838Z x_106: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_63, l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.7600899Z 2025-03-04T20:49:58.7601171Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7602811Z x_107: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_106 = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7602886Z 2025-03-04T20:49:58.7603175Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7603311Z out_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_107); x_107 = None 2025-03-04T20:49:58.7603380Z 2025-03-04T20:49:58.7603632Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7604761Z x_108: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_64, l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_64 = l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.7604826Z 2025-03-04T20:49:58.7605100Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7606609Z x_109: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_108, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_108 = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7606697Z 2025-03-04T20:49:58.7606992Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7607123Z out_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_109); x_109 = None 2025-03-04T20:49:58.7607191Z 2025-03-04T20:49:58.7607433Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7607855Z x_110: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_65, l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_65 = l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.7607915Z 2025-03-04T20:49:58.7608182Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7609672Z x_111: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_110, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_110 = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7609740Z 2025-03-04T20:49:58.7610020Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.7610164Z x_111 += out_63; out_66: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_111; x_111 = out_63 = None 2025-03-04T20:49:58.7610229Z 2025-03-04T20:49:58.7610503Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7610644Z out_67: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_66); out_66 = None 2025-03-04T20:49:58.7610720Z 2025-03-04T20:49:58.7611006Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7611411Z x_112: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_67, l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.7611482Z 2025-03-04T20:49:58.7611740Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7613237Z x_113: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_112, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_112 = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7613319Z 2025-03-04T20:49:58.7613593Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7613728Z out_68: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_113); x_113 = None 2025-03-04T20:49:58.7613786Z 2025-03-04T20:49:58.7614033Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7614454Z x_114: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_68, l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_68 = l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.7614519Z 2025-03-04T20:49:58.7614778Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7616312Z x_115: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_114, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_114 = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7616381Z 2025-03-04T20:49:58.7616657Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7616793Z out_69: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_115); x_115 = None 2025-03-04T20:49:58.7616852Z 2025-03-04T20:49:58.7617100Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7617579Z x_116: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_69, l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_69 = l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.7617647Z 2025-03-04T20:49:58.7617903Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7619419Z x_117: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_116, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_116 = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7619502Z 2025-03-04T20:49:58.7619776Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.7619928Z x_117 += out_67; out_70: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_117; x_117 = out_67 = None 2025-03-04T20:49:58.7619986Z 2025-03-04T20:49:58.7620270Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7620404Z out_71: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_70); out_70 = None 2025-03-04T20:49:58.7620471Z 2025-03-04T20:49:58.7620716Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7621132Z x_118: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_71, l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.7621192Z 2025-03-04T20:49:58.7621458Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7622972Z x_119: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_118, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_118 = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7623032Z 2025-03-04T20:49:58.7623315Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7623443Z out_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_119); x_119 = None 2025-03-04T20:49:58.7623537Z 2025-03-04T20:49:58.7623812Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7624234Z x_120: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_72, l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_72 = l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.7624299Z 2025-03-04T20:49:58.7624554Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7626061Z x_121: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_120, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_120 = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7626136Z 2025-03-04T20:49:58.7626422Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7626553Z out_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_121); x_121 = None 2025-03-04T20:49:58.7626620Z 2025-03-04T20:49:58.7626870Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7627299Z x_122: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_73, l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_73 = l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.7627366Z 2025-03-04T20:49:58.7627623Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7629200Z x_123: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_122, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_122 = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7629262Z 2025-03-04T20:49:58.7629556Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.7629713Z x_123 += out_71; out_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_123; x_123 = out_71 = None 2025-03-04T20:49:58.7629779Z 2025-03-04T20:49:58.7630055Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7630247Z out_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_74); out_74 = None 2025-03-04T20:49:58.7630317Z 2025-03-04T20:49:58.7630590Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7631037Z x_124: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_75, l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.7631100Z 2025-03-04T20:49:58.7631391Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7633003Z x_125: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_124, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_124 = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7633090Z 2025-03-04T20:49:58.7633381Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7633518Z out_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_125); x_125 = None 2025-03-04T20:49:58.7633586Z 2025-03-04T20:49:58.7633896Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7634333Z x_126: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_76, l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_76 = l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.7634394Z 2025-03-04T20:49:58.7634670Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7636220Z x_127: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_126, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_126 = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7636289Z 2025-03-04T20:49:58.7636615Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7636746Z out_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_127); x_127 = None 2025-03-04T20:49:58.7636834Z 2025-03-04T20:49:58.7637111Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7637624Z x_128: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_77, l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_77 = l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.7637710Z 2025-03-04T20:49:58.7638112Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7640004Z x_129: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_128, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_128 = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7640099Z 2025-03-04T20:49:58.7640387Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.7640532Z x_129 += out_75; out_78: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_129; x_129 = out_75 = None 2025-03-04T20:49:58.7640602Z 2025-03-04T20:49:58.7640917Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7641057Z out_79: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_78); out_78 = None 2025-03-04T20:49:58.7641117Z 2025-03-04T20:49:58.7641372Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7641959Z x_130: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_79, l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.7642037Z 2025-03-04T20:49:58.7642304Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7643997Z x_131: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_130, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_130 = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7644080Z 2025-03-04T20:49:58.7644518Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7644683Z out_80: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_131); x_131 = None 2025-03-04T20:49:58.7644742Z 2025-03-04T20:49:58.7644995Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7645603Z x_132: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_80, l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_80 = l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.7645670Z 2025-03-04T20:49:58.7645933Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7647571Z x_133: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_132, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_132 = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7647639Z 2025-03-04T20:49:58.7647925Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7648067Z out_81: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_133); x_133 = None 2025-03-04T20:49:58.7648127Z 2025-03-04T20:49:58.7648384Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7649022Z x_134: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_81, l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_81 = l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.7649110Z 2025-03-04T20:49:58.7649493Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7651928Z x_135: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_134, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_134 = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7652003Z 2025-03-04T20:49:58.7652313Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.7652537Z x_135 += out_79; out_82: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_135; x_135 = out_79 = None 2025-03-04T20:49:58.7652614Z 2025-03-04T20:49:58.7652924Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7653070Z out_83: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_82); out_82 = None 2025-03-04T20:49:58.7653143Z 2025-03-04T20:49:58.7653425Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7653911Z x_136: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_83, l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.7653997Z 2025-03-04T20:49:58.7654299Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7656053Z x_137: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_136, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_136 = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7656123Z 2025-03-04T20:49:58.7656511Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7656659Z out_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_137); x_137 = None 2025-03-04T20:49:58.7656733Z 2025-03-04T20:49:58.7657010Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7657490Z x_138: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_84, l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_84 = l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.7657554Z 2025-03-04T20:49:58.7657933Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7660455Z x_139: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_138, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_138 = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7660565Z 2025-03-04T20:49:58.7661111Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7661297Z out_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_139); x_139 = None 2025-03-04T20:49:58.7661379Z 2025-03-04T20:49:58.7661747Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7662325Z x_140: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_85, l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_85 = l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.7662387Z 2025-03-04T20:49:58.7662659Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7664254Z x_141: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_140, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_140 = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7664317Z 2025-03-04T20:49:58.7664611Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.7664757Z x_141 += out_83; out_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_141; x_141 = out_83 = None 2025-03-04T20:49:58.7664825Z 2025-03-04T20:49:58.7665108Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7665255Z out_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_86); out_86 = None 2025-03-04T20:49:58.7665315Z 2025-03-04T20:49:58.7665571Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7665988Z x_142: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_87, l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.7666058Z 2025-03-04T20:49:58.7666329Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7667911Z x_143: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_142, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_142 = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7668008Z 2025-03-04T20:49:58.7668288Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7668429Z out_88: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_143); x_143 = None 2025-03-04T20:49:58.7668490Z 2025-03-04T20:49:58.7668747Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7669181Z x_144: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_88, l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_88 = l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.7669265Z 2025-03-04T20:49:58.7669542Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7671183Z x_145: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_144, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_144 = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7671256Z 2025-03-04T20:49:58.7671585Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7671801Z out_89: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_145); x_145 = None 2025-03-04T20:49:58.7671887Z 2025-03-04T20:49:58.7672279Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7672767Z x_146: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_89, l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_89 = l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.7672843Z 2025-03-04T20:49:58.7673121Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7675179Z x_147: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_146, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_146 = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7675318Z 2025-03-04T20:49:58.7675663Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.7675825Z x_147 += out_87; out_90: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_147; x_147 = out_87 = None 2025-03-04T20:49:58.7675900Z 2025-03-04T20:49:58.7676203Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7676354Z out_91: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_90); out_90 = None 2025-03-04T20:49:58.7676417Z 2025-03-04T20:49:58.7676686Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7677113Z x_148: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_91, l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.7677206Z 2025-03-04T20:49:58.7677475Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7679054Z x_149: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_148, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_148 = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7679125Z 2025-03-04T20:49:58.7679409Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7679552Z out_92: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_149); x_149 = None 2025-03-04T20:49:58.7679613Z 2025-03-04T20:49:58.7679885Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7680311Z x_150: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_92, l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_92 = l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.7680383Z 2025-03-04T20:49:58.7680649Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7682249Z x_151: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_150, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_150 = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7682331Z 2025-03-04T20:49:58.7682618Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7682755Z out_93: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_151); x_151 = None 2025-03-04T20:49:58.7682813Z 2025-03-04T20:49:58.7683065Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7683492Z x_152: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_93, l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_93 = l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.7683579Z 2025-03-04T20:49:58.7683840Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7685397Z x_153: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_152, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_152 = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7685465Z 2025-03-04T20:49:58.7685745Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.7685895Z x_153 += out_91; out_94: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_153; x_153 = out_91 = None 2025-03-04T20:49:58.7685954Z 2025-03-04T20:49:58.7686241Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7686377Z out_95: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_94); out_94 = None 2025-03-04T20:49:58.7686446Z 2025-03-04T20:49:58.7686695Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7687126Z x_154: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_95, l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.7687186Z 2025-03-04T20:49:58.7687460Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7689029Z x_155: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_154, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_154 = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7689107Z 2025-03-04T20:49:58.7689389Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7689523Z out_96: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_155); x_155 = None 2025-03-04T20:49:58.7689592Z 2025-03-04T20:49:58.7689841Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7690309Z x_156: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_96, l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_96 = l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.7690373Z 2025-03-04T20:49:58.7690666Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7692208Z x_157: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_156, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_156 = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7692270Z 2025-03-04T20:49:58.7692562Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7692695Z out_97: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_157); x_157 = None 2025-03-04T20:49:58.7692763Z 2025-03-04T20:49:58.7693013Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7693449Z x_158: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_97, l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_97 = l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.7693509Z 2025-03-04T20:49:58.7693773Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7695302Z x_159: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_158, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_158 = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7695378Z 2025-03-04T20:49:58.7695663Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.7695806Z x_159 += out_95; out_98: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_159; x_159 = out_95 = None 2025-03-04T20:49:58.7695873Z 2025-03-04T20:49:58.7696150Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7696292Z out_99: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_98); out_98 = None 2025-03-04T20:49:58.7696365Z 2025-03-04T20:49:58.7696618Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7697032Z x_160: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_99, l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.7697090Z 2025-03-04T20:49:58.7697357Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7698894Z x_161: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_160, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_160 = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7698962Z 2025-03-04T20:49:58.7699253Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7699400Z out_100: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_161); x_161 = None 2025-03-04T20:49:58.7699459Z 2025-03-04T20:49:58.7699728Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7700155Z x_162: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_100, l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_100 = l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.7700212Z 2025-03-04T20:49:58.7700480Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7702143Z x_163: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_162, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_162 = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7702239Z 2025-03-04T20:49:58.7702517Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7702658Z out_101: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_163); x_163 = None 2025-03-04T20:49:58.7702716Z 2025-03-04T20:49:58.7702964Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7703419Z x_164: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_101, l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_101 = l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.7703478Z 2025-03-04T20:49:58.7703745Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7705260Z x_165: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_164, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_164 = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7705331Z 2025-03-04T20:49:58.7705610Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.7705755Z x_165 += out_99; out_102: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_165; x_165 = out_99 = None 2025-03-04T20:49:58.7705821Z 2025-03-04T20:49:58.7706096Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7706248Z out_103: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_102); out_102 = None 2025-03-04T20:49:58.7706304Z 2025-03-04T20:49:58.7706551Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7706962Z x_166: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_103, l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.7707023Z 2025-03-04T20:49:58.7707278Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7708832Z x_167: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_166, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_166 = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7708911Z 2025-03-04T20:49:58.7709187Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7709325Z out_104: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_167); x_167 = None 2025-03-04T20:49:58.7709397Z 2025-03-04T20:49:58.7709644Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7710058Z x_168: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_104, l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_104 = l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.7710123Z 2025-03-04T20:49:58.7710376Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7711916Z x_169: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_168, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_168 = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7711984Z 2025-03-04T20:49:58.7712267Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7712406Z out_105: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_169); x_169 = None 2025-03-04T20:49:58.7712469Z 2025-03-04T20:49:58.7712725Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7713153Z x_170: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_105, l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_105 = l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.7713220Z 2025-03-04T20:49:58.7713481Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7715208Z x_171: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_170, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_170 = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7715302Z 2025-03-04T20:49:58.7715582Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.7715746Z x_171 += out_103; out_106: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_171; x_171 = out_103 = None 2025-03-04T20:49:58.7715809Z 2025-03-04T20:49:58.7716117Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7716262Z out_107: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_106); out_106 = None 2025-03-04T20:49:58.7716331Z 2025-03-04T20:49:58.7716579Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7717009Z x_172: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_107, l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.7717069Z 2025-03-04T20:49:58.7717341Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7718900Z x_173: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_172, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_172 = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7718962Z 2025-03-04T20:49:58.7719252Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7719389Z out_108: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_173); x_173 = None 2025-03-04T20:49:58.7719458Z 2025-03-04T20:49:58.7719706Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7720143Z x_174: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_108, l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_108 = l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.7720203Z 2025-03-04T20:49:58.7720477Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7722071Z x_175: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_174, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_174 = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7722147Z 2025-03-04T20:49:58.7722439Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7722591Z out_109: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_175); x_175 = None 2025-03-04T20:49:58.7722657Z 2025-03-04T20:49:58.7722907Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7723362Z x_176: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_109, l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_109 = l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.7723423Z 2025-03-04T20:49:58.7723693Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7725282Z x_177: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_176, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_176 = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7725344Z 2025-03-04T20:49:58.7725631Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.7725789Z x_177 += out_107; out_110: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_177; x_177 = out_107 = None 2025-03-04T20:49:58.7725858Z 2025-03-04T20:49:58.7726141Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7726292Z out_111: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_110); out_110 = None 2025-03-04T20:49:58.7726351Z 2025-03-04T20:49:58.7726609Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7727032Z x_178: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_111, l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.7727098Z 2025-03-04T20:49:58.7727408Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7728945Z x_179: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_178, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_178 = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7729028Z 2025-03-04T20:49:58.7729318Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7729459Z out_112: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_179); x_179 = None 2025-03-04T20:49:58.7729520Z 2025-03-04T20:49:58.7729784Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7730198Z x_180: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_112, l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_112 = l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.7730264Z 2025-03-04T20:49:58.7730528Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7732100Z x_181: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_180, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_180 = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7732169Z 2025-03-04T20:49:58.7732451Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7732587Z out_113: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_181); x_181 = None 2025-03-04T20:49:58.7732646Z 2025-03-04T20:49:58.7732896Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7733313Z x_182: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_113, l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_113 = l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.7733377Z 2025-03-04T20:49:58.7733641Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7735194Z x_183: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_182, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_182 = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7735262Z 2025-03-04T20:49:58.7735535Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.7735712Z x_183 += out_111; out_114: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_183; x_183 = out_111 = None 2025-03-04T20:49:58.7735770Z 2025-03-04T20:49:58.7736050Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7736186Z out_115: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_114); out_114 = None 2025-03-04T20:49:58.7736251Z 2025-03-04T20:49:58.7736493Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7736906Z x_184: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_115, l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.7736972Z 2025-03-04T20:49:58.7737231Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7738749Z x_185: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_184, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_184 = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7738810Z 2025-03-04T20:49:58.7739092Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7739230Z out_116: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_185); x_185 = None 2025-03-04T20:49:58.7739289Z 2025-03-04T20:49:58.7739544Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7739968Z x_186: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_116, l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_116 = l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.7740051Z 2025-03-04T20:49:58.7740360Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7741906Z x_187: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_186, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_186 = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7741990Z 2025-03-04T20:49:58.7742270Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7742408Z out_117: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_187); x_187 = None 2025-03-04T20:49:58.7742465Z 2025-03-04T20:49:58.7742718Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7743145Z x_188: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_117, l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_117 = l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.7743213Z 2025-03-04T20:49:58.7743479Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7745023Z x_189: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_188, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_188 = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7745093Z 2025-03-04T20:49:58.7745375Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.7745533Z x_189 += out_115; out_118: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_189; x_189 = out_115 = None 2025-03-04T20:49:58.7745594Z 2025-03-04T20:49:58.7745891Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7746034Z out_119: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_118); out_118 = None 2025-03-04T20:49:58.7746101Z 2025-03-04T20:49:58.7746362Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7746816Z x_190: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.7746892Z 2025-03-04T20:49:58.7747159Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7748684Z x_191: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_190, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_190 = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7748774Z 2025-03-04T20:49:58.7749070Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7749206Z out_120: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_191); x_191 = None 2025-03-04T20:49:58.7749271Z 2025-03-04T20:49:58.7749521Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7749965Z x_192: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_120, l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1); out_120 = l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.7750026Z 2025-03-04T20:49:58.7750300Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7751949Z x_193: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_192, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_192 = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7752025Z 2025-03-04T20:49:58.7752332Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7752472Z out_121: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_193); x_193 = None 2025-03-04T20:49:58.7752541Z 2025-03-04T20:49:58.7752806Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7753279Z x_194: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_121, l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_121 = l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.7753358Z 2025-03-04T20:49:58.7753673Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7755377Z x_195: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_194, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_194 = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7755461Z 2025-03-04T20:49:58.7755721Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7756162Z x_196: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_119 = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:49:58.7756229Z 2025-03-04T20:49:58.7756494Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7758089Z x_197: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_196, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_196 = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7758160Z 2025-03-04T20:49:58.7758437Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.7758591Z x_195 += x_197; out_122: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_195; x_195 = x_197 = None 2025-03-04T20:49:58.7758654Z 2025-03-04T20:49:58.7758941Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7759085Z out_123: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_122); out_122 = None 2025-03-04T20:49:58.7759152Z 2025-03-04T20:49:58.7759403Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7759827Z x_198: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_123, l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.7759904Z 2025-03-04T20:49:58.7760209Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7761741Z x_199: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_198, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_198 = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7761819Z 2025-03-04T20:49:58.7762112Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7762244Z out_124: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_199); x_199 = None 2025-03-04T20:49:58.7762310Z 2025-03-04T20:49:58.7762557Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7762989Z x_200: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_124, l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1); out_124 = l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.7763048Z 2025-03-04T20:49:58.7763319Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7764873Z x_201: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_200, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_200 = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7764935Z 2025-03-04T20:49:58.7765229Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7765361Z out_125: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_201); x_201 = None 2025-03-04T20:49:58.7765428Z 2025-03-04T20:49:58.7765676Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7766111Z x_202: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_125, l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_125 = l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.7766168Z 2025-03-04T20:49:58.7766447Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7768018Z x_203: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_202, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_202 = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7768079Z 2025-03-04T20:49:58.7768367Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.7768538Z x_203 += out_123; out_126: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_203; x_203 = out_123 = None 2025-03-04T20:49:58.7768607Z 2025-03-04T20:49:58.7768884Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7769036Z out_127: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_126); out_126 = None 2025-03-04T20:49:58.7769096Z 2025-03-04T20:49:58.7769351Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7769767Z x_204: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_127, l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.7769838Z 2025-03-04T20:49:58.7770109Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7771645Z x_205: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_204, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_204 = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7771714Z 2025-03-04T20:49:58.7771990Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7772128Z out_128: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_205); x_205 = None 2025-03-04T20:49:58.7772187Z 2025-03-04T20:49:58.7772439Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7772858Z x_206: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_128, l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1); out_128 = l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.7772943Z 2025-03-04T20:49:58.7773253Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7774787Z x_207: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_206, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_206 = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7774903Z 2025-03-04T20:49:58.7775194Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7775339Z out_129: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_207); x_207 = None 2025-03-04T20:49:58.7775403Z 2025-03-04T20:49:58.7775664Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7776103Z x_208: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_129, l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_129 = l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.7776168Z 2025-03-04T20:49:58.7776447Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.7777997Z x_209: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_208, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_208 = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.7778070Z 2025-03-04T20:49:58.7778355Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.7778524Z x_209 += out_127; out_130: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_209; x_209 = out_127 = None 2025-03-04T20:49:58.7778588Z 2025-03-04T20:49:58.7778883Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.7779036Z out_131: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_130); out_130 = None 2025-03-04T20:49:58.7779100Z 2025-03-04T20:49:58.7779566Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:49:58.7779742Z arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32) 2025-03-04T20:49:58.7779843Z 2025-03-04T20:49:58.7780155Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:49:58.7780302Z shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T20:49:58.7780366Z 2025-03-04T20:49:58.7780825Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:49:58.7780973Z arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32) 2025-03-04T20:49:58.7781041Z 2025-03-04T20:49:58.7781333Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:49:58.7781493Z shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T20:49:58.7781554Z 2025-03-04T20:49:58.7781940Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:49:58.7782115Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T20:49:58.7782220Z shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0] 2025-03-04T20:49:58.7782336Z shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T20:49:58.7782405Z 2025-03-04T20:49:58.7782740Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:49:58.7782873Z shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T20:49:58.7782935Z 2025-03-04T20:49:58.7783269Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:49:58.7783385Z shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T20:49:58.7783452Z 2025-03-04T20:49:58.7783834Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:49:58.7784050Z shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T20:49:58.7784109Z 2025-03-04T20:49:58.7784540Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:49:58.7784667Z view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T20:49:58.7785099Z view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T20:49:58.7785223Z add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T20:49:58.7785339Z x_210: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4); add = None 2025-03-04T20:49:58.7785399Z 2025-03-04T20:49:58.7785705Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:49:58.7785835Z tensor: "f32[82125, 4][4, 1]cpu" = x_210.to(torch.float32); x_210 = None 2025-03-04T20:49:58.7785911Z 2025-03-04T20:49:58.7786200Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.7786986Z x_211: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_131, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); out_131 = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T20:49:58.7787051Z 2025-03-04T20:49:58.7787326Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:49:58.7787520Z x_212: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_211, inplace = False); x_211 = None 2025-03-04T20:49:58.7787599Z 2025-03-04T20:49:58.7787989Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:49:58.7788934Z score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_212, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T20:49:58.7789002Z 2025-03-04T20:49:58.7789370Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:49:58.7790244Z x_213: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_212, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_212 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T20:49:58.7790313Z 2025-03-04T20:49:58.7790671Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T20:49:58.7790836Z permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T20:49:58.7790978Z logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T20:49:58.7791050Z 2025-03-04T20:49:58.7791494Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T20:49:58.7791668Z view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_213.view(4, -1, 4, 73, 75); x_213 = None 2025-03-04T20:49:58.7791845Z permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2); view_2 = None 2025-03-04T20:49:58.7792036Z pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2); permute_1 = None 2025-03-04T20:49:58.7792100Z 2025-03-04T20:49:58.7792531Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:49:58.7792797Z pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T20:49:58.7792869Z 2025-03-04T20:49:58.7793329Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:49:58.7793492Z unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T20:49:58.7793650Z expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T20:49:58.7793855Z anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T20:49:58.7793932Z 2025-03-04T20:49:58.7794311Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:49:58.7794515Z deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T20:49:58.7794575Z 2025-03-04T20:49:58.7794899Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:49:58.7795036Z boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T20:49:58.7795104Z 2025-03-04T20:49:58.7795418Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:49:58.7795553Z getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:49:58.7795679Z getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:49:58.7795834Z widths: "f32[328500][1]cpu" = getitem_2 - getitem_3; getitem_2 = getitem_3 = None 2025-03-04T20:49:58.7795898Z 2025-03-04T20:49:58.7796226Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:49:58.7796347Z getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:49:58.7796473Z getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:49:58.7796615Z heights: "f32[328500][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T20:49:58.7796683Z 2025-03-04T20:49:58.7796989Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:49:58.7797113Z getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:49:58.7797200Z mul: "f32[328500][1]cpu" = 0.5 * widths 2025-03-04T20:49:58.7797328Z ctr_x: "f32[328500][1]cpu" = getitem_6 + mul; getitem_6 = mul = None 2025-03-04T20:49:58.7797388Z 2025-03-04T20:49:58.7797704Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:49:58.7797841Z getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:49:58.7797933Z mul_1: "f32[328500][1]cpu" = 0.5 * heights 2025-03-04T20:49:58.7798059Z ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1; getitem_7 = mul_1 = None 2025-03-04T20:49:58.7798124Z 2025-03-04T20:49:58.7798496Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:49:58.7798654Z getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:49:58.7798827Z dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0; getitem_8 = None 2025-03-04T20:49:58.7798900Z 2025-03-04T20:49:58.7799206Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:49:58.7799364Z getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:49:58.7799475Z dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0; getitem_9 = None 2025-03-04T20:49:58.7799547Z 2025-03-04T20:49:58.7799848Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:49:58.7800007Z getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:49:58.7800116Z dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0; getitem_10 = None 2025-03-04T20:49:58.7800203Z 2025-03-04T20:49:58.7800508Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:49:58.7800708Z getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:49:58.7800830Z dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0; getitem_11 = None 2025-03-04T20:49:58.7800895Z 2025-03-04T20:49:58.7801257Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:49:58.7801401Z dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:49:58.7801473Z 2025-03-04T20:49:58.7801830Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:49:58.7802084Z dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:49:58.7802153Z 2025-03-04T20:49:58.7802533Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:49:58.7802673Z getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:49:58.7802808Z mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12; dx = getitem_12 = None 2025-03-04T20:49:58.7802963Z getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:49:58.7803108Z pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13; mul_2 = getitem_13 = None 2025-03-04T20:49:58.7803174Z 2025-03-04T20:49:58.7803535Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:49:58.7803673Z getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:49:58.7803801Z mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14; dy = getitem_14 = None 2025-03-04T20:49:58.7803953Z getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:49:58.7804093Z pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15; mul_3 = getitem_15 = None 2025-03-04T20:49:58.7804154Z 2025-03-04T20:49:58.7804500Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:49:58.7804617Z exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:49:58.7804868Z getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:49:58.7805000Z pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16; exp = getitem_16 = None 2025-03-04T20:49:58.7805069Z 2025-03-04T20:49:58.7805408Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:49:58.7805531Z exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:49:58.7805698Z getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:49:58.7805838Z pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17; exp_1 = getitem_17 = None 2025-03-04T20:49:58.7805901Z 2025-03-04T20:49:58.7806228Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:49:58.7806347Z mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T20:49:58.7806472Z x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:49:58.7806537Z 2025-03-04T20:49:58.7806863Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:49:58.7806955Z mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T20:49:58.7807073Z y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:49:58.7807135Z 2025-03-04T20:49:58.7807452Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:49:58.7807563Z mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:49:58.7807698Z x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:49:58.7807761Z 2025-03-04T20:49:58.7808081Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:49:58.7808191Z mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:49:58.7808326Z y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:49:58.7808388Z 2025-03-04T20:49:58.7808749Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:49:58.7808934Z pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:49:58.7809003Z 2025-03-04T20:49:58.7809345Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:49:58.7809513Z proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4)); pred_boxes = None 2025-03-04T20:49:58.7809582Z 2025-03-04T20:49:58.7809976Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:49:58.7810158Z proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T20:49:58.7810220Z 2025-03-04T20:49:58.7810723Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T20:49:58.7810882Z arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T20:49:58.7810977Z 2025-03-04T20:49:58.7811271Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:49:58.7811412Z batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T20:49:58.7811472Z 2025-03-04T20:49:58.7811925Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:49:58.7812035Z topk = logits_i.topk(6000, dim = 1); logits_i = None 2025-03-04T20:49:58.7812139Z topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0] 2025-03-04T20:49:58.7812249Z topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1]; topk = None 2025-03-04T20:49:58.7812332Z 2025-03-04T20:49:58.7812815Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:49:58.7812982Z getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T20:49:58.7813214Z topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)]; proposals_i_1 = getitem_20 = topk_idx = None 2025-03-04T20:49:58.7813282Z 2025-03-04T20:49:58.7813738Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:49:58.7813912Z full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:49:58.7813974Z 2025-03-04T20:49:58.7814279Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:49:58.7814425Z level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu')); full = level_ids = None 2025-03-04T20:49:58.7814493Z 2025-03-04T20:49:58.7814876Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T20:49:58.7815026Z getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0]; topk_proposals_i = None 2025-03-04T20:49:58.7815085Z 2025-03-04T20:49:58.7815389Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:49:58.7815531Z tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32); getitem_22 = None 2025-03-04T20:49:58.7815602Z 2025-03-04T20:49:58.7815980Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T20:49:58.7816121Z scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0]; topk_scores_i = None 2025-03-04T20:49:58.7816183Z 2025-03-04T20:49:58.7816678Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T20:49:58.7816816Z isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1); tensor_1 = None 2025-03-04T20:49:58.7816931Z all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:49:58.7817085Z isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T20:49:58.7817257Z valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T20:49:58.7817322Z 2025-03-04T20:49:58.7817679Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T20:49:58.7817797Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T20:49:58.7817856Z 2025-03-04T20:49:58.7818394Z 2025-03-04T20:49:58.7818493Z class GraphModule(torch.nn.Module): 2025-03-04T20:49:58.7923090Z def forward(self, L_stack0_tensor: "f32[4, 3, 1156, 1199][4158132, 1386044, 1199, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", 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1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_: 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"f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_: "f32[512, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_: "f32[2048, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[2048, 2048, 3, 3][18432, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[2048][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"): 2025-03-04T20:49:58.8023964Z l_stack0_tensor = L_stack0_tensor 2025-03-04T20:49:58.8024375Z l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.8025070Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8025902Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8026730Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8027526Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8028275Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.8029068Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8029929Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8030789Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8031614Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8032392Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.8033166Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8034046Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8034996Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8035795Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8036515Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.8037280Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8038021Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8038758Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8039440Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8040105Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:49:58.8040812Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8041569Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8042315Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8043020Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8043672Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.8044346Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8045084Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8045791Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8046471Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8047115Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.8047793Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8048569Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8049271Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8049944Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8050611Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.8051299Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8052037Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8052724Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8053387Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8054028Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.8054690Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8055412Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8056107Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8056772Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8057414Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.8058080Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8058800Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8059515Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8060186Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8060831Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.8061556Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8062310Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8063031Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8063723Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8064403Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.8065134Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8065912Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8066628Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8067327Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8067999Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.8068720Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8069475Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8070192Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8070881Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8071540Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.8072225Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8072960Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8073676Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8074431Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8075106Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:49:58.8075908Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8076686Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8077440Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8078165Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8078845Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.8079554Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8080296Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8081018Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8081710Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8082368Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.8083052Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8083768Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8084461Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8085133Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8085780Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.8086449Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8087168Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8087868Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8088554Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8089262Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.8089930Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8090643Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8091344Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8092017Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8092707Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.8093375Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8094095Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8094797Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8095474Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8096119Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.8096799Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8097524Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8098232Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8098911Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8099563Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.8100254Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8101000Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8101729Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8102557Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8103324Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.8104019Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8104761Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8105484Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8106193Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8106903Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.8107620Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8108374Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8109103Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8109823Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8110486Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.8111242Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8112007Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8112739Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8113442Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8114233Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.8115002Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8115766Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8116495Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8117262Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8117925Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.8118595Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8119320Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8120016Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8120714Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8121373Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:49:58.8122080Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8122835Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8123575Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8124287Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8124942Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.8125627Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8126408Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8127176Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8127902Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8128767Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.8129506Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8130340Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8131166Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8131900Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8132662Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.8154225Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8155023Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8155926Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8156628Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8157308Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.8158014Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8158777Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8159518Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8160229Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8160901Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.8161600Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8162412Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8163152Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8163856Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8164536Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.8165248Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8166011Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8166846Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8167529Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8168174Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.8168847Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8169582Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8170327Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8171009Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8171657Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.8172350Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8173127Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8173851Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8174547Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8175204Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.8175896Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8176645Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8177364Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8178057Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8178718Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.8179405Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8180203Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8180923Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8181609Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8182280Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.8182960Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8183702Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8184405Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8185083Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8185727Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.8186396Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8187127Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8187820Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8188497Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8189133Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.8189806Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8190532Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8191232Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8191903Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8192540Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.8193209Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8194058Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8194786Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8195484Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8196119Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.8196795Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8197563Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8198262Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8198935Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8199579Z l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.8200276Z l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8201003Z l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8201706Z l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8202543Z l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8203193Z l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.8203875Z l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8204608Z l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8205316Z l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8205996Z l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8206644Z l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.8207440Z l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8208164Z l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8208869Z l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8209547Z l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8210191Z l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.8210892Z l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8211615Z l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8212317Z l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8212988Z l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8213631Z l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.8214303Z l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8215027Z l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8215743Z l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8216413Z l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8217050Z l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.8217721Z l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8218441Z l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8219142Z l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8219824Z l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8220462Z l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.8221183Z l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8221915Z l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8222623Z l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8223304Z l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8223957Z l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.8224654Z l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8225385Z l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8226095Z l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8226775Z l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8227426Z l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.8228099Z l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8228827Z l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8229526Z l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8230220Z l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8230883Z l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.8231574Z l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8232319Z l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8233037Z l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8233737Z l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8234508Z l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.8235194Z l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8235932Z l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8236645Z l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8237337Z l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8238021Z l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.8238715Z l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8239466Z l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8240186Z l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8240872Z l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8241541Z l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.8242241Z l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8242986Z l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8243711Z l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8244413Z l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8245084Z l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.8245793Z l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8246522Z l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8247227Z l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8247973Z l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8248623Z l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.8249292Z l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8250018Z l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8250730Z l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8251453Z l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8252126Z l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.8252824Z l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8253593Z l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8254347Z l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8255060Z l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8255729Z l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.8256405Z l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8257137Z l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8257849Z l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8258532Z l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8259175Z l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.8259856Z l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8260587Z l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8261295Z l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8262009Z l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8262652Z l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.8263329Z l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8264060Z l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8264768Z l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8265482Z l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8266127Z l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.8266795Z l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8267517Z l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8268220Z l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8268901Z l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8269590Z l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.8270285Z l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8271026Z l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8271752Z l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8272459Z l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8273129Z l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.8273881Z l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8274649Z l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8275456Z l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8276164Z l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8276833Z l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.8277531Z l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8278282Z l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8279028Z l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8279725Z l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8280383Z l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.8281068Z l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8281812Z l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8282540Z l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8283234Z l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8283890Z l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.8284572Z l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8285322Z l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8286042Z l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8286736Z l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8287388Z l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.8288056Z l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8288828Z l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8289531Z l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8290210Z l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8290852Z l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.8291522Z l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8292272Z l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8292975Z l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8293654Z l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8294305Z l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.8294976Z l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8295706Z l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8296410Z l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8297091Z l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8297738Z l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.8298411Z l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8299147Z l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8299861Z l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8300539Z l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8301184Z l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.8302056Z l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8302818Z l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8303521Z l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8304197Z l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8304844Z l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.8305522Z l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8306281Z l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8306988Z l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8307671Z l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8308315Z l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.8309013Z l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8309762Z l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8310493Z l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8311195Z l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8311861Z l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.8312551Z l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8313296Z l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8314059Z l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8314764Z l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8315417Z l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.8316180Z l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8316929Z l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8317654Z l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8318344Z l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8319029Z l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.8319737Z l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8320480Z l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8321210Z l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8321905Z l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8322568Z l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.8323257Z l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8324001Z l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8324723Z l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8325420Z l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8326080Z l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.8326756Z l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8327479Z l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8328195Z l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8328894Z l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8329601Z l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.8330287Z l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8331014Z l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8331718Z l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8332400Z l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8333063Z l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.8333737Z l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8334466Z l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8335170Z l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8335857Z l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8336505Z l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.8337177Z l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8337901Z l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8338602Z l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8339284Z l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8339928Z l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.8340600Z l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8341325Z l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8342066Z l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8342797Z l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8343441Z l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.8344110Z l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8344839Z l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8345537Z l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8346238Z l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8346883Z l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.8347554Z l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8348397Z l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8349293Z l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8349987Z l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8350636Z l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.8351311Z l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8352045Z l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8352751Z l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8353455Z l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8354207Z l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.8354909Z l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8355635Z l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8356443Z l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8357160Z l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8357820Z l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.8358512Z l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8359260Z l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8359989Z l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8360716Z l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8361377Z l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.8362076Z l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8362850Z l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8363597Z l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8364312Z l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8365000Z l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.8365709Z l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8366490Z l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8367235Z l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8367934Z l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8368578Z l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.8369378Z l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8370104Z l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8370869Z l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8371558Z l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8372218Z l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.8372911Z l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8373652Z l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8374375Z l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8375050Z l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8375700Z l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.8376365Z l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8377088Z l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8377788Z l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8378465Z l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8379108Z l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.8379773Z l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8380497Z l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8381194Z l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8381856Z l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8382494Z l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.8383159Z l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8383926Z l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8384626Z l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8385296Z l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8385937Z l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.8386606Z l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8387344Z l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8388041Z l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8388713Z l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8389389Z l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:49:58.8390108Z l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8390883Z l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8391632Z l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8392347Z l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8393023Z l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.8393713Z l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8394530Z l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8395268Z l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8395955Z l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8396616Z l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.8397339Z l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8398094Z l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8398812Z l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8399499Z l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8399794Z l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.8400142Z l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8400517Z l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8400838Z l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8401165Z l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8401453Z l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.8401810Z l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8402246Z l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8402572Z l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8402886Z l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8403172Z l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.8403525Z l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8403865Z l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8404192Z l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8404503Z l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8404795Z l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.8405220Z l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8405558Z l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8405882Z l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8406192Z l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8406548Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T20:49:58.8406891Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T20:49:58.8407212Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T20:49:58.8407587Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T20:49:58.8407957Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T20:49:58.8408322Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T20:49:58.8408667Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T20:49:58.8408745Z 2025-03-04T20:49:58.8409052Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8409550Z x: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1); l_stack0_tensor = l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.8409616Z 2025-03-04T20:49:58.8409905Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8411397Z x_1: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8411467Z 2025-03-04T20:49:58.8411774Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x) 2025-03-04T20:49:58.8411938Z x_2: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.relu_(x_1); x_1 = None 2025-03-04T20:49:58.8412005Z 2025-03-04T20:49:58.8412365Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1) 2025-03-04T20:49:58.8412604Z x_3: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1); x_2 = None 2025-03-04T20:49:58.8412663Z 2025-03-04T20:49:58.8412929Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8413347Z x_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.8413432Z 2025-03-04T20:49:58.8413704Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8415277Z x_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_4 = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8415348Z 2025-03-04T20:49:58.8415638Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8415797Z out: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_5); x_5 = None 2025-03-04T20:49:58.8415856Z 2025-03-04T20:49:58.8416114Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8416540Z x_6: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out = l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.8416611Z 2025-03-04T20:49:58.8416876Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8418435Z x_7: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_6 = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8418518Z 2025-03-04T20:49:58.8418805Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8418948Z out_1: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_7); x_7 = None 2025-03-04T20:49:58.8419008Z 2025-03-04T20:49:58.8419265Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8419700Z x_8: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_1 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.8419788Z 2025-03-04T20:49:58.8420055Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8421621Z x_9: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_8 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8421689Z 2025-03-04T20:49:58.8421943Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8422391Z x_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); x_3 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:49:58.8422452Z 2025-03-04T20:49:58.8422724Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8424337Z x_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_10 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8424399Z 2025-03-04T20:49:58.8424685Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.8424832Z x_9 += x_11; out_2: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_9; x_9 = x_11 = None 2025-03-04T20:49:58.8424922Z 2025-03-04T20:49:58.8425246Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8425404Z out_3: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_2); out_2 = None 2025-03-04T20:49:58.8425463Z 2025-03-04T20:49:58.8425725Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8426150Z x_12: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.8426217Z 2025-03-04T20:49:58.8426483Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8428044Z x_13: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_12 = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8428113Z 2025-03-04T20:49:58.8428405Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8428552Z out_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_13); x_13 = None 2025-03-04T20:49:58.8428614Z 2025-03-04T20:49:58.8428871Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8429306Z x_14: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_4 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.8429372Z 2025-03-04T20:49:58.8429640Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8431200Z x_15: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_14 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8431268Z 2025-03-04T20:49:58.8431550Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8431742Z out_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_15); x_15 = None 2025-03-04T20:49:58.8431803Z 2025-03-04T20:49:58.8432058Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8432493Z x_16: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_5 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.8432560Z 2025-03-04T20:49:58.8432822Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8434456Z x_17: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_16 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8434555Z 2025-03-04T20:49:58.8434862Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.8435021Z x_17 += out_3; out_6: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_17; x_17 = out_3 = None 2025-03-04T20:49:58.8435080Z 2025-03-04T20:49:58.8435362Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8435504Z out_7: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_6); out_6 = None 2025-03-04T20:49:58.8435569Z 2025-03-04T20:49:58.8435813Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8436240Z x_18: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.8436307Z 2025-03-04T20:49:58.8436573Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8438091Z x_19: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_18 = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8438702Z 2025-03-04T20:49:58.8439022Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8439156Z out_8: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_19); x_19 = None 2025-03-04T20:49:58.8439222Z 2025-03-04T20:49:58.8439465Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8439890Z x_20: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_8 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.8439959Z 2025-03-04T20:49:58.8440218Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8441784Z x_21: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_20 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8441844Z 2025-03-04T20:49:58.8442134Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8442271Z out_9: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_21); x_21 = None 2025-03-04T20:49:58.8442331Z 2025-03-04T20:49:58.8442580Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8442999Z x_22: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_9 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.8443064Z 2025-03-04T20:49:58.8443326Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8444838Z x_23: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_22 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8444903Z 2025-03-04T20:49:58.8445176Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.8445375Z x_23 += out_7; out_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_23; x_23 = out_7 = None 2025-03-04T20:49:58.8445435Z 2025-03-04T20:49:58.8445718Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8445867Z out_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_10); out_10 = None 2025-03-04T20:49:58.8445932Z 2025-03-04T20:49:58.8446177Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8446605Z x_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.8446680Z 2025-03-04T20:49:58.8446951Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8448476Z x_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_24 = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8448544Z 2025-03-04T20:49:58.8448830Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8448968Z out_12: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_25); x_25 = None 2025-03-04T20:49:58.8449044Z 2025-03-04T20:49:58.8449287Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8449721Z x_26: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_12 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.8449783Z 2025-03-04T20:49:58.8450052Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8451572Z x_27: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_26 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8451654Z 2025-03-04T20:49:58.8451978Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8452116Z out_13: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_27); x_27 = None 2025-03-04T20:49:58.8452181Z 2025-03-04T20:49:58.8452424Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8452856Z x_28: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_13 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.8452915Z 2025-03-04T20:49:58.8453186Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8454698Z x_29: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_28 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8454765Z 2025-03-04T20:49:58.8455017Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8455452Z x_30: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); out_11 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:49:58.8455516Z 2025-03-04T20:49:58.8455774Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8457338Z x_31: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_30 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8457406Z 2025-03-04T20:49:58.8457678Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.8457825Z x_29 += x_31; out_14: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_29; x_29 = x_31 = None 2025-03-04T20:49:58.8457904Z 2025-03-04T20:49:58.8458221Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8458368Z out_15: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_14); out_14 = None 2025-03-04T20:49:58.8458433Z 2025-03-04T20:49:58.8458678Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8459103Z x_32: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.8459162Z 2025-03-04T20:49:58.8459430Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8460943Z x_33: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_32 = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8461003Z 2025-03-04T20:49:58.8461286Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8461425Z out_16: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_33); x_33 = None 2025-03-04T20:49:58.8461489Z 2025-03-04T20:49:58.8461731Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8462152Z x_34: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_16 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.8462210Z 2025-03-04T20:49:58.8462472Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8463969Z x_35: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_34 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8464031Z 2025-03-04T20:49:58.8464315Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8464495Z out_17: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_35); x_35 = None 2025-03-04T20:49:58.8464561Z 2025-03-04T20:49:58.8464803Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8465226Z x_36: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_17 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.8465285Z 2025-03-04T20:49:58.8465548Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8467061Z x_37: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_36 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8467134Z 2025-03-04T20:49:58.8467415Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.8467566Z x_37 += out_15; out_18: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_37; x_37 = out_15 = None 2025-03-04T20:49:58.8467636Z 2025-03-04T20:49:58.8467913Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8468066Z out_19: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_18); out_18 = None 2025-03-04T20:49:58.8468124Z 2025-03-04T20:49:58.8468376Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8468793Z x_38: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.8468859Z 2025-03-04T20:49:58.8469125Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8470694Z x_39: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_38 = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8470781Z 2025-03-04T20:49:58.8471096Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8471241Z out_20: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_39); x_39 = None 2025-03-04T20:49:58.8471301Z 2025-03-04T20:49:58.8471559Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8471995Z x_40: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_20 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.8472062Z 2025-03-04T20:49:58.8472328Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8473958Z x_41: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_40 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8474032Z 2025-03-04T20:49:58.8474340Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8474492Z out_21: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_41); x_41 = None 2025-03-04T20:49:58.8474554Z 2025-03-04T20:49:58.8474833Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8475272Z x_42: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_21 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.8475342Z 2025-03-04T20:49:58.8475613Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8477184Z x_43: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_42 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8477255Z 2025-03-04T20:49:58.8477537Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.8477817Z x_43 += out_19; out_22: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_43; x_43 = out_19 = None 2025-03-04T20:49:58.8477878Z 2025-03-04T20:49:58.8478167Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8478316Z out_23: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_22); out_22 = None 2025-03-04T20:49:58.8478383Z 2025-03-04T20:49:58.8478632Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8479060Z x_44: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.8479145Z 2025-03-04T20:49:58.8479411Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8480961Z x_45: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_44 = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8481022Z 2025-03-04T20:49:58.8481312Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8481454Z out_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_45); x_45 = None 2025-03-04T20:49:58.8481514Z 2025-03-04T20:49:58.8481771Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8482198Z x_46: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_24 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.8482264Z 2025-03-04T20:49:58.8482529Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8484089Z x_47: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_46 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8484205Z 2025-03-04T20:49:58.8484523Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8484667Z out_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_47); x_47 = None 2025-03-04T20:49:58.8484727Z 2025-03-04T20:49:58.8484990Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8485411Z x_48: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_25 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.8485477Z 2025-03-04T20:49:58.8485738Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8487254Z x_49: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_48 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8487322Z 2025-03-04T20:49:58.8487597Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.8487747Z x_49 += out_23; out_26: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_49; x_49 = out_23 = None 2025-03-04T20:49:58.8487805Z 2025-03-04T20:49:58.8488084Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8490481Z out_27: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_26); out_26 = None 2025-03-04T20:49:58.8490562Z 2025-03-04T20:49:58.8490827Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8491257Z x_50: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.8491321Z 2025-03-04T20:49:58.8491597Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8493175Z x_51: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_50 = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8493259Z 2025-03-04T20:49:58.8493535Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8493669Z out_28: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_51); x_51 = None 2025-03-04T20:49:58.8493727Z 2025-03-04T20:49:58.8493979Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8494392Z x_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_28 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.8494475Z 2025-03-04T20:49:58.8494737Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8496233Z x_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_52 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8496301Z 2025-03-04T20:49:58.8496577Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8496709Z out_29: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_53); x_53 = None 2025-03-04T20:49:58.8496768Z 2025-03-04T20:49:58.8497083Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8497496Z x_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_29 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.8497563Z 2025-03-04T20:49:58.8497820Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8499338Z x_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_54 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8499421Z 2025-03-04T20:49:58.8499737Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8500163Z x_56: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); out_27 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:49:58.8500220Z 2025-03-04T20:49:58.8500482Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8502158Z x_57: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_56 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8502273Z 2025-03-04T20:49:58.8502554Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.8502687Z x_55 += x_57; out_30: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_55; x_55 = x_57 = None 2025-03-04T20:49:58.8502755Z 2025-03-04T20:49:58.8503033Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8503174Z out_31: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_30); out_30 = None 2025-03-04T20:49:58.8503231Z 2025-03-04T20:49:58.8503480Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8503923Z x_58: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.8503991Z 2025-03-04T20:49:58.8504255Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8505812Z x_59: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_58 = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8505895Z 2025-03-04T20:49:58.8506195Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8506332Z out_32: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_59); x_59 = None 2025-03-04T20:49:58.8506390Z 2025-03-04T20:49:58.8506641Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8507064Z x_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_32 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.8507129Z 2025-03-04T20:49:58.8507393Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8508945Z x_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_60 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8509013Z 2025-03-04T20:49:58.8509293Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8509431Z out_33: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_61); x_61 = None 2025-03-04T20:49:58.8509494Z 2025-03-04T20:49:58.8509745Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8510181Z x_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_33 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.8510250Z 2025-03-04T20:49:58.8510516Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8512065Z x_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_62 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8512132Z 2025-03-04T20:49:58.8512413Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.8512584Z x_63 += out_31; out_34: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_63; x_63 = out_31 = None 2025-03-04T20:49:58.8512657Z 2025-03-04T20:49:58.8512942Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8513080Z out_35: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_34); out_34 = None 2025-03-04T20:49:58.8513148Z 2025-03-04T20:49:58.8513397Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8513911Z x_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.8513979Z 2025-03-04T20:49:58.8514282Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8515977Z x_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_64 = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8516061Z 2025-03-04T20:49:58.8516509Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8516641Z out_36: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_65); x_65 = None 2025-03-04T20:49:58.8516708Z 2025-03-04T20:49:58.8516959Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8517406Z x_66: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_36 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.8517466Z 2025-03-04T20:49:58.8517736Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8519287Z x_67: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_66 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8519347Z 2025-03-04T20:49:58.8519678Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8519810Z out_37: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_67); x_67 = None 2025-03-04T20:49:58.8519878Z 2025-03-04T20:49:58.8520132Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8520559Z x_68: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_37 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.8520620Z 2025-03-04T20:49:58.8520895Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8522438Z x_69: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_68 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8522495Z 2025-03-04T20:49:58.8522775Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.8522916Z x_69 += out_35; out_38: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_69; x_69 = out_35 = None 2025-03-04T20:49:58.8522983Z 2025-03-04T20:49:58.8523259Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8523402Z out_39: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_38); out_38 = None 2025-03-04T20:49:58.8523459Z 2025-03-04T20:49:58.8523727Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8524132Z x_70: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.8524200Z 2025-03-04T20:49:58.8524472Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8525968Z x_71: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_70 = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8526078Z 2025-03-04T20:49:58.8526356Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8526493Z out_40: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_71); x_71 = None 2025-03-04T20:49:58.8526550Z 2025-03-04T20:49:58.8526799Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8527212Z x_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_40 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.8527279Z 2025-03-04T20:49:58.8527561Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8529037Z x_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_72 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8529104Z 2025-03-04T20:49:58.8529383Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8529515Z out_41: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_73); x_73 = None 2025-03-04T20:49:58.8529573Z 2025-03-04T20:49:58.8529819Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8530248Z x_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_41 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.8530307Z 2025-03-04T20:49:58.8530571Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8532062Z x_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_74 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8532128Z 2025-03-04T20:49:58.8532428Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.8532574Z x_75 += out_39; out_42: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_75; x_75 = out_39 = None 2025-03-04T20:49:58.8532632Z 2025-03-04T20:49:58.8532913Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8533056Z out_43: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_42); out_42 = None 2025-03-04T20:49:58.8533115Z 2025-03-04T20:49:58.8533367Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8533770Z x_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.8533855Z 2025-03-04T20:49:58.8534114Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8535594Z x_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_76 = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8535661Z 2025-03-04T20:49:58.8535936Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8536068Z out_44: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_77); x_77 = None 2025-03-04T20:49:58.8536126Z 2025-03-04T20:49:58.8536393Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8536801Z x_78: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_44 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.8536870Z 2025-03-04T20:49:58.8537129Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8538622Z x_79: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_78 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8538720Z 2025-03-04T20:49:58.8538999Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8539131Z out_45: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_79); x_79 = None 2025-03-04T20:49:58.8539190Z 2025-03-04T20:49:58.8539439Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8539851Z x_80: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_45 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.8539918Z 2025-03-04T20:49:58.8540194Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8541688Z x_81: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_80 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8541756Z 2025-03-04T20:49:58.8542026Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.8542171Z x_81 += out_43; out_46: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_81; x_81 = out_43 = None 2025-03-04T20:49:58.8542228Z 2025-03-04T20:49:58.8542505Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8542655Z out_47: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_46); out_46 = None 2025-03-04T20:49:58.8542722Z 2025-03-04T20:49:58.8542966Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8543375Z x_82: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.8543435Z 2025-03-04T20:49:58.8543698Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8545196Z x_83: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_82 = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8545276Z 2025-03-04T20:49:58.8545558Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8545685Z out_48: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_83); x_83 = None 2025-03-04T20:49:58.8545749Z 2025-03-04T20:49:58.8545999Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8546414Z x_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_48 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.8546490Z 2025-03-04T20:49:58.8546755Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8548251Z x_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_84 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8548318Z 2025-03-04T20:49:58.8548612Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8548741Z out_49: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_85); x_85 = None 2025-03-04T20:49:58.8548807Z 2025-03-04T20:49:58.8549081Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8549498Z x_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_49 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.8549558Z 2025-03-04T20:49:58.8549823Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8551346Z x_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_86 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8551449Z 2025-03-04T20:49:58.8551755Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.8551903Z x_87 += out_47; out_50: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_87; x_87 = out_47 = None 2025-03-04T20:49:58.8551972Z 2025-03-04T20:49:58.8552270Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8552424Z out_51: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_50); out_50 = None 2025-03-04T20:49:58.8552486Z 2025-03-04T20:49:58.8552753Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8553208Z x_88: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.8553279Z 2025-03-04T20:49:58.8553558Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8555241Z x_89: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_88 = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8555315Z 2025-03-04T20:49:58.8555612Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8555776Z out_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_89); x_89 = None 2025-03-04T20:49:58.8555841Z 2025-03-04T20:49:58.8556110Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8556556Z x_90: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_52 = l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.8556629Z 2025-03-04T20:49:58.8556908Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8558557Z x_91: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_90 = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8558647Z 2025-03-04T20:49:58.8559006Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8559149Z out_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_91); x_91 = None 2025-03-04T20:49:58.8559213Z 2025-03-04T20:49:58.8559481Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8559931Z x_92: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_53 = l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.8560027Z 2025-03-04T20:49:58.8560305Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8561940Z x_93: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_92 = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8562008Z 2025-03-04T20:49:58.8562283Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.8562420Z x_93 += out_51; out_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_93; x_93 = out_51 = None 2025-03-04T20:49:58.8562486Z 2025-03-04T20:49:58.8562776Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8562918Z out_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_54); out_54 = None 2025-03-04T20:49:58.8562980Z 2025-03-04T20:49:58.8563234Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8563640Z x_94: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.8563709Z 2025-03-04T20:49:58.8563969Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8565511Z x_95: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_94 = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8565593Z 2025-03-04T20:49:58.8565884Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8566023Z out_56: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_95); x_95 = None 2025-03-04T20:49:58.8566083Z 2025-03-04T20:49:58.8566347Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8566772Z x_96: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_56 = l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.8566838Z 2025-03-04T20:49:58.8567099Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8568587Z x_97: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_96 = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8568654Z 2025-03-04T20:49:58.8568931Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8569081Z out_57: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_97); x_97 = None 2025-03-04T20:49:58.8569138Z 2025-03-04T20:49:58.8569388Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8569794Z x_98: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_57 = l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.8569862Z 2025-03-04T20:49:58.8570117Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8571618Z x_99: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_98 = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8571699Z 2025-03-04T20:49:58.8571971Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.8572112Z x_99 += out_55; out_58: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_99; x_99 = out_55 = None 2025-03-04T20:49:58.8572171Z 2025-03-04T20:49:58.8572454Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8572588Z out_59: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_58); out_58 = None 2025-03-04T20:49:58.8572652Z 2025-03-04T20:49:58.8572910Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8573320Z x_100: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.8573378Z 2025-03-04T20:49:58.8573642Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8575138Z x_101: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_100 = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8575198Z 2025-03-04T20:49:58.8575504Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8575639Z out_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_101); x_101 = None 2025-03-04T20:49:58.8575705Z 2025-03-04T20:49:58.8575947Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8576371Z x_102: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_60 = l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.8576430Z 2025-03-04T20:49:58.8576698Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8578273Z x_103: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_102 = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8578360Z 2025-03-04T20:49:58.8578641Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8578775Z out_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_103); x_103 = None 2025-03-04T20:49:58.8578844Z 2025-03-04T20:49:58.8579092Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8579543Z x_104: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_61 = l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.8579602Z 2025-03-04T20:49:58.8579877Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8581430Z x_105: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_104 = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8581492Z 2025-03-04T20:49:58.8581781Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.8581947Z x_105 += out_59; out_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_105; x_105 = out_59 = None 2025-03-04T20:49:58.8582016Z 2025-03-04T20:49:58.8582295Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8582441Z out_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_62); out_62 = None 2025-03-04T20:49:58.8582503Z 2025-03-04T20:49:58.8582761Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8583173Z x_106: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_63, l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.8583241Z 2025-03-04T20:49:58.8583515Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8585055Z x_107: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_106 = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8585139Z 2025-03-04T20:49:58.8585426Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8585567Z out_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_107); x_107 = None 2025-03-04T20:49:58.8585678Z 2025-03-04T20:49:58.8585936Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8586354Z x_108: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_64, l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_64 = l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.8586422Z 2025-03-04T20:49:58.8586693Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8588213Z x_109: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_108, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_108 = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8588283Z 2025-03-04T20:49:58.8588583Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8588725Z out_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_109); x_109 = None 2025-03-04T20:49:58.8588784Z 2025-03-04T20:49:58.8589043Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8589483Z x_110: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_65, l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_65 = l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.8589544Z 2025-03-04T20:49:58.8589816Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8591380Z x_111: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_110, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_110 = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8591466Z 2025-03-04T20:49:58.8591746Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.8591906Z x_111 += out_63; out_66: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_111; x_111 = out_63 = None 2025-03-04T20:49:58.8591968Z 2025-03-04T20:49:58.8592253Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8592419Z out_67: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_66); out_66 = None 2025-03-04T20:49:58.8592480Z 2025-03-04T20:49:58.8592736Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8593176Z x_112: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_67, l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.8593247Z 2025-03-04T20:49:58.8593526Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8595253Z x_113: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_112, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_112 = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8595329Z 2025-03-04T20:49:58.8595630Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8595780Z out_68: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_113); x_113 = None 2025-03-04T20:49:58.8595847Z 2025-03-04T20:49:58.8596122Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8596579Z x_114: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_68, l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_68 = l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.8596649Z 2025-03-04T20:49:58.8596934Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8598644Z x_115: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_114, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_114 = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8598732Z 2025-03-04T20:49:58.8599030Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8599176Z out_69: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_115); x_115 = None 2025-03-04T20:49:58.8599267Z 2025-03-04T20:49:58.8599534Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8599983Z x_116: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_69, l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_69 = l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.8600053Z 2025-03-04T20:49:58.8600334Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8602066Z x_117: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_116, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_116 = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8602144Z 2025-03-04T20:49:58.8602506Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.8602673Z x_117 += out_67; out_70: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_117; x_117 = out_67 = None 2025-03-04T20:49:58.8602737Z 2025-03-04T20:49:58.8603047Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8603192Z out_71: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_70); out_70 = None 2025-03-04T20:49:58.8603262Z 2025-03-04T20:49:58.8603525Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8603979Z x_118: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_71, l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.8604037Z 2025-03-04T20:49:58.8604307Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8605852Z x_119: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_118, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_118 = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8605919Z 2025-03-04T20:49:58.8606203Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8606360Z out_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_119); x_119 = None 2025-03-04T20:49:58.8606425Z 2025-03-04T20:49:58.8606666Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8607091Z x_120: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_72, l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_72 = l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.8607148Z 2025-03-04T20:49:58.8607415Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8608967Z x_121: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_120, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_120 = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8609028Z 2025-03-04T20:49:58.8609311Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8609444Z out_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_121); x_121 = None 2025-03-04T20:49:58.8609511Z 2025-03-04T20:49:58.8609755Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8610183Z x_122: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_73, l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_73 = l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.8610244Z 2025-03-04T20:49:58.8610508Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8612058Z x_123: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_122, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_122 = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8612133Z 2025-03-04T20:49:58.8612411Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.8612556Z x_123 += out_71; out_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_123; x_123 = out_71 = None 2025-03-04T20:49:58.8612639Z 2025-03-04T20:49:58.8612916Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8613057Z out_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_74); out_74 = None 2025-03-04T20:49:58.8613116Z 2025-03-04T20:49:58.8613366Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8613776Z x_124: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_75, l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.8613842Z 2025-03-04T20:49:58.8614103Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8615638Z x_125: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_124, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_124 = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8615708Z 2025-03-04T20:49:58.8615988Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8616126Z out_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_125); x_125 = None 2025-03-04T20:49:58.8616184Z 2025-03-04T20:49:58.8616433Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8616853Z x_126: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_76, l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_76 = l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.8616920Z 2025-03-04T20:49:58.8617181Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8618726Z x_127: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_126, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_126 = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8618794Z 2025-03-04T20:49:58.8619087Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8619222Z out_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_127); x_127 = None 2025-03-04T20:49:58.8619280Z 2025-03-04T20:49:58.8619529Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8619946Z x_128: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_77, l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_77 = l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.8620012Z 2025-03-04T20:49:58.8620269Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8621816Z x_129: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_128, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_128 = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8621884Z 2025-03-04T20:49:58.8622166Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.8622322Z x_129 += out_75; out_78: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_129; x_129 = out_75 = None 2025-03-04T20:49:58.8622379Z 2025-03-04T20:49:58.8622661Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8622795Z out_79: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_78); out_78 = None 2025-03-04T20:49:58.8622862Z 2025-03-04T20:49:58.8623106Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8623524Z x_130: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_79, l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.8623607Z 2025-03-04T20:49:58.8623890Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8625407Z x_131: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_130, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_130 = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8625482Z 2025-03-04T20:49:58.8625768Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8625896Z out_80: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_131); x_131 = None 2025-03-04T20:49:58.8625962Z 2025-03-04T20:49:58.8626206Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8626628Z x_132: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_80, l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_80 = l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.8626693Z 2025-03-04T20:49:58.8626954Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8628507Z x_133: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_132, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_132 = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8628571Z 2025-03-04T20:49:58.8628862Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8628992Z out_81: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_133); x_133 = None 2025-03-04T20:49:58.8629059Z 2025-03-04T20:49:58.8629307Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8629745Z x_134: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_81, l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_81 = l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.8629809Z 2025-03-04T20:49:58.8630093Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8631612Z x_135: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_134, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_134 = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8631694Z 2025-03-04T20:49:58.8631967Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.8632114Z x_135 += out_79; out_82: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_135; x_135 = out_79 = None 2025-03-04T20:49:58.8632172Z 2025-03-04T20:49:58.8632452Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8632586Z out_83: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_82); out_82 = None 2025-03-04T20:49:58.8632651Z 2025-03-04T20:49:58.8632895Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8633312Z x_136: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_83, l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.8633374Z 2025-03-04T20:49:58.8633635Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8635222Z x_137: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_136, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_136 = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8635295Z 2025-03-04T20:49:58.8635579Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8635706Z out_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_137); x_137 = None 2025-03-04T20:49:58.8635774Z 2025-03-04T20:49:58.8636019Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8636452Z x_138: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_84, l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_84 = l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.8636527Z 2025-03-04T20:49:58.8636791Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8638336Z x_139: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_138, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_138 = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8638423Z 2025-03-04T20:49:58.8638714Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8638847Z out_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_139); x_139 = None 2025-03-04T20:49:58.8638916Z 2025-03-04T20:49:58.8639166Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8639597Z x_140: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_85, l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_85 = l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.8639660Z 2025-03-04T20:49:58.8639934Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8641499Z x_141: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_140, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_140 = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8641570Z 2025-03-04T20:49:58.8641858Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.8642005Z x_141 += out_83; out_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_141; x_141 = out_83 = None 2025-03-04T20:49:58.8642072Z 2025-03-04T20:49:58.8642357Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8642501Z out_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_86); out_86 = None 2025-03-04T20:49:58.8642559Z 2025-03-04T20:49:58.8642813Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8643264Z x_142: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_87, l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.8643333Z 2025-03-04T20:49:58.8643598Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8645147Z x_143: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_142, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_142 = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8645230Z 2025-03-04T20:49:58.8645514Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8645654Z out_88: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_143); x_143 = None 2025-03-04T20:49:58.8645715Z 2025-03-04T20:49:58.8645970Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8646399Z x_144: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_88, l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_88 = l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.8646471Z 2025-03-04T20:49:58.8646735Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8648314Z x_145: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_144, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_144 = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8648385Z 2025-03-04T20:49:58.8648669Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8648807Z out_89: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_145); x_145 = None 2025-03-04T20:49:58.8648868Z 2025-03-04T20:49:58.8649123Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8649575Z x_146: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_89, l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_89 = l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.8649660Z 2025-03-04T20:49:58.8649925Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8651484Z x_147: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_146, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_146 = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8651568Z 2025-03-04T20:49:58.8651850Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.8652005Z x_147 += out_87; out_90: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_147; x_147 = out_87 = None 2025-03-04T20:49:58.8652064Z 2025-03-04T20:49:58.8652355Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8652493Z out_91: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_90); out_90 = None 2025-03-04T20:49:58.8652560Z 2025-03-04T20:49:58.8652815Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8653243Z x_148: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_91, l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.8653302Z 2025-03-04T20:49:58.8653569Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8655097Z x_149: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_148, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_148 = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8655158Z 2025-03-04T20:49:58.8655447Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8655575Z out_92: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_149); x_149 = None 2025-03-04T20:49:58.8655640Z 2025-03-04T20:49:58.8655881Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8656328Z x_150: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_92, l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_92 = l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.8656386Z 2025-03-04T20:49:58.8656648Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8658150Z x_151: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_150, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_150 = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8658225Z 2025-03-04T20:49:58.8658515Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8658646Z out_93: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_151); x_151 = None 2025-03-04T20:49:58.8658713Z 2025-03-04T20:49:58.8658964Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8659397Z x_152: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_93, l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_93 = l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.8659463Z 2025-03-04T20:49:58.8659726Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8661294Z x_153: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_152, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_152 = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8661356Z 2025-03-04T20:49:58.8661645Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.8661792Z x_153 += out_91; out_94: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_153; x_153 = out_91 = None 2025-03-04T20:49:58.8661858Z 2025-03-04T20:49:58.8662144Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8662303Z out_95: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_94); out_94 = None 2025-03-04T20:49:58.8662379Z 2025-03-04T20:49:58.8662632Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8663045Z x_154: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_95, l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.8663105Z 2025-03-04T20:49:58.8663372Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8664912Z x_155: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_154, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_154 = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8665002Z 2025-03-04T20:49:58.8665285Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8665426Z out_96: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_155); x_155 = None 2025-03-04T20:49:58.8665487Z 2025-03-04T20:49:58.8665746Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8666184Z x_156: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_96, l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_96 = l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.8666244Z 2025-03-04T20:49:58.8666525Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8668014Z x_157: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_156, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_156 = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8668083Z 2025-03-04T20:49:58.8668368Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8668500Z out_97: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_157); x_157 = None 2025-03-04T20:49:58.8668567Z 2025-03-04T20:49:58.8668851Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8669275Z x_158: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_97, l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_97 = l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.8669344Z 2025-03-04T20:49:58.8669616Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8671147Z x_159: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_158, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_158 = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8671231Z 2025-03-04T20:49:58.8671516Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.8671663Z x_159 += out_95; out_98: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_159; x_159 = out_95 = None 2025-03-04T20:49:58.8671730Z 2025-03-04T20:49:58.8672011Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8672159Z out_99: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_98); out_98 = None 2025-03-04T20:49:58.8672218Z 2025-03-04T20:49:58.8672476Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8672918Z x_160: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_99, l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.8672987Z 2025-03-04T20:49:58.8673253Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8674962Z x_161: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_160, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_160 = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8675037Z 2025-03-04T20:49:58.8675322Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8675503Z out_100: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_161); x_161 = None 2025-03-04T20:49:58.8675565Z 2025-03-04T20:49:58.8675822Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8676247Z x_162: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_100, l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_100 = l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.8676317Z 2025-03-04T20:49:58.8676583Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8678150Z x_163: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_162, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_162 = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8678236Z 2025-03-04T20:49:58.8678524Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8678671Z out_101: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_163); x_163 = None 2025-03-04T20:49:58.8678733Z 2025-03-04T20:49:58.8678993Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8679422Z x_164: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_101, l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_101 = l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.8679488Z 2025-03-04T20:49:58.8679771Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8681325Z x_165: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_164, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_164 = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8681394Z 2025-03-04T20:49:58.8681671Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.8681829Z x_165 += out_99; out_102: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_165; x_165 = out_99 = None 2025-03-04T20:49:58.8681906Z 2025-03-04T20:49:58.8682215Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8682360Z out_103: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_102); out_102 = None 2025-03-04T20:49:58.8682427Z 2025-03-04T20:49:58.8682678Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8683107Z x_166: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_103, l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.8683168Z 2025-03-04T20:49:58.8683444Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8685007Z x_167: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_166, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_166 = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8685066Z 2025-03-04T20:49:58.8685348Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8685479Z out_104: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_167); x_167 = None 2025-03-04T20:49:58.8685543Z 2025-03-04T20:49:58.8685784Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8686216Z x_168: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_104, l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_104 = l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.8686275Z 2025-03-04T20:49:58.8686537Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8688043Z x_169: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_168, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_168 = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8688102Z 2025-03-04T20:49:58.8688382Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8688571Z out_105: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_169); x_169 = None 2025-03-04T20:49:58.8688637Z 2025-03-04T20:49:58.8688877Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8689300Z x_170: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_105, l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_105 = l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.8689359Z 2025-03-04T20:49:58.8689630Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8691162Z x_171: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_170, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_170 = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8691524Z 2025-03-04T20:49:58.8691805Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.8691962Z x_171 += out_103; out_106: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_171; x_171 = out_103 = None 2025-03-04T20:49:58.8692031Z 2025-03-04T20:49:58.8692304Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8692451Z out_107: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_106); out_106 = None 2025-03-04T20:49:58.8692510Z 2025-03-04T20:49:58.8692777Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8693185Z x_172: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_107, l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.8693253Z 2025-03-04T20:49:58.8693514Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8695012Z x_173: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_172, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_172 = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8695115Z 2025-03-04T20:49:58.8695392Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8695536Z out_108: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_173); x_173 = None 2025-03-04T20:49:58.8695597Z 2025-03-04T20:49:58.8695852Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8696282Z x_174: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_108, l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_108 = l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.8696347Z 2025-03-04T20:49:58.8696635Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8698154Z x_175: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_174, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_174 = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8698222Z 2025-03-04T20:49:58.8698502Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8698640Z out_109: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_175); x_175 = None 2025-03-04T20:49:58.8698697Z 2025-03-04T20:49:58.8698949Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8699392Z x_176: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_109, l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_109 = l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.8699454Z 2025-03-04T20:49:58.8699721Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8701330Z x_177: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_176, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_176 = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8701417Z 2025-03-04T20:49:58.8701729Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.8701995Z x_177 += out_107; out_110: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_177; x_177 = out_107 = None 2025-03-04T20:49:58.8702065Z 2025-03-04T20:49:58.8702370Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8702520Z out_111: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_110); out_110 = None 2025-03-04T20:49:58.8702585Z 2025-03-04T20:49:58.8702841Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8703333Z x_178: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_111, l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.8703447Z 2025-03-04T20:49:58.8703725Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8705375Z x_179: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_178, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_178 = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8705448Z 2025-03-04T20:49:58.8705748Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8705897Z out_112: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_179); x_179 = None 2025-03-04T20:49:58.8705958Z 2025-03-04T20:49:58.8706256Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8706709Z x_180: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_112, l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_112 = l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.8706780Z 2025-03-04T20:49:58.8707062Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8708738Z x_181: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_180, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_180 = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8708832Z 2025-03-04T20:49:58.8709131Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8709276Z out_113: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_181); x_181 = None 2025-03-04T20:49:58.8709338Z 2025-03-04T20:49:58.8709606Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8710055Z x_182: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_113, l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_113 = l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.8710142Z 2025-03-04T20:49:58.8710424Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8711976Z x_183: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_182, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_182 = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8712045Z 2025-03-04T20:49:58.8712331Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.8712493Z x_183 += out_111; out_114: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_183; x_183 = out_111 = None 2025-03-04T20:49:58.8712553Z 2025-03-04T20:49:58.8712840Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8712997Z out_115: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_114); out_114 = None 2025-03-04T20:49:58.8713064Z 2025-03-04T20:49:58.8713316Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8713753Z x_184: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_115, l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.8713870Z 2025-03-04T20:49:58.8714174Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8715885Z x_185: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_184, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_184 = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8715968Z 2025-03-04T20:49:58.8716261Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8716396Z out_116: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_185); x_185 = None 2025-03-04T20:49:58.8716461Z 2025-03-04T20:49:58.8716709Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8717140Z x_186: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_116, l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_116 = l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.8717220Z 2025-03-04T20:49:58.8717490Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8719046Z x_187: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_186, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_186 = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8719109Z 2025-03-04T20:49:58.8719397Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8719530Z out_117: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_187); x_187 = None 2025-03-04T20:49:58.8719613Z 2025-03-04T20:49:58.8719864Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8720301Z x_188: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_117, l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_117 = l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.8720363Z 2025-03-04T20:49:58.8720637Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8722198Z x_189: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_188, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_188 = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8722284Z 2025-03-04T20:49:58.8722575Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.8722731Z x_189 += out_115; out_118: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_189; x_189 = out_115 = None 2025-03-04T20:49:58.8722800Z 2025-03-04T20:49:58.8723089Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8723236Z out_119: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_118); out_118 = None 2025-03-04T20:49:58.8723296Z 2025-03-04T20:49:58.8723569Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8723984Z x_190: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.8724052Z 2025-03-04T20:49:58.8724316Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8725853Z x_191: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_190, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_190 = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8725923Z 2025-03-04T20:49:58.8726214Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8726354Z out_120: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_191); x_191 = None 2025-03-04T20:49:58.8726413Z 2025-03-04T20:49:58.8726663Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8727078Z x_192: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_120, l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1); out_120 = l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.8727143Z 2025-03-04T20:49:58.8727400Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8728917Z x_193: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_192, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_192 = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8728999Z 2025-03-04T20:49:58.8729273Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8729411Z out_121: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_193); x_193 = None 2025-03-04T20:49:58.8729468Z 2025-03-04T20:49:58.8729720Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8730173Z x_194: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_121, l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_121 = l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.8730239Z 2025-03-04T20:49:58.8730503Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8732025Z x_195: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_194, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_194 = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8732097Z 2025-03-04T20:49:58.8732343Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8732801Z x_196: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_119 = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:49:58.8732861Z 2025-03-04T20:49:58.8733127Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8734682Z x_197: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_196, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_196 = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8734758Z 2025-03-04T20:49:58.8735054Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.8735199Z x_195 += x_197; out_122: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_195; x_195 = x_197 = None 2025-03-04T20:49:58.8735266Z 2025-03-04T20:49:58.8735542Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8735689Z out_123: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_122); out_122 = None 2025-03-04T20:49:58.8735750Z 2025-03-04T20:49:58.8736002Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8736420Z x_198: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_123, l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.8736496Z 2025-03-04T20:49:58.8736760Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8738296Z x_199: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_198, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_198 = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8738364Z 2025-03-04T20:49:58.8738647Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8738788Z out_124: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_199); x_199 = None 2025-03-04T20:49:58.8738847Z 2025-03-04T20:49:58.8739116Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8739551Z x_200: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_124, l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1); out_124 = l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.8739614Z 2025-03-04T20:49:58.8739894Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8741400Z x_201: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_200, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_200 = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8741483Z 2025-03-04T20:49:58.8741766Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8741909Z out_125: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_201); x_201 = None 2025-03-04T20:49:58.8741969Z 2025-03-04T20:49:58.8742236Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8742663Z x_202: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_125, l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_125 = l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.8742737Z 2025-03-04T20:49:58.8743004Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8744510Z x_203: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_202, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_202 = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8744579Z 2025-03-04T20:49:58.8744861Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.8745026Z x_203 += out_123; out_126: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_203; x_203 = out_123 = None 2025-03-04T20:49:58.8745091Z 2025-03-04T20:49:58.8745365Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8745527Z out_127: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_126); out_126 = None 2025-03-04T20:49:58.8745587Z 2025-03-04T20:49:58.8745843Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8746261Z x_204: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_127, l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.8746328Z 2025-03-04T20:49:58.8746625Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8748191Z x_205: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_204, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_204 = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8748274Z 2025-03-04T20:49:58.8748558Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8748702Z out_128: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_205); x_205 = None 2025-03-04T20:49:58.8748762Z 2025-03-04T20:49:58.8749018Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8749443Z x_206: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_128, l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1); out_128 = l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.8749527Z 2025-03-04T20:49:58.8749792Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8751346Z x_207: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_206, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_206 = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8751415Z 2025-03-04T20:49:58.8751705Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8751846Z out_129: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_207); x_207 = None 2025-03-04T20:49:58.8751905Z 2025-03-04T20:49:58.8752179Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8752609Z x_208: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_129, l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_129 = l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.8752676Z 2025-03-04T20:49:58.8752943Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.8754559Z x_209: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_208, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_208 = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.8754649Z 2025-03-04T20:49:58.8754925Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.8755089Z x_209 += out_127; out_130: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_209; x_209 = out_127 = None 2025-03-04T20:49:58.8755149Z 2025-03-04T20:49:58.8755439Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.8755582Z out_131: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_130); out_130 = None 2025-03-04T20:49:58.8755648Z 2025-03-04T20:49:58.8756091Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:49:58.8756275Z arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32) 2025-03-04T20:49:58.8756336Z 2025-03-04T20:49:58.8756636Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:49:58.8756771Z shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T20:49:58.8756842Z 2025-03-04T20:49:58.8757274Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:49:58.8757429Z arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32) 2025-03-04T20:49:58.8757490Z 2025-03-04T20:49:58.8757791Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:49:58.8757923Z shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T20:49:58.8757993Z 2025-03-04T20:49:58.8758365Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:49:58.8758569Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T20:49:58.8758664Z shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0] 2025-03-04T20:49:58.8758788Z shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T20:49:58.8758849Z 2025-03-04T20:49:58.8759193Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:49:58.8759317Z shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T20:49:58.8759385Z 2025-03-04T20:49:58.8759716Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:49:58.8759840Z shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T20:49:58.8759900Z 2025-03-04T20:49:58.8760291Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:49:58.8760507Z shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T20:49:58.8760585Z 2025-03-04T20:49:58.8761026Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:49:58.8761153Z view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T20:49:58.8761589Z view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T20:49:58.8761707Z add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T20:49:58.8761822Z x_210: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4); add = None 2025-03-04T20:49:58.8761883Z 2025-03-04T20:49:58.8762191Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:49:58.8762333Z tensor: "f32[82125, 4][4, 1]cpu" = x_210.to(torch.float32); x_210 = None 2025-03-04T20:49:58.8762398Z 2025-03-04T20:49:58.8762646Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.8763429Z x_211: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_131, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); out_131 = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T20:49:58.8763489Z 2025-03-04T20:49:58.8763767Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:49:58.8763957Z x_212: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_211, inplace = False); x_211 = None 2025-03-04T20:49:58.8764025Z 2025-03-04T20:49:58.8764418Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:49:58.8765277Z score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_212, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T20:49:58.8765347Z 2025-03-04T20:49:58.8765697Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:49:58.8766498Z x_213: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_212, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_212 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T20:49:58.8766556Z 2025-03-04T20:49:58.8766894Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T20:49:58.8767077Z permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T20:49:58.8767218Z logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T20:49:58.8767277Z 2025-03-04T20:49:58.8767690Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T20:49:58.8767850Z view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_213.view(4, -1, 4, 73, 75); x_213 = None 2025-03-04T20:49:58.8768014Z permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2); view_2 = None 2025-03-04T20:49:58.8768189Z pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2); permute_1 = None 2025-03-04T20:49:58.8768268Z 2025-03-04T20:49:58.8768669Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:49:58.8768868Z pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T20:49:58.8768936Z 2025-03-04T20:49:58.8769359Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:49:58.8769510Z unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T20:49:58.8769652Z expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T20:49:58.8769791Z anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T20:49:58.8769853Z 2025-03-04T20:49:58.8770227Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:49:58.8770391Z deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T20:49:58.8770458Z 2025-03-04T20:49:58.8770762Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:49:58.8770930Z boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T20:49:58.8770990Z 2025-03-04T20:49:58.8771303Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:49:58.8771430Z getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:49:58.8771559Z getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:49:58.8771696Z widths: "f32[328500][1]cpu" = getitem_2 - getitem_3; getitem_2 = getitem_3 = None 2025-03-04T20:49:58.8771769Z 2025-03-04T20:49:58.8772081Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:49:58.8772207Z getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:49:58.8772324Z getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:49:58.8772470Z heights: "f32[328500][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T20:49:58.8772529Z 2025-03-04T20:49:58.8772844Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:49:58.8772990Z getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:49:58.8773082Z mul: "f32[328500][1]cpu" = 0.5 * widths 2025-03-04T20:49:58.8773196Z ctr_x: "f32[328500][1]cpu" = getitem_6 + mul; getitem_6 = mul = None 2025-03-04T20:49:58.8773263Z 2025-03-04T20:49:58.8773567Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:49:58.8773714Z getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:49:58.8773798Z mul_1: "f32[328500][1]cpu" = 0.5 * heights 2025-03-04T20:49:58.8773927Z ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1; getitem_7 = mul_1 = None 2025-03-04T20:49:58.8773987Z 2025-03-04T20:49:58.8774367Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:49:58.8774535Z getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:49:58.8774651Z dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0; getitem_8 = None 2025-03-04T20:49:58.8774712Z 2025-03-04T20:49:58.8775253Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:49:58.8775409Z getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:49:58.8775518Z dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0; getitem_9 = None 2025-03-04T20:49:58.8775583Z 2025-03-04T20:49:58.8775876Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:49:58.8776030Z getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:49:58.8776137Z dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0; getitem_10 = None 2025-03-04T20:49:58.8776202Z 2025-03-04T20:49:58.8776496Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:49:58.8776678Z getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:49:58.8776800Z dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0; getitem_11 = None 2025-03-04T20:49:58.8776867Z 2025-03-04T20:49:58.8777198Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:49:58.8777344Z dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:49:58.8777403Z 2025-03-04T20:49:58.8777734Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:49:58.8777859Z dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:49:58.8777924Z 2025-03-04T20:49:58.8778260Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:49:58.8778400Z getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:49:58.8778517Z mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12; dx = getitem_12 = None 2025-03-04T20:49:58.8778669Z getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:49:58.8778854Z pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13; mul_2 = getitem_13 = None 2025-03-04T20:49:58.8778921Z 2025-03-04T20:49:58.8779273Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:49:58.8779408Z getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:49:58.8779523Z mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14; dy = getitem_14 = None 2025-03-04T20:49:58.8779675Z getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:49:58.8779806Z pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15; mul_3 = getitem_15 = None 2025-03-04T20:49:58.8779872Z 2025-03-04T20:49:58.8780194Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:49:58.8780331Z exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:49:58.8780481Z getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:49:58.8780611Z pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16; exp = getitem_16 = None 2025-03-04T20:49:58.8780670Z 2025-03-04T20:49:58.8781006Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:49:58.8781123Z exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:49:58.8781282Z getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:49:58.8781415Z pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17; exp_1 = getitem_17 = None 2025-03-04T20:49:58.8781476Z 2025-03-04T20:49:58.8781793Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:49:58.8781884Z mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T20:49:58.8782000Z x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:49:58.8782057Z 2025-03-04T20:49:58.8782387Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:49:58.8782478Z mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T20:49:58.8782592Z y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:49:58.8782649Z 2025-03-04T20:49:58.8782952Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:49:58.8783062Z mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:49:58.8783192Z x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:49:58.8783251Z 2025-03-04T20:49:58.8783552Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:49:58.8783654Z mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:49:58.8783781Z y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:49:58.8783842Z 2025-03-04T20:49:58.8784186Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:49:58.8784391Z pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:49:58.8784458Z 2025-03-04T20:49:58.8784779Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:49:58.8784940Z proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4)); pred_boxes = None 2025-03-04T20:49:58.8784998Z 2025-03-04T20:49:58.8785378Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:49:58.8785543Z proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T20:49:58.8785609Z 2025-03-04T20:49:58.8786078Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T20:49:58.8786229Z arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T20:49:58.8786287Z 2025-03-04T20:49:58.8786587Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:49:58.8786717Z batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T20:49:58.8786783Z 2025-03-04T20:49:58.8787222Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:49:58.8787337Z topk = logits_i.topk(6000, dim = 1); logits_i = None 2025-03-04T20:49:58.8787436Z topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0] 2025-03-04T20:49:58.8787557Z topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1]; topk = None 2025-03-04T20:49:58.8787616Z 2025-03-04T20:49:58.8788085Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:49:58.8788246Z getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T20:49:58.8788499Z topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)]; proposals_i_1 = getitem_20 = topk_idx = None 2025-03-04T20:49:58.8788561Z 2025-03-04T20:49:58.8789012Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:49:58.8789181Z full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:49:58.8789239Z 2025-03-04T20:49:58.8789533Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:49:58.8789678Z level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu')); full = level_ids = None 2025-03-04T20:49:58.8789746Z 2025-03-04T20:49:58.8790122Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T20:49:58.8790267Z getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0]; topk_proposals_i = None 2025-03-04T20:49:58.8790324Z 2025-03-04T20:49:58.8790686Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:49:58.8790825Z tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32); getitem_22 = None 2025-03-04T20:49:58.8790889Z 2025-03-04T20:49:58.8791269Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T20:49:58.8791409Z scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0]; topk_scores_i = None 2025-03-04T20:49:58.8791470Z 2025-03-04T20:49:58.8791965Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T20:49:58.8792100Z isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1); tensor_1 = None 2025-03-04T20:49:58.8792251Z all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:49:58.8792407Z isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T20:49:58.8792541Z valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T20:49:58.8792602Z 2025-03-04T20:49:58.8792978Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T20:49:58.8793091Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T20:49:58.8793159Z 2025-03-04T20:49:58.8793169Z 2025-03-04T20:49:58.8793256Z class GraphModule(torch.nn.Module): 2025-03-04T20:49:58.8893360Z def forward(self, L_stack0_tensor: "f32[4, 3, 1156, 1199][4158132, 1386044, 1199, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_: "f32[512, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_: "f32[1024, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_: 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"f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 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"f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[2048, 2048, 3, 3][18432, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[2048][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"): 2025-03-04T20:49:58.8894341Z l_stack0_tensor = L_stack0_tensor 2025-03-04T20:49:58.8894617Z l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.8894938Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8895264Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8895559Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8895857Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8896144Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.8896479Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8896847Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8897166Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8897481Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8897765Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.8898111Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8898459Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8898779Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8899091Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8899371Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.8899715Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8900047Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8900365Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8900683Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8900984Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:49:58.8901335Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8901688Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8902131Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8902458Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8902747Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.8903165Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8903554Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8903887Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8904202Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8904482Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.8904848Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8905186Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8905498Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8905812Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8906115Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.8906509Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8906896Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8907305Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8907676Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8908030Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.8908440Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8908838Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8909239Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8909601Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8909948Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.8910398Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8910810Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8911215Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8911600Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8911961Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.8912378Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8912758Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8913158Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8913533Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8913921Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.8914344Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8914748Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8915147Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8915528Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8915887Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.8916304Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8916707Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8917112Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8917488Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8917880Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.8918285Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8918692Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8919098Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8919475Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8919872Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:49:58.8920320Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8920734Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8921126Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8921455Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8921735Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.8922071Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8922444Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8922763Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8923076Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8923358Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.8923700Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8924032Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8924355Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8924690Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8924974Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.8925312Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8925642Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8925960Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8926285Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8926570Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.8926900Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8927236Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8927547Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8927858Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8928144Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.8928477Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8928829Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8929147Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8929462Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8929742Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.8930084Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8930415Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8930738Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8931089Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8931369Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.8931711Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8932040Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8932364Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8932686Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8932971Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.8933303Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8933636Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8933953Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8934257Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8934541Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.8934887Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8935223Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8935537Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8935846Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8936124Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.8936461Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8936796Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8937141Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8937451Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8937727Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.8938070Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8938396Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8938733Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8939038Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8939323Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.8939666Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8940034Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8940388Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8940690Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8940991Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:49:58.8941351Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8941701Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8942031Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8942353Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8942640Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.8942975Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8943326Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8943656Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8943966Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8944249Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.8944592Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8944926Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8945261Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8945570Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8945849Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.8946194Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8946528Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8946846Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8947146Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8947446Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.8947818Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8948197Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8948551Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8948889Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8949209Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.8949564Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8949941Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8950289Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8950630Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8950939Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.8951315Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8951708Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8952057Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8952403Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8952712Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.8953085Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8953455Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8953870Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8954253Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8954572Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.8954959Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8955331Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8955686Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8956039Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8956357Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.8956760Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8957138Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8957486Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8957835Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8958152Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.8958546Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8958919Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8959268Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8959600Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8959877Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.8960219Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8960550Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8960874Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8961200Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8961478Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.8961815Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8962140Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8962459Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8962762Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8963045Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.8963404Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8963740Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8964063Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8964379Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8964663Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.8965006Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8965341Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8965651Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8965959Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8966236Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.8966577Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8966906Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8967231Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8967545Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8967823Z l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.8968162Z l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8968492Z l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8968813Z l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8969115Z l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8969431Z l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.8969771Z l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8970102Z l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8970458Z l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8970797Z l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8971103Z l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.8971438Z l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8971773Z l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8972089Z l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8972400Z l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8972689Z l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.8973022Z l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8973358Z l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8973685Z l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8973995Z l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8974277Z l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.8974619Z l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8974948Z l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8975268Z l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8975578Z l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8975888Z l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.8976224Z l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8976549Z l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8976865Z l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8977167Z l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8977479Z l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.8977811Z l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8978147Z l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8978463Z l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8978770Z l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8979055Z l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.8979388Z l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8979735Z l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8980047Z l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8980363Z l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8980645Z l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.8980986Z l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8981322Z l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8981637Z l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8981977Z l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8982256Z l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.8982591Z l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8982921Z l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8983240Z l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8983560Z l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8983844Z l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.8984187Z l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8984517Z l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8984838Z l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8985144Z l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8985431Z l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.8985767Z l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8986118Z l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8986433Z l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8986746Z l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8987037Z l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.8987382Z l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8987727Z l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8988059Z l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8988388Z l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8988672Z l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.8989021Z l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8989354Z l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8989681Z l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8990012Z l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8990314Z l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.8990697Z l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8991076Z l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8991452Z l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8991799Z l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8992118Z l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.8992511Z l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8992891Z l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8993254Z l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8993593Z l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8993967Z l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.8994363Z l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8994744Z l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8995123Z l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8995466Z l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8995770Z l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.8996157Z l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8996514Z l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8996866Z l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8997199Z l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8997501Z l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.8997865Z l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8998225Z l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.8998570Z l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.8998901Z l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.8999219Z l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.8999581Z l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.8999935Z l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.9000271Z l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.9000592Z l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.9000903Z l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.9001267Z l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.9001670Z l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.9002122Z l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.9002448Z l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.9002756Z l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.9003121Z l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.9003529Z l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.9003864Z l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.9004181Z l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.9004463Z l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.9004816Z l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.9005178Z l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.9005496Z l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.9005830Z l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.9006113Z l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.9006462Z l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.9006807Z l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.9007139Z l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.9007458Z l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.9007754Z l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.9008122Z l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.9008484Z l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.9008809Z l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.9009116Z l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.9009403Z l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.9009754Z l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.9010095Z l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.9010408Z l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.9010722Z l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.9011019Z l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.9011373Z l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.9011710Z l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.9012026Z l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.9012353Z l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.9012635Z l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.9012981Z l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.9013311Z l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.9013635Z l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.9013950Z l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.9014228Z l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.9014634Z l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.9014965Z l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.9015289Z l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.9015597Z l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.9015889Z l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.9016241Z l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.9016583Z l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.9016910Z l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.9017218Z l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.9017510Z l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.9017847Z l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.9018183Z l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.9018514Z l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.9018834Z l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.9019121Z l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.9019468Z l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.9019808Z l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.9020130Z l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.9020445Z l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.9020757Z l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.9021097Z l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.9021435Z l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.9021762Z l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.9022075Z l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.9022387Z l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.9022741Z l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.9023074Z l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.9023394Z l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.9023698Z l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.9023988Z l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.9024322Z l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.9024679Z l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.9024999Z l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.9025323Z l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.9025620Z l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.9025973Z l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.9026333Z l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.9026666Z l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.9027036Z l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.9027325Z l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.9027686Z l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.9028036Z l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.9028365Z l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.9028705Z l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.9028994Z l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.9029344Z l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.9029685Z l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.9030016Z l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.9030332Z l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.9030629Z l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.9030986Z l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.9031333Z l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.9031664Z l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.9031981Z l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.9032290Z l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.9032650Z l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.9033011Z l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.9033365Z l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.9033686Z l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.9034027Z l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.9034387Z l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.9034738Z l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.9035083Z l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.9035403Z l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.9035688Z l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.9036050Z l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.9036393Z l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.9036730Z l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.9037043Z l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.9037339Z l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.9037706Z l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.9038046Z l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.9038383Z l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.9038693Z l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.9038990Z l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.9039335Z l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.9039681Z l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.9040038Z l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.9040358Z l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.9040655Z l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.9041006Z l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.9041346Z l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.9041678Z l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.9041990Z l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.9042270Z l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.9042614Z l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.9042947Z l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.9043269Z l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.9043578Z l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.9043871Z l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.9044216Z l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.9044552Z l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.9044874Z l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.9045178Z l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.9045469Z l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.9045810Z l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.9046178Z l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.9046503Z l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.9046807Z l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.9047097Z l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.9047433Z l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.9047788Z l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.9048102Z l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.9048415Z l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.9048695Z l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.9049035Z l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.9049372Z l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.9049690Z l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.9050020Z l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.9050300Z l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.9050642Z l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.9050976Z l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.9051297Z l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.9051602Z l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.9051889Z l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.9052259Z l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.9052593Z l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.9052913Z l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.9053216Z l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.9053504Z l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.9053857Z l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.9054189Z l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.9054499Z l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.9054810Z l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.9055092Z l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.9055426Z l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.9055762Z l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.9056074Z l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.9056401Z l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.9056699Z l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:49:58.9057053Z l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.9057395Z l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.9057732Z l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:49:58.9058057Z l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:49:58.9058336Z l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.9058710Z l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.9059041Z l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.9059363Z l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.9059677Z l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.9059973Z l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.9060335Z l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.9060682Z l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.9061011Z l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.9061324Z l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.9061620Z l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.9061964Z l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.9062308Z l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.9062654Z l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.9062985Z l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.9063275Z l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:49:58.9063659Z l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.9064047Z l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.9064412Z l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:49:58.9064760Z l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:49:58.9065090Z l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:49:58.9065468Z l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.9065854Z l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.9066195Z l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:49:58.9066506Z l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:49:58.9066820Z l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:49:58.9067177Z l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:49:58.9067525Z l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:49:58.9067855Z l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:49:58.9068172Z l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:49:58.9068536Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T20:49:58.9068868Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T20:49:58.9069205Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T20:49:58.9069579Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T20:49:58.9069962Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T20:49:58.9070324Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T20:49:58.9070668Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T20:49:58.9070741Z 2025-03-04T20:49:58.9071030Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9071510Z x: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1); l_stack0_tensor = l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.9071603Z 2025-03-04T20:49:58.9071893Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9073393Z x_1: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9073480Z 2025-03-04T20:49:58.9073817Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x) 2025-03-04T20:49:58.9073963Z x_2: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.relu_(x_1); x_1 = None 2025-03-04T20:49:58.9074031Z 2025-03-04T20:49:58.9074423Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1) 2025-03-04T20:49:58.9074675Z x_3: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1); x_2 = None 2025-03-04T20:49:58.9074748Z 2025-03-04T20:49:58.9075013Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9075438Z x_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.9075508Z 2025-03-04T20:49:58.9075781Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9077370Z x_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_4 = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9077443Z 2025-03-04T20:49:58.9077737Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9077892Z out: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_5); x_5 = None 2025-03-04T20:49:58.9077951Z 2025-03-04T20:49:58.9078205Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9078647Z x_6: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out = l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.9078718Z 2025-03-04T20:49:58.9078979Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9080493Z x_7: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_6 = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9080579Z 2025-03-04T20:49:58.9080862Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9081006Z out_1: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_7); x_7 = None 2025-03-04T20:49:58.9081066Z 2025-03-04T20:49:58.9081321Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9081751Z x_8: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_1 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.9081818Z 2025-03-04T20:49:58.9082077Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9083600Z x_9: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_8 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9083670Z 2025-03-04T20:49:58.9083912Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9084343Z x_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); x_3 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:49:58.9084403Z 2025-03-04T20:49:58.9084667Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9086250Z x_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_10 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9086308Z 2025-03-04T20:49:58.9086587Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.9086746Z x_9 += x_11; out_2: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_9; x_9 = x_11 = None 2025-03-04T20:49:58.9086811Z 2025-03-04T20:49:58.9087088Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9087239Z out_3: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_2); out_2 = None 2025-03-04T20:49:58.9087297Z 2025-03-04T20:49:58.9087549Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9087963Z x_12: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.9088029Z 2025-03-04T20:49:58.9088293Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9089808Z x_13: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_12 = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9089877Z 2025-03-04T20:49:58.9090159Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9090303Z out_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_13); x_13 = None 2025-03-04T20:49:58.9090361Z 2025-03-04T20:49:58.9090615Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9091033Z x_14: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_4 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.9091116Z 2025-03-04T20:49:58.9091394Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9092893Z x_15: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_14 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9092974Z 2025-03-04T20:49:58.9093252Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9093393Z out_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_15); x_15 = None 2025-03-04T20:49:58.9093452Z 2025-03-04T20:49:58.9093701Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9094130Z x_16: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_5 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.9094189Z 2025-03-04T20:49:58.9094460Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9095970Z x_17: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_16 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9096039Z 2025-03-04T20:49:58.9096311Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.9096464Z x_17 += out_3; out_6: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_17; x_17 = out_3 = None 2025-03-04T20:49:58.9096524Z 2025-03-04T20:49:58.9096807Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9096956Z out_7: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_6); out_6 = None 2025-03-04T20:49:58.9097015Z 2025-03-04T20:49:58.9097264Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9097687Z x_18: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.9097772Z 2025-03-04T20:49:58.9098028Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9099545Z x_19: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_18 = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9099625Z 2025-03-04T20:49:58.9099902Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9100040Z out_8: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_19); x_19 = None 2025-03-04T20:49:58.9100100Z 2025-03-04T20:49:58.9100350Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9100762Z x_20: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_8 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.9100831Z 2025-03-04T20:49:58.9101090Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9102765Z x_21: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_20 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9102840Z 2025-03-04T20:49:58.9103129Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9103268Z out_9: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_21); x_21 = None 2025-03-04T20:49:58.9103326Z 2025-03-04T20:49:58.9103576Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9103999Z x_22: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_9 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.9104085Z 2025-03-04T20:49:58.9104364Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9105871Z x_23: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_22 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9105964Z 2025-03-04T20:49:58.9106238Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.9106391Z x_23 += out_7; out_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_23; x_23 = out_7 = None 2025-03-04T20:49:58.9106452Z 2025-03-04T20:49:58.9106738Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9106889Z out_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_10); out_10 = None 2025-03-04T20:49:58.9106956Z 2025-03-04T20:49:58.9107200Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9107630Z x_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.9107689Z 2025-03-04T20:49:58.9107954Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9109485Z x_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_24 = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9109554Z 2025-03-04T20:49:58.9109838Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9109975Z out_12: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_25); x_25 = None 2025-03-04T20:49:58.9110040Z 2025-03-04T20:49:58.9110285Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9110730Z x_26: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_12 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.9110804Z 2025-03-04T20:49:58.9111065Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9112575Z x_27: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_26 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9112658Z 2025-03-04T20:49:58.9112950Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9113091Z out_13: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_27); x_27 = None 2025-03-04T20:49:58.9113155Z 2025-03-04T20:49:58.9113406Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9113909Z x_28: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_13 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.9113977Z 2025-03-04T20:49:58.9114250Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9115848Z x_29: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_28 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9115911Z 2025-03-04T20:49:58.9116180Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9116631Z x_30: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); out_11 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:49:58.9116699Z 2025-03-04T20:49:58.9116973Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9118589Z x_31: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_30 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9118673Z 2025-03-04T20:49:58.9118954Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.9119125Z x_29 += x_31; out_14: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_29; x_29 = x_31 = None 2025-03-04T20:49:58.9119189Z 2025-03-04T20:49:58.9119485Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9119637Z out_15: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_14); out_14 = None 2025-03-04T20:49:58.9119708Z 2025-03-04T20:49:58.9119963Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9120401Z x_32: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.9120466Z 2025-03-04T20:49:58.9120745Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9122342Z x_33: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_32 = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9122408Z 2025-03-04T20:49:58.9122706Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9122844Z out_16: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_33); x_33 = None 2025-03-04T20:49:58.9122913Z 2025-03-04T20:49:58.9123163Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9123618Z x_34: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_16 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.9123702Z 2025-03-04T20:49:58.9123987Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9125549Z x_35: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_34 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9125626Z 2025-03-04T20:49:58.9125917Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9126055Z out_17: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_35); x_35 = None 2025-03-04T20:49:58.9126121Z 2025-03-04T20:49:58.9126376Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9126836Z x_36: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_17 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.9126896Z 2025-03-04T20:49:58.9127169Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9128774Z x_37: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_36 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9128837Z 2025-03-04T20:49:58.9129128Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.9129280Z x_37 += out_15; out_18: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_37; x_37 = out_15 = None 2025-03-04T20:49:58.9129349Z 2025-03-04T20:49:58.9129643Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9129802Z out_19: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_18); out_18 = None 2025-03-04T20:49:58.9129863Z 2025-03-04T20:49:58.9130136Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9130567Z x_38: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.9130650Z 2025-03-04T20:49:58.9130925Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9132444Z x_39: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_38 = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9132527Z 2025-03-04T20:49:58.9132806Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9132947Z out_20: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_39); x_39 = None 2025-03-04T20:49:58.9133005Z 2025-03-04T20:49:58.9133256Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9133682Z x_40: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_20 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.9133744Z 2025-03-04T20:49:58.9134010Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9135544Z x_41: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_40 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9135613Z 2025-03-04T20:49:58.9135891Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9136032Z out_21: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_41); x_41 = None 2025-03-04T20:49:58.9136090Z 2025-03-04T20:49:58.9136346Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9136779Z x_42: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_21 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.9136855Z 2025-03-04T20:49:58.9137133Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9138630Z x_43: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_42 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9138712Z 2025-03-04T20:49:58.9138984Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.9139141Z x_43 += out_19; out_22: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_43; x_43 = out_19 = None 2025-03-04T20:49:58.9139206Z 2025-03-04T20:49:58.9139483Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9139636Z out_23: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_22); out_22 = None 2025-03-04T20:49:58.9139694Z 2025-03-04T20:49:58.9139940Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9140360Z x_44: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.9140425Z 2025-03-04T20:49:58.9140684Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9142214Z x_45: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_44 = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9142284Z 2025-03-04T20:49:58.9142558Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9142697Z out_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_45); x_45 = None 2025-03-04T20:49:58.9142756Z 2025-03-04T20:49:58.9143003Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9143440Z x_46: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_24 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.9143522Z 2025-03-04T20:49:58.9143777Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9145273Z x_47: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_46 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9145353Z 2025-03-04T20:49:58.9145629Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9145768Z out_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_47); x_47 = None 2025-03-04T20:49:58.9145826Z 2025-03-04T20:49:58.9146077Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9146498Z x_48: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_25 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.9146565Z 2025-03-04T20:49:58.9146820Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9148347Z x_49: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_48 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9148418Z 2025-03-04T20:49:58.9148688Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.9148844Z x_49 += out_23; out_26: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_49; x_49 = out_23 = None 2025-03-04T20:49:58.9148902Z 2025-03-04T20:49:58.9149184Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9149326Z out_27: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_26); out_26 = None 2025-03-04T20:49:58.9149394Z 2025-03-04T20:49:58.9149636Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9150083Z x_50: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.9150143Z 2025-03-04T20:49:58.9150420Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9151997Z x_51: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_50 = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9152086Z 2025-03-04T20:49:58.9152411Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9152553Z out_28: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_51); x_51 = None 2025-03-04T20:49:58.9152623Z 2025-03-04T20:49:58.9152910Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9153400Z x_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_28 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.9153466Z 2025-03-04T20:49:58.9153875Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9155580Z x_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_52 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9155653Z 2025-03-04T20:49:58.9155984Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9156133Z out_29: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_53); x_53 = None 2025-03-04T20:49:58.9156211Z 2025-03-04T20:49:58.9156498Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9157003Z x_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_29 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.9157091Z 2025-03-04T20:49:58.9157397Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9159087Z x_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_54 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9159176Z 2025-03-04T20:49:58.9159475Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9159964Z x_56: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); out_27 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:49:58.9160043Z 2025-03-04T20:49:58.9160342Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9162172Z x_57: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_56 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9162248Z 2025-03-04T20:49:58.9162568Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.9162733Z x_55 += x_57; out_30: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_55; x_55 = x_57 = None 2025-03-04T20:49:58.9162799Z 2025-03-04T20:49:58.9163137Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9163296Z out_31: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_30); out_30 = None 2025-03-04T20:49:58.9163371Z 2025-03-04T20:49:58.9163655Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9164136Z x_58: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.9164222Z 2025-03-04T20:49:58.9164549Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9166079Z x_59: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_58 = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9166155Z 2025-03-04T20:49:58.9166441Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9166569Z out_32: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_59); x_59 = None 2025-03-04T20:49:58.9166635Z 2025-03-04T20:49:58.9166877Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9167292Z x_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_32 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.9167353Z 2025-03-04T20:49:58.9167618Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9169129Z x_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_60 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9169190Z 2025-03-04T20:49:58.9169480Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9169609Z out_33: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_61); x_61 = None 2025-03-04T20:49:58.9169675Z 2025-03-04T20:49:58.9169918Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9170334Z x_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_33 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.9170392Z 2025-03-04T20:49:58.9170673Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9172173Z x_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_62 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9172256Z 2025-03-04T20:49:58.9172538Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.9172680Z x_63 += out_31; out_34: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_63; x_63 = out_31 = None 2025-03-04T20:49:58.9172748Z 2025-03-04T20:49:58.9173022Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9173167Z out_35: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_34); out_34 = None 2025-03-04T20:49:58.9173225Z 2025-03-04T20:49:58.9173478Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9173882Z x_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.9173949Z 2025-03-04T20:49:58.9174214Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9175714Z x_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_64 = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9175784Z 2025-03-04T20:49:58.9176063Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9176197Z out_36: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_65); x_65 = None 2025-03-04T20:49:58.9176257Z 2025-03-04T20:49:58.9176508Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9176919Z x_66: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_36 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.9177003Z 2025-03-04T20:49:58.9177285Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9178759Z x_67: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_66 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9178855Z 2025-03-04T20:49:58.9179142Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9179279Z out_37: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_67); x_67 = None 2025-03-04T20:49:58.9179341Z 2025-03-04T20:49:58.9179598Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9180032Z x_68: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_37 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.9180093Z 2025-03-04T20:49:58.9180360Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9181911Z x_69: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_68 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9181982Z 2025-03-04T20:49:58.9182263Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.9182414Z x_69 += out_35; out_38: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_69; x_69 = out_35 = None 2025-03-04T20:49:58.9182472Z 2025-03-04T20:49:58.9182761Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9182907Z out_39: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_38); out_38 = None 2025-03-04T20:49:58.9182969Z 2025-03-04T20:49:58.9183228Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9183670Z x_70: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.9183748Z 2025-03-04T20:49:58.9184010Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9185569Z x_71: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_70 = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9185654Z 2025-03-04T20:49:58.9185938Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9186076Z out_40: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_71); x_71 = None 2025-03-04T20:49:58.9186138Z 2025-03-04T20:49:58.9186396Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9186831Z x_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_40 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.9186900Z 2025-03-04T20:49:58.9187164Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9188715Z x_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_72 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9188784Z 2025-03-04T20:49:58.9189081Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9189218Z out_41: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_73); x_73 = None 2025-03-04T20:49:58.9189279Z 2025-03-04T20:49:58.9189538Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9189961Z x_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_41 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.9190046Z 2025-03-04T20:49:58.9190324Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9191858Z x_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_74 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9191942Z 2025-03-04T20:49:58.9192218Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.9192366Z x_75 += out_39; out_42: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_75; x_75 = out_39 = None 2025-03-04T20:49:58.9192427Z 2025-03-04T20:49:58.9192712Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9192846Z out_43: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_42); out_42 = None 2025-03-04T20:49:58.9192912Z 2025-03-04T20:49:58.9193157Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9193580Z x_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.9193639Z 2025-03-04T20:49:58.9193970Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9195528Z x_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_76 = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9195598Z 2025-03-04T20:49:58.9195888Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9196020Z out_44: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_77); x_77 = None 2025-03-04T20:49:58.9196088Z 2025-03-04T20:49:58.9196336Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9196779Z x_78: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_44 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.9196861Z 2025-03-04T20:49:58.9197133Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9198678Z x_79: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_78 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9198762Z 2025-03-04T20:49:58.9199048Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9199173Z out_45: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_79); x_79 = None 2025-03-04T20:49:58.9199242Z 2025-03-04T20:49:58.9199489Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9199906Z x_80: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_45 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.9199967Z 2025-03-04T20:49:58.9200229Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9201727Z x_81: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_80 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9201795Z 2025-03-04T20:49:58.9202168Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.9202310Z x_81 += out_43; out_46: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_81; x_81 = out_43 = None 2025-03-04T20:49:58.9202375Z 2025-03-04T20:49:58.9202658Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9202798Z out_47: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_46); out_46 = None 2025-03-04T20:49:58.9202860Z 2025-03-04T20:49:58.9203561Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9203990Z x_82: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.9204060Z 2025-03-04T20:49:58.9204320Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9205844Z x_83: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_82 = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9205938Z 2025-03-04T20:49:58.9206217Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9206354Z out_48: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_83); x_83 = None 2025-03-04T20:49:58.9206413Z 2025-03-04T20:49:58.9206662Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9207075Z x_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_48 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.9207142Z 2025-03-04T20:49:58.9207397Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9208950Z x_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_84 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9209021Z 2025-03-04T20:49:58.9209307Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9209444Z out_49: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_85); x_85 = None 2025-03-04T20:49:58.9209504Z 2025-03-04T20:49:58.9209759Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9210201Z x_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_49 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.9210284Z 2025-03-04T20:49:58.9210542Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9212048Z x_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_86 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9212134Z 2025-03-04T20:49:58.9212405Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.9212550Z x_87 += out_47; out_50: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_87; x_87 = out_47 = None 2025-03-04T20:49:58.9212609Z 2025-03-04T20:49:58.9212889Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9213023Z out_51: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_50); out_50 = None 2025-03-04T20:49:58.9213091Z 2025-03-04T20:49:58.9213335Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9213744Z x_88: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.9213804Z 2025-03-04T20:49:58.9214084Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9215580Z x_89: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_88 = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9215640Z 2025-03-04T20:49:58.9215930Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9216059Z out_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_89); x_89 = None 2025-03-04T20:49:58.9216128Z 2025-03-04T20:49:58.9216394Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9216839Z x_90: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_52 = l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.9216901Z 2025-03-04T20:49:58.9217173Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9218722Z x_91: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_90 = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9218822Z 2025-03-04T20:49:58.9219123Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9219255Z out_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_91); x_91 = None 2025-03-04T20:49:58.9219322Z 2025-03-04T20:49:58.9219578Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9220019Z x_92: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_53 = l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.9220080Z 2025-03-04T20:49:58.9220358Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9221936Z x_93: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_92 = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9221999Z 2025-03-04T20:49:58.9222291Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.9222433Z x_93 += out_51; out_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_93; x_93 = out_51 = None 2025-03-04T20:49:58.9222500Z 2025-03-04T20:49:58.9222783Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9222949Z out_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_54); out_54 = None 2025-03-04T20:49:58.9223024Z 2025-03-04T20:49:58.9223277Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9223693Z x_94: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.9223752Z 2025-03-04T20:49:58.9224021Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9225546Z x_95: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_94 = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9225629Z 2025-03-04T20:49:58.9225914Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9226050Z out_56: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_95); x_95 = None 2025-03-04T20:49:58.9226112Z 2025-03-04T20:49:58.9226365Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9226787Z x_96: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_56 = l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.9226845Z 2025-03-04T20:49:58.9227129Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9228644Z x_97: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_96 = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9228712Z 2025-03-04T20:49:58.9228997Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9229141Z out_57: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_97); x_97 = None 2025-03-04T20:49:58.9229200Z 2025-03-04T20:49:58.9229463Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9229895Z x_98: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_57 = l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.9229958Z 2025-03-04T20:49:58.9230230Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9231745Z x_99: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_98 = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9231829Z 2025-03-04T20:49:58.9232110Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.9232259Z x_99 += out_55; out_58: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_99; x_99 = out_55 = None 2025-03-04T20:49:58.9232325Z 2025-03-04T20:49:58.9232604Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9232753Z out_59: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_58); out_58 = None 2025-03-04T20:49:58.9232813Z 2025-03-04T20:49:58.9233073Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9233491Z x_100: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.9233576Z 2025-03-04T20:49:58.9233891Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9235446Z x_101: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_100 = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9235515Z 2025-03-04T20:49:58.9235795Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9235959Z out_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_101); x_101 = None 2025-03-04T20:49:58.9236033Z 2025-03-04T20:49:58.9236285Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9236694Z x_102: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_60 = l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.9236762Z 2025-03-04T20:49:58.9237022Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9238517Z x_103: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_102 = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9238604Z 2025-03-04T20:49:58.9238884Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9239025Z out_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_103); x_103 = None 2025-03-04T20:49:58.9239087Z 2025-03-04T20:49:58.9239337Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9239758Z x_104: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_61 = l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.9239823Z 2025-03-04T20:49:58.9240093Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9241593Z x_105: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_104 = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9241662Z 2025-03-04T20:49:58.9241936Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.9242090Z x_105 += out_59; out_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_105; x_105 = out_59 = None 2025-03-04T20:49:58.9242149Z 2025-03-04T20:49:58.9242463Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9242598Z out_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_62); out_62 = None 2025-03-04T20:49:58.9242664Z 2025-03-04T20:49:58.9242908Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9243324Z x_106: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_63, l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.9243382Z 2025-03-04T20:49:58.9243646Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9245215Z x_107: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_106 = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9245283Z 2025-03-04T20:49:58.9245573Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9245709Z out_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_107); x_107 = None 2025-03-04T20:49:58.9245775Z 2025-03-04T20:49:58.9246019Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9246461Z x_108: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_64, l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_64 = l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.9246520Z 2025-03-04T20:49:58.9246783Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9248294Z x_109: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_108, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_108 = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9248355Z 2025-03-04T20:49:58.9248638Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9248784Z out_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_109); x_109 = None 2025-03-04T20:49:58.9248864Z 2025-03-04T20:49:58.9249106Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9249519Z x_110: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_65, l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_65 = l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.9249576Z 2025-03-04T20:49:58.9249845Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9251347Z x_111: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_110, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_110 = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9251423Z 2025-03-04T20:49:58.9251701Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.9251846Z x_111 += out_63; out_66: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_111; x_111 = out_63 = None 2025-03-04T20:49:58.9251913Z 2025-03-04T20:49:58.9252189Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9252328Z out_67: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_66); out_66 = None 2025-03-04T20:49:58.9252386Z 2025-03-04T20:49:58.9252634Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9253057Z x_112: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_67, l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.9253122Z 2025-03-04T20:49:58.9253384Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9254893Z x_113: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_112, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_112 = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9254974Z 2025-03-04T20:49:58.9255267Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9255405Z out_68: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_113); x_113 = None 2025-03-04T20:49:58.9255463Z 2025-03-04T20:49:58.9255712Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9256132Z x_114: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_68, l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_68 = l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.9256198Z 2025-03-04T20:49:58.9256456Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9257978Z x_115: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_114, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_114 = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9258045Z 2025-03-04T20:49:58.9258323Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9258461Z out_69: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_115); x_115 = None 2025-03-04T20:49:58.9258520Z 2025-03-04T20:49:58.9258770Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9259205Z x_116: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_69, l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_69 = l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.9259273Z 2025-03-04T20:49:58.9259530Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9261048Z x_117: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_116, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_116 = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9261116Z 2025-03-04T20:49:58.9261389Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.9261574Z x_117 += out_67; out_70: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_117; x_117 = out_67 = None 2025-03-04T20:49:58.9261633Z 2025-03-04T20:49:58.9261916Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9262050Z out_71: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_70); out_70 = None 2025-03-04T20:49:58.9262116Z 2025-03-04T20:49:58.9262360Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9262782Z x_118: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_71, l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.9262867Z 2025-03-04T20:49:58.9263139Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9264694Z x_119: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_118, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_118 = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9264759Z 2025-03-04T20:49:58.9265072Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9265205Z out_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_119); x_119 = None 2025-03-04T20:49:58.9265272Z 2025-03-04T20:49:58.9265519Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9265968Z x_120: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_72, l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_72 = l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.9266035Z 2025-03-04T20:49:58.9266302Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9267852Z x_121: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_120, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_120 = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9267928Z 2025-03-04T20:49:58.9268237Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9268371Z out_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_121); x_121 = None 2025-03-04T20:49:58.9268438Z 2025-03-04T20:49:58.9268689Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9269125Z x_122: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_73, l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_73 = l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.9269190Z 2025-03-04T20:49:58.9269454Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9271027Z x_123: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_122, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_122 = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9271089Z 2025-03-04T20:49:58.9271376Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.9271530Z x_123 += out_71; out_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_123; x_123 = out_71 = None 2025-03-04T20:49:58.9271592Z 2025-03-04T20:49:58.9271883Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9272019Z out_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_74); out_74 = None 2025-03-04T20:49:58.9272101Z 2025-03-04T20:49:58.9272352Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9272780Z x_124: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_75, l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.9272843Z 2025-03-04T20:49:58.9273115Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9274729Z x_125: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_124, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_124 = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9274818Z 2025-03-04T20:49:58.9275111Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9275244Z out_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_125); x_125 = None 2025-03-04T20:49:58.9275312Z 2025-03-04T20:49:58.9275563Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9275995Z x_126: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_76, l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_76 = l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.9276070Z 2025-03-04T20:49:58.9276346Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9277908Z x_127: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_126, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_126 = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9277977Z 2025-03-04T20:49:58.9278270Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9278402Z out_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_127); x_127 = None 2025-03-04T20:49:58.9278468Z 2025-03-04T20:49:58.9278733Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9279167Z x_128: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_77, l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_77 = l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.9279228Z 2025-03-04T20:49:58.9279502Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9281058Z x_129: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_128, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_128 = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9281155Z 2025-03-04T20:49:58.9281439Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.9281585Z x_129 += out_75; out_78: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_129; x_129 = out_75 = None 2025-03-04T20:49:58.9281650Z 2025-03-04T20:49:58.9281925Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9282065Z out_79: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_78); out_78 = None 2025-03-04T20:49:58.9282123Z 2025-03-04T20:49:58.9282372Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9282796Z x_130: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_79, l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.9282862Z 2025-03-04T20:49:58.9283119Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9284619Z x_131: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_130, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_130 = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9284688Z 2025-03-04T20:49:58.9284964Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9285115Z out_80: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_131); x_131 = None 2025-03-04T20:49:58.9285175Z 2025-03-04T20:49:58.9285426Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9285832Z x_132: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_80, l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_80 = l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.9285902Z 2025-03-04T20:49:58.9286161Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9287668Z x_133: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_132, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_132 = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9287749Z 2025-03-04T20:49:58.9288028Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9288165Z out_81: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_133); x_133 = None 2025-03-04T20:49:58.9288223Z 2025-03-04T20:49:58.9288475Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9288888Z x_134: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_81, l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_81 = l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.9288974Z 2025-03-04T20:49:58.9289229Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9290722Z x_135: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_134, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_134 = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9290790Z 2025-03-04T20:49:58.9291061Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.9291209Z x_135 += out_79; out_82: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_135; x_135 = out_79 = None 2025-03-04T20:49:58.9291268Z 2025-03-04T20:49:58.9291566Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9291701Z out_83: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_82); out_82 = None 2025-03-04T20:49:58.9291769Z 2025-03-04T20:49:58.9292029Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9292456Z x_136: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_83, l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.9292515Z 2025-03-04T20:49:58.9292788Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9294353Z x_137: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_136, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_136 = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9294432Z 2025-03-04T20:49:58.9294716Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9294844Z out_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_137); x_137 = None 2025-03-04T20:49:58.9294909Z 2025-03-04T20:49:58.9295152Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9295585Z x_138: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_84, l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_84 = l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.9295643Z 2025-03-04T20:49:58.9295907Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9297411Z x_139: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_138, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_138 = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9297472Z 2025-03-04T20:49:58.9297755Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9297894Z out_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_139); x_139 = None 2025-03-04T20:49:58.9297962Z 2025-03-04T20:49:58.9298207Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9298629Z x_140: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_85, l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_85 = l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.9298698Z 2025-03-04T20:49:58.9298956Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9300481Z x_141: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_140, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_140 = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9300554Z 2025-03-04T20:49:58.9300837Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.9300979Z x_141 += out_83; out_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_141; x_141 = out_83 = None 2025-03-04T20:49:58.9301045Z 2025-03-04T20:49:58.9301324Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9301468Z out_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_86); out_86 = None 2025-03-04T20:49:58.9301543Z 2025-03-04T20:49:58.9301796Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9302307Z x_142: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_87, l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.9302373Z 2025-03-04T20:49:58.9302648Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9304216Z x_143: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_142, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_142 = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9304291Z 2025-03-04T20:49:58.9304636Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9304777Z out_88: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_143); x_143 = None 2025-03-04T20:49:58.9304838Z 2025-03-04T20:49:58.9305099Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9305539Z x_144: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_88, l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_88 = l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.9305599Z 2025-03-04T20:49:58.9305869Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9307434Z x_145: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_144, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_144 = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9307523Z 2025-03-04T20:49:58.9307814Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9307943Z out_89: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_145); x_145 = None 2025-03-04T20:49:58.9308009Z 2025-03-04T20:49:58.9308257Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9308718Z x_146: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_89, l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_89 = l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.9308777Z 2025-03-04T20:49:58.9309047Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9310593Z x_147: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_146, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_146 = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9310665Z 2025-03-04T20:49:58.9310987Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.9311143Z x_147 += out_87; out_90: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_147; x_147 = out_87 = None 2025-03-04T20:49:58.9311214Z 2025-03-04T20:49:58.9311515Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9311671Z out_91: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_90); out_90 = None 2025-03-04T20:49:58.9311734Z 2025-03-04T20:49:58.9312047Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9312468Z x_148: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_91, l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.9312536Z 2025-03-04T20:49:58.9312802Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9314431Z x_149: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_148, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_148 = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9314525Z 2025-03-04T20:49:58.9314822Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9314970Z out_92: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_149); x_149 = None 2025-03-04T20:49:58.9315061Z 2025-03-04T20:49:58.9315316Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9315735Z x_150: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_92, l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_92 = l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.9315804Z 2025-03-04T20:49:58.9316070Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9317631Z x_151: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_150, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_150 = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9317701Z 2025-03-04T20:49:58.9317987Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9318129Z out_93: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_151); x_151 = None 2025-03-04T20:49:58.9318190Z 2025-03-04T20:49:58.9318450Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9318880Z x_152: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_93, l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_93 = l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.9318949Z 2025-03-04T20:49:58.9319213Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9320792Z x_153: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_152, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_152 = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9320906Z 2025-03-04T20:49:58.9321183Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.9321333Z x_153 += out_91; out_94: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_153; x_153 = out_91 = None 2025-03-04T20:49:58.9321395Z 2025-03-04T20:49:58.9321701Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9321838Z out_95: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_94); out_94 = None 2025-03-04T20:49:58.9321904Z 2025-03-04T20:49:58.9322152Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9322579Z x_154: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_95, l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.9322638Z 2025-03-04T20:49:58.9322902Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9324422Z x_155: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_154, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_154 = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9324483Z 2025-03-04T20:49:58.9324769Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9324900Z out_96: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_155); x_155 = None 2025-03-04T20:49:58.9324966Z 2025-03-04T20:49:58.9325209Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9325633Z x_156: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_96, l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_96 = l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.9325692Z 2025-03-04T20:49:58.9325959Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9327488Z x_157: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_156, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_156 = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9327561Z 2025-03-04T20:49:58.9327846Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9327989Z out_97: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_157); x_157 = None 2025-03-04T20:49:58.9328057Z 2025-03-04T20:49:58.9328297Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9328716Z x_158: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_97, l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_97 = l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.9328776Z 2025-03-04T20:49:58.9329043Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9330589Z x_159: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_158, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_158 = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9330652Z 2025-03-04T20:49:58.9330941Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.9331086Z x_159 += out_95; out_98: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_159; x_159 = out_95 = None 2025-03-04T20:49:58.9331166Z 2025-03-04T20:49:58.9331443Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9331587Z out_99: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_98); out_98 = None 2025-03-04T20:49:58.9331645Z 2025-03-04T20:49:58.9331899Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9332310Z x_160: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_99, l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.9332376Z 2025-03-04T20:49:58.9332667Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9334199Z x_161: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_160, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_160 = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9334280Z 2025-03-04T20:49:58.9334560Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9334703Z out_100: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_161); x_161 = None 2025-03-04T20:49:58.9334761Z 2025-03-04T20:49:58.9335012Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9335434Z x_162: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_100, l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_100 = l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.9335500Z 2025-03-04T20:49:58.9335773Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9337398Z x_163: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_162, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_162 = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9337469Z 2025-03-04T20:49:58.9337750Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9337895Z out_101: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_163); x_163 = None 2025-03-04T20:49:58.9337957Z 2025-03-04T20:49:58.9338214Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9338657Z x_164: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_101, l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_101 = l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.9338718Z 2025-03-04T20:49:58.9338990Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9340570Z x_165: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_164, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_164 = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9340640Z 2025-03-04T20:49:58.9340918Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.9341093Z x_165 += out_99; out_102: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_165; x_165 = out_99 = None 2025-03-04T20:49:58.9341154Z 2025-03-04T20:49:58.9341443Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9341596Z out_103: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_102); out_102 = None 2025-03-04T20:49:58.9341656Z 2025-03-04T20:49:58.9341915Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9342337Z x_166: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_103, l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.9342407Z 2025-03-04T20:49:58.9342679Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9344263Z x_167: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_166, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_166 = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9344333Z 2025-03-04T20:49:58.9344614Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9344755Z out_104: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_167); x_167 = None 2025-03-04T20:49:58.9344814Z 2025-03-04T20:49:58.9345069Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9345495Z x_168: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_104, l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_104 = l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.9345577Z 2025-03-04T20:49:58.9345857Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9347430Z x_169: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_168, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_168 = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9347513Z 2025-03-04T20:49:58.9347799Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9347939Z out_105: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_169); x_169 = None 2025-03-04T20:49:58.9347999Z 2025-03-04T20:49:58.9348255Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9348684Z x_170: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_105, l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_105 = l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.9348752Z 2025-03-04T20:49:58.9349022Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9350595Z x_171: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_170, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_170 = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9350665Z 2025-03-04T20:49:58.9350945Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.9351109Z x_171 += out_103; out_106: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_171; x_171 = out_103 = None 2025-03-04T20:49:58.9351170Z 2025-03-04T20:49:58.9351461Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9351607Z out_107: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_106); out_106 = None 2025-03-04T20:49:58.9351674Z 2025-03-04T20:49:58.9351924Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9352372Z x_172: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_107, l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.9352447Z 2025-03-04T20:49:58.9352714Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9354314Z x_173: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_172, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_172 = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9354407Z 2025-03-04T20:49:58.9354700Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9354834Z out_108: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_173); x_173 = None 2025-03-04T20:49:58.9354906Z 2025-03-04T20:49:58.9355154Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9355589Z x_174: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_108, l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_108 = l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.9355652Z 2025-03-04T20:49:58.9355922Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9357486Z x_175: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_174, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_174 = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9357551Z 2025-03-04T20:49:58.9357839Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9357971Z out_109: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_175); x_175 = None 2025-03-04T20:49:58.9358038Z 2025-03-04T20:49:58.9358286Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9358721Z x_176: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_109, l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_109 = l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.9358814Z 2025-03-04T20:49:58.9359085Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9360624Z x_177: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_176, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_176 = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9360700Z 2025-03-04T20:49:58.9360985Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.9361141Z x_177 += out_107; out_110: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_177; x_177 = out_107 = None 2025-03-04T20:49:58.9361207Z 2025-03-04T20:49:58.9361489Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9361639Z out_111: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_110); out_110 = None 2025-03-04T20:49:58.9361699Z 2025-03-04T20:49:58.9361950Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9362371Z x_178: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_111, l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.9362437Z 2025-03-04T20:49:58.9362703Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9364240Z x_179: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_178, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_178 = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9364307Z 2025-03-04T20:49:58.9364583Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9364723Z out_112: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_179); x_179 = None 2025-03-04T20:49:58.9364781Z 2025-03-04T20:49:58.9365031Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9365475Z x_180: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_112, l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_112 = l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.9365541Z 2025-03-04T20:49:58.9365798Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9367309Z x_181: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_180, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_180 = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9367401Z 2025-03-04T20:49:58.9367680Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9367821Z out_113: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_181); x_181 = None 2025-03-04T20:49:58.9367881Z 2025-03-04T20:49:58.9368133Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9368561Z x_182: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_113, l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_113 = l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.9368632Z 2025-03-04T20:49:58.9368898Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9370452Z x_183: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_182, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_182 = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9370523Z 2025-03-04T20:49:58.9370793Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.9370951Z x_183 += out_111; out_114: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_183; x_183 = out_111 = None 2025-03-04T20:49:58.9371013Z 2025-03-04T20:49:58.9371296Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9371434Z out_115: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_114); out_114 = None 2025-03-04T20:49:58.9371516Z 2025-03-04T20:49:58.9371771Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9372196Z x_184: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_115, l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.9372263Z 2025-03-04T20:49:58.9372540Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9374053Z x_185: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_184, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_184 = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9374128Z 2025-03-04T20:49:58.9374423Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9374555Z out_116: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_185); x_185 = None 2025-03-04T20:49:58.9374624Z 2025-03-04T20:49:58.9374874Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9375307Z x_186: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_116, l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_116 = l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.9375373Z 2025-03-04T20:49:58.9375638Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9377196Z x_187: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_186, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_186 = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9377260Z 2025-03-04T20:49:58.9377551Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9377693Z out_117: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_187); x_187 = None 2025-03-04T20:49:58.9377754Z 2025-03-04T20:49:58.9378000Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9378464Z x_188: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_117, l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_117 = l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.9378532Z 2025-03-04T20:49:58.9378804Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9380367Z x_189: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_188, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_188 = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9380453Z 2025-03-04T20:49:58.9380737Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.9380902Z x_189 += out_115; out_118: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_189; x_189 = out_115 = None 2025-03-04T20:49:58.9380964Z 2025-03-04T20:49:58.9381257Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9381404Z out_119: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_118); out_118 = None 2025-03-04T20:49:58.9381473Z 2025-03-04T20:49:58.9381723Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9382153Z x_190: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.9382228Z 2025-03-04T20:49:58.9382502Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9384038Z x_191: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_190, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_190 = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9384108Z 2025-03-04T20:49:58.9384400Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9384538Z out_120: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_191); x_191 = None 2025-03-04T20:49:58.9384620Z 2025-03-04T20:49:58.9384885Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9385313Z x_192: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_120, l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1); out_120 = l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.9385373Z 2025-03-04T20:49:58.9385643Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9387169Z x_193: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_192, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_192 = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9387257Z 2025-03-04T20:49:58.9387544Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9387675Z out_121: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_193); x_193 = None 2025-03-04T20:49:58.9387745Z 2025-03-04T20:49:58.9387991Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9388426Z x_194: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_121, l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_121 = l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.9388485Z 2025-03-04T20:49:58.9388774Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9390324Z x_195: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_194, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_194 = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9390392Z 2025-03-04T20:49:58.9390652Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9391089Z x_196: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_119 = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:49:58.9391191Z 2025-03-04T20:49:58.9391455Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9393061Z x_197: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_196, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_196 = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9393149Z 2025-03-04T20:49:58.9393428Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.9393584Z x_195 += x_197; out_122: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_195; x_195 = x_197 = None 2025-03-04T20:49:58.9393644Z 2025-03-04T20:49:58.9394003Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9394153Z out_123: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_122); out_122 = None 2025-03-04T20:49:58.9394222Z 2025-03-04T20:49:58.9394474Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9394904Z x_198: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_123, l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.9394963Z 2025-03-04T20:49:58.9395235Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9396810Z x_199: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_198, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_198 = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9396876Z 2025-03-04T20:49:58.9397170Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9397305Z out_124: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_199); x_199 = None 2025-03-04T20:49:58.9397374Z 2025-03-04T20:49:58.9397623Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9398083Z x_200: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_124, l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1); out_124 = l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.9398144Z 2025-03-04T20:49:58.9398417Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9399926Z x_201: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_200, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_200 = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9400002Z 2025-03-04T20:49:58.9400289Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9400420Z out_125: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_201); x_201 = None 2025-03-04T20:49:58.9400489Z 2025-03-04T20:49:58.9400735Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9401162Z x_202: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_125, l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_125 = l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.9401222Z 2025-03-04T20:49:58.9401491Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9403129Z x_203: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_202, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_202 = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9403198Z 2025-03-04T20:49:58.9403484Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.9403641Z x_203 += out_123; out_126: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_203; x_203 = out_123 = None 2025-03-04T20:49:58.9403710Z 2025-03-04T20:49:58.9403991Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9404139Z out_127: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_126); out_126 = None 2025-03-04T20:49:58.9404218Z 2025-03-04T20:49:58.9404486Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9404896Z x_204: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_127, l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:49:58.9404965Z 2025-03-04T20:49:58.9405226Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9406728Z x_205: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_204, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_204 = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9406815Z 2025-03-04T20:49:58.9407096Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9407236Z out_128: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_205); x_205 = None 2025-03-04T20:49:58.9407295Z 2025-03-04T20:49:58.9407547Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9407968Z x_206: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_128, l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1); out_128 = l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:49:58.9408034Z 2025-03-04T20:49:58.9408291Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9409809Z x_207: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_206, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_206 = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9409881Z 2025-03-04T20:49:58.9410162Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9410300Z out_129: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_207); x_207 = None 2025-03-04T20:49:58.9410358Z 2025-03-04T20:49:58.9410610Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9411055Z x_208: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_129, l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_129 = l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:49:58.9411121Z 2025-03-04T20:49:58.9411383Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:49:58.9412882Z x_209: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_208, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_208 = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:49:58.9412965Z 2025-03-04T20:49:58.9413238Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:49:58.9413398Z x_209 += out_127; out_130: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_209; x_209 = out_127 = None 2025-03-04T20:49:58.9413458Z 2025-03-04T20:49:58.9413738Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:49:58.9413884Z out_131: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_130); out_130 = None 2025-03-04T20:49:58.9413948Z 2025-03-04T20:49:58.9414379Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:49:58.9414531Z arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32) 2025-03-04T20:49:58.9414596Z 2025-03-04T20:49:58.9414901Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:49:58.9415044Z shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T20:49:58.9415104Z 2025-03-04T20:49:58.9415538Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:49:58.9415684Z arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32) 2025-03-04T20:49:58.9415748Z 2025-03-04T20:49:58.9416032Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:49:58.9416170Z shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T20:49:58.9416230Z 2025-03-04T20:49:58.9416604Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:49:58.9416779Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T20:49:58.9416900Z shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0] 2025-03-04T20:49:58.9417027Z shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T20:49:58.9417094Z 2025-03-04T20:49:58.9417417Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:49:58.9417545Z shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T20:49:58.9417606Z 2025-03-04T20:49:58.9417934Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:49:58.9418049Z shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T20:49:58.9418114Z 2025-03-04T20:49:58.9418501Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:49:58.9418734Z shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T20:49:58.9418792Z 2025-03-04T20:49:58.9419211Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:49:58.9419332Z view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T20:49:58.9419754Z view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T20:49:58.9419872Z add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T20:49:58.9419994Z x_210: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4); add = None 2025-03-04T20:49:58.9420055Z 2025-03-04T20:49:58.9420358Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:49:58.9420479Z tensor: "f32[82125, 4][4, 1]cpu" = x_210.to(torch.float32); x_210 = None 2025-03-04T20:49:58.9420545Z 2025-03-04T20:49:58.9420793Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:49:58.9421580Z x_211: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_131, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); out_131 = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T20:49:58.9421649Z 2025-03-04T20:49:58.9421925Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:49:58.9422116Z x_212: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_211, inplace = False); x_211 = None 2025-03-04T20:49:58.9422175Z 2025-03-04T20:49:58.9422560Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:49:58.9423478Z score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_212, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T20:49:58.9423563Z 2025-03-04T20:49:58.9423915Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:49:58.9424729Z x_213: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_212, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_212 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T20:49:58.9424810Z 2025-03-04T20:49:58.9425144Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T20:49:58.9425296Z permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T20:49:58.9425429Z logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T20:49:58.9425492Z 2025-03-04T20:49:58.9425898Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T20:49:58.9426058Z view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_213.view(4, -1, 4, 73, 75); x_213 = None 2025-03-04T20:49:58.9426224Z permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2); view_2 = None 2025-03-04T20:49:58.9426402Z pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2); permute_1 = None 2025-03-04T20:49:58.9426460Z 2025-03-04T20:49:58.9426859Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:49:58.9427057Z pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T20:49:58.9427123Z 2025-03-04T20:49:58.9427559Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:49:58.9427713Z unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T20:49:58.9427861Z expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T20:49:58.9428003Z anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T20:49:58.9428063Z 2025-03-04T20:49:58.9428455Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:49:58.9428627Z deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T20:49:58.9428688Z 2025-03-04T20:49:58.9429010Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:49:58.9429153Z boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T20:49:58.9429237Z 2025-03-04T20:49:58.9429563Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:49:58.9429699Z getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:49:58.9429822Z getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:49:58.9429970Z widths: "f32[328500][1]cpu" = getitem_2 - getitem_3; getitem_2 = getitem_3 = None 2025-03-04T20:49:58.9430031Z 2025-03-04T20:49:58.9430357Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:49:58.9430475Z getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:49:58.9430598Z getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:49:58.9430758Z heights: "f32[328500][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T20:49:58.9430837Z 2025-03-04T20:49:58.9431139Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:49:58.9431258Z getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:49:58.9431341Z mul: "f32[328500][1]cpu" = 0.5 * widths 2025-03-04T20:49:58.9431463Z ctr_x: "f32[328500][1]cpu" = getitem_6 + mul; getitem_6 = mul = None 2025-03-04T20:49:58.9431522Z 2025-03-04T20:49:58.9431830Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:49:58.9431967Z getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:49:58.9432063Z mul_1: "f32[328500][1]cpu" = 0.5 * heights 2025-03-04T20:49:58.9432187Z ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1; getitem_7 = mul_1 = None 2025-03-04T20:49:58.9432253Z 2025-03-04T20:49:58.9432588Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:49:58.9432739Z getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:49:58.9432846Z dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0; getitem_8 = None 2025-03-04T20:49:58.9432910Z 2025-03-04T20:49:58.9433225Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:49:58.9433381Z getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:49:58.9433488Z dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0; getitem_9 = None 2025-03-04T20:49:58.9433560Z 2025-03-04T20:49:58.9433926Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:49:58.9434090Z getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:49:58.9434198Z dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0; getitem_10 = None 2025-03-04T20:49:58.9434273Z 2025-03-04T20:49:58.9434596Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:49:58.9434796Z getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:49:58.9434913Z dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0; getitem_11 = None 2025-03-04T20:49:58.9435003Z 2025-03-04T20:49:58.9435356Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:49:58.9435497Z dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:49:58.9435567Z 2025-03-04T20:49:58.9435893Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:49:58.9436030Z dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:49:58.9436093Z 2025-03-04T20:49:58.9436448Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:49:58.9436585Z getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:49:58.9436735Z mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12; dx = getitem_12 = None 2025-03-04T20:49:58.9436883Z getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:49:58.9437025Z pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13; mul_2 = getitem_13 = None 2025-03-04T20:49:58.9437086Z 2025-03-04T20:49:58.9437439Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:49:58.9437574Z getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:49:58.9437699Z mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14; dy = getitem_14 = None 2025-03-04T20:49:58.9437845Z getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:49:58.9437988Z pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15; mul_3 = getitem_15 = None 2025-03-04T20:49:58.9438050Z 2025-03-04T20:49:58.9438384Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:49:58.9438497Z exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:49:58.9438659Z getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:49:58.9438809Z pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16; exp = getitem_16 = None 2025-03-04T20:49:58.9438877Z 2025-03-04T20:49:58.9439213Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:49:58.9439334Z exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:49:58.9439498Z getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:49:58.9439634Z pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17; exp_1 = getitem_17 = None 2025-03-04T20:49:58.9439693Z 2025-03-04T20:49:58.9440012Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:49:58.9440108Z mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T20:49:58.9440229Z x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:49:58.9440291Z 2025-03-04T20:49:58.9440608Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:49:58.9440698Z mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T20:49:58.9440837Z y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:49:58.9440911Z 2025-03-04T20:49:58.9441225Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:49:58.9441333Z mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:49:58.9441463Z x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:49:58.9441522Z 2025-03-04T20:49:58.9441833Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:49:58.9441940Z mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:49:58.9442067Z y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:49:58.9442129Z 2025-03-04T20:49:58.9442504Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:49:58.9442681Z pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:49:58.9442749Z 2025-03-04T20:49:58.9443085Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:49:58.9443252Z proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4)); pred_boxes = None 2025-03-04T20:49:58.9443312Z 2025-03-04T20:49:58.9443704Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:49:58.9443882Z proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T20:49:58.9443941Z 2025-03-04T20:49:58.9444434Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T20:49:58.9444566Z arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T20:49:58.9444633Z 2025-03-04T20:49:58.9444947Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:49:58.9445090Z batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T20:49:58.9445149Z 2025-03-04T20:49:58.9445593Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:49:58.9445704Z topk = logits_i.topk(6000, dim = 1); logits_i = None 2025-03-04T20:49:58.9445808Z topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0] 2025-03-04T20:49:58.9445919Z topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1]; topk = None 2025-03-04T20:49:58.9445985Z 2025-03-04T20:49:58.9446451Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:49:58.9446618Z getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T20:49:58.9446852Z topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)]; proposals_i_1 = getitem_20 = topk_idx = None 2025-03-04T20:49:58.9446935Z 2025-03-04T20:49:58.9447406Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:49:58.9447576Z full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:49:58.9447636Z 2025-03-04T20:49:58.9447936Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:49:58.9448084Z level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu')); full = level_ids = None 2025-03-04T20:49:58.9448151Z 2025-03-04T20:49:58.9448537Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T20:49:58.9448706Z getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0]; topk_proposals_i = None 2025-03-04T20:49:58.9448766Z 2025-03-04T20:49:58.9449071Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:49:58.9449212Z tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32); getitem_22 = None 2025-03-04T20:49:58.9449279Z 2025-03-04T20:49:58.9449664Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T20:49:58.9449805Z scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0]; topk_scores_i = None 2025-03-04T20:49:58.9449864Z 2025-03-04T20:49:58.9450340Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T20:49:58.9450480Z isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1); tensor_1 = None 2025-03-04T20:49:58.9450594Z all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:49:58.9450747Z isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T20:49:58.9450874Z valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T20:49:58.9450940Z 2025-03-04T20:49:58.9451318Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T20:49:58.9451437Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T20:49:58.9451498Z 2025-03-04T20:50:16.5479520Z 2025-03-04T20:50:16.5480227Z class GraphModule(torch.nn.Module): 2025-03-04T20:50:16.5481849Z def forward(self, L_features_res5_: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_weight_: "f32[2048, 2048, 3, 3][18432, 9, 3, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_bias_: "f32[2048][1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"): 2025-03-04T20:50:16.5483678Z l_features_res5_ = L_features_res5_ 2025-03-04T20:50:16.5484086Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T20:50:16.5485086Z l_self_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T20:50:16.5489971Z l_self_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T20:50:16.5492279Z l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T20:50:16.5493127Z l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T20:50:16.5498119Z l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T20:50:16.5500297Z l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T20:50:16.5501022Z 2025-03-04T20:50:16.5506134Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:50:16.5507591Z arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32) 2025-03-04T20:50:16.5507994Z 2025-03-04T20:50:16.5508490Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:50:16.5508980Z shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T20:50:16.5509296Z 2025-03-04T20:50:16.5514218Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:50:16.5515018Z arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32) 2025-03-04T20:50:16.5515290Z 2025-03-04T20:50:16.5515692Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:50:16.5516196Z shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T20:50:16.5516457Z 2025-03-04T20:50:16.5517106Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:50:16.5517733Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T20:50:16.5518068Z shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0] 2025-03-04T20:50:16.5518342Z shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T20:50:16.5518692Z 2025-03-04T20:50:16.5519130Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:50:16.5519658Z shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T20:50:16.5519901Z 2025-03-04T20:50:16.5520311Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:50:16.5520811Z shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T20:50:16.5521046Z 2025-03-04T20:50:16.5521507Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:50:16.5522225Z shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T20:50:16.5522548Z 2025-03-04T20:50:16.5523041Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:50:16.5523616Z view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T20:50:16.5524108Z view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T20:50:16.5524582Z add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T20:50:16.5524859Z x: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4); add = None 2025-03-04T20:50:16.5525079Z 2025-03-04T20:50:16.5525492Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:50:16.5525957Z tensor: "f32[82125, 4][4, 1]cpu" = x.to(torch.float32); x = None 2025-03-04T20:50:16.5526185Z 2025-03-04T20:50:16.5526519Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:50:16.5527424Z x_1: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(l_features_res5_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_res5_ = l_self_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T20:50:16.5528114Z 2025-03-04T20:50:16.5528470Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:50:16.5528978Z x_2: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False); x_1 = None 2025-03-04T20:50:16.5529273Z 2025-03-04T20:50:16.5529729Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:50:16.5530794Z score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T20:50:16.5531525Z 2025-03-04T20:50:16.5531963Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:50:16.5532963Z x_3: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_2 = l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T20:50:16.5533657Z 2025-03-04T20:50:16.5534077Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T20:50:16.5534616Z permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T20:50:16.5534950Z logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T20:50:16.5535217Z 2025-03-04T20:50:16.5535720Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T20:50:16.5536324Z view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_3.view(4, -1, 4, 73, 75); x_3 = None 2025-03-04T20:50:16.5536694Z permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2); view_2 = None 2025-03-04T20:50:16.5537084Z pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2); permute_1 = None 2025-03-04T20:50:16.5537369Z 2025-03-04T20:50:16.5537850Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:50:16.5538511Z pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T20:50:16.5538821Z 2025-03-04T20:50:16.5539324Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:50:16.5539937Z unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T20:50:16.5540280Z expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T20:50:16.5540616Z anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T20:50:16.5540870Z 2025-03-04T20:50:16.5541336Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:50:16.5541922Z deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T20:50:16.5542199Z 2025-03-04T20:50:16.5542589Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:50:16.5543093Z boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T20:50:16.5543346Z 2025-03-04T20:50:16.5543758Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:50:16.5544262Z getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:50:16.5544573Z getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:50:16.5544897Z widths: "f32[328500][1]cpu" = getitem_2 - getitem_3; getitem_2 = getitem_3 = None 2025-03-04T20:50:16.5545163Z 2025-03-04T20:50:16.5545578Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:50:16.5546081Z getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:50:16.5546387Z getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:50:16.5546716Z heights: "f32[328500][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T20:50:16.5546982Z 2025-03-04T20:50:16.5547390Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:50:16.5547892Z getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:50:16.5548163Z mul: "f32[328500][1]cpu" = 0.5 * widths 2025-03-04T20:50:16.5548469Z ctr_x: "f32[328500][1]cpu" = getitem_6 + mul; getitem_6 = mul = None 2025-03-04T20:50:16.5548716Z 2025-03-04T20:50:16.5549113Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:50:16.5549622Z getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:50:16.5549905Z mul_1: "f32[328500][1]cpu" = 0.5 * heights 2025-03-04T20:50:16.5550172Z ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1; getitem_7 = mul_1 = None 2025-03-04T20:50:16.5550415Z 2025-03-04T20:50:16.5550846Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:50:16.5551349Z getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:50:16.5551691Z dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0; getitem_8 = None 2025-03-04T20:50:16.5551921Z 2025-03-04T20:50:16.5552303Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:50:16.5552800Z getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:50:16.5553115Z dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0; getitem_9 = None 2025-03-04T20:50:16.5553343Z 2025-03-04T20:50:16.5553725Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:50:16.5554229Z getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:50:16.5554548Z dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0; getitem_10 = None 2025-03-04T20:50:16.5554779Z 2025-03-04T20:50:16.5555166Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:50:16.5555699Z getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:50:16.5556038Z dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0; getitem_11 = None 2025-03-04T20:50:16.5556263Z 2025-03-04T20:50:16.5557899Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:50:16.5558524Z dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:50:16.5558798Z 2025-03-04T20:50:16.5559218Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:50:16.5559764Z dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:50:16.5560029Z 2025-03-04T20:50:16.5560455Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:50:16.5560994Z getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:50:16.5561313Z mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12; dx = getitem_12 = None 2025-03-04T20:50:16.5561645Z getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:50:16.5561990Z pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13; mul_2 = getitem_13 = None 2025-03-04T20:50:16.5562249Z 2025-03-04T20:50:16.5562689Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:50:16.5563232Z getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:50:16.5563539Z mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14; dy = getitem_14 = None 2025-03-04T20:50:16.5563859Z getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:50:16.5564190Z pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15; mul_3 = getitem_15 = None 2025-03-04T20:50:16.5564476Z 2025-03-04T20:50:16.5564885Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:50:16.5565379Z exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:50:16.5565696Z getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:50:16.5566050Z pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16; exp = getitem_16 = None 2025-03-04T20:50:16.5566292Z 2025-03-04T20:50:16.5566705Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:50:16.5567202Z exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:50:16.5567530Z getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:50:16.5567877Z pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17; exp_1 = getitem_17 = None 2025-03-04T20:50:16.5568123Z 2025-03-04T20:50:16.5568517Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:50:16.5568974Z mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T20:50:16.5569229Z x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:50:16.5569454Z 2025-03-04T20:50:16.5569841Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:50:16.5570289Z mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T20:50:16.5570540Z y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:50:16.5570760Z 2025-03-04T20:50:16.5571150Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:50:16.5571613Z mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:50:16.5571895Z x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:50:16.5572136Z 2025-03-04T20:50:16.5572517Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:50:16.5572976Z mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:50:16.5573256Z y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:50:16.5573493Z 2025-03-04T20:50:16.5573916Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:50:16.5574480Z pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:50:16.5574765Z 2025-03-04T20:50:16.5575171Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:50:16.5575851Z proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4)); pred_boxes = None 2025-03-04T20:50:16.5576489Z 2025-03-04T20:50:16.5577017Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:50:16.5577669Z proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T20:50:16.5578058Z 2025-03-04T20:50:16.5578683Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T20:50:16.5579430Z arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T20:50:16.5579762Z 2025-03-04T20:50:16.5580206Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:50:16.5580763Z batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T20:50:16.5581084Z 2025-03-04T20:50:16.5581728Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:50:16.5582401Z topk = logits_i.topk(6000, dim = 1); logits_i = None 2025-03-04T20:50:16.5582732Z topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0] 2025-03-04T20:50:16.5583059Z topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1]; topk = None 2025-03-04T20:50:16.5583362Z 2025-03-04T20:50:16.5583969Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:50:16.5584796Z getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T20:50:16.5585294Z topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)]; proposals_i_1 = getitem_20 = topk_idx = None 2025-03-04T20:50:16.5585716Z 2025-03-04T20:50:16.5586349Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:50:16.5587161Z full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:50:16.5587534Z 2025-03-04T20:50:16.5587978Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:50:16.5588520Z level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu')); full = level_ids = None 2025-03-04T20:50:16.5588886Z 2025-03-04T20:50:16.5589409Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T20:50:16.5590062Z getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0]; topk_proposals_i = None 2025-03-04T20:50:16.5590389Z 2025-03-04T20:50:16.5590823Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:50:16.5591386Z tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32); getitem_22 = None 2025-03-04T20:50:16.5591807Z 2025-03-04T20:50:16.5592318Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T20:50:16.5592988Z scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0]; topk_scores_i = None 2025-03-04T20:50:16.5593271Z 2025-03-04T20:50:16.5593931Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T20:50:16.5594662Z isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1); tensor_1 = None 2025-03-04T20:50:16.5595039Z all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:50:16.5595443Z isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T20:50:16.5595855Z valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T20:50:16.5596137Z 2025-03-04T20:50:16.5596664Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T20:50:16.5597336Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T20:50:16.5597616Z 2025-03-04T20:50:16.5597777Z 2025-03-04T20:50:16.5597890Z class GraphModule(torch.nn.Module): 2025-03-04T20:50:16.5599373Z def forward(self, L_features_res5_: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_weight_: "f32[2048, 2048, 3, 3][18432, 9, 3, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_bias_: "f32[2048][1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"): 2025-03-04T20:50:16.5600720Z l_features_res5_ = L_features_res5_ 2025-03-04T20:50:16.5601199Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T20:50:16.5601805Z l_self_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T20:50:16.5602650Z l_self_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T20:50:16.5603248Z l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T20:50:16.5603958Z l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T20:50:16.5604706Z l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T20:50:16.5605313Z l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T20:50:16.5605781Z 2025-03-04T20:50:16.5606381Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:50:16.5607082Z arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32) 2025-03-04T20:50:16.5607439Z 2025-03-04T20:50:16.5607942Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:50:16.5608511Z shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T20:50:16.5608826Z 2025-03-04T20:50:16.5609405Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:50:16.5610182Z arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32) 2025-03-04T20:50:16.5610504Z 2025-03-04T20:50:16.5610930Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:50:16.5611495Z shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T20:50:16.5611833Z 2025-03-04T20:50:16.5612355Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:50:16.5613030Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T20:50:16.5613410Z shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0] 2025-03-04T20:50:16.5613751Z shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T20:50:16.5624996Z 2025-03-04T20:50:16.5625585Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:50:16.5626115Z shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T20:50:16.5626364Z 2025-03-04T20:50:16.5626786Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:50:16.5627295Z shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T20:50:16.5627532Z 2025-03-04T20:50:16.5627996Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:50:16.5628633Z shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T20:50:16.5629082Z 2025-03-04T20:50:16.5629597Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:50:16.5630193Z view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T20:50:16.5630701Z view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T20:50:16.5631187Z add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T20:50:16.5631479Z x: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4); add = None 2025-03-04T20:50:16.5631709Z 2025-03-04T20:50:16.5632104Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:50:16.5632585Z tensor: "f32[82125, 4][4, 1]cpu" = x.to(torch.float32); x = None 2025-03-04T20:50:16.5632822Z 2025-03-04T20:50:16.5633165Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:50:16.5634133Z x_1: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(l_features_res5_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_res5_ = l_self_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T20:50:16.5634846Z 2025-03-04T20:50:16.5635211Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:50:16.5635734Z x_2: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False); x_1 = None 2025-03-04T20:50:16.5636037Z 2025-03-04T20:50:16.5636506Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:50:16.5637576Z score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T20:50:16.5638308Z 2025-03-04T20:50:16.5638841Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:50:16.5639866Z x_3: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_2 = l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T20:50:16.5640554Z 2025-03-04T20:50:16.5640971Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T20:50:16.5641585Z permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T20:50:16.5641934Z logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T20:50:16.5642196Z 2025-03-04T20:50:16.5642724Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T20:50:16.5643356Z view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_3.view(4, -1, 4, 73, 75); x_3 = None 2025-03-04T20:50:16.5643745Z permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2); view_2 = None 2025-03-04T20:50:16.5644151Z pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2); permute_1 = None 2025-03-04T20:50:16.5644443Z 2025-03-04T20:50:16.5644937Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:50:16.5645601Z pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T20:50:16.5645920Z 2025-03-04T20:50:16.5646450Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:50:16.5647096Z unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T20:50:16.5647480Z expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T20:50:16.5647823Z anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T20:50:16.5648084Z 2025-03-04T20:50:16.5648553Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:50:16.5649159Z deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T20:50:16.5649451Z 2025-03-04T20:50:16.5649858Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:50:16.5650376Z boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T20:50:16.5650656Z 2025-03-04T20:50:16.5651057Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:50:16.5651561Z getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:50:16.5651871Z getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:50:16.5652197Z widths: "f32[328500][1]cpu" = getitem_2 - getitem_3; getitem_2 = getitem_3 = None 2025-03-04T20:50:16.5652459Z 2025-03-04T20:50:16.5652867Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:50:16.5653368Z getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:50:16.5653661Z getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:50:16.5653978Z heights: "f32[328500][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T20:50:16.5654241Z 2025-03-04T20:50:16.5654630Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:50:16.5655104Z getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:50:16.5655366Z mul: "f32[328500][1]cpu" = 0.5 * widths 2025-03-04T20:50:16.5655624Z ctr_x: "f32[328500][1]cpu" = getitem_6 + mul; getitem_6 = mul = None 2025-03-04T20:50:16.5655860Z 2025-03-04T20:50:16.5656263Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:50:16.5656762Z getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:50:16.5657051Z mul_1: "f32[328500][1]cpu" = 0.5 * heights 2025-03-04T20:50:16.5657312Z ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1; getitem_7 = mul_1 = None 2025-03-04T20:50:16.5657549Z 2025-03-04T20:50:16.5657959Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:50:16.5658451Z getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:50:16.5658767Z dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0; getitem_8 = None 2025-03-04T20:50:16.5658994Z 2025-03-04T20:50:16.5659372Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:50:16.5659860Z getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:50:16.5660292Z dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0; getitem_9 = None 2025-03-04T20:50:16.5660513Z 2025-03-04T20:50:16.5660907Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:50:16.5661397Z getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:50:16.5661711Z dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0; getitem_10 = None 2025-03-04T20:50:16.5661935Z 2025-03-04T20:50:16.5662317Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:50:16.5662843Z getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:50:16.5663179Z dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0; getitem_11 = None 2025-03-04T20:50:16.5663419Z 2025-03-04T20:50:16.5663830Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:50:16.5664348Z dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:50:16.5664601Z 2025-03-04T20:50:16.5665010Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:50:16.5665516Z dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:50:16.5665764Z 2025-03-04T20:50:16.5666179Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:50:16.5666701Z getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:50:16.5667014Z mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12; dx = getitem_12 = None 2025-03-04T20:50:16.5667332Z getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:50:16.5667668Z pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13; mul_2 = getitem_13 = None 2025-03-04T20:50:16.5667915Z 2025-03-04T20:50:16.5668337Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:50:16.5668878Z getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:50:16.5669191Z mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14; dy = getitem_14 = None 2025-03-04T20:50:16.5669520Z getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:50:16.5669863Z pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15; mul_3 = getitem_15 = None 2025-03-04T20:50:16.5670117Z 2025-03-04T20:50:16.5670535Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:50:16.5671036Z exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:50:16.5671359Z getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:50:16.5671703Z pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16; exp = getitem_16 = None 2025-03-04T20:50:16.5671952Z 2025-03-04T20:50:16.5672371Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:50:16.5672883Z exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:50:16.5673247Z getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:50:16.5673711Z pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17; exp_1 = getitem_17 = None 2025-03-04T20:50:16.5673965Z 2025-03-04T20:50:16.5674367Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:50:16.5674857Z mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T20:50:16.5675115Z x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:50:16.5675344Z 2025-03-04T20:50:16.5675729Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:50:16.5676179Z mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T20:50:16.5676469Z y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:50:16.5676703Z 2025-03-04T20:50:16.5677092Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:50:16.5677578Z mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:50:16.5677881Z x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:50:16.5678126Z 2025-03-04T20:50:16.5678582Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:50:16.5679054Z mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:50:16.5679348Z y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:50:16.5679605Z 2025-03-04T20:50:16.5680031Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:50:16.5680597Z pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:50:16.5680882Z 2025-03-04T20:50:16.5681293Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:50:16.5681843Z proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4)); pred_boxes = None 2025-03-04T20:50:16.5682122Z 2025-03-04T20:50:16.5682579Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:50:16.5683169Z proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T20:50:16.5683447Z 2025-03-04T20:50:16.5684005Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T20:50:16.5684659Z arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T20:50:16.5684899Z 2025-03-04T20:50:16.5685270Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:50:16.5685744Z batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T20:50:16.5686002Z 2025-03-04T20:50:16.5686527Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:50:16.5687129Z topk = logits_i.topk(6000, dim = 1); logits_i = None 2025-03-04T20:50:16.5687387Z topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0] 2025-03-04T20:50:16.5687645Z topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1]; topk = None 2025-03-04T20:50:16.5687863Z 2025-03-04T20:50:16.5688400Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:50:16.5689065Z getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T20:50:16.5689508Z topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)]; proposals_i_1 = getitem_20 = topk_idx = None 2025-03-04T20:50:16.5689861Z 2025-03-04T20:50:16.5690394Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:50:16.5691051Z full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:50:16.5691323Z 2025-03-04T20:50:16.5691697Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:50:16.5692187Z level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu')); full = level_ids = None 2025-03-04T20:50:16.5692445Z 2025-03-04T20:50:16.5692902Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T20:50:16.5693471Z getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0]; topk_proposals_i = None 2025-03-04T20:50:16.5693721Z 2025-03-04T20:50:16.5694090Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:50:16.5694569Z tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32); getitem_22 = None 2025-03-04T20:50:16.5694820Z 2025-03-04T20:50:16.5695287Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T20:50:16.5695842Z scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0]; topk_scores_i = None 2025-03-04T20:50:16.5696088Z 2025-03-04T20:50:16.5696641Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T20:50:16.5697294Z isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1); tensor_1 = None 2025-03-04T20:50:16.5697593Z all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:50:16.5697910Z isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T20:50:16.5698239Z valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T20:50:16.5698479Z 2025-03-04T20:50:16.5698918Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T20:50:16.5699438Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T20:50:16.5699681Z 2025-03-04T20:50:19.6793444Z 2025-03-04T20:50:19.6794095Z class GraphModule(torch.nn.Module): 2025-03-04T20:50:19.6795974Z def forward(self, L_pred_anchor_deltas_0_: "f32[4, 82125, 4][328500, 4, 1]cpu", L_anchors_0_tensor: "f32[82125, 4][4, 1]cpu", L_pred_objectness_logits_0_: "f32[4, 82125][82125, 1]cpu"): 2025-03-04T20:50:19.6796570Z l_pred_anchor_deltas_0_ = L_pred_anchor_deltas_0_ 2025-03-04T20:50:19.6796871Z l_anchors_0_tensor = L_anchors_0_tensor 2025-03-04T20:50:19.6797153Z l_pred_objectness_logits_0_ = L_pred_objectness_logits_0_ 2025-03-04T20:50:19.6797407Z 2025-03-04T20:50:19.6797983Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:50:19.6798708Z pred_anchor_deltas_i: "f32[328500, 4][4, 1]cpu" = l_pred_anchor_deltas_0_.reshape(-1, 4); l_pred_anchor_deltas_0_ = None 2025-03-04T20:50:19.6799378Z 2025-03-04T20:50:19.6800039Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:50:19.6800844Z unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = l_anchors_0_tensor.unsqueeze(0); l_anchors_0_tensor = None 2025-03-04T20:50:19.6801281Z expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T20:50:19.6801632Z anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T20:50:19.6802136Z 2025-03-04T20:50:19.6802655Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:50:19.6803271Z deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.float(); pred_anchor_deltas_i = None 2025-03-04T20:50:19.6803568Z 2025-03-04T20:50:19.6803980Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:50:19.6804513Z boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T20:50:19.6804777Z 2025-03-04T20:50:19.6805194Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:50:19.6805765Z getitem: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:50:19.6806085Z getitem_1: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:50:19.6806415Z widths: "f32[328500][1]cpu" = getitem - getitem_1; getitem = getitem_1 = None 2025-03-04T20:50:19.6806679Z 2025-03-04T20:50:19.6807102Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:50:19.6807623Z getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:50:19.6807935Z getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:50:19.6808267Z heights: "f32[328500][1]cpu" = getitem_2 - getitem_3; getitem_2 = getitem_3 = None 2025-03-04T20:50:19.6808539Z 2025-03-04T20:50:19.6808949Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:50:19.6809453Z getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:50:19.6809721Z mul: "f32[328500][1]cpu" = 0.5 * widths 2025-03-04T20:50:19.6809982Z ctr_x: "f32[328500][1]cpu" = getitem_4 + mul; getitem_4 = mul = None 2025-03-04T20:50:19.6810274Z 2025-03-04T20:50:19.6810710Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:50:19.6811240Z getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:50:19.6811530Z mul_1: "f32[328500][1]cpu" = 0.5 * heights 2025-03-04T20:50:19.6811804Z ctr_y: "f32[328500][1]cpu" = getitem_5 + mul_1; getitem_5 = mul_1 = None 2025-03-04T20:50:19.6812055Z 2025-03-04T20:50:19.6812481Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:50:19.6812987Z getitem_6: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:50:19.6813301Z dx: "f32[328500, 1][1, 1]cpu" = getitem_6 / 1.0; getitem_6 = None 2025-03-04T20:50:19.6813526Z 2025-03-04T20:50:19.6813934Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:50:19.6814431Z getitem_7: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:50:19.6814746Z dy: "f32[328500, 1][1, 1]cpu" = getitem_7 / 1.0; getitem_7 = None 2025-03-04T20:50:19.6814974Z 2025-03-04T20:50:19.6815360Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:50:19.6815864Z getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:50:19.6816176Z dw: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0; getitem_8 = None 2025-03-04T20:50:19.6816401Z 2025-03-04T20:50:19.6816795Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:50:19.6817317Z getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:50:19.6817649Z dh: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0; getitem_9 = None 2025-03-04T20:50:19.6817868Z 2025-03-04T20:50:19.6818284Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:50:19.6818814Z dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:50:19.6819100Z 2025-03-04T20:50:19.6819517Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:50:19.6820036Z dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:50:19.6820291Z 2025-03-04T20:50:19.6820722Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:50:19.6821259Z getitem_10: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:50:19.6821578Z mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_10; dx = getitem_10 = None 2025-03-04T20:50:19.6821902Z getitem_11: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:50:19.6822259Z pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_11; mul_2 = getitem_11 = None 2025-03-04T20:50:19.6822507Z 2025-03-04T20:50:19.6822928Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:50:19.6823478Z getitem_12: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:50:19.6823802Z mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_12; dy = getitem_12 = None 2025-03-04T20:50:19.6824117Z getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:50:19.6824452Z pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_13; mul_3 = getitem_13 = None 2025-03-04T20:50:19.6824697Z 2025-03-04T20:50:19.6825103Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:50:19.6825593Z exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:50:19.6825907Z getitem_14: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:50:19.6826247Z pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_14; exp = getitem_14 = None 2025-03-04T20:50:19.6826515Z 2025-03-04T20:50:19.6826928Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:50:19.6827431Z exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:50:19.6827761Z getitem_15: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:50:19.6828109Z pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_15; exp_1 = getitem_15 = None 2025-03-04T20:50:19.6828356Z 2025-03-04T20:50:19.6828753Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:50:19.6829214Z mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T20:50:19.6829470Z x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:50:19.6829702Z 2025-03-04T20:50:19.6830094Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:50:19.6830561Z mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T20:50:19.6830814Z y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:50:19.6831043Z 2025-03-04T20:50:19.6831457Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:50:19.6831954Z mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:50:19.6832247Z x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:50:19.6832490Z 2025-03-04T20:50:19.6832879Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:50:19.6833361Z mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:50:19.6833648Z y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:50:19.6833889Z 2025-03-04T20:50:19.6834331Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:50:19.6834915Z pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:50:19.6835203Z 2025-03-04T20:50:19.6835625Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:50:19.6836206Z proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4)); pred_boxes = None 2025-03-04T20:50:19.6836494Z 2025-03-04T20:50:19.6836994Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:50:19.6837621Z proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T20:50:19.6837919Z 2025-03-04T20:50:19.6838506Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T20:50:19.6839463Z arange: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T20:50:19.6839725Z 2025-03-04T20:50:19.6840126Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:50:19.6840653Z batch_idx: "i64[4][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T20:50:19.6840904Z 2025-03-04T20:50:19.6841424Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:50:19.6842089Z topk = l_pred_objectness_logits_0_.topk(6000, dim = 1); l_pred_objectness_logits_0_ = None 2025-03-04T20:50:19.6842417Z topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0] 2025-03-04T20:50:19.6842682Z topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1]; topk = None 2025-03-04T20:50:19.6842904Z 2025-03-04T20:50:19.6843470Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:50:19.6844152Z getitem_18: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T20:50:19.6844596Z topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_18, topk_idx)]; proposals_i_1 = getitem_18 = topk_idx = None 2025-03-04T20:50:19.6844939Z 2025-03-04T20:50:19.6845515Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:50:19.6846187Z full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:50:19.6846473Z 2025-03-04T20:50:19.6846842Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:50:19.6847339Z level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu')); full = level_ids = None 2025-03-04T20:50:19.6847603Z 2025-03-04T20:50:19.6848064Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T20:50:19.6848645Z getitem_20: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0]; topk_proposals_i = None 2025-03-04T20:50:19.6848908Z 2025-03-04T20:50:19.6849286Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:50:19.6849776Z tensor: "f32[6000, 4][4, 1]cpu" = getitem_20.to(torch.float32); getitem_20 = None 2025-03-04T20:50:19.6850032Z 2025-03-04T20:50:19.6850507Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T20:50:19.6851095Z scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0]; topk_scores_i = None 2025-03-04T20:50:19.6851349Z 2025-03-04T20:50:19.6851911Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T20:50:19.6852578Z isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor); tensor = None 2025-03-04T20:50:19.6852881Z all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:50:19.6853204Z isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T20:50:19.6853544Z valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T20:50:19.6853813Z 2025-03-04T20:50:19.6854269Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T20:50:19.6854808Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T20:50:19.6855031Z 2025-03-04T20:50:26.3078056Z 2025-03-04T20:50:26.3083421Z class GraphModule(torch.nn.Module): 2025-03-04T20:50:26.3086916Z def forward(self, L_stack0_: "f32[3225, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 100352][100352, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1225 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1225 - s0, 4][4, 1]cpu"): 2025-03-04T20:50:26.3089264Z l_stack0_ = L_stack0_ 2025-03-04T20:50:26.3091441Z l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_ 2025-03-04T20:50:26.3092224Z l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_ 2025-03-04T20:50:26.3092797Z l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_ 2025-03-04T20:50:26.3098999Z l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_ 2025-03-04T20:50:26.3101490Z l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_ 2025-03-04T20:50:26.3102478Z l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_ 2025-03-04T20:50:26.3108043Z l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ 2025-03-04T20:50:26.3110277Z l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ 2025-03-04T20:50:26.3110944Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T20:50:26.3115042Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T20:50:26.3116210Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T20:50:26.3116699Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T20:50:26.3117028Z 2025-03-04T20:50:26.3117488Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x) 2025-03-04T20:50:26.3118598Z x: "f32[3225, 100352][100352, 1]cpu" = l_stack0_.flatten(1, -1); l_stack0_ = None 2025-03-04T20:50:26.3119539Z x_1: "f32[3225, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_); x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None 2025-03-04T20:50:26.3120436Z x_2: "f32[3225, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False); x_1 = None 2025-03-04T20:50:26.3121351Z x_3: "f32[3225, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_); x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None 2025-03-04T20:50:26.3122159Z x_4: "f32[3225, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False); x_3 = None 2025-03-04T20:50:26.3122472Z 2025-03-04T20:50:26.3122932Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x) 2025-03-04T20:50:26.3124024Z scores: "f32[3225, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_); l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None 2025-03-04T20:50:26.3124816Z 2025-03-04T20:50:26.3125277Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x) 2025-03-04T20:50:26.3126448Z proposal_deltas: "f32[3225, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_); x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None 2025-03-04T20:50:26.3127274Z 2025-03-04T20:50:26.3127692Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:50:26.3128209Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T20:50:26.3128493Z getitem: "Sym(s0)" = size[0] 2025-03-04T20:50:26.3128753Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T20:50:26.3129056Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T20:50:26.3129341Z getitem_2: "Sym(1225 - s0)" = size_1[0] 2025-03-04T20:50:26.3129609Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T20:50:26.3129850Z 2025-03-04T20:50:26.3130263Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:50:26.3131319Z proposal_boxes: "f32[3225, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T20:50:26.3132145Z 2025-03-04T20:50:26.3132675Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:50:26.3133252Z deltas: "f32[3225, 320][320, 1]cpu" = proposal_deltas.float(); proposal_deltas = None 2025-03-04T20:50:26.3133538Z 2025-03-04T20:50:26.3133957Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:50:26.3134514Z boxes: "f32[3225, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T20:50:26.3134803Z 2025-03-04T20:50:26.3135224Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:50:26.3135784Z getitem_4: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:50:26.3136096Z getitem_5: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:50:26.3136418Z widths: "f32[3225][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T20:50:26.3136683Z 2025-03-04T20:50:26.3137105Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:50:26.3137610Z getitem_6: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:50:26.3137911Z getitem_7: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:50:26.3138231Z heights: "f32[3225][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T20:50:26.3138491Z 2025-03-04T20:50:26.3138891Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:50:26.3139396Z getitem_8: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:50:26.3139647Z mul: "f32[3225][1]cpu" = 0.5 * widths 2025-03-04T20:50:26.3139910Z ctr_x: "f32[3225][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T20:50:26.3140157Z 2025-03-04T20:50:26.3140573Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:50:26.3141125Z getitem_9: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:50:26.3141427Z mul_1: "f32[3225][1]cpu" = 0.5 * heights 2025-03-04T20:50:26.3141707Z ctr_y: "f32[3225][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T20:50:26.3141948Z 2025-03-04T20:50:26.3142384Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:50:26.3142912Z getitem_10: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:50:26.3143252Z dx: "f32[3225, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T20:50:26.3143478Z 2025-03-04T20:50:26.3143862Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:50:26.3144389Z getitem_11: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:50:26.3144725Z dy: "f32[3225, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T20:50:26.3144964Z 2025-03-04T20:50:26.3145365Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:50:26.3145925Z getitem_12: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:50:26.3146258Z dw: "f32[3225, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T20:50:26.3146499Z 2025-03-04T20:50:26.3146885Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:50:26.3147420Z getitem_13: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:50:26.3147761Z dh: "f32[3225, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T20:50:26.3147985Z 2025-03-04T20:50:26.3148407Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:50:26.3148938Z dw_1: "f32[3225, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:50:26.3149209Z 2025-03-04T20:50:26.3149622Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:50:26.3150144Z dh_1: "f32[3225, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:50:26.3150399Z 2025-03-04T20:50:26.3150838Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:50:26.3151386Z getitem_14: "f32[3225, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:50:26.3151709Z mul_2: "f32[3225, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T20:50:26.3152043Z getitem_15: "f32[3225, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:50:26.3152394Z pred_ctr_x: "f32[3225, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T20:50:26.3152651Z 2025-03-04T20:50:26.3153094Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:50:26.3153641Z getitem_16: "f32[3225, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:50:26.3153962Z mul_3: "f32[3225, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T20:50:26.3154317Z getitem_17: "f32[3225, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:50:26.3154673Z pred_ctr_y: "f32[3225, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T20:50:26.3154942Z 2025-03-04T20:50:26.3155392Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:50:26.3155972Z exp: "f32[3225, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:50:26.3156321Z getitem_18: "f32[3225, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:50:26.3156683Z pred_w: "f32[3225, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T20:50:26.3156951Z 2025-03-04T20:50:26.3157414Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:50:26.3157988Z exp_1: "f32[3225, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:50:26.3158354Z getitem_19: "f32[3225, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:50:26.3158748Z pred_h: "f32[3225, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T20:50:26.3159044Z 2025-03-04T20:50:26.3159597Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:50:26.3160115Z mul_6: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T20:50:26.3160402Z x1: "f32[3225, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:50:26.3160659Z 2025-03-04T20:50:26.3161059Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:50:26.3161503Z mul_7: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T20:50:26.3161762Z y1: "f32[3225, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:50:26.3161991Z 2025-03-04T20:50:26.3162388Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:50:26.3162900Z mul_8: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:50:26.3163177Z x2: "f32[3225, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:50:26.3163415Z 2025-03-04T20:50:26.3163803Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:50:26.3164275Z mul_9: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:50:26.3164562Z y2: "f32[3225, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:50:26.3164804Z 2025-03-04T20:50:26.3165241Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:50:26.3165821Z pred_boxes: "f32[3225, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:50:26.3166108Z 2025-03-04T20:50:26.3166540Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:50:26.3167075Z predict_boxes: "f32[3225, 320][320, 1]cpu" = pred_boxes.reshape((3225, 320)); pred_boxes = None 2025-03-04T20:50:26.3167347Z 2025-03-04T20:50:26.3167813Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T20:50:26.3168414Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T20:50:26.3168773Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T20:50:26.3169058Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T20:50:26.3169363Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T20:50:26.3169674Z getitem_23: "f32[1225 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T20:50:26.3169937Z 2025-03-04T20:50:26.3170321Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:50:26.3170885Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T20:50:26.3171237Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T20:50:26.3171480Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T20:50:26.3171852Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T20:50:26.3172208Z getitem_26: "Sym(1225 - s0)" = size_3[0] 2025-03-04T20:50:26.3172471Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T20:50:26.3172684Z 2025-03-04T20:50:26.3173119Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T20:50:26.3173679Z probs: "f32[3225, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1); scores = None 2025-03-04T20:50:26.3173959Z 2025-03-04T20:50:26.3174396Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T20:50:26.3174996Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T20:50:26.3175351Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T20:50:26.3175631Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T20:50:26.3175947Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T20:50:26.3176261Z getitem_31: "f32[1225 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T20:50:26.3176516Z 2025-03-04T20:50:26.3177065Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T20:50:26.3177757Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T20:50:26.3178100Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:50:26.3178434Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T20:50:26.3178775Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T20:50:26.3179085Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T20:50:26.3179326Z 2025-03-04T20:50:26.3179764Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T20:50:26.3180282Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T20:50:26.3180514Z 2025-03-04T20:50:26.3180646Z 2025-03-04T20:50:26.3180736Z class GraphModule(torch.nn.Module): 2025-03-04T20:50:26.3182700Z def forward(self, L_stack0_: "f32[3225, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 100352][100352, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1225 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1225 - s0, 4][4, 1]cpu"): 2025-03-04T20:50:26.3184750Z l_stack0_ = L_stack0_ 2025-03-04T20:50:26.3185098Z l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_ 2025-03-04T20:50:26.3185577Z l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_ 2025-03-04T20:50:26.3186102Z l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_ 2025-03-04T20:50:26.3186586Z l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_ 2025-03-04T20:50:26.3187123Z l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_ 2025-03-04T20:50:26.3187690Z l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_ 2025-03-04T20:50:26.3188262Z l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ 2025-03-04T20:50:26.3188836Z l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ 2025-03-04T20:50:26.3189343Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T20:50:26.3189757Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T20:50:26.3190159Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T20:50:26.3190570Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T20:50:26.3190861Z 2025-03-04T20:50:26.3191225Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x) 2025-03-04T20:50:26.3191693Z x: "f32[3225, 100352][100352, 1]cpu" = l_stack0_.flatten(1, -1); l_stack0_ = None 2025-03-04T20:50:26.3192387Z x_1: "f32[3225, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_); x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None 2025-03-04T20:50:26.3193103Z x_2: "f32[3225, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False); x_1 = None 2025-03-04T20:50:26.3193817Z x_3: "f32[3225, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_); x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None 2025-03-04T20:50:26.3194544Z x_4: "f32[3225, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False); x_3 = None 2025-03-04T20:50:26.3194823Z 2025-03-04T20:50:26.3195224Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x) 2025-03-04T20:50:26.3196182Z scores: "f32[3225, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_); l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None 2025-03-04T20:50:26.3196890Z 2025-03-04T20:50:26.3197310Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x) 2025-03-04T20:50:26.3198369Z proposal_deltas: "f32[3225, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_); x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None 2025-03-04T20:50:26.3199139Z 2025-03-04T20:50:26.3199608Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:50:26.3200151Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T20:50:26.3200429Z getitem: "Sym(s0)" = size[0] 2025-03-04T20:50:26.3200686Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T20:50:26.3200988Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T20:50:26.3201280Z getitem_2: "Sym(1225 - s0)" = size_1[0] 2025-03-04T20:50:26.3201551Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T20:50:26.3201792Z 2025-03-04T20:50:26.3202299Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:50:26.3203350Z proposal_boxes: "f32[3225, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T20:50:26.3204120Z 2025-03-04T20:50:26.3204583Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:50:26.3205165Z deltas: "f32[3225, 320][320, 1]cpu" = proposal_deltas.float(); proposal_deltas = None 2025-03-04T20:50:26.3205436Z 2025-03-04T20:50:26.3205834Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:50:26.3206359Z boxes: "f32[3225, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T20:50:26.3206638Z 2025-03-04T20:50:26.3207050Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:50:26.3207540Z getitem_4: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:50:26.3207834Z getitem_5: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:50:26.3208147Z widths: "f32[3225][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T20:50:26.3208408Z 2025-03-04T20:50:26.3208815Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:50:26.3209344Z getitem_6: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:50:26.3209634Z getitem_7: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:50:26.3209943Z heights: "f32[3225][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T20:50:26.3210205Z 2025-03-04T20:50:26.3210601Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:50:26.3211091Z getitem_8: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:50:26.3211336Z mul: "f32[3225][1]cpu" = 0.5 * widths 2025-03-04T20:50:26.3211582Z ctr_x: "f32[3225][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T20:50:26.3211806Z 2025-03-04T20:50:26.3212197Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:50:26.3212690Z getitem_9: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:50:26.3212961Z mul_1: "f32[3225][1]cpu" = 0.5 * heights 2025-03-04T20:50:26.3213203Z ctr_y: "f32[3225][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T20:50:26.3213474Z 2025-03-04T20:50:26.3213928Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:50:26.3214442Z getitem_10: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:50:26.3214770Z dx: "f32[3225, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T20:50:26.3214993Z 2025-03-04T20:50:26.3215367Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:50:26.3215863Z getitem_11: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:50:26.3216169Z dy: "f32[3225, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T20:50:26.3216392Z 2025-03-04T20:50:26.3216772Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:50:26.3217291Z getitem_12: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:50:26.3217596Z dw: "f32[3225, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T20:50:26.3217817Z 2025-03-04T20:50:26.3218191Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:50:26.3218719Z getitem_13: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:50:26.3219058Z dh: "f32[3225, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T20:50:26.3219291Z 2025-03-04T20:50:26.3219704Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:50:26.3220221Z dw_1: "f32[3225, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:50:26.3220468Z 2025-03-04T20:50:26.3220884Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:50:26.3221399Z dh_1: "f32[3225, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:50:26.3221646Z 2025-03-04T20:50:26.3222089Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:50:26.3222626Z getitem_14: "f32[3225, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:50:26.3222932Z mul_2: "f32[3225, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T20:50:26.3223257Z getitem_15: "f32[3225, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:50:26.3223596Z pred_ctr_x: "f32[3225, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T20:50:26.3223843Z 2025-03-04T20:50:26.3224269Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:50:26.3224797Z getitem_16: "f32[3225, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:50:26.3225102Z mul_3: "f32[3225, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T20:50:26.3225416Z getitem_17: "f32[3225, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:50:26.3225753Z pred_ctr_y: "f32[3225, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T20:50:26.3226026Z 2025-03-04T20:50:26.3226447Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:50:26.3226935Z exp: "f32[3225, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:50:26.3227245Z getitem_18: "f32[3225, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:50:26.3227573Z pred_w: "f32[3225, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T20:50:26.3227809Z 2025-03-04T20:50:26.3228215Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:50:26.3228699Z exp_1: "f32[3225, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:50:26.3229014Z getitem_19: "f32[3225, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:50:26.3229363Z pred_h: "f32[3225, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T20:50:26.3229606Z 2025-03-04T20:50:26.3229997Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:50:26.3230460Z mul_6: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T20:50:26.3230713Z x1: "f32[3225, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:50:26.3230933Z 2025-03-04T20:50:26.3231328Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:50:26.3231768Z mul_7: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T20:50:26.3232013Z y1: "f32[3225, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:50:26.3232236Z 2025-03-04T20:50:26.3232625Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:50:26.3233085Z mul_8: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:50:26.3233366Z x2: "f32[3225, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:50:26.3233606Z 2025-03-04T20:50:26.3233993Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:50:26.3234482Z mul_9: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:50:26.3234768Z y2: "f32[3225, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:50:26.3235006Z 2025-03-04T20:50:26.3235450Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:50:26.3236038Z pred_boxes: "f32[3225, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:50:26.3236324Z 2025-03-04T20:50:26.3236745Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:50:26.3237313Z predict_boxes: "f32[3225, 320][320, 1]cpu" = pred_boxes.reshape((3225, 320)); pred_boxes = None 2025-03-04T20:50:26.3237596Z 2025-03-04T20:50:26.3238045Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T20:50:26.3238661Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T20:50:26.3239061Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T20:50:26.3239383Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T20:50:26.3239803Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T20:50:26.3240154Z getitem_23: "f32[1225 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T20:50:26.3240440Z 2025-03-04T20:50:26.3240860Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:50:26.3241502Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T20:50:26.3241846Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T20:50:26.3242079Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T20:50:26.3242437Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T20:50:26.3242816Z getitem_26: "Sym(1225 - s0)" = size_3[0] 2025-03-04T20:50:26.3243057Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T20:50:26.3243271Z 2025-03-04T20:50:26.3243689Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T20:50:26.3244246Z probs: "f32[3225, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1); scores = None 2025-03-04T20:50:26.3244525Z 2025-03-04T20:50:26.3244967Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T20:50:26.3245565Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T20:50:26.3245925Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T20:50:26.3246221Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T20:50:26.3246510Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T20:50:26.3246810Z getitem_31: "f32[1225 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T20:50:26.3247065Z 2025-03-04T20:50:26.3247631Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T20:50:26.3248319Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T20:50:26.3248669Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:50:26.3249012Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T20:50:26.3249348Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T20:50:26.3249633Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T20:50:26.3249868Z 2025-03-04T20:50:26.3250317Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T20:50:26.3250844Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T20:50:26.3251076Z 2025-03-04T20:50:26.3251236Z 2025-03-04T20:50:26.3251330Z class GraphModule(torch.nn.Module): 2025-03-04T20:50:26.3253275Z def forward(self, L_stack0_: "f32[3225, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 100352][100352, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1225 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1225 - s0, 4][4, 1]cpu"): 2025-03-04T20:50:26.3255411Z l_stack0_ = L_stack0_ 2025-03-04T20:50:26.3255776Z l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_ 2025-03-04T20:50:26.3256299Z l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_ 2025-03-04T20:50:26.3256789Z l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_ 2025-03-04T20:50:26.3257333Z l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_ 2025-03-04T20:50:26.3257863Z l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_ 2025-03-04T20:50:26.3258446Z l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_ 2025-03-04T20:50:26.3259025Z l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ 2025-03-04T20:50:26.3259599Z l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ 2025-03-04T20:50:26.3260093Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T20:50:26.3260498Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T20:50:26.3260900Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T20:50:26.3261339Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T20:50:26.3261659Z 2025-03-04T20:50:26.3262087Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x) 2025-03-04T20:50:26.3262582Z x: "f32[3225, 100352][100352, 1]cpu" = l_stack0_.flatten(1, -1); l_stack0_ = None 2025-03-04T20:50:26.3263302Z x_1: "f32[3225, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_); x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None 2025-03-04T20:50:26.3264054Z x_2: "f32[3225, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False); x_1 = None 2025-03-04T20:50:26.3264802Z x_3: "f32[3225, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_); x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None 2025-03-04T20:50:26.3265512Z x_4: "f32[3225, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False); x_3 = None 2025-03-04T20:50:26.3265787Z 2025-03-04T20:50:26.3266180Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x) 2025-03-04T20:50:26.3267196Z scores: "f32[3225, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_); l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None 2025-03-04T20:50:26.3267910Z 2025-03-04T20:50:26.3268317Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x) 2025-03-04T20:50:26.3269321Z proposal_deltas: "f32[3225, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_); x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None 2025-03-04T20:50:26.3270074Z 2025-03-04T20:50:26.3270443Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:50:26.3270907Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T20:50:26.3271159Z getitem: "Sym(s0)" = size[0] 2025-03-04T20:50:26.3271389Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T20:50:26.3271657Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T20:50:26.3271913Z getitem_2: "Sym(1225 - s0)" = size_1[0] 2025-03-04T20:50:26.3272152Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T20:50:26.3272367Z 2025-03-04T20:50:26.3272735Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:50:26.3273670Z proposal_boxes: "f32[3225, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T20:50:26.3274381Z 2025-03-04T20:50:26.3274848Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:50:26.3275463Z deltas: "f32[3225, 320][320, 1]cpu" = proposal_deltas.float(); proposal_deltas = None 2025-03-04T20:50:26.3275789Z 2025-03-04T20:50:26.3276228Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:50:26.3276809Z boxes: "f32[3225, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T20:50:26.3277126Z 2025-03-04T20:50:26.3277545Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:50:26.3278078Z getitem_4: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:50:26.3278407Z getitem_5: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:50:26.3278750Z widths: "f32[3225][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T20:50:26.3279031Z 2025-03-04T20:50:26.3279547Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:50:26.3280111Z getitem_6: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:50:26.3280435Z getitem_7: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:50:26.3280808Z heights: "f32[3225][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T20:50:26.3281111Z 2025-03-04T20:50:26.3281555Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:50:26.3282097Z getitem_8: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:50:26.3282371Z mul: "f32[3225][1]cpu" = 0.5 * widths 2025-03-04T20:50:26.3282651Z ctr_x: "f32[3225][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T20:50:26.3282913Z 2025-03-04T20:50:26.3283359Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:50:26.3283932Z getitem_9: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:50:26.3284244Z mul_1: "f32[3225][1]cpu" = 0.5 * heights 2025-03-04T20:50:26.3284553Z ctr_y: "f32[3225][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T20:50:26.3284816Z 2025-03-04T20:50:26.3285258Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:50:26.3285826Z getitem_10: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:50:26.3286181Z dx: "f32[3225, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T20:50:26.3286433Z 2025-03-04T20:50:26.3286860Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:50:26.3287416Z getitem_11: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:50:26.3287769Z dy: "f32[3225, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T20:50:26.3288019Z 2025-03-04T20:50:26.3288451Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:50:26.3288963Z getitem_12: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:50:26.3289268Z dw: "f32[3225, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T20:50:26.3289486Z 2025-03-04T20:50:26.3289886Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:50:26.3290429Z getitem_13: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:50:26.3290771Z dh: "f32[3225, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T20:50:26.3290998Z 2025-03-04T20:50:26.3291426Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:50:26.3291963Z dw_1: "f32[3225, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:50:26.3292227Z 2025-03-04T20:50:26.3292631Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:50:26.3293135Z dh_1: "f32[3225, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:50:26.3293378Z 2025-03-04T20:50:26.3293798Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:50:26.3294330Z getitem_14: "f32[3225, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:50:26.3294688Z mul_2: "f32[3225, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T20:50:26.3295009Z getitem_15: "f32[3225, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:50:26.3295359Z pred_ctr_x: "f32[3225, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T20:50:26.3295617Z 2025-03-04T20:50:26.3296057Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:50:26.3296587Z getitem_16: "f32[3225, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:50:26.3296894Z mul_3: "f32[3225, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T20:50:26.3297211Z getitem_17: "f32[3225, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:50:26.3297554Z pred_ctr_y: "f32[3225, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T20:50:26.3297799Z 2025-03-04T20:50:26.3298208Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:50:26.3298698Z exp: "f32[3225, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:50:26.3299007Z getitem_18: "f32[3225, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:50:26.3299342Z pred_w: "f32[3225, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T20:50:26.3299587Z 2025-03-04T20:50:26.3300005Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:50:26.3300505Z exp_1: "f32[3225, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:50:26.3300824Z getitem_19: "f32[3225, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:50:26.3301163Z pred_h: "f32[3225, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T20:50:26.3301408Z 2025-03-04T20:50:26.3301808Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:50:26.3302444Z mul_6: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T20:50:26.3302767Z x1: "f32[3225, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:50:26.3302995Z 2025-03-04T20:50:26.3303378Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:50:26.3303823Z mul_7: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T20:50:26.3304080Z y1: "f32[3225, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:50:26.3304319Z 2025-03-04T20:50:26.3304713Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:50:26.3305195Z mul_8: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:50:26.3305475Z x2: "f32[3225, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:50:26.3305711Z 2025-03-04T20:50:26.3306090Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:50:26.3306550Z mul_9: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:50:26.3306827Z y2: "f32[3225, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:50:26.3307091Z 2025-03-04T20:50:26.3307536Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:50:26.3308098Z pred_boxes: "f32[3225, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:50:26.3308382Z 2025-03-04T20:50:26.3308791Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:50:26.3309330Z predict_boxes: "f32[3225, 320][320, 1]cpu" = pred_boxes.reshape((3225, 320)); pred_boxes = None 2025-03-04T20:50:26.3309617Z 2025-03-04T20:50:26.3310048Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T20:50:26.3310670Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T20:50:26.3311021Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T20:50:26.3311303Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T20:50:26.3311597Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T20:50:26.3311904Z getitem_23: "f32[1225 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T20:50:26.3312155Z 2025-03-04T20:50:26.3312523Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:50:26.3313068Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T20:50:26.3313405Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T20:50:26.3313641Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T20:50:26.3313999Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T20:50:26.3314340Z getitem_26: "Sym(1225 - s0)" = size_3[0] 2025-03-04T20:50:26.3314582Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T20:50:26.3314792Z 2025-03-04T20:50:26.3315212Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T20:50:26.3315798Z probs: "f32[3225, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1); scores = None 2025-03-04T20:50:26.3316076Z 2025-03-04T20:50:26.3316505Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T20:50:26.3317091Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T20:50:26.3317445Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T20:50:26.3317729Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T20:50:26.3318024Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T20:50:26.3318334Z getitem_31: "f32[1225 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T20:50:26.3318586Z 2025-03-04T20:50:26.3319135Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T20:50:26.3319898Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T20:50:26.3320286Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:50:26.3320644Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T20:50:26.3320992Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T20:50:26.3321275Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T20:50:26.3321502Z 2025-03-04T20:50:26.3321943Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T20:50:26.3322463Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T20:50:26.3322697Z 2025-03-04T20:50:26.3322788Z 2025-03-04T20:50:26.3322876Z class GraphModule(torch.nn.Module): 2025-03-04T20:50:26.3324777Z def forward(self, L_stack0_: "f32[3225, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 100352][100352, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1225 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1225 - s0, 4][4, 1]cpu"): 2025-03-04T20:50:26.3326857Z l_stack0_ = L_stack0_ 2025-03-04T20:50:26.3327202Z l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_ 2025-03-04T20:50:26.3327688Z l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_ 2025-03-04T20:50:26.3328166Z l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_ 2025-03-04T20:50:26.3328641Z l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_ 2025-03-04T20:50:26.3329180Z l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_ 2025-03-04T20:50:26.3329753Z l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_ 2025-03-04T20:50:26.3330323Z l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ 2025-03-04T20:50:26.3330891Z l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ 2025-03-04T20:50:26.3331374Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T20:50:26.3331782Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T20:50:26.3332189Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T20:50:26.3332570Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T20:50:26.3332857Z 2025-03-04T20:50:26.3333206Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x) 2025-03-04T20:50:26.3333663Z x: "f32[3225, 100352][100352, 1]cpu" = l_stack0_.flatten(1, -1); l_stack0_ = None 2025-03-04T20:50:26.3334399Z x_1: "f32[3225, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_); x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None 2025-03-04T20:50:26.3335100Z x_2: "f32[3225, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False); x_1 = None 2025-03-04T20:50:26.3335808Z x_3: "f32[3225, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_); x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None 2025-03-04T20:50:26.3336513Z x_4: "f32[3225, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False); x_3 = None 2025-03-04T20:50:26.3336791Z 2025-03-04T20:50:26.3337200Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x) 2025-03-04T20:50:26.3338142Z scores: "f32[3225, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_); l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None 2025-03-04T20:50:26.3338847Z 2025-03-04T20:50:26.3339261Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x) 2025-03-04T20:50:26.3340251Z proposal_deltas: "f32[3225, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_); x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None 2025-03-04T20:50:26.3340965Z 2025-03-04T20:50:26.3341329Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:50:26.3341782Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T20:50:26.3342024Z getitem: "Sym(s0)" = size[0] 2025-03-04T20:50:26.3342246Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T20:50:26.3342525Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T20:50:26.3342779Z getitem_2: "Sym(1225 - s0)" = size_1[0] 2025-03-04T20:50:26.3343014Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T20:50:26.3343226Z 2025-03-04T20:50:26.3343585Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:50:26.3344509Z proposal_boxes: "f32[3225, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T20:50:26.3345205Z 2025-03-04T20:50:26.3345653Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:50:26.3346214Z deltas: "f32[3225, 320][320, 1]cpu" = proposal_deltas.float(); proposal_deltas = None 2025-03-04T20:50:26.3346478Z 2025-03-04T20:50:26.3346866Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:50:26.3347409Z boxes: "f32[3225, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T20:50:26.3347677Z 2025-03-04T20:50:26.3348066Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:50:26.3348547Z getitem_4: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:50:26.3348832Z getitem_5: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:50:26.3349141Z widths: "f32[3225][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T20:50:26.3349392Z 2025-03-04T20:50:26.3349783Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:50:26.3350264Z getitem_6: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:50:26.3350574Z getitem_7: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:50:26.3350877Z heights: "f32[3225][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T20:50:26.3351129Z 2025-03-04T20:50:26.3351513Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:50:26.3351984Z getitem_8: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:50:26.3352234Z mul: "f32[3225][1]cpu" = 0.5 * widths 2025-03-04T20:50:26.3352483Z ctr_x: "f32[3225][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T20:50:26.3352714Z 2025-03-04T20:50:26.3353101Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:50:26.3353606Z getitem_9: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:50:26.3353880Z mul_1: "f32[3225][1]cpu" = 0.5 * heights 2025-03-04T20:50:26.3354139Z ctr_y: "f32[3225][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T20:50:26.3354374Z 2025-03-04T20:50:26.3354776Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:50:26.3355288Z getitem_10: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:50:26.3355624Z dx: "f32[3225, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T20:50:26.3355854Z 2025-03-04T20:50:26.3356241Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:50:26.3356753Z getitem_11: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:50:26.3357076Z dy: "f32[3225, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T20:50:26.3357304Z 2025-03-04T20:50:26.3357683Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:50:26.3358199Z getitem_12: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:50:26.3358531Z dw: "f32[3225, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T20:50:26.3358771Z 2025-03-04T20:50:26.3359177Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:50:26.3359829Z getitem_13: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:50:26.3360259Z dh: "f32[3225, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T20:50:26.3360586Z 2025-03-04T20:50:26.3361038Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:50:26.3361604Z dw_1: "f32[3225, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:50:26.3361876Z 2025-03-04T20:50:26.3362296Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:50:26.3362821Z dh_1: "f32[3225, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:50:26.3363070Z 2025-03-04T20:50:26.3363504Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:50:26.3364059Z getitem_14: "f32[3225, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:50:26.3364386Z mul_2: "f32[3225, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T20:50:26.3364732Z getitem_15: "f32[3225, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:50:26.3365086Z pred_ctr_x: "f32[3225, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T20:50:26.3365334Z 2025-03-04T20:50:26.3365761Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:50:26.3366295Z getitem_16: "f32[3225, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:50:26.3366596Z mul_3: "f32[3225, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T20:50:26.3366917Z getitem_17: "f32[3225, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:50:26.3367254Z pred_ctr_y: "f32[3225, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T20:50:26.3367504Z 2025-03-04T20:50:26.3367927Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:50:26.3368426Z exp: "f32[3225, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:50:26.3368778Z getitem_18: "f32[3225, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:50:26.3369115Z pred_w: "f32[3225, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T20:50:26.3369359Z 2025-03-04T20:50:26.3369780Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:50:26.3370291Z exp_1: "f32[3225, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:50:26.3370618Z getitem_19: "f32[3225, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:50:26.3370957Z pred_h: "f32[3225, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T20:50:26.3371203Z 2025-03-04T20:50:26.3371602Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:50:26.3372069Z mul_6: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T20:50:26.3372329Z x1: "f32[3225, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:50:26.3372555Z 2025-03-04T20:50:26.3372946Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:50:26.3373444Z mul_7: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T20:50:26.3373694Z y1: "f32[3225, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:50:26.3373916Z 2025-03-04T20:50:26.3374294Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:50:26.3374761Z mul_8: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:50:26.3375049Z x2: "f32[3225, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:50:26.3375290Z 2025-03-04T20:50:26.3375678Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:50:26.3376149Z mul_9: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:50:26.3376456Z y2: "f32[3225, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:50:26.3376699Z 2025-03-04T20:50:26.3377136Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:50:26.3377717Z pred_boxes: "f32[3225, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:50:26.3378006Z 2025-03-04T20:50:26.3378426Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:50:26.3378988Z predict_boxes: "f32[3225, 320][320, 1]cpu" = pred_boxes.reshape((3225, 320)); pred_boxes = None 2025-03-04T20:50:26.3379274Z 2025-03-04T20:50:26.3379719Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T20:50:26.3380338Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T20:50:26.3380708Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T20:50:26.3380993Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T20:50:26.3381293Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T20:50:26.3381612Z getitem_23: "f32[1225 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T20:50:26.3381885Z 2025-03-04T20:50:26.3382263Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:50:26.3382819Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T20:50:26.3383165Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T20:50:26.3383408Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T20:50:26.3383770Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T20:50:26.3384123Z getitem_26: "Sym(1225 - s0)" = size_3[0] 2025-03-04T20:50:26.3384362Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T20:50:26.3384575Z 2025-03-04T20:50:26.3384992Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T20:50:26.3385545Z probs: "f32[3225, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1); scores = None 2025-03-04T20:50:26.3385825Z 2025-03-04T20:50:26.3386263Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T20:50:26.3386898Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T20:50:26.3387248Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T20:50:26.3387532Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T20:50:26.3387823Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T20:50:26.3388132Z getitem_31: "f32[1225 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T20:50:26.3388387Z 2025-03-04T20:50:26.3388928Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T20:50:26.3389609Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T20:50:26.3389967Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:50:26.3390313Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T20:50:26.3390655Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T20:50:26.3390950Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T20:50:26.3391191Z 2025-03-04T20:50:26.3391631Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T20:50:26.3392154Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T20:50:26.3392390Z 2025-03-04T20:50:28.2177357Z 2025-03-04T20:50:28.2183581Z class GraphModule(torch.nn.Module): 2025-03-04T20:50:28.2186005Z def forward(self, L_predictions_0_: "f32[3225, 81][81, 1]cpu", L_predictions_1_: "f32[3225, 320][320, 1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1225 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1225 - s0, 4][4, 1]cpu"): 2025-03-04T20:50:28.2186995Z l_predictions_0_ = L_predictions_0_ 2025-03-04T20:50:28.2187245Z l_predictions_1_ = L_predictions_1_ 2025-03-04T20:50:28.2187838Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T20:50:28.2188243Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T20:50:28.2188633Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T20:50:28.2189018Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T20:50:28.2189308Z 2025-03-04T20:50:28.2189721Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:50:28.2190188Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T20:50:28.2190435Z getitem: "Sym(s0)" = size[0] 2025-03-04T20:50:28.2190657Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T20:50:28.2190926Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T20:50:28.2191186Z getitem_2: "Sym(1225 - s0)" = size_1[0] 2025-03-04T20:50:28.2191422Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T20:50:28.2191638Z 2025-03-04T20:50:28.2192010Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:50:28.2193040Z proposal_boxes: "f32[3225, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T20:50:28.2193736Z 2025-03-04T20:50:28.2194190Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:50:28.2194753Z deltas: "f32[3225, 320][320, 1]cpu" = l_predictions_1_.float(); l_predictions_1_ = None 2025-03-04T20:50:28.2195018Z 2025-03-04T20:50:28.2195409Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:50:28.2195931Z boxes: "f32[3225, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T20:50:28.2196263Z 2025-03-04T20:50:28.2196665Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:50:28.2197160Z getitem_4: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:50:28.2197462Z getitem_5: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:50:28.2197774Z widths: "f32[3225][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T20:50:28.2198034Z 2025-03-04T20:50:28.2198444Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:50:28.2198940Z getitem_6: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:50:28.2199230Z getitem_7: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:50:28.2199547Z heights: "f32[3225][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T20:50:28.2199957Z 2025-03-04T20:50:28.2200382Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:50:28.2200901Z getitem_8: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:50:28.2201150Z mul: "f32[3225][1]cpu" = 0.5 * widths 2025-03-04T20:50:28.2201415Z ctr_x: "f32[3225][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T20:50:28.2201650Z 2025-03-04T20:50:28.2202225Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:50:28.2202730Z getitem_9: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:50:28.2203010Z mul_1: "f32[3225][1]cpu" = 0.5 * heights 2025-03-04T20:50:28.2203297Z ctr_y: "f32[3225][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T20:50:28.2203527Z 2025-03-04T20:50:28.2203948Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:50:28.2204448Z getitem_10: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:50:28.2204764Z dx: "f32[3225, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T20:50:28.2204993Z 2025-03-04T20:50:28.2205371Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:50:28.2205861Z getitem_11: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:50:28.2206198Z dy: "f32[3225, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T20:50:28.2206449Z 2025-03-04T20:50:28.2206821Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:50:28.2207312Z getitem_12: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:50:28.2207621Z dw: "f32[3225, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T20:50:28.2207852Z 2025-03-04T20:50:28.2208231Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:50:28.2208758Z getitem_13: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:50:28.2209091Z dh: "f32[3225, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T20:50:28.2209344Z 2025-03-04T20:50:28.2209755Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:50:28.2210280Z dw_1: "f32[3225, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:50:28.2210536Z 2025-03-04T20:50:28.2210955Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:50:28.2211472Z dh_1: "f32[3225, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:50:28.2211723Z 2025-03-04T20:50:28.2212152Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:50:28.2212686Z getitem_14: "f32[3225, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:50:28.2212999Z mul_2: "f32[3225, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T20:50:28.2213330Z getitem_15: "f32[3225, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:50:28.2213671Z pred_ctr_x: "f32[3225, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T20:50:28.2213925Z 2025-03-04T20:50:28.2214387Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:50:28.2214910Z getitem_16: "f32[3225, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:50:28.2215210Z mul_3: "f32[3225, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T20:50:28.2215519Z getitem_17: "f32[3225, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:50:28.2215850Z pred_ctr_y: "f32[3225, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T20:50:28.2216093Z 2025-03-04T20:50:28.2216506Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:50:28.2216995Z exp: "f32[3225, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:50:28.2217308Z getitem_18: "f32[3225, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:50:28.2217638Z pred_w: "f32[3225, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T20:50:28.2217880Z 2025-03-04T20:50:28.2218293Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:50:28.2218799Z exp_1: "f32[3225, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:50:28.2219133Z getitem_19: "f32[3225, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:50:28.2219478Z pred_h: "f32[3225, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T20:50:28.2219726Z 2025-03-04T20:50:28.2220134Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:50:28.2220587Z mul_6: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T20:50:28.2220842Z x1: "f32[3225, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:50:28.2221065Z 2025-03-04T20:50:28.2221454Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:50:28.2221917Z mul_7: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T20:50:28.2222169Z y1: "f32[3225, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:50:28.2222389Z 2025-03-04T20:50:28.2222761Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:50:28.2223220Z mul_8: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:50:28.2223499Z x2: "f32[3225, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:50:28.2223735Z 2025-03-04T20:50:28.2224121Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:50:28.2224590Z mul_9: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:50:28.2224884Z y2: "f32[3225, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:50:28.2225129Z 2025-03-04T20:50:28.2225561Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:50:28.2226134Z pred_boxes: "f32[3225, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:50:28.2226418Z 2025-03-04T20:50:28.2226828Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:50:28.2227393Z predict_boxes: "f32[3225, 320][320, 1]cpu" = pred_boxes.reshape((3225, 320)); pred_boxes = None 2025-03-04T20:50:28.2227667Z 2025-03-04T20:50:28.2228143Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T20:50:28.2228760Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T20:50:28.2229111Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T20:50:28.2229392Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T20:50:28.2229684Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T20:50:28.2229992Z getitem_23: "f32[1225 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T20:50:28.2230243Z 2025-03-04T20:50:28.2230607Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:50:28.2231154Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T20:50:28.2231503Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T20:50:28.2231738Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T20:50:28.2232128Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T20:50:28.2232467Z getitem_26: "Sym(1225 - s0)" = size_3[0] 2025-03-04T20:50:28.2232701Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T20:50:28.2232911Z 2025-03-04T20:50:28.2233315Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T20:50:28.2233901Z probs: "f32[3225, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1); l_predictions_0_ = None 2025-03-04T20:50:28.2234222Z 2025-03-04T20:50:28.2234667Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T20:50:28.2235297Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T20:50:28.2235655Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T20:50:28.2235943Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T20:50:28.2236242Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T20:50:28.2236551Z getitem_31: "f32[1225 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T20:50:28.2236805Z 2025-03-04T20:50:28.2237360Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T20:50:28.2238063Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T20:50:28.2238410Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:50:28.2238752Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T20:50:28.2239097Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T20:50:28.2239390Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T20:50:28.2239629Z 2025-03-04T20:50:28.2240199Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T20:50:28.2240743Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T20:50:28.2240974Z 2025-03-04T20:50:28.2241100Z 2025-03-04T20:50:28.2241191Z class GraphModule(torch.nn.Module): 2025-03-04T20:50:28.2242006Z def forward(self, L_predictions_0_: "f32[3225, 81][81, 1]cpu", L_predictions_1_: "f32[3225, 320][320, 1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1225 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1225 - s0, 4][4, 1]cpu"): 2025-03-04T20:50:28.2242806Z l_predictions_0_ = L_predictions_0_ 2025-03-04T20:50:28.2243027Z l_predictions_1_ = L_predictions_1_ 2025-03-04T20:50:28.2243330Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T20:50:28.2243731Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T20:50:28.2244126Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T20:50:28.2244519Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T20:50:28.2244833Z 2025-03-04T20:50:28.2245255Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:50:28.2245716Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T20:50:28.2245965Z getitem: "Sym(s0)" = size[0] 2025-03-04T20:50:28.2246192Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T20:50:28.2246460Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T20:50:28.2246716Z getitem_2: "Sym(1225 - s0)" = size_1[0] 2025-03-04T20:50:28.2246957Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T20:50:28.2256300Z 2025-03-04T20:50:28.2256813Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:50:28.2257795Z proposal_boxes: "f32[3225, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T20:50:28.2258655Z 2025-03-04T20:50:28.2259119Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:50:28.2259699Z deltas: "f32[3225, 320][320, 1]cpu" = l_predictions_1_.float(); l_predictions_1_ = None 2025-03-04T20:50:28.2259968Z 2025-03-04T20:50:28.2260369Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:50:28.2260893Z boxes: "f32[3225, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T20:50:28.2261170Z 2025-03-04T20:50:28.2261572Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:50:28.2262069Z getitem_4: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:50:28.2262369Z getitem_5: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:50:28.2262683Z widths: "f32[3225][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T20:50:28.2262938Z 2025-03-04T20:50:28.2263377Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:50:28.2263875Z getitem_6: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:50:28.2264165Z getitem_7: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:50:28.2264479Z heights: "f32[3225][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T20:50:28.2264741Z 2025-03-04T20:50:28.2265129Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:50:28.2265609Z getitem_8: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:50:28.2265864Z mul: "f32[3225][1]cpu" = 0.5 * widths 2025-03-04T20:50:28.2266119Z ctr_x: "f32[3225][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T20:50:28.2266352Z 2025-03-04T20:50:28.2266764Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:50:28.2267257Z getitem_9: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:50:28.2267572Z mul_1: "f32[3225][1]cpu" = 0.5 * heights 2025-03-04T20:50:28.2267843Z ctr_y: "f32[3225][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T20:50:28.2268075Z 2025-03-04T20:50:28.2268477Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:50:28.2268999Z getitem_10: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:50:28.2269329Z dx: "f32[3225, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T20:50:28.2269557Z 2025-03-04T20:50:28.2269935Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:50:28.2270447Z getitem_11: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:50:28.2270769Z dy: "f32[3225, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T20:50:28.2271017Z 2025-03-04T20:50:28.2271403Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:50:28.2271917Z getitem_12: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:50:28.2272230Z dw: "f32[3225, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T20:50:28.2272459Z 2025-03-04T20:50:28.2272867Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:50:28.2273419Z getitem_13: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:50:28.2273758Z dh: "f32[3225, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T20:50:28.2273994Z 2025-03-04T20:50:28.2274496Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:50:28.2275056Z dw_1: "f32[3225, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:50:28.2275320Z 2025-03-04T20:50:28.2275764Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:50:28.2276333Z dh_1: "f32[3225, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:50:28.2276595Z 2025-03-04T20:50:28.2277053Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:50:28.2277623Z getitem_14: "f32[3225, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:50:28.2277952Z mul_2: "f32[3225, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T20:50:28.2278296Z getitem_15: "f32[3225, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:50:28.2278656Z pred_ctr_x: "f32[3225, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T20:50:28.2278924Z 2025-03-04T20:50:28.2279369Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:50:28.2280014Z getitem_16: "f32[3225, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:50:28.2280346Z mul_3: "f32[3225, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T20:50:28.2280684Z getitem_17: "f32[3225, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:50:28.2281088Z pred_ctr_y: "f32[3225, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T20:50:28.2281356Z 2025-03-04T20:50:28.2281783Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:50:28.2282282Z exp: "f32[3225, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:50:28.2282619Z getitem_18: "f32[3225, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:50:28.2282986Z pred_w: "f32[3225, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T20:50:28.2283253Z 2025-03-04T20:50:28.2283705Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:50:28.2284241Z exp_1: "f32[3225, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:50:28.2284611Z getitem_19: "f32[3225, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:50:28.2284975Z pred_h: "f32[3225, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T20:50:28.2285236Z 2025-03-04T20:50:28.2285658Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:50:28.2286150Z mul_6: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T20:50:28.2286427Z x1: "f32[3225, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:50:28.2286669Z 2025-03-04T20:50:28.2287079Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:50:28.2287569Z mul_7: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T20:50:28.2287836Z y1: "f32[3225, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:50:28.2288081Z 2025-03-04T20:50:28.2288497Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:50:28.2289004Z mul_8: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:50:28.2289309Z x2: "f32[3225, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:50:28.2289566Z 2025-03-04T20:50:28.2290030Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:50:28.2290493Z mul_9: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:50:28.2290773Z y2: "f32[3225, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:50:28.2291009Z 2025-03-04T20:50:28.2291432Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:50:28.2291996Z pred_boxes: "f32[3225, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:50:28.2292277Z 2025-03-04T20:50:28.2292683Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:50:28.2293230Z predict_boxes: "f32[3225, 320][320, 1]cpu" = pred_boxes.reshape((3225, 320)); pred_boxes = None 2025-03-04T20:50:28.2293510Z 2025-03-04T20:50:28.2293945Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T20:50:28.2294588Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T20:50:28.2294941Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T20:50:28.2295218Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T20:50:28.2295513Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T20:50:28.2295820Z getitem_23: "f32[1225 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T20:50:28.2296070Z 2025-03-04T20:50:28.2296436Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:50:28.2296978Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T20:50:28.2297318Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T20:50:28.2297575Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T20:50:28.2297951Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T20:50:28.2298313Z getitem_26: "Sym(1225 - s0)" = size_3[0] 2025-03-04T20:50:28.2298563Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T20:50:28.2298786Z 2025-03-04T20:50:28.2299217Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T20:50:28.2299828Z probs: "f32[3225, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1); l_predictions_0_ = None 2025-03-04T20:50:28.2300149Z 2025-03-04T20:50:28.2300585Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T20:50:28.2301181Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T20:50:28.2301535Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T20:50:28.2301829Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T20:50:28.2302347Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T20:50:28.2302659Z getitem_31: "f32[1225 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T20:50:28.2302902Z 2025-03-04T20:50:28.2303516Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T20:50:28.2304201Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T20:50:28.2304543Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:50:28.2304864Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T20:50:28.2305196Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T20:50:28.2305481Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T20:50:28.2305716Z 2025-03-04T20:50:28.2306146Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T20:50:28.2306656Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T20:50:28.2306882Z 2025-03-04T20:50:28.2307014Z 2025-03-04T20:50:28.2307107Z class GraphModule(torch.nn.Module): 2025-03-04T20:50:28.2307927Z def forward(self, L_predictions_0_: "f32[3225, 81][81, 1]cpu", L_predictions_1_: "f32[3225, 320][320, 1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1225 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1225 - s0, 4][4, 1]cpu"): 2025-03-04T20:50:28.2308727Z l_predictions_0_ = L_predictions_0_ 2025-03-04T20:50:28.2308947Z l_predictions_1_ = L_predictions_1_ 2025-03-04T20:50:28.2309252Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T20:50:28.2309647Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T20:50:28.2310035Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T20:50:28.2310418Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T20:50:28.2310725Z 2025-03-04T20:50:28.2311092Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:50:28.2311540Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T20:50:28.2311778Z getitem: "Sym(s0)" = size[0] 2025-03-04T20:50:28.2312002Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T20:50:28.2312267Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T20:50:28.2312519Z getitem_2: "Sym(1225 - s0)" = size_1[0] 2025-03-04T20:50:28.2312752Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T20:50:28.2312953Z 2025-03-04T20:50:28.2313311Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:50:28.2314224Z proposal_boxes: "f32[3225, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T20:50:28.2314932Z 2025-03-04T20:50:28.2315380Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:50:28.2315956Z deltas: "f32[3225, 320][320, 1]cpu" = l_predictions_1_.float(); l_predictions_1_ = None 2025-03-04T20:50:28.2316223Z 2025-03-04T20:50:28.2316628Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:50:28.2317151Z boxes: "f32[3225, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T20:50:28.2317430Z 2025-03-04T20:50:28.2317831Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:50:28.2318329Z getitem_4: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:50:28.2318629Z getitem_5: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:50:28.2318942Z widths: "f32[3225][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T20:50:28.2319197Z 2025-03-04T20:50:28.2319605Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:50:28.2322634Z getitem_6: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:50:28.2322948Z getitem_7: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:50:28.2323325Z heights: "f32[3225][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T20:50:28.2323594Z 2025-03-04T20:50:28.2324035Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:50:28.2324530Z getitem_8: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:50:28.2324788Z mul: "f32[3225][1]cpu" = 0.5 * widths 2025-03-04T20:50:28.2325045Z ctr_x: "f32[3225][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T20:50:28.2325281Z 2025-03-04T20:50:28.2325683Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:50:28.2326193Z getitem_9: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:50:28.2326477Z mul_1: "f32[3225][1]cpu" = 0.5 * heights 2025-03-04T20:50:28.2326763Z ctr_y: "f32[3225][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T20:50:28.2327007Z 2025-03-04T20:50:28.2327412Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:50:28.2327925Z getitem_10: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:50:28.2328243Z dx: "f32[3225, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T20:50:28.2328472Z 2025-03-04T20:50:28.2328857Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:50:28.2329361Z getitem_11: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:50:28.2329679Z dy: "f32[3225, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T20:50:28.2329907Z 2025-03-04T20:50:28.2330290Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:50:28.2330798Z getitem_12: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:50:28.2331109Z dw: "f32[3225, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T20:50:28.2331335Z 2025-03-04T20:50:28.2331747Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:50:28.2332291Z getitem_13: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:50:28.2332638Z dh: "f32[3225, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T20:50:28.2332862Z 2025-03-04T20:50:28.2333279Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:50:28.2333799Z dw_1: "f32[3225, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:50:28.2334045Z 2025-03-04T20:50:28.2334460Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:50:28.2334970Z dh_1: "f32[3225, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:50:28.2335215Z 2025-03-04T20:50:28.2335628Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:50:28.2336160Z getitem_14: "f32[3225, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:50:28.2337517Z mul_2: "f32[3225, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T20:50:28.2337865Z getitem_15: "f32[3225, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:50:28.2338205Z pred_ctr_x: "f32[3225, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T20:50:28.2338455Z 2025-03-04T20:50:28.2338884Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:50:28.2339412Z getitem_16: "f32[3225, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:50:28.2339718Z mul_3: "f32[3225, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T20:50:28.2340032Z getitem_17: "f32[3225, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:50:28.2340365Z pred_ctr_y: "f32[3225, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T20:50:28.2340635Z 2025-03-04T20:50:28.2341050Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:50:28.2341544Z exp: "f32[3225, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:50:28.2341860Z getitem_18: "f32[3225, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:50:28.2342193Z pred_w: "f32[3225, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T20:50:28.2342440Z 2025-03-04T20:50:28.2342848Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:50:28.2343339Z exp_1: "f32[3225, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:50:28.2343662Z getitem_19: "f32[3225, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:50:28.2344000Z pred_h: "f32[3225, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T20:50:28.2344243Z 2025-03-04T20:50:28.2344639Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:50:28.2345096Z mul_6: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T20:50:28.2345350Z x1: "f32[3225, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:50:28.2345616Z 2025-03-04T20:50:28.2346010Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:50:28.2346459Z mul_7: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T20:50:28.2346707Z y1: "f32[3225, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:50:28.2346938Z 2025-03-04T20:50:28.2347324Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:50:28.2347800Z mul_8: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:50:28.2348082Z x2: "f32[3225, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:50:28.2348324Z 2025-03-04T20:50:28.2348710Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:50:28.2349177Z mul_9: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:50:28.2349458Z y2: "f32[3225, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:50:28.2349693Z 2025-03-04T20:50:28.2350155Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:50:28.2350729Z pred_boxes: "f32[3225, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:50:28.2351013Z 2025-03-04T20:50:28.2351427Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:50:28.2351973Z predict_boxes: "f32[3225, 320][320, 1]cpu" = pred_boxes.reshape((3225, 320)); pred_boxes = None 2025-03-04T20:50:28.2352256Z 2025-03-04T20:50:28.2352686Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T20:50:28.2353290Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T20:50:28.2353673Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T20:50:28.2353963Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T20:50:28.2354266Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T20:50:28.2354584Z getitem_23: "f32[1225 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T20:50:28.2354846Z 2025-03-04T20:50:28.2355226Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:50:28.2355784Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T20:50:28.2356131Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T20:50:28.2356371Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T20:50:28.2356740Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T20:50:28.2357092Z getitem_26: "Sym(1225 - s0)" = size_3[0] 2025-03-04T20:50:28.2357335Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T20:50:28.2357550Z 2025-03-04T20:50:28.2357974Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T20:50:28.2358594Z probs: "f32[3225, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1); l_predictions_0_ = None 2025-03-04T20:50:28.2358916Z 2025-03-04T20:50:28.2359363Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T20:50:28.2360079Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T20:50:28.2360456Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T20:50:28.2360757Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T20:50:28.2361058Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T20:50:28.2361372Z getitem_31: "f32[1225 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T20:50:28.2361629Z 2025-03-04T20:50:28.2362180Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T20:50:28.2362873Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T20:50:28.2363211Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:50:28.2363606Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T20:50:28.2363951Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T20:50:28.2364247Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T20:50:28.2364482Z 2025-03-04T20:50:28.2364922Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T20:50:28.2365446Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T20:50:28.2365679Z 2025-03-04T20:50:30.9553961Z 2025-03-04T20:50:30.9556461Z class GraphModule(torch.nn.Module): 2025-03-04T20:50:30.9556903Z def forward(self, L_scores_0_: "f32[1000, 81][81, 1]cpu", L_boxes_0_: "f32[1000, 320][320, 1]cpu"): 2025-03-04T20:50:30.9557590Z l_scores_0_ = L_scores_0_ 2025-03-04T20:50:30.9557792Z l_boxes_0_ = L_boxes_0_ 2025-03-04T20:50:30.9558025Z 2025-03-04T20:50:30.9558655Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T20:50:30.9559403Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(l_boxes_0_); l_boxes_0_ = None 2025-03-04T20:50:30.9559735Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:50:30.9560195Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(l_scores_0_); l_scores_0_ = None 2025-03-04T20:50:30.9560533Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T20:50:30.9560830Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T20:50:30.9561079Z 2025-03-04T20:50:30.9561559Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T20:50:30.9562081Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T20:50:30.9562311Z 2025-03-04T20:50:30.9562405Z 2025-03-04T20:50:30.9562492Z class GraphModule(torch.nn.Module): 2025-03-04T20:50:30.9562789Z def forward(self, L_scores_0_: "f32[1000, 81][81, 1]cpu", L_boxes_0_: "f32[1000, 320][320, 1]cpu"): 2025-03-04T20:50:30.9563075Z l_scores_0_ = L_scores_0_ 2025-03-04T20:50:30.9563337Z l_boxes_0_ = L_boxes_0_ 2025-03-04T20:50:30.9563519Z 2025-03-04T20:50:30.9564064Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T20:50:30.9564712Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(l_boxes_0_); l_boxes_0_ = None 2025-03-04T20:50:30.9565019Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:50:30.9565319Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(l_scores_0_); l_scores_0_ = None 2025-03-04T20:50:30.9565623Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T20:50:30.9565905Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T20:50:30.9566130Z 2025-03-04T20:50:30.9566559Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T20:50:30.9567058Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T20:50:30.9567277Z 2025-03-04T20:50:45.5965784Z Compilation time (from dynamo_timed): 46.193790479 2025-03-04T20:50:45.5967905Z pass 2025-03-04T20:50:45.5974696Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T20:50:45.5976616Z TIMING: entire_frame_compile:46.19379 gc:0.03451 _recursive_pre_grad_passes:0.03194 async_compile.wait:10.9264 backend_compile:31.0027 _recursive_joint_graph_passes:0.56314 _recursive_post_grad_passes:0.13634 code_gen:16.21212 inductor_compile:18.77385 total_wall_time:46.19379 2025-03-04T20:50:45.5977798Z STATS: call_* op count: 781 | FakeTensorMode.__torch_dispatch__:27911 | FakeTensor.__torch_dispatch__:3329 | ProxyTorchDispatchMode.__torch_dispatch__:10376 | attempt fast:51 | slow no contiguity match:20 | fast is_contiguous:31 2025-03-04T20:50:45.5978510Z Dynamo produced 52 graphs covering 781 ops with 42 graph breaks (6 unique) 2025-03-04T20:50:51.1445815Z 2025-03-04T20:50:57.9739221Z loading model: 0it [00:00, ?it/s] 2025-03-04T20:50:57.9741010Z loading model: 0it [00:06, ?it/s] 2025-03-04T20:50:57.9750870Z cpu eval detectron2_fasterrcnn_r_101_fpn 2025-03-04T20:51:13.4559513Z WARNING:common:fp64 golden ref were not generated for detectron2_fasterrcnn_r_101_fpn. Setting accuracy check to cosine 2025-03-04T20:51:13.4727354Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T20:51:19.6098951Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T20:51:26.0441560Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T20:51:41.0499133Z 2025-03-04T20:51:41.0499755Z class GraphModule(torch.nn.Module): 2025-03-04T20:51:41.0663304Z def forward(self, L_stack0_tensor: "f32[4, 3, 1184, 1216][4319232, 1439744, 1216, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", 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L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_: "f32[512, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_: "f32[2048, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_lateral_convs_0_parameters_weight_: "f32[256, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_0_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_0_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_0_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_1_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_1_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_1_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_2_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_2_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_2_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_3_parameters_weight_: "f32[256, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_3_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_3_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_3_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[3, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[3][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[12, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[12][1]cpu"): 2025-03-04T20:51:41.0806033Z l_stack0_tensor = L_stack0_tensor 2025-03-04T20:51:41.0806560Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.0807362Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.0808195Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.0809017Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.0809800Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.0810659Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.0811482Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.0812370Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.0813216Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.0814036Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.0814822Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.0815726Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.0816584Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.0817419Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.0818226Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.0819043Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.0819889Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.0820776Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.0821646Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.0822486Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.0823307Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:51:41.0824207Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.0825159Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.0827434Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:51:41.0828380Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:51:41.0829260Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.0830149Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.0831114Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.0832055Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.0832936Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.0833775Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.0834662Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.0835593Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.0836444Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.0837294Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.0838086Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.0838910Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.0839787Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.0840656Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.0841481Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.0842272Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.0843084Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.0843953Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.0844803Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.0845663Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.0846458Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.0847322Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.0848259Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.0849177Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.0850053Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.0850845Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.0851667Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.0852547Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.0853400Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.0854231Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.0855041Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.0855839Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.0856694Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.0857532Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.0858358Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.0859146Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.0859998Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.0860893Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.0861790Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.0862663Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.0863521Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.0864444Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.0865427Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.0866481Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.0867345Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.0868140Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:51:41.0869016Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.0869922Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.0870802Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:51:41.0871655Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:51:41.0872453Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.0873270Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.0874171Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.0875019Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.0875840Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.0876629Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.0877442Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.0878346Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.0879195Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.0880020Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.0880816Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.0881636Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.0882512Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.0883377Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.0884197Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.0884985Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.0885808Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.0886677Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.0887535Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.0888404Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.0889190Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.0890025Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.0890881Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.0891730Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.0892547Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.0893317Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.0894128Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.0894995Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.0895827Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.0896644Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.0897409Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.0898228Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.0899106Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.0899959Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.0900824Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.0901686Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.0902749Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.0903700Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.0904653Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.0905544Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.0907054Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.0907981Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.0908941Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.0909866Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.0910770Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.0911565Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.0912392Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.0913243Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.0914079Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.0914941Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.0915709Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.0916500Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.0917407Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.0918239Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.0919051Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.0919839Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.0920680Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.0921527Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.0922363Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.0923167Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.0923964Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:51:41.0924824Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.0925754Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.0926620Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:51:41.0927456Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:51:41.0928235Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.0929037Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.0929933Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.0930830Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.0931676Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.0932467Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.0933293Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.0934163Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.0935031Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.0935852Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.0936642Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.0937463Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.0938346Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.0939195Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.0940030Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.0940819Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.0941638Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.0942513Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.0943363Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.0944189Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.0945014Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.0945913Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.0946827Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.0947699Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.0948570Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.0949380Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.0950223Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.0951118Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.0951987Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.0952839Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.0953641Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.0954501Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.0955409Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.0956292Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.0957152Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.0957975Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.0958785Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.0959691Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.0960545Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.0961371Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.0962170Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.0962990Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.0963870Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.0964696Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.0965497Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.0966266Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.0967063Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.0967932Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.0968764Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.0969569Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.0970338Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.0971146Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.0972024Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.0972909Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.0973733Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.0974514Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.0975313Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.0976182Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.0977060Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.0977887Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.0978679Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.0979500Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.0980390Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.0981243Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.0982083Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.0982871Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.0983687Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.0984577Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.0985450Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.0987339Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.0988205Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.0989037Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.0989931Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.0990802Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.0991645Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.0992430Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.0993255Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.0994129Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.0994977Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.0995771Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.0996542Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.0997373Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.0998252Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.0999124Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.1000004Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.1000815Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.1001648Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.1002703Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.1003586Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.1004439Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.1005226Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.1006095Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.1006966Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.1007824Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.1008621Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.1009393Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.1010204Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.1011110Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.1011944Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.1012763Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.1013553Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.1014386Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.1015266Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.1016182Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.1017010Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.1017799Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.1018622Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.1019510Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.1020380Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.1021193Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.1021982Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.1022802Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.1023694Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.1024563Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.1025417Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.1026298Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.1027171Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.1028066Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.1028915Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.1029765Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.1030634Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.1031500Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.1032424Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.1033323Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.1034230Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.1035073Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.1035941Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.1036854Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.1037684Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.1038487Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.1039335Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.1040141Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.1040991Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.1041822Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.1042697Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.1043477Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.1044306Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.1045193Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.1046039Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.1046867Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.1047654Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.1048482Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.1049339Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.1050182Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.1051019Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.1051830Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.1052633Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.1053525Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.1054383Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.1055215Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.1056007Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.1056837Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.1057715Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.1059468Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.1060313Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.1061113Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.1061933Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.1062843Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.1063727Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.1064576Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.1065385Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.1066861Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.1067763Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.1068673Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.1069505Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.1070300Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.1071176Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.1072048Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.1072884Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.1073756Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.1074533Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.1075369Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.1076232Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.1077061Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.1077890Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.1078672Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.1079488Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.1080365Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.1081221Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.1082040Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.1082844Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.1083664Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.1084538Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.1085391Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.1086224Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.1086990Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.1087866Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.1088750Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.1089607Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.1090439Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.1091263Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.1092090Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.1092966Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.1093819Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.1094652Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.1095450Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.1096292Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.1097177Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.1098039Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.1098868Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.1099665Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.1100490Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.1101405Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.1102440Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.1103282Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.1104083Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.1104911Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.1105938Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.1106826Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.1107677Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.1108478Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.1109313Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.1110187Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.1111059Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.1111866Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.1112639Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.1113441Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.1114293Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.1115133Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.1115979Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.1116754Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.1117559Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.1118416Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.1119274Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.1120113Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.1120901Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.1121725Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.1122605Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.1123460Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.1124301Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.1125094Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.1125932Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.1126818Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.1127675Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.1128505Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.1129344Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.1130184Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.1131075Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.1131950Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.1132795Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.1133611Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.1134444Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.1135327Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.1136180Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.1137016Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.1137808Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.1138645Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.1139527Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.1140381Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.1141207Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.1142001Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.1142840Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.1143788Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.1144665Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.1145543Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.1147536Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.1148852Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.1149789Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.1150676Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.1151544Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.1152372Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.1153222Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.1154163Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.1155051Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.1155914Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.1156729Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.1157593Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.1158501Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.1159418Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.1160271Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.1161071Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.1161894Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.1162793Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.1163680Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.1164518Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.1165319Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.1166152Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.1167043Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.1167912Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.1168971Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.1172187Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.1173025Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.1173901Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.1174753Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.1175578Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.1176402Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.1177226Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.1178109Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.1178975Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.1179854Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.1180662Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.1181516Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.1182424Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.1184215Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.1185150Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.1186048Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.1186923Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.1188513Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.1189888Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.1191856Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.1194583Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.1197376Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.1205279Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.1206168Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.1207005Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.1207812Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.1209233Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.1214409Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.1220604Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.1221721Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.1224659Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.1225657Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.1226694Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.1227655Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.1228582Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.1229468Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.1230384Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.1231403Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.1232388Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.1233266Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.1234069Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.1234899Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.1235799Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.1236654Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.1237486Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.1238287Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.1239119Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.1239993Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.1240861Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.1241694Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.1242492Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.1243318Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.1244210Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.1245082Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.1245950Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.1246747Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.1247564Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.1248456Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.1249329Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.1250188Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.1250998Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.1251841Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.1252749Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.1253627Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.1254478Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.1255327Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:51:41.1256203Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.1257127Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.1258037Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:51:41.1258913Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:51:41.1259726Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.1260596Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.1261492Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.1262363Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.1263206Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.1264033Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.1264885Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.1265842Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.1266761Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.1267618Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.1268418Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.1269254Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.1270130Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.1270992Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.1271814Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.1272601Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.1273423Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.1274326Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.1275175Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.1275996Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.1276792Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.1277622Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.1278516Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.1279365Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.1280194Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.1280977Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.1281797Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.1282692Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.1283566Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.1284393Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.1285069Z l_self_modules_backbone_lateral_convs_0_parameters_weight_ = L_self_modules_backbone_lateral_convs_0_parameters_weight_ 2025-03-04T20:51:41.1285591Z l_self_modules_backbone_lateral_convs_0_parameters_bias_ = L_self_modules_backbone_lateral_convs_0_parameters_bias_ 2025-03-04T20:51:41.1286099Z l_self_modules_backbone_output_convs_0_parameters_weight_ = L_self_modules_backbone_output_convs_0_parameters_weight_ 2025-03-04T20:51:41.1286605Z l_self_modules_backbone_output_convs_0_parameters_bias_ = L_self_modules_backbone_output_convs_0_parameters_bias_ 2025-03-04T20:51:41.1287103Z l_self_modules_backbone_lateral_convs_1_parameters_weight_ = L_self_modules_backbone_lateral_convs_1_parameters_weight_ 2025-03-04T20:51:41.1287602Z l_self_modules_backbone_lateral_convs_1_parameters_bias_ = L_self_modules_backbone_lateral_convs_1_parameters_bias_ 2025-03-04T20:51:41.1288132Z l_self_modules_backbone_output_convs_1_parameters_weight_ = L_self_modules_backbone_output_convs_1_parameters_weight_ 2025-03-04T20:51:41.1288628Z l_self_modules_backbone_output_convs_1_parameters_bias_ = L_self_modules_backbone_output_convs_1_parameters_bias_ 2025-03-04T20:51:41.1289133Z l_self_modules_backbone_lateral_convs_2_parameters_weight_ = L_self_modules_backbone_lateral_convs_2_parameters_weight_ 2025-03-04T20:51:41.1289632Z l_self_modules_backbone_lateral_convs_2_parameters_bias_ = L_self_modules_backbone_lateral_convs_2_parameters_bias_ 2025-03-04T20:51:41.1290137Z l_self_modules_backbone_output_convs_2_parameters_weight_ = L_self_modules_backbone_output_convs_2_parameters_weight_ 2025-03-04T20:51:41.1290636Z l_self_modules_backbone_output_convs_2_parameters_bias_ = L_self_modules_backbone_output_convs_2_parameters_bias_ 2025-03-04T20:51:41.1291137Z l_self_modules_backbone_lateral_convs_3_parameters_weight_ = L_self_modules_backbone_lateral_convs_3_parameters_weight_ 2025-03-04T20:51:41.1291777Z l_self_modules_backbone_lateral_convs_3_parameters_bias_ = L_self_modules_backbone_lateral_convs_3_parameters_bias_ 2025-03-04T20:51:41.1292283Z l_self_modules_backbone_output_convs_3_parameters_weight_ = L_self_modules_backbone_output_convs_3_parameters_weight_ 2025-03-04T20:51:41.1292784Z l_self_modules_backbone_output_convs_3_parameters_bias_ = L_self_modules_backbone_output_convs_3_parameters_bias_ 2025-03-04T20:51:41.1293429Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T20:51:41.1294215Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ 2025-03-04T20:51:41.1295022Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ 2025-03-04T20:51:41.1318598Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ 2025-03-04T20:51:41.1319372Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ 2025-03-04T20:51:41.1320245Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T20:51:41.1320962Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T20:51:41.1321725Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T20:51:41.1322533Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T20:51:41.1323296Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T20:51:41.1324064Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T20:51:41.1324554Z 2025-03-04T20:51:41.1324946Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1325918Z x: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1); l_stack0_tensor = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.1326582Z 2025-03-04T20:51:41.1326952Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1329060Z x_1: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1330970Z 2025-03-04T20:51:41.1331357Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x) 2025-03-04T20:51:41.1331911Z x_2: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.relu_(x_1); x_1 = None 2025-03-04T20:51:41.1332177Z 2025-03-04T20:51:41.1332643Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1) 2025-03-04T20:51:41.1333316Z x_3: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1); x_2 = None 2025-03-04T20:51:41.1333672Z 2025-03-04T20:51:41.1334026Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1334871Z x_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.1335492Z 2025-03-04T20:51:41.1335860Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1338059Z x_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1340089Z 2025-03-04T20:51:41.1340494Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1340987Z out: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_5); x_5 = None 2025-03-04T20:51:41.1341246Z 2025-03-04T20:51:41.1341596Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1342423Z x_6: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.1343047Z 2025-03-04T20:51:41.1343421Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1345686Z x_7: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_6 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1347691Z 2025-03-04T20:51:41.1348060Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1348547Z out_1: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_7); x_7 = None 2025-03-04T20:51:41.1348808Z 2025-03-04T20:51:41.1349167Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1349991Z x_8: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_1 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.1350617Z 2025-03-04T20:51:41.1350973Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1353142Z x_9: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1355073Z 2025-03-04T20:51:41.1355399Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1356203Z x_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); x_3 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:51:41.1356815Z 2025-03-04T20:51:41.1357157Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1359332Z x_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_10 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1361418Z 2025-03-04T20:51:41.1361776Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.1362247Z x_9 += x_11; out_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_9; x_9 = x_11 = None 2025-03-04T20:51:41.1362496Z 2025-03-04T20:51:41.1362856Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1363355Z out_3: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_2); out_2 = None 2025-03-04T20:51:41.1363616Z 2025-03-04T20:51:41.1363941Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1364732Z x_12: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.1365323Z 2025-03-04T20:51:41.1365666Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1367771Z x_13: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_12 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1369671Z 2025-03-04T20:51:41.1370034Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1370513Z out_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_13); x_13 = None 2025-03-04T20:51:41.1370766Z 2025-03-04T20:51:41.1371095Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1371909Z x_14: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.1372509Z 2025-03-04T20:51:41.1372853Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1374956Z x_15: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_14 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1376826Z 2025-03-04T20:51:41.1377203Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1377677Z out_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_15); x_15 = None 2025-03-04T20:51:41.1377927Z 2025-03-04T20:51:41.1378255Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1379057Z x_16: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_5 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.1379681Z 2025-03-04T20:51:41.1380034Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1382215Z x_17: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_16 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1384140Z 2025-03-04T20:51:41.1384518Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.1385036Z x_17 += out_3; out_6: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_17; x_17 = out_3 = None 2025-03-04T20:51:41.1385334Z 2025-03-04T20:51:41.1387070Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1387626Z out_7: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_6); out_6 = None 2025-03-04T20:51:41.1387907Z 2025-03-04T20:51:41.1388250Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1389083Z x_18: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.1389695Z 2025-03-04T20:51:41.1390044Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1392187Z x_19: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_18 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1394126Z 2025-03-04T20:51:41.1394492Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1394969Z out_8: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_19); x_19 = None 2025-03-04T20:51:41.1395217Z 2025-03-04T20:51:41.1395545Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1396345Z x_20: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.1396970Z 2025-03-04T20:51:41.1397356Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1399488Z x_21: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_20 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1401402Z 2025-03-04T20:51:41.1401758Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1402382Z out_9: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_21); x_21 = None 2025-03-04T20:51:41.1402631Z 2025-03-04T20:51:41.1402959Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1403767Z x_22: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_9 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.1404385Z 2025-03-04T20:51:41.1404734Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1406905Z x_23: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_22 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1408769Z 2025-03-04T20:51:41.1409121Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.1409590Z x_23 += out_7; out_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_23; x_23 = out_7 = None 2025-03-04T20:51:41.1409847Z 2025-03-04T20:51:41.1410198Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1410676Z out_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_10); out_10 = None 2025-03-04T20:51:41.1410964Z 2025-03-04T20:51:41.1411318Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1412109Z x_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.1412700Z 2025-03-04T20:51:41.1413041Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1415131Z x_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1417022Z 2025-03-04T20:51:41.1417379Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1417848Z out_12: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_25); x_25 = None 2025-03-04T20:51:41.1418105Z 2025-03-04T20:51:41.1418428Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1419234Z x_26: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_12 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.1419845Z 2025-03-04T20:51:41.1420206Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1422361Z x_27: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_26 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1424275Z 2025-03-04T20:51:41.1424644Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1425163Z out_13: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_27); x_27 = None 2025-03-04T20:51:41.1425417Z 2025-03-04T20:51:41.1425811Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1426844Z x_28: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_13 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.1427460Z 2025-03-04T20:51:41.1427800Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1429919Z x_29: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_28 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1431859Z 2025-03-04T20:51:41.1432194Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1433019Z x_30: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:51:41.1433644Z 2025-03-04T20:51:41.1434010Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1436198Z x_31: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_30 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1438323Z 2025-03-04T20:51:41.1438692Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.1439173Z x_29 += x_31; out_14: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_29; x_29 = x_31 = None 2025-03-04T20:51:41.1439436Z 2025-03-04T20:51:41.1439795Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1440283Z out_15: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_14); out_14 = None 2025-03-04T20:51:41.1440547Z 2025-03-04T20:51:41.1440873Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1441669Z x_32: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.1442272Z 2025-03-04T20:51:41.1442610Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1444676Z x_33: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_32 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1446589Z 2025-03-04T20:51:41.1446955Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1447433Z out_16: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_33); x_33 = None 2025-03-04T20:51:41.1447692Z 2025-03-04T20:51:41.1448039Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1448843Z x_34: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_16 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.1449460Z 2025-03-04T20:51:41.1449802Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1451897Z x_35: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_34 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1453809Z 2025-03-04T20:51:41.1454170Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1454643Z out_17: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_35); x_35 = None 2025-03-04T20:51:41.1454895Z 2025-03-04T20:51:41.1455222Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1456016Z x_36: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_17 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.1456637Z 2025-03-04T20:51:41.1456973Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1459051Z x_37: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_36 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1460953Z 2025-03-04T20:51:41.1461312Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.1461815Z x_37 += out_15; out_18: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_37; x_37 = out_15 = None 2025-03-04T20:51:41.1462100Z 2025-03-04T20:51:41.1462481Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1462997Z out_19: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_18); out_18 = None 2025-03-04T20:51:41.1463282Z 2025-03-04T20:51:41.1463631Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1464481Z x_38: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.1465118Z 2025-03-04T20:51:41.1465485Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1467871Z x_39: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_38 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1469826Z 2025-03-04T20:51:41.1470197Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1470693Z out_20: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_39); x_39 = None 2025-03-04T20:51:41.1470953Z 2025-03-04T20:51:41.1471283Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1472088Z x_40: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_20 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.1472701Z 2025-03-04T20:51:41.1473045Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1475177Z x_41: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_40 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1477065Z 2025-03-04T20:51:41.1477423Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1477885Z out_21: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_41); x_41 = None 2025-03-04T20:51:41.1478135Z 2025-03-04T20:51:41.1478457Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1479247Z x_42: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_21 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.1479838Z 2025-03-04T20:51:41.1480177Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1482328Z x_43: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_42 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1484234Z 2025-03-04T20:51:41.1484586Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.1485058Z x_43 += out_19; out_22: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_43; x_43 = out_19 = None 2025-03-04T20:51:41.1485320Z 2025-03-04T20:51:41.1485668Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1486147Z out_23: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_22); out_22 = None 2025-03-04T20:51:41.1486407Z 2025-03-04T20:51:41.1486732Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1487515Z x_44: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.1488127Z 2025-03-04T20:51:41.1488465Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1490554Z x_45: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_44 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1492419Z 2025-03-04T20:51:41.1492782Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1493252Z out_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_45); x_45 = None 2025-03-04T20:51:41.1493502Z 2025-03-04T20:51:41.1493827Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1494646Z x_46: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.1495244Z 2025-03-04T20:51:41.1495583Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1497678Z x_47: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_46 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1499557Z 2025-03-04T20:51:41.1499929Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1500410Z out_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_47); x_47 = None 2025-03-04T20:51:41.1500664Z 2025-03-04T20:51:41.1501002Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1501811Z x_48: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_25 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.1502569Z 2025-03-04T20:51:41.1502959Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1505222Z x_49: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_48 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1507274Z 2025-03-04T20:51:41.1507640Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.1508131Z x_49 += out_23; out_26: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_49; x_49 = out_23 = None 2025-03-04T20:51:41.1508430Z 2025-03-04T20:51:41.1508822Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1509318Z out_27: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_26); out_26 = None 2025-03-04T20:51:41.1509590Z 2025-03-04T20:51:41.1509926Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1510734Z x_50: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.1511335Z 2025-03-04T20:51:41.1511686Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1513838Z x_51: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_50 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1515763Z 2025-03-04T20:51:41.1516132Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1516616Z out_28: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_51); x_51 = None 2025-03-04T20:51:41.1516870Z 2025-03-04T20:51:41.1517204Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1518034Z x_52: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_28 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.1518635Z 2025-03-04T20:51:41.1518978Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1521071Z x_53: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_52 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1522955Z 2025-03-04T20:51:41.1523319Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1523782Z out_29: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_53); x_53 = None 2025-03-04T20:51:41.1524029Z 2025-03-04T20:51:41.1524358Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1525141Z x_54: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_29 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.1525756Z 2025-03-04T20:51:41.1526098Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1528233Z x_55: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_54 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1530150Z 2025-03-04T20:51:41.1530479Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1531287Z x_56: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:51:41.1531882Z 2025-03-04T20:51:41.1532224Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1534386Z x_57: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_56 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1536491Z 2025-03-04T20:51:41.1536847Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.1537308Z x_55 += x_57; out_30: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_55; x_55 = x_57 = None 2025-03-04T20:51:41.1537561Z 2025-03-04T20:51:41.1537922Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1538405Z out_31: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_30); out_30 = None 2025-03-04T20:51:41.1538656Z 2025-03-04T20:51:41.1538982Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1539797Z x_58: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.1540402Z 2025-03-04T20:51:41.1540743Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1543066Z x_59: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_58 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1546151Z 2025-03-04T20:51:41.1546606Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1547148Z out_32: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_59); x_59 = None 2025-03-04T20:51:41.1547402Z 2025-03-04T20:51:41.1547740Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1548544Z x_60: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_32 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.1549151Z 2025-03-04T20:51:41.1549503Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1551641Z x_61: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_60 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1553574Z 2025-03-04T20:51:41.1553939Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1554410Z out_33: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_61); x_61 = None 2025-03-04T20:51:41.1554659Z 2025-03-04T20:51:41.1555010Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1555806Z x_62: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_33 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.1556405Z 2025-03-04T20:51:41.1556759Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1558841Z x_63: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_62 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1560692Z 2025-03-04T20:51:41.1561046Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.1561515Z x_63 += out_31; out_34: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_63; x_63 = out_31 = None 2025-03-04T20:51:41.1561771Z 2025-03-04T20:51:41.1562134Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1562601Z out_35: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_34); out_34 = None 2025-03-04T20:51:41.1562853Z 2025-03-04T20:51:41.1563178Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1563954Z x_64: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.1564535Z 2025-03-04T20:51:41.1564907Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1566979Z x_65: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_64 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1568848Z 2025-03-04T20:51:41.1569214Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1569679Z out_36: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_65); x_65 = None 2025-03-04T20:51:41.1569927Z 2025-03-04T20:51:41.1570257Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1571054Z x_66: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_36 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.1571660Z 2025-03-04T20:51:41.1571999Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1574123Z x_67: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_66 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1576002Z 2025-03-04T20:51:41.1576366Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1576832Z out_37: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_67); x_67 = None 2025-03-04T20:51:41.1577083Z 2025-03-04T20:51:41.1577415Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1578213Z x_68: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_37 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.1578854Z 2025-03-04T20:51:41.1579201Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1581335Z x_69: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_68 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1583264Z 2025-03-04T20:51:41.1583620Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.1584092Z x_69 += out_35; out_38: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_69; x_69 = out_35 = None 2025-03-04T20:51:41.1584348Z 2025-03-04T20:51:41.1584712Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1585212Z out_39: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_38); out_38 = None 2025-03-04T20:51:41.1585480Z 2025-03-04T20:51:41.1585900Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1586738Z x_70: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.1587347Z 2025-03-04T20:51:41.1587710Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1589856Z x_71: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_70 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1591781Z 2025-03-04T20:51:41.1592146Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1592631Z out_40: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_71); x_71 = None 2025-03-04T20:51:41.1592890Z 2025-03-04T20:51:41.1593228Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1594006Z x_72: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_40 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.1594594Z 2025-03-04T20:51:41.1594930Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1597078Z x_73: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_72 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1599001Z 2025-03-04T20:51:41.1599365Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1599837Z out_41: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_73); x_73 = None 2025-03-04T20:51:41.1600085Z 2025-03-04T20:51:41.1600414Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1601252Z x_74: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_41 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.1602008Z 2025-03-04T20:51:41.1602372Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1604514Z x_75: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_74 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1606391Z 2025-03-04T20:51:41.1606812Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.1607274Z x_75 += out_39; out_42: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_75; x_75 = out_39 = None 2025-03-04T20:51:41.1607522Z 2025-03-04T20:51:41.1607868Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1608335Z out_43: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_42); out_42 = None 2025-03-04T20:51:41.1608568Z 2025-03-04T20:51:41.1608892Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1609668Z x_76: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.1610268Z 2025-03-04T20:51:41.1610602Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1612693Z x_77: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_76 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1614548Z 2025-03-04T20:51:41.1614912Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1615399Z out_44: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_77); x_77 = None 2025-03-04T20:51:41.1615646Z 2025-03-04T20:51:41.1615969Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1616749Z x_78: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_44 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.1617344Z 2025-03-04T20:51:41.1617686Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1619787Z x_79: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_78 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1621714Z 2025-03-04T20:51:41.1622081Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1622556Z out_45: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_79); x_79 = None 2025-03-04T20:51:41.1622808Z 2025-03-04T20:51:41.1623142Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1623963Z x_80: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_45 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.1624567Z 2025-03-04T20:51:41.1624915Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1627270Z x_81: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_80 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1629182Z 2025-03-04T20:51:41.1629561Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.1630040Z x_81 += out_43; out_46: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_81; x_81 = out_43 = None 2025-03-04T20:51:41.1630295Z 2025-03-04T20:51:41.1630660Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1631144Z out_47: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_46); out_46 = None 2025-03-04T20:51:41.1631401Z 2025-03-04T20:51:41.1631727Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1632511Z x_82: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.1633101Z 2025-03-04T20:51:41.1633442Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1635582Z x_83: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_82 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1637508Z 2025-03-04T20:51:41.1637888Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1638357Z out_48: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_83); x_83 = None 2025-03-04T20:51:41.1638596Z 2025-03-04T20:51:41.1638920Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1639709Z x_84: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_48 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.1640300Z 2025-03-04T20:51:41.1640643Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1642753Z x_85: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_84 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1644634Z 2025-03-04T20:51:41.1645007Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1645466Z out_49: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_85); x_85 = None 2025-03-04T20:51:41.1645708Z 2025-03-04T20:51:41.1646033Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1646808Z x_86: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_49 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.1647393Z 2025-03-04T20:51:41.1647775Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1649843Z x_87: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_86 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1651735Z 2025-03-04T20:51:41.1652097Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.1652576Z x_87 += out_47; out_50: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_87; x_87 = out_47 = None 2025-03-04T20:51:41.1652834Z 2025-03-04T20:51:41.1653205Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1653671Z out_51: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_50); out_50 = None 2025-03-04T20:51:41.1653922Z 2025-03-04T20:51:41.1654245Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1655020Z x_88: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.1655602Z 2025-03-04T20:51:41.1655944Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1658043Z x_89: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_88 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1659916Z 2025-03-04T20:51:41.1660273Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1660730Z out_52: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_89); x_89 = None 2025-03-04T20:51:41.1660971Z 2025-03-04T20:51:41.1661970Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1662795Z x_90: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_52 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.1663399Z 2025-03-04T20:51:41.1663747Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1665966Z x_91: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_90 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1667934Z 2025-03-04T20:51:41.1668310Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1668787Z out_53: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_91); x_91 = None 2025-03-04T20:51:41.1669048Z 2025-03-04T20:51:41.1669390Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1670194Z x_92: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_53 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.1670813Z 2025-03-04T20:51:41.1671183Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1673321Z x_93: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_92 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1675245Z 2025-03-04T20:51:41.1675605Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.1676113Z x_93 += out_51; out_54: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_93; x_93 = out_51 = None 2025-03-04T20:51:41.1676391Z 2025-03-04T20:51:41.1676766Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1677248Z out_55: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_54); out_54 = None 2025-03-04T20:51:41.1677508Z 2025-03-04T20:51:41.1677837Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1678659Z x_94: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.1679260Z 2025-03-04T20:51:41.1679606Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1681689Z x_95: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_94 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1683540Z 2025-03-04T20:51:41.1683901Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1684366Z out_56: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_95); x_95 = None 2025-03-04T20:51:41.1684619Z 2025-03-04T20:51:41.1684965Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1685743Z x_96: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_56 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.1686329Z 2025-03-04T20:51:41.1686668Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1688737Z x_97: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_96 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1690599Z 2025-03-04T20:51:41.1690957Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1691416Z out_57: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_97); x_97 = None 2025-03-04T20:51:41.1691661Z 2025-03-04T20:51:41.1691983Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1692756Z x_98: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_57 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.1693355Z 2025-03-04T20:51:41.1693692Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1695764Z x_99: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_98 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1697625Z 2025-03-04T20:51:41.1697904Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.1698057Z x_99 += out_55; out_58: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_99; x_99 = out_55 = None 2025-03-04T20:51:41.1698125Z 2025-03-04T20:51:41.1698403Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1698544Z out_59: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_58); out_58 = None 2025-03-04T20:51:41.1698606Z 2025-03-04T20:51:41.1698860Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1699332Z x_100: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.1699397Z 2025-03-04T20:51:41.1699667Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1701430Z x_101: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_100 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1701517Z 2025-03-04T20:51:41.1701813Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1702209Z out_60: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_101); x_101 = None 2025-03-04T20:51:41.1702283Z 2025-03-04T20:51:41.1702535Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1703039Z x_102: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_60 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.1703100Z 2025-03-04T20:51:41.1703384Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1705342Z x_103: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_102 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1705422Z 2025-03-04T20:51:41.1705779Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1705933Z out_61: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_103); x_103 = None 2025-03-04T20:51:41.1706003Z 2025-03-04T20:51:41.1706273Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1706805Z x_104: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_61 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.1706870Z 2025-03-04T20:51:41.1707161Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1709130Z x_105: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_104 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1709214Z 2025-03-04T20:51:41.1709531Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.1709689Z x_105 += out_59; out_62: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_105; x_105 = out_59 = None 2025-03-04T20:51:41.1709760Z 2025-03-04T20:51:41.1710062Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1710219Z out_63: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_62); out_62 = None 2025-03-04T20:51:41.1710282Z 2025-03-04T20:51:41.1710553Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1711064Z x_106: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_63, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.1711138Z 2025-03-04T20:51:41.1711420Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1713342Z x_107: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_106 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1713418Z 2025-03-04T20:51:41.1713720Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1713870Z out_64: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_107); x_107 = None 2025-03-04T20:51:41.1713942Z 2025-03-04T20:51:41.1714192Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1714696Z x_108: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_64, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_64 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.1714762Z 2025-03-04T20:51:41.1715028Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1716787Z x_109: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_108, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_108 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1716873Z 2025-03-04T20:51:41.1717158Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1717295Z out_65: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_109); x_109 = None 2025-03-04T20:51:41.1717360Z 2025-03-04T20:51:41.1717606Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1718102Z x_110: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_65, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_65 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.1718162Z 2025-03-04T20:51:41.1718430Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1720231Z x_111: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_110, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_110 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1720303Z 2025-03-04T20:51:41.1720583Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.1720724Z x_111 += out_63; out_66: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_111; x_111 = out_63 = None 2025-03-04T20:51:41.1720806Z 2025-03-04T20:51:41.1721097Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1721241Z out_67: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_66); out_66 = None 2025-03-04T20:51:41.1721301Z 2025-03-04T20:51:41.1721555Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1722036Z x_112: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_67, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.1722104Z 2025-03-04T20:51:41.1722363Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1724154Z x_113: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_112, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_112 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1724222Z 2025-03-04T20:51:41.1724500Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1724636Z out_68: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_113); x_113 = None 2025-03-04T20:51:41.1724694Z 2025-03-04T20:51:41.1724942Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1725434Z x_114: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_68, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_68 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.1725503Z 2025-03-04T20:51:41.1725764Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1727587Z x_115: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_114, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_114 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1727673Z 2025-03-04T20:51:41.1727956Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1728092Z out_69: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_115); x_115 = None 2025-03-04T20:51:41.1728149Z 2025-03-04T20:51:41.1728401Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1728882Z x_116: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_69, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_69 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.1728966Z 2025-03-04T20:51:41.1729232Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1730989Z x_117: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_116, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_116 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1731054Z 2025-03-04T20:51:41.1731333Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.1731476Z x_117 += out_67; out_70: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_117; x_117 = out_67 = None 2025-03-04T20:51:41.1731542Z 2025-03-04T20:51:41.1731830Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1731971Z out_71: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_70); out_70 = None 2025-03-04T20:51:41.1732032Z 2025-03-04T20:51:41.1732286Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1732757Z x_118: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_71, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.1732823Z 2025-03-04T20:51:41.1733082Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1734861Z x_119: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_118, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_118 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1734944Z 2025-03-04T20:51:41.1735220Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1735356Z out_72: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_119); x_119 = None 2025-03-04T20:51:41.1735439Z 2025-03-04T20:51:41.1735689Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1736163Z x_120: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_72, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_72 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.1736229Z 2025-03-04T20:51:41.1736486Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1738260Z x_121: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_120, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_120 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1738329Z 2025-03-04T20:51:41.1738613Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1738755Z out_73: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_121); x_121 = None 2025-03-04T20:51:41.1738814Z 2025-03-04T20:51:41.1739064Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1739545Z x_122: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_73, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_73 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.1739611Z 2025-03-04T20:51:41.1739871Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1741662Z x_123: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_122, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_122 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1741748Z 2025-03-04T20:51:41.1742038Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.1742189Z x_123 += out_71; out_74: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_123; x_123 = out_71 = None 2025-03-04T20:51:41.1742248Z 2025-03-04T20:51:41.1742531Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1742665Z out_75: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_74); out_74 = None 2025-03-04T20:51:41.1742730Z 2025-03-04T20:51:41.1742976Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1743472Z x_124: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_75, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.1743540Z 2025-03-04T20:51:41.1743803Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1745742Z x_125: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_124, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_124 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1745821Z 2025-03-04T20:51:41.1746113Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1746257Z out_76: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_125); x_125 = None 2025-03-04T20:51:41.1746318Z 2025-03-04T20:51:41.1746623Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1747266Z x_126: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_76, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_76 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.1747359Z 2025-03-04T20:51:41.1747644Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1749516Z x_127: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_126, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_126 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1749601Z 2025-03-04T20:51:41.1749889Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1750029Z out_77: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_127); x_127 = None 2025-03-04T20:51:41.1750090Z 2025-03-04T20:51:41.1750349Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1750850Z x_128: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_77, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_77 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.1750917Z 2025-03-04T20:51:41.1751185Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1753037Z x_129: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_128, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_128 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1753109Z 2025-03-04T20:51:41.1753391Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.1753547Z x_129 += out_75; out_78: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_129; x_129 = out_75 = None 2025-03-04T20:51:41.1753622Z 2025-03-04T20:51:41.1753933Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1754070Z out_79: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_78); out_78 = None 2025-03-04T20:51:41.1754138Z 2025-03-04T20:51:41.1754404Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1754925Z x_130: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_79, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.1754988Z 2025-03-04T20:51:41.1755276Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1757176Z x_131: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_130, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_130 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1757240Z 2025-03-04T20:51:41.1757535Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1757666Z out_80: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_131); x_131 = None 2025-03-04T20:51:41.1757733Z 2025-03-04T20:51:41.1758012Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1758523Z x_132: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_80, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_80 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.1758588Z 2025-03-04T20:51:41.1758857Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1760708Z x_133: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_132, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_132 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1760799Z 2025-03-04T20:51:41.1761085Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1761227Z out_81: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_133); x_133 = None 2025-03-04T20:51:41.1761287Z 2025-03-04T20:51:41.1761544Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1762072Z x_134: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_81, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_81 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.1762172Z 2025-03-04T20:51:41.1762438Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1764299Z x_135: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_134, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_134 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1764368Z 2025-03-04T20:51:41.1764647Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.1764801Z x_135 += out_79; out_82: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_135; x_135 = out_79 = None 2025-03-04T20:51:41.1764862Z 2025-03-04T20:51:41.1765165Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1765303Z out_83: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_82); out_82 = None 2025-03-04T20:51:41.1765371Z 2025-03-04T20:51:41.1765620Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1766110Z x_136: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_83, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.1766169Z 2025-03-04T20:51:41.1766441Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1768280Z x_137: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_136, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_136 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1768358Z 2025-03-04T20:51:41.1768649Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1768782Z out_84: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_137); x_137 = None 2025-03-04T20:51:41.1768872Z 2025-03-04T20:51:41.1769126Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1769627Z x_138: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_84, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_84 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.1769697Z 2025-03-04T20:51:41.1769965Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1771815Z x_139: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_138, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_138 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1771880Z 2025-03-04T20:51:41.1772171Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1772313Z out_85: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_139); x_139 = None 2025-03-04T20:51:41.1772377Z 2025-03-04T20:51:41.1772632Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1773131Z x_140: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_85, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_85 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.1773198Z 2025-03-04T20:51:41.1773465Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1775323Z x_141: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_140, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_140 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1775407Z 2025-03-04T20:51:41.1775688Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.1775855Z x_141 += out_83; out_86: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_141; x_141 = out_83 = None 2025-03-04T20:51:41.1775915Z 2025-03-04T20:51:41.1776206Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1776342Z out_87: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_86); out_86 = None 2025-03-04T20:51:41.1776408Z 2025-03-04T20:51:41.1776661Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1777152Z x_142: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_87, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.1777214Z 2025-03-04T20:51:41.1777486Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1779329Z x_143: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_142, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_142 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1779392Z 2025-03-04T20:51:41.1779679Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1779814Z out_88: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_143); x_143 = None 2025-03-04T20:51:41.1779881Z 2025-03-04T20:51:41.1780131Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1780643Z x_144: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_88, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_88 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.1780725Z 2025-03-04T20:51:41.1781010Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1782954Z x_145: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_144, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_144 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1783035Z 2025-03-04T20:51:41.1783339Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1783478Z out_89: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_145); x_145 = None 2025-03-04T20:51:41.1783548Z 2025-03-04T20:51:41.1783812Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1784341Z x_146: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_89, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_89 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.1784409Z 2025-03-04T20:51:41.1784688Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1786786Z x_147: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_146, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_146 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1786878Z 2025-03-04T20:51:41.1787177Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.1787335Z x_147 += out_87; out_90: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_147; x_147 = out_87 = None 2025-03-04T20:51:41.1787396Z 2025-03-04T20:51:41.1787737Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1787886Z out_91: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_90); out_90 = None 2025-03-04T20:51:41.1787953Z 2025-03-04T20:51:41.1788218Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1788739Z x_148: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_91, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.1788800Z 2025-03-04T20:51:41.1789088Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1791015Z x_149: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_148, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_148 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1791091Z 2025-03-04T20:51:41.1791401Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1791541Z out_92: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_149); x_149 = None 2025-03-04T20:51:41.1791611Z 2025-03-04T20:51:41.1791878Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1792420Z x_150: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_92, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_92 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.1792483Z 2025-03-04T20:51:41.1792772Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1794605Z x_151: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_150, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_150 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1794682Z 2025-03-04T20:51:41.1794965Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1795093Z out_93: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_151); x_151 = None 2025-03-04T20:51:41.1795157Z 2025-03-04T20:51:41.1795398Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1795886Z x_152: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_93, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_93 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.1795970Z 2025-03-04T20:51:41.1796240Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1798035Z x_153: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_152, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_152 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1798096Z 2025-03-04T20:51:41.1798380Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.1798522Z x_153 += out_91; out_94: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_153; x_153 = out_91 = None 2025-03-04T20:51:41.1798588Z 2025-03-04T20:51:41.1798881Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1799023Z out_95: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_94); out_94 = None 2025-03-04T20:51:41.1799083Z 2025-03-04T20:51:41.1799333Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1799812Z x_154: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_95, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.1799872Z 2025-03-04T20:51:41.1800136Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1802071Z x_155: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_154, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_154 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1802168Z 2025-03-04T20:51:41.1802456Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1802590Z out_96: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_155); x_155 = None 2025-03-04T20:51:41.1802682Z 2025-03-04T20:51:41.1802929Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1803416Z x_156: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_96, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_96 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.1803477Z 2025-03-04T20:51:41.1803747Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1805584Z x_157: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_156, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_156 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1805661Z 2025-03-04T20:51:41.1805946Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1806078Z out_97: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_157); x_157 = None 2025-03-04T20:51:41.1806145Z 2025-03-04T20:51:41.1806384Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1806866Z x_158: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_97, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_97 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.1806925Z 2025-03-04T20:51:41.1807188Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1808967Z x_159: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_158, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_158 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1809043Z 2025-03-04T20:51:41.1809321Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.1809480Z x_159 += out_95; out_98: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_159; x_159 = out_95 = None 2025-03-04T20:51:41.1809549Z 2025-03-04T20:51:41.1809828Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1809969Z out_99: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_98); out_98 = None 2025-03-04T20:51:41.1810027Z 2025-03-04T20:51:41.1810282Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1810755Z x_160: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_99, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.1810824Z 2025-03-04T20:51:41.1811081Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1812896Z x_161: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_160, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_160 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1812968Z 2025-03-04T20:51:41.1813243Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1813389Z out_100: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_161); x_161 = None 2025-03-04T20:51:41.1813448Z 2025-03-04T20:51:41.1813698Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1814199Z x_162: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_100, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_100 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.1814273Z 2025-03-04T20:51:41.1814543Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1816365Z x_163: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_162, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_162 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1816447Z 2025-03-04T20:51:41.1816736Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1816873Z out_101: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_163); x_163 = None 2025-03-04T20:51:41.1816936Z 2025-03-04T20:51:41.1817183Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1817687Z x_164: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_101, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_101 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.1817747Z 2025-03-04T20:51:41.1818017Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1819842Z x_165: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_164, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_164 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1819912Z 2025-03-04T20:51:41.1820196Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.1820344Z x_165 += out_99; out_102: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_165; x_165 = out_99 = None 2025-03-04T20:51:41.1820408Z 2025-03-04T20:51:41.1820722Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1820878Z out_103: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_102); out_102 = None 2025-03-04T20:51:41.1820937Z 2025-03-04T20:51:41.1821190Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1821680Z x_166: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_103, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.1821747Z 2025-03-04T20:51:41.1822013Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1823939Z x_167: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_166, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_166 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1824015Z 2025-03-04T20:51:41.1824315Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1824470Z out_104: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_167); x_167 = None 2025-03-04T20:51:41.1824530Z 2025-03-04T20:51:41.1824786Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1825316Z x_168: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_104, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_104 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.1825389Z 2025-03-04T20:51:41.1825723Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1827635Z x_169: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_168, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_168 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1827720Z 2025-03-04T20:51:41.1828014Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1828157Z out_105: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_169); x_169 = None 2025-03-04T20:51:41.1828225Z 2025-03-04T20:51:41.1828476Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1828983Z x_170: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_105, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_105 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.1829060Z 2025-03-04T20:51:41.1829343Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1831155Z x_171: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_170, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_170 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1831227Z 2025-03-04T20:51:41.1831507Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.1831661Z x_171 += out_103; out_106: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_171; x_171 = out_103 = None 2025-03-04T20:51:41.1831731Z 2025-03-04T20:51:41.1832048Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1832197Z out_107: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_106); out_106 = None 2025-03-04T20:51:41.1832256Z 2025-03-04T20:51:41.1832510Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1832986Z x_172: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_107, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.1833051Z 2025-03-04T20:51:41.1833310Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1835139Z x_173: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_172, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_172 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1835221Z 2025-03-04T20:51:41.1835501Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1835637Z out_108: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_173); x_173 = None 2025-03-04T20:51:41.1835731Z 2025-03-04T20:51:41.1835981Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1836458Z x_174: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_108, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_108 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.1836525Z 2025-03-04T20:51:41.1836783Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1838557Z x_175: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_174, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_174 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1838629Z 2025-03-04T20:51:41.1838906Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1839047Z out_109: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_175); x_175 = None 2025-03-04T20:51:41.1839107Z 2025-03-04T20:51:41.1839356Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1839843Z x_176: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_109, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_109 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.1839909Z 2025-03-04T20:51:41.1840172Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1841945Z x_177: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_176, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_176 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1842029Z 2025-03-04T20:51:41.1842312Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.1842479Z x_177 += out_107; out_110: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_177; x_177 = out_107 = None 2025-03-04T20:51:41.1842545Z 2025-03-04T20:51:41.1842821Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1842968Z out_111: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_110); out_110 = None 2025-03-04T20:51:41.1843027Z 2025-03-04T20:51:41.1843280Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1843757Z x_178: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_111, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.1843825Z 2025-03-04T20:51:41.1844086Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1845869Z x_179: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_178, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_178 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1845938Z 2025-03-04T20:51:41.1846213Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1846349Z out_112: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_179); x_179 = None 2025-03-04T20:51:41.1846406Z 2025-03-04T20:51:41.1846654Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1847150Z x_180: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_112, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_112 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.1847229Z 2025-03-04T20:51:41.1847487Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1849268Z x_181: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_180, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_180 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1849350Z 2025-03-04T20:51:41.1849631Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1849767Z out_113: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_181); x_181 = None 2025-03-04T20:51:41.1849827Z 2025-03-04T20:51:41.1850075Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1850563Z x_182: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_113, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_113 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.1850630Z 2025-03-04T20:51:41.1850893Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1852730Z x_183: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_182, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_182 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1852801Z 2025-03-04T20:51:41.1853082Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.1853244Z x_183 += out_111; out_114: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_183; x_183 = out_111 = None 2025-03-04T20:51:41.1853303Z 2025-03-04T20:51:41.1853630Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1853775Z out_115: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_114); out_114 = None 2025-03-04T20:51:41.1853842Z 2025-03-04T20:51:41.1854096Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1854581Z x_184: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_115, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.1854646Z 2025-03-04T20:51:41.1854905Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1856739Z x_185: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_184, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_184 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1856808Z 2025-03-04T20:51:41.1857097Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1857237Z out_116: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_185); x_185 = None 2025-03-04T20:51:41.1857298Z 2025-03-04T20:51:41.1857556Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1858067Z x_186: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_116, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_116 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.1858134Z 2025-03-04T20:51:41.1858409Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1860249Z x_187: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_186, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_186 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1860379Z 2025-03-04T20:51:41.1860665Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1860804Z out_117: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_187); x_187 = None 2025-03-04T20:51:41.1860866Z 2025-03-04T20:51:41.1861120Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1861624Z x_188: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_117, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_117 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.1861709Z 2025-03-04T20:51:41.1861976Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1863785Z x_189: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_188, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_188 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1863855Z 2025-03-04T20:51:41.1864135Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.1864294Z x_189 += out_115; out_118: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_189; x_189 = out_115 = None 2025-03-04T20:51:41.1864355Z 2025-03-04T20:51:41.1864659Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1864799Z out_119: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_118); out_118 = None 2025-03-04T20:51:41.1864870Z 2025-03-04T20:51:41.1865121Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1865677Z x_190: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.1865744Z 2025-03-04T20:51:41.1866036Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1867894Z x_191: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_190, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_190 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1867974Z 2025-03-04T20:51:41.1868269Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1868405Z out_120: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_191); x_191 = None 2025-03-04T20:51:41.1868489Z 2025-03-04T20:51:41.1868741Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1869244Z x_192: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_120, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_120 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.1869313Z 2025-03-04T20:51:41.1869581Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1871431Z x_193: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_192, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_192 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1873748Z 2025-03-04T20:51:41.1874054Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1874201Z out_121: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_193); x_193 = None 2025-03-04T20:51:41.1874259Z 2025-03-04T20:51:41.1874513Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1875003Z x_194: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_121, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_121 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.1875068Z 2025-03-04T20:51:41.1875329Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1877171Z x_195: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_194, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_194 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1877234Z 2025-03-04T20:51:41.1877491Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1878005Z x_196: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:51:41.1878073Z 2025-03-04T20:51:41.1878338Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1880178Z x_197: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_196, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_196 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1880264Z 2025-03-04T20:51:41.1880535Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.1880748Z x_195 += x_197; out_122: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_195; x_195 = x_197 = None 2025-03-04T20:51:41.1880810Z 2025-03-04T20:51:41.1881099Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1881239Z out_123: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_122); out_122 = None 2025-03-04T20:51:41.1881308Z 2025-03-04T20:51:41.1881555Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1882037Z x_198: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_123, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.1882104Z 2025-03-04T20:51:41.1882366Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1884167Z x_199: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_198, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_198 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1884252Z 2025-03-04T20:51:41.1884543Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1884681Z out_124: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_199); x_199 = None 2025-03-04T20:51:41.1884740Z 2025-03-04T20:51:41.1884988Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1887798Z x_200: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_124, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_124 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.1891455Z 2025-03-04T20:51:41.1891795Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1893638Z x_201: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_200, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_200 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1893752Z 2025-03-04T20:51:41.1897598Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1898070Z out_125: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_201); x_201 = None 2025-03-04T20:51:41.1898319Z 2025-03-04T20:51:41.1899582Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1900198Z x_202: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_125, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_125 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.1900305Z 2025-03-04T20:51:41.1900616Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1902788Z x_203: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_202, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_202 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1902905Z 2025-03-04T20:51:41.1903221Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.1903406Z x_203 += out_123; out_126: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_203; x_203 = out_123 = None 2025-03-04T20:51:41.1903475Z 2025-03-04T20:51:41.1903803Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1903966Z out_127: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_126); out_126 = None 2025-03-04T20:51:41.1904044Z 2025-03-04T20:51:41.1904325Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1904877Z x_204: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_127, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.1904946Z 2025-03-04T20:51:41.1905273Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1907425Z x_205: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_204, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_204 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1907533Z 2025-03-04T20:51:41.1907857Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1908008Z out_128: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_205); x_205 = None 2025-03-04T20:51:41.1908081Z 2025-03-04T20:51:41.1908380Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1908928Z x_206: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_128, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_128 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.1909000Z 2025-03-04T20:51:41.1909297Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1912180Z x_207: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_206, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_206 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1912296Z 2025-03-04T20:51:41.1913374Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1915557Z out_129: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_207); x_207 = None 2025-03-04T20:51:41.1915797Z 2025-03-04T20:51:41.1924312Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1924864Z x_208: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_129, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_129 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.1924937Z 2025-03-04T20:51:41.1925211Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.1927084Z x_209: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_208, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_208 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.1927156Z 2025-03-04T20:51:41.1927471Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.1927651Z x_209 += out_127; out_130: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_209; x_209 = out_127 = None 2025-03-04T20:51:41.1927712Z 2025-03-04T20:51:41.1928004Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.1928147Z out_131: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_130); out_130 = None 2025-03-04T20:51:41.1928213Z 2025-03-04T20:51:41.1928463Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1929066Z x_210: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(out_131, l_self_modules_backbone_lateral_convs_0_parameters_weight_, l_self_modules_backbone_lateral_convs_0_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_131 = l_self_modules_backbone_lateral_convs_0_parameters_weight_ = l_self_modules_backbone_lateral_convs_0_parameters_bias_ = None 2025-03-04T20:51:41.1929154Z 2025-03-04T20:51:41.1929441Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1929993Z x_211: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_210, l_self_modules_backbone_output_convs_0_parameters_weight_, l_self_modules_backbone_output_convs_0_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_self_modules_backbone_output_convs_0_parameters_weight_ = l_self_modules_backbone_output_convs_0_parameters_bias_ = None 2025-03-04T20:51:41.1930058Z 2025-03-04T20:51:41.1930478Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest") 2025-03-04T20:51:41.1930761Z top_down_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.interpolate(x_210, scale_factor = 2.0, mode = 'nearest'); x_210 = None 2025-03-04T20:51:41.1930833Z 2025-03-04T20:51:41.1931096Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1931719Z x_212: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_lateral_convs_1_parameters_weight_, l_self_modules_backbone_lateral_convs_1_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_119 = l_self_modules_backbone_lateral_convs_1_parameters_weight_ = l_self_modules_backbone_lateral_convs_1_parameters_bias_ = None 2025-03-04T20:51:41.1931800Z 2025-03-04T20:51:41.1932179Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features 2025-03-04T20:51:41.1932384Z prev_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = x_212 + top_down_features; x_212 = top_down_features = None 2025-03-04T20:51:41.1932453Z 2025-03-04T20:51:41.1932716Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1933332Z x_213: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(prev_features, l_self_modules_backbone_output_convs_1_parameters_weight_, l_self_modules_backbone_output_convs_1_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_self_modules_backbone_output_convs_1_parameters_weight_ = l_self_modules_backbone_output_convs_1_parameters_bias_ = None 2025-03-04T20:51:41.1933401Z 2025-03-04T20:51:41.1933828Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest") 2025-03-04T20:51:41.1934192Z top_down_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.interpolate(prev_features, scale_factor = 2.0, mode = 'nearest'); prev_features = None 2025-03-04T20:51:41.1934258Z 2025-03-04T20:51:41.1934533Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1935139Z x_214: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_lateral_convs_2_parameters_weight_, l_self_modules_backbone_lateral_convs_2_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_27 = l_self_modules_backbone_lateral_convs_2_parameters_weight_ = l_self_modules_backbone_lateral_convs_2_parameters_bias_ = None 2025-03-04T20:51:41.1935212Z 2025-03-04T20:51:41.1935573Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features 2025-03-04T20:51:41.1935820Z prev_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = x_214 + top_down_features_1; x_214 = top_down_features_1 = None 2025-03-04T20:51:41.1935884Z 2025-03-04T20:51:41.1936156Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1936775Z x_215: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(prev_features_1, l_self_modules_backbone_output_convs_2_parameters_weight_, l_self_modules_backbone_output_convs_2_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_self_modules_backbone_output_convs_2_parameters_weight_ = l_self_modules_backbone_output_convs_2_parameters_bias_ = None 2025-03-04T20:51:41.1936841Z 2025-03-04T20:51:41.1937272Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest") 2025-03-04T20:51:41.1937612Z top_down_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.interpolate(prev_features_1, scale_factor = 2.0, mode = 'nearest'); prev_features_1 = None 2025-03-04T20:51:41.1937682Z 2025-03-04T20:51:41.1937946Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1938592Z x_216: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_lateral_convs_3_parameters_weight_, l_self_modules_backbone_lateral_convs_3_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_11 = l_self_modules_backbone_lateral_convs_3_parameters_weight_ = l_self_modules_backbone_lateral_convs_3_parameters_bias_ = None 2025-03-04T20:51:41.1938670Z 2025-03-04T20:51:41.1939022Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features 2025-03-04T20:51:41.1939231Z prev_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_216 + top_down_features_2; x_216 = top_down_features_2 = None 2025-03-04T20:51:41.1939300Z 2025-03-04T20:51:41.1939548Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1940166Z x_217: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(prev_features_2, l_self_modules_backbone_output_convs_3_parameters_weight_, l_self_modules_backbone_output_convs_3_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); prev_features_2 = l_self_modules_backbone_output_convs_3_parameters_weight_ = l_self_modules_backbone_output_convs_3_parameters_bias_ = None 2025-03-04T20:51:41.1940236Z 2025-03-04T20:51:41.1940613Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:200 in forward, code: return [F.max_pool2d(x, kernel_size=1, stride=2, padding=0)] 2025-03-04T20:51:41.1940829Z res: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.max_pool2d(x_211, kernel_size = 1, stride = 2, padding = 0) 2025-03-04T20:51:41.1940888Z 2025-03-04T20:51:41.1941333Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:51:41.1941494Z arange: "f32[304][1]cpu" = torch.arange(0.0, 1216, step = 4, dtype = torch.float32) 2025-03-04T20:51:41.1941562Z 2025-03-04T20:51:41.1941876Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:51:41.1942045Z shifts_x: "f32[304][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T20:51:41.1942112Z 2025-03-04T20:51:41.1942581Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:51:41.1942738Z arange_1: "f32[296][1]cpu" = torch.arange(0.0, 1184, step = 4, dtype = torch.float32) 2025-03-04T20:51:41.1942810Z 2025-03-04T20:51:41.1943121Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:51:41.1943272Z shifts_y: "f32[296][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T20:51:41.1943337Z 2025-03-04T20:51:41.1943735Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:51:41.1943926Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T20:51:41.1944039Z shift_y: "f32[296, 304][1, 0]cpu" = meshgrid[0] 2025-03-04T20:51:41.1944170Z shift_x: "f32[296, 304][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T20:51:41.1944246Z 2025-03-04T20:51:41.1944614Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:51:41.1944785Z shift_x_1: "f32[89984][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T20:51:41.1944850Z 2025-03-04T20:51:41.1945203Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:51:41.1945353Z shift_y_1: "f32[89984][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T20:51:41.1945428Z 2025-03-04T20:51:41.1945919Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:51:41.1946168Z shifts: "f32[89984, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T20:51:41.1946235Z 2025-03-04T20:51:41.1946714Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:51:41.1946853Z view: "f32[89984, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T20:51:41.1947361Z view_1: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T20:51:41.1947509Z add_3: "f32[89984, 3, 4][12, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T20:51:41.1947638Z x_218: "f32[269952, 4][4, 1]cpu" = add_3.reshape(-1, 4); add_3 = None 2025-03-04T20:51:41.1947714Z 2025-03-04T20:51:41.1948197Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:51:41.1948369Z arange_2: "f32[152][1]cpu" = torch.arange(0.0, 1216, step = 8, dtype = torch.float32) 2025-03-04T20:51:41.1948437Z 2025-03-04T20:51:41.1948771Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:51:41.1948947Z shifts_x_1: "f32[152][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T20:51:41.1949020Z 2025-03-04T20:51:41.1949503Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:51:41.1949674Z arange_3: "f32[148][1]cpu" = torch.arange(0.0, 1184, step = 8, dtype = torch.float32) 2025-03-04T20:51:41.1949741Z 2025-03-04T20:51:41.1950078Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:51:41.1950228Z shifts_y_1: "f32[148][1]cpu" = arange_3.to(device(type='cpu')); arange_3 = None 2025-03-04T20:51:41.1950302Z 2025-03-04T20:51:41.1950721Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:51:41.1950949Z meshgrid_1 = torch.functional.meshgrid(shifts_y_1, shifts_x_1); shifts_y_1 = shifts_x_1 = None 2025-03-04T20:51:41.1951060Z shift_y_2: "f32[148, 152][1, 0]cpu" = meshgrid_1[0] 2025-03-04T20:51:41.1951204Z shift_x_2: "f32[148, 152][0, 1]cpu" = meshgrid_1[1]; meshgrid_1 = None 2025-03-04T20:51:41.1951271Z 2025-03-04T20:51:41.1951644Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:51:41.1951801Z shift_x_3: "f32[22496][1]cpu" = shift_x_2.reshape(-1); shift_x_2 = None 2025-03-04T20:51:41.1951875Z 2025-03-04T20:51:41.1952228Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:51:41.1952359Z shift_y_3: "f32[22496][1]cpu" = shift_y_2.reshape(-1); shift_y_2 = None 2025-03-04T20:51:41.1952419Z 2025-03-04T20:51:41.1952804Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:51:41.1953015Z shifts_1: "f32[22496, 4][4, 1]cpu" = torch.stack((shift_x_3, shift_y_3, shift_x_3, shift_y_3), dim = 1); shift_x_3 = shift_y_3 = None 2025-03-04T20:51:41.1953080Z 2025-03-04T20:51:41.1953495Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:51:41.1953632Z view_2: "f32[22496, 1, 4][4, 4, 1]cpu" = shifts_1.view(-1, 1, 4); shifts_1 = None 2025-03-04T20:51:41.1954071Z view_3: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = None 2025-03-04T20:51:41.1954196Z add_4: "f32[22496, 3, 4][12, 4, 1]cpu" = view_2 + view_3; view_2 = view_3 = None 2025-03-04T20:51:41.1954315Z x_219: "f32[67488, 4][4, 1]cpu" = add_4.reshape(-1, 4); add_4 = None 2025-03-04T20:51:41.1954376Z 2025-03-04T20:51:41.1954810Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:51:41.1954954Z arange_4: "f32[76][1]cpu" = torch.arange(0.0, 1216, step = 16, dtype = torch.float32) 2025-03-04T20:51:41.1955023Z 2025-03-04T20:51:41.1955317Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:51:41.1955478Z shifts_x_2: "f32[76][1]cpu" = arange_4.to(device(type='cpu')); arange_4 = None 2025-03-04T20:51:41.1955537Z 2025-03-04T20:51:41.1955974Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:51:41.1956115Z arange_5: "f32[74][1]cpu" = torch.arange(0.0, 1184, step = 16, dtype = torch.float32) 2025-03-04T20:51:41.1956182Z 2025-03-04T20:51:41.1956479Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:51:41.1956617Z shifts_y_2: "f32[74][1]cpu" = arange_5.to(device(type='cpu')); arange_5 = None 2025-03-04T20:51:41.1956678Z 2025-03-04T20:51:41.1957054Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:51:41.1957247Z meshgrid_2 = torch.functional.meshgrid(shifts_y_2, shifts_x_2); shifts_y_2 = shifts_x_2 = None 2025-03-04T20:51:41.1957354Z shift_y_4: "f32[74, 76][1, 0]cpu" = meshgrid_2[0] 2025-03-04T20:51:41.1957474Z shift_x_4: "f32[74, 76][0, 1]cpu" = meshgrid_2[1]; meshgrid_2 = None 2025-03-04T20:51:41.1957543Z 2025-03-04T20:51:41.1957884Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:51:41.1958015Z shift_x_5: "f32[5624][1]cpu" = shift_x_4.reshape(-1); shift_x_4 = None 2025-03-04T20:51:41.1958091Z 2025-03-04T20:51:41.1958421Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:51:41.1958541Z shift_y_5: "f32[5624][1]cpu" = shift_y_4.reshape(-1); shift_y_4 = None 2025-03-04T20:51:41.1958608Z 2025-03-04T20:51:41.1958982Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:51:41.1959198Z shifts_2: "f32[5624, 4][4, 1]cpu" = torch.stack((shift_x_5, shift_y_5, shift_x_5, shift_y_5), dim = 1); shift_x_5 = shift_y_5 = None 2025-03-04T20:51:41.1959257Z 2025-03-04T20:51:41.1959674Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:51:41.1959801Z view_4: "f32[5624, 1, 4][4, 4, 1]cpu" = shifts_2.view(-1, 1, 4); shifts_2 = None 2025-03-04T20:51:41.1960242Z view_5: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = None 2025-03-04T20:51:41.1960368Z add_5: "f32[5624, 3, 4][12, 4, 1]cpu" = view_4 + view_5; view_4 = view_5 = None 2025-03-04T20:51:41.1960480Z x_220: "f32[16872, 4][4, 1]cpu" = add_5.reshape(-1, 4); add_5 = None 2025-03-04T20:51:41.1960545Z 2025-03-04T20:51:41.1960974Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:51:41.1961126Z arange_6: "f32[38][1]cpu" = torch.arange(0.0, 1216, step = 32, dtype = torch.float32) 2025-03-04T20:51:41.1961186Z 2025-03-04T20:51:41.1961489Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:51:41.1961672Z shifts_x_3: "f32[38][1]cpu" = arange_6.to(device(type='cpu')); arange_6 = None 2025-03-04T20:51:41.1961739Z 2025-03-04T20:51:41.1962166Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:51:41.1962312Z arange_7: "f32[37][1]cpu" = torch.arange(0.0, 1184, step = 32, dtype = torch.float32) 2025-03-04T20:51:41.1962374Z 2025-03-04T20:51:41.1962670Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:51:41.1962802Z shifts_y_3: "f32[37][1]cpu" = arange_7.to(device(type='cpu')); arange_7 = None 2025-03-04T20:51:41.1962870Z 2025-03-04T20:51:41.1963242Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:51:41.1963437Z meshgrid_3 = torch.functional.meshgrid(shifts_y_3, shifts_x_3); shifts_y_3 = shifts_x_3 = None 2025-03-04T20:51:41.1963533Z shift_y_6: "f32[37, 38][1, 0]cpu" = meshgrid_3[0] 2025-03-04T20:51:41.1963657Z shift_x_6: "f32[37, 38][0, 1]cpu" = meshgrid_3[1]; meshgrid_3 = None 2025-03-04T20:51:41.1963716Z 2025-03-04T20:51:41.1964628Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:51:41.1964777Z shift_x_7: "f32[1406][1]cpu" = shift_x_6.reshape(-1); shift_x_6 = None 2025-03-04T20:51:41.1964844Z 2025-03-04T20:51:41.1965179Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:51:41.1965307Z shift_y_7: "f32[1406][1]cpu" = shift_y_6.reshape(-1); shift_y_6 = None 2025-03-04T20:51:41.1965366Z 2025-03-04T20:51:41.1965748Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:51:41.1965952Z shifts_3: "f32[1406, 4][4, 1]cpu" = torch.stack((shift_x_7, shift_y_7, shift_x_7, shift_y_7), dim = 1); shift_x_7 = shift_y_7 = None 2025-03-04T20:51:41.1966021Z 2025-03-04T20:51:41.1966421Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:51:41.1966552Z view_6: "f32[1406, 1, 4][4, 4, 1]cpu" = shifts_3.view(-1, 1, 4); shifts_3 = None 2025-03-04T20:51:41.1966972Z view_7: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = None 2025-03-04T20:51:41.1967091Z add_6: "f32[1406, 3, 4][12, 4, 1]cpu" = view_6 + view_7; view_6 = view_7 = None 2025-03-04T20:51:41.1967203Z x_221: "f32[4218, 4][4, 1]cpu" = add_6.reshape(-1, 4); add_6 = None 2025-03-04T20:51:41.1967261Z 2025-03-04T20:51:41.1967681Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:51:41.1967822Z arange_8: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32) 2025-03-04T20:51:41.1967889Z 2025-03-04T20:51:41.1968189Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:51:41.1968326Z shifts_x_4: "f32[19][1]cpu" = arange_8.to(device(type='cpu')); arange_8 = None 2025-03-04T20:51:41.1968385Z 2025-03-04T20:51:41.1968808Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:51:41.1968944Z arange_9: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32) 2025-03-04T20:51:41.1969007Z 2025-03-04T20:51:41.1969290Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:51:41.1969429Z shifts_y_4: "f32[19][1]cpu" = arange_9.to(device(type='cpu')); arange_9 = None 2025-03-04T20:51:41.1969489Z 2025-03-04T20:51:41.1969865Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:51:41.1970051Z meshgrid_4 = torch.functional.meshgrid(shifts_y_4, shifts_x_4); shifts_y_4 = shifts_x_4 = None 2025-03-04T20:51:41.1970152Z shift_y_8: "f32[19, 19][1, 0]cpu" = meshgrid_4[0] 2025-03-04T20:51:41.1970264Z shift_x_8: "f32[19, 19][0, 1]cpu" = meshgrid_4[1]; meshgrid_4 = None 2025-03-04T20:51:41.1970331Z 2025-03-04T20:51:41.1970671Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:51:41.1970817Z shift_x_9: "f32[361][1]cpu" = shift_x_8.reshape(-1); shift_x_8 = None 2025-03-04T20:51:41.1970878Z 2025-03-04T20:51:41.1971217Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:51:41.1971330Z shift_y_9: "f32[361][1]cpu" = shift_y_8.reshape(-1); shift_y_8 = None 2025-03-04T20:51:41.1971396Z 2025-03-04T20:51:41.1971761Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:51:41.1971970Z shifts_4: "f32[361, 4][4, 1]cpu" = torch.stack((shift_x_9, shift_y_9, shift_x_9, shift_y_9), dim = 1); shift_x_9 = shift_y_9 = None 2025-03-04T20:51:41.1972028Z 2025-03-04T20:51:41.1972432Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:51:41.1972553Z view_8: "f32[361, 1, 4][4, 4, 1]cpu" = shifts_4.view(-1, 1, 4); shifts_4 = None 2025-03-04T20:51:41.1972975Z view_9: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = None 2025-03-04T20:51:41.1973098Z add_7: "f32[361, 3, 4][12, 4, 1]cpu" = view_8 + view_9; view_8 = view_9 = None 2025-03-04T20:51:41.1973205Z x_222: "f32[1083, 4][4, 1]cpu" = add_7.reshape(-1, 4); add_7 = None 2025-03-04T20:51:41.1973272Z 2025-03-04T20:51:41.1973568Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:51:41.1973701Z tensor: "f32[269952, 4][4, 1]cpu" = x_218.to(torch.float32); x_218 = None 2025-03-04T20:51:41.1973827Z tensor_1: "f32[67488, 4][4, 1]cpu" = x_219.to(torch.float32); x_219 = None 2025-03-04T20:51:41.1973973Z tensor_2: "f32[16872, 4][4, 1]cpu" = x_220.to(torch.float32); x_220 = None 2025-03-04T20:51:41.1974092Z tensor_3: "f32[4218, 4][4, 1]cpu" = x_221.to(torch.float32); x_221 = None 2025-03-04T20:51:41.1974224Z tensor_4: "f32[1083, 4][4, 1]cpu" = x_222.to(torch.float32); x_222 = None 2025-03-04T20:51:41.1974282Z 2025-03-04T20:51:41.1974540Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1975033Z x_223: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_217, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_217 = None 2025-03-04T20:51:41.1975099Z 2025-03-04T20:51:41.1975366Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:51:41.1975567Z x_224: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.relu(x_223, inplace = False); x_223 = None 2025-03-04T20:51:41.1975625Z 2025-03-04T20:51:41.1976008Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:51:41.1976534Z score: "f32[4, 3, 296, 304][269952, 89984, 304, 1]cpu" = torch.conv2d(x_224, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T20:51:41.1976624Z 2025-03-04T20:51:41.1976977Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:51:41.1977499Z x_233: "f32[4, 12, 296, 304][1079808, 89984, 304, 1]cpu" = torch.conv2d(x_224, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_224 = None 2025-03-04T20:51:41.1977566Z 2025-03-04T20:51:41.1977818Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1978310Z x_225: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(x_215, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_215 = None 2025-03-04T20:51:41.1978371Z 2025-03-04T20:51:41.1978663Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:51:41.1978854Z x_226: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.relu(x_225, inplace = False); x_225 = None 2025-03-04T20:51:41.1978923Z 2025-03-04T20:51:41.1979295Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:51:41.1979815Z score_1: "f32[4, 3, 148, 152][67488, 22496, 152, 1]cpu" = torch.conv2d(x_226, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T20:51:41.1979876Z 2025-03-04T20:51:41.1980236Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:51:41.1980768Z x_234: "f32[4, 12, 148, 152][269952, 22496, 152, 1]cpu" = torch.conv2d(x_226, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_226 = None 2025-03-04T20:51:41.1980834Z 2025-03-04T20:51:41.1981094Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1981568Z x_227: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(x_213, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_213 = None 2025-03-04T20:51:41.1981636Z 2025-03-04T20:51:41.1981909Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:51:41.1982097Z x_228: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.relu(x_227, inplace = False); x_227 = None 2025-03-04T20:51:41.1982158Z 2025-03-04T20:51:41.1982539Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:51:41.1983052Z score_2: "f32[4, 3, 74, 76][16872, 5624, 76, 1]cpu" = torch.conv2d(x_228, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T20:51:41.1983141Z 2025-03-04T20:51:41.1983513Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:51:41.1984044Z x_235: "f32[4, 12, 74, 76][67488, 5624, 76, 1]cpu" = torch.conv2d(x_228, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_228 = None 2025-03-04T20:51:41.1984107Z 2025-03-04T20:51:41.1984382Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1984883Z x_229: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_211, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_211 = None 2025-03-04T20:51:41.1984949Z 2025-03-04T20:51:41.1985260Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:51:41.1985446Z x_230: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.nn.functional.relu(x_229, inplace = False); x_229 = None 2025-03-04T20:51:41.1985514Z 2025-03-04T20:51:41.1986015Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:51:41.1986607Z score_3: "f32[4, 3, 37, 38][4218, 1406, 38, 1]cpu" = torch.conv2d(x_230, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T20:51:41.1986674Z 2025-03-04T20:51:41.1987071Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:51:41.1987593Z x_236: "f32[4, 12, 37, 38][16872, 1406, 38, 1]cpu" = torch.conv2d(x_230, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_230 = None 2025-03-04T20:51:41.1987661Z 2025-03-04T20:51:41.1987913Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.1988681Z x_231: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.conv2d(res, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); res = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T20:51:41.1988751Z 2025-03-04T20:51:41.1989023Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:51:41.1989201Z x_232: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.relu(x_231, inplace = False); x_231 = None 2025-03-04T20:51:41.1989263Z 2025-03-04T20:51:41.1989659Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:51:41.1990515Z score_4: "f32[4, 3, 19, 19][1083, 361, 19, 1]cpu" = torch.conv2d(x_232, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T20:51:41.1990601Z 2025-03-04T20:51:41.1990953Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:51:41.1991776Z x_237: "f32[4, 12, 19, 19][4332, 361, 19, 1]cpu" = torch.conv2d(x_232, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_232 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T20:51:41.1991846Z 2025-03-04T20:51:41.1992194Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T20:51:41.1992364Z permute: "f32[4, 296, 304, 3][269952, 304, 1, 89984]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T20:51:41.1992505Z logits_i: "f32[4, 269952][269952, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T20:51:41.1992670Z permute_1: "f32[4, 148, 152, 3][67488, 152, 1, 22496]cpu" = score_1.permute(0, 2, 3, 1); score_1 = None 2025-03-04T20:51:41.1992813Z logits_i_1: "f32[4, 67488][67488, 1]cpu" = permute_1.flatten(1); permute_1 = None 2025-03-04T20:51:41.1992972Z permute_2: "f32[4, 74, 76, 3][16872, 76, 1, 5624]cpu" = score_2.permute(0, 2, 3, 1); score_2 = None 2025-03-04T20:51:41.1993108Z logits_i_2: "f32[4, 16872][16872, 1]cpu" = permute_2.flatten(1); permute_2 = None 2025-03-04T20:51:41.1993272Z permute_3: "f32[4, 37, 38, 3][4218, 38, 1, 1406]cpu" = score_3.permute(0, 2, 3, 1); score_3 = None 2025-03-04T20:51:41.1993404Z logits_i_3: "f32[4, 4218][4218, 1]cpu" = permute_3.flatten(1); permute_3 = None 2025-03-04T20:51:41.1993551Z permute_4: "f32[4, 19, 19, 3][1083, 19, 1, 361]cpu" = score_4.permute(0, 2, 3, 1); score_4 = None 2025-03-04T20:51:41.1993677Z logits_i_4: "f32[4, 1083][1083, 1]cpu" = permute_4.flatten(1); permute_4 = None 2025-03-04T20:51:41.1993745Z 2025-03-04T20:51:41.1994168Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T20:51:41.1994350Z view_10: "f32[4, 3, 4, 296, 304][1079808, 359936, 89984, 304, 1]cpu" = x_233.view(4, -1, 4, 296, 304); x_233 = None 2025-03-04T20:51:41.1994535Z permute_5: "f32[4, 296, 304, 3, 4][1079808, 304, 1, 359936, 89984]cpu" = view_10.permute(0, 3, 4, 1, 2); view_10 = None 2025-03-04T20:51:41.1994722Z pred_anchor_deltas_i: "f32[4, 269952, 4][1079808, 4, 1]cpu" = permute_5.flatten(1, -2); permute_5 = None 2025-03-04T20:51:41.1994883Z view_11: "f32[4, 3, 4, 148, 152][269952, 89984, 22496, 152, 1]cpu" = x_234.view(4, -1, 4, 148, 152); x_234 = None 2025-03-04T20:51:41.1995062Z permute_6: "f32[4, 148, 152, 3, 4][269952, 152, 1, 89984, 22496]cpu" = view_11.permute(0, 3, 4, 1, 2); view_11 = None 2025-03-04T20:51:41.1995248Z pred_anchor_deltas_i_2: "f32[4, 67488, 4][269952, 4, 1]cpu" = permute_6.flatten(1, -2); permute_6 = None 2025-03-04T20:51:41.1995405Z view_12: "f32[4, 3, 4, 74, 76][67488, 22496, 5624, 76, 1]cpu" = x_235.view(4, -1, 4, 74, 76); x_235 = None 2025-03-04T20:51:41.1995582Z permute_7: "f32[4, 74, 76, 3, 4][67488, 76, 1, 22496, 5624]cpu" = view_12.permute(0, 3, 4, 1, 2); view_12 = None 2025-03-04T20:51:41.1995756Z pred_anchor_deltas_i_4: "f32[4, 16872, 4][67488, 4, 1]cpu" = permute_7.flatten(1, -2); permute_7 = None 2025-03-04T20:51:41.1995902Z view_13: "f32[4, 3, 4, 37, 38][16872, 5624, 1406, 38, 1]cpu" = x_236.view(4, -1, 4, 37, 38); x_236 = None 2025-03-04T20:51:41.1996056Z permute_8: "f32[4, 37, 38, 3, 4][16872, 38, 1, 5624, 1406]cpu" = view_13.permute(0, 3, 4, 1, 2); view_13 = None 2025-03-04T20:51:41.1996226Z pred_anchor_deltas_i_6: "f32[4, 4218, 4][16872, 4, 1]cpu" = permute_8.flatten(1, -2); permute_8 = None 2025-03-04T20:51:41.1996369Z view_14: "f32[4, 3, 4, 19, 19][4332, 1444, 361, 19, 1]cpu" = x_237.view(4, -1, 4, 19, 19); x_237 = None 2025-03-04T20:51:41.1996528Z permute_9: "f32[4, 19, 19, 3, 4][4332, 19, 1, 1444, 361]cpu" = view_14.permute(0, 3, 4, 1, 2); view_14 = None 2025-03-04T20:51:41.1996687Z pred_anchor_deltas_i_8: "f32[4, 1083, 4][4332, 4, 1]cpu" = permute_9.flatten(1, -2); permute_9 = None 2025-03-04T20:51:41.1996755Z 2025-03-04T20:51:41.1997162Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:51:41.1997368Z pred_anchor_deltas_i_1: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T20:51:41.1997429Z 2025-03-04T20:51:41.1997859Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:51:41.1998009Z unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T20:51:41.1998156Z expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T20:51:41.1998289Z anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T20:51:41.1998370Z 2025-03-04T20:51:41.1998744Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:51:41.1998923Z deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T20:51:41.1998981Z 2025-03-04T20:51:41.1999313Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:51:41.1999450Z boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T20:51:41.1999516Z 2025-03-04T20:51:41.1999822Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:51:41.1999959Z getitem_10: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:51:41.2000083Z getitem_11: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:51:41.2000236Z widths: "f32[1079808][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T20:51:41.2000294Z 2025-03-04T20:51:41.2000611Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:51:41.2000733Z getitem_12: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:51:41.2000874Z getitem_13: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:51:41.2001019Z heights: "f32[1079808][1]cpu" = getitem_12 - getitem_13; getitem_12 = getitem_13 = None 2025-03-04T20:51:41.2001101Z 2025-03-04T20:51:41.2001410Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:51:41.2001536Z getitem_14: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:51:41.2001620Z mul: "f32[1079808][1]cpu" = 0.5 * widths 2025-03-04T20:51:41.2001751Z ctr_x: "f32[1079808][1]cpu" = getitem_14 + mul; getitem_14 = mul = None 2025-03-04T20:51:41.2001810Z 2025-03-04T20:51:41.2002251Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:51:41.2002408Z getitem_15: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:51:41.2002495Z mul_1: "f32[1079808][1]cpu" = 0.5 * heights 2025-03-04T20:51:41.2002627Z ctr_y: "f32[1079808][1]cpu" = getitem_15 + mul_1; getitem_15 = mul_1 = None 2025-03-04T20:51:41.2002685Z 2025-03-04T20:51:41.2003086Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:51:41.2003245Z getitem_16: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:51:41.2003376Z dx: "f32[1079808, 1][1, 1]cpu" = getitem_16 / 1.0; getitem_16 = None 2025-03-04T20:51:41.2003436Z 2025-03-04T20:51:41.2003735Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:51:41.2003886Z getitem_17: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:51:41.2004001Z dy: "f32[1079808, 1][1, 1]cpu" = getitem_17 / 1.0; getitem_17 = None 2025-03-04T20:51:41.2004063Z 2025-03-04T20:51:41.2004360Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:51:41.2004531Z getitem_18: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:51:41.2004645Z dw: "f32[1079808, 1][1, 1]cpu" = getitem_18 / 1.0; getitem_18 = None 2025-03-04T20:51:41.2004703Z 2025-03-04T20:51:41.2005003Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:51:41.2005180Z getitem_19: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:51:41.2005295Z dh: "f32[1079808, 1][1, 1]cpu" = getitem_19 / 1.0; getitem_19 = None 2025-03-04T20:51:41.2005355Z 2025-03-04T20:51:41.2005688Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:51:41.2005826Z dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:51:41.2005893Z 2025-03-04T20:51:41.2006216Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:51:41.2006356Z dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:51:41.2006415Z 2025-03-04T20:51:41.2006775Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:51:41.2006908Z getitem_20: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:51:41.2007081Z mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_20; dx = getitem_20 = None 2025-03-04T20:51:41.2007226Z getitem_21: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:51:41.2007366Z pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_21; mul_2 = getitem_21 = None 2025-03-04T20:51:41.2007423Z 2025-03-04T20:51:41.2007772Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:51:41.2007914Z getitem_22: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:51:41.2008033Z mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_22; dy = getitem_22 = None 2025-03-04T20:51:41.2008185Z getitem_23: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:51:41.2008316Z pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_23; mul_3 = getitem_23 = None 2025-03-04T20:51:41.2008382Z 2025-03-04T20:51:41.2008719Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:51:41.2008842Z exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:51:41.2008996Z getitem_24: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:51:41.2009128Z pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_24; exp = getitem_24 = None 2025-03-04T20:51:41.2009187Z 2025-03-04T20:51:41.2009513Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:51:41.2009623Z exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:51:41.2009792Z getitem_25: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:51:41.2009921Z pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_25; exp_1 = getitem_25 = None 2025-03-04T20:51:41.2010005Z 2025-03-04T20:51:41.2010313Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:51:41.2010411Z mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T20:51:41.2010523Z x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:51:41.2010591Z 2025-03-04T20:51:41.2010895Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:51:41.2010987Z mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T20:51:41.2011097Z y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:51:41.2011161Z 2025-03-04T20:51:41.2011458Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:51:41.2011574Z mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:51:41.2011699Z x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:51:41.2011766Z 2025-03-04T20:51:41.2012063Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:51:41.2012174Z mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:51:41.2012310Z y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:51:41.2012377Z 2025-03-04T20:51:41.2012737Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:51:41.2012934Z pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:51:41.2012992Z 2025-03-04T20:51:41.2013325Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:51:41.2013480Z proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4)); pred_boxes = None 2025-03-04T20:51:41.2013544Z 2025-03-04T20:51:41.2013917Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:51:41.2014098Z proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T20:51:41.2014158Z 2025-03-04T20:51:41.2014580Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:51:41.2014788Z pred_anchor_deltas_i_3: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_2.reshape(-1, 4); pred_anchor_deltas_i_2 = None 2025-03-04T20:51:41.2014856Z 2025-03-04T20:51:41.2015335Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:51:41.2015494Z unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = tensor_1.unsqueeze(0); tensor_1 = None 2025-03-04T20:51:41.2015649Z expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1); unsqueeze_1 = None 2025-03-04T20:51:41.2015787Z anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4); expand_1 = None 2025-03-04T20:51:41.2015871Z 2025-03-04T20:51:41.2016245Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:51:41.2016416Z deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float(); pred_anchor_deltas_i_3 = None 2025-03-04T20:51:41.2016477Z 2025-03-04T20:51:41.2016799Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:51:41.2016943Z boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32); anchors_i_1 = None 2025-03-04T20:51:41.2017009Z 2025-03-04T20:51:41.2017323Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:51:41.2017459Z getitem_26: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)] 2025-03-04T20:51:41.2017587Z getitem_27: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T20:51:41.2017742Z widths_1: "f32[269952][1]cpu" = getitem_26 - getitem_27; getitem_26 = getitem_27 = None 2025-03-04T20:51:41.2017801Z 2025-03-04T20:51:41.2018122Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:51:41.2018246Z getitem_28: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)] 2025-03-04T20:51:41.2018395Z getitem_29: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)] 2025-03-04T20:51:41.2018547Z heights_1: "f32[269952][1]cpu" = getitem_28 - getitem_29; getitem_28 = getitem_29 = None 2025-03-04T20:51:41.2018630Z 2025-03-04T20:51:41.2018938Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:51:41.2019067Z getitem_30: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T20:51:41.2019160Z mul_10: "f32[269952][1]cpu" = 0.5 * widths_1 2025-03-04T20:51:41.2019295Z ctr_x_1: "f32[269952][1]cpu" = getitem_30 + mul_10; getitem_30 = mul_10 = None 2025-03-04T20:51:41.2019354Z 2025-03-04T20:51:41.2019668Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:51:41.2019814Z getitem_31: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]; boxes_1 = None 2025-03-04T20:51:41.2019913Z mul_11: "f32[269952][1]cpu" = 0.5 * heights_1 2025-03-04T20:51:41.2020041Z ctr_y_1: "f32[269952][1]cpu" = getitem_31 + mul_11; getitem_31 = mul_11 = None 2025-03-04T20:51:41.2020106Z 2025-03-04T20:51:41.2020422Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:51:41.2020584Z getitem_32: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:51:41.2020695Z dx_1: "f32[269952, 1][1, 1]cpu" = getitem_32 / 1.0; getitem_32 = None 2025-03-04T20:51:41.2020761Z 2025-03-04T20:51:41.2021057Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:51:41.2021213Z getitem_33: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:51:41.2021324Z dy_1: "f32[269952, 1][1, 1]cpu" = getitem_33 / 1.0; getitem_33 = None 2025-03-04T20:51:41.2021394Z 2025-03-04T20:51:41.2021692Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:51:41.2021862Z getitem_34: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:51:41.2021971Z dw_2: "f32[269952, 1][1, 1]cpu" = getitem_34 / 1.0; getitem_34 = None 2025-03-04T20:51:41.2022039Z 2025-03-04T20:51:41.2022345Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:51:41.2022528Z getitem_35: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))]; deltas_1 = None 2025-03-04T20:51:41.2022645Z dh_2: "f32[269952, 1][1, 1]cpu" = getitem_35 / 1.0; getitem_35 = None 2025-03-04T20:51:41.2022704Z 2025-03-04T20:51:41.2023046Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:51:41.2023189Z dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356); dw_2 = None 2025-03-04T20:51:41.2023254Z 2025-03-04T20:51:41.2023583Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:51:41.2023723Z dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356); dh_2 = None 2025-03-04T20:51:41.2023783Z 2025-03-04T20:51:41.2024156Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:51:41.2024292Z getitem_36: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)] 2025-03-04T20:51:41.2024440Z mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_36; dx_1 = getitem_36 = None 2025-03-04T20:51:41.2024595Z getitem_37: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)]; ctr_x_1 = None 2025-03-04T20:51:41.2024742Z pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_37; mul_12 = getitem_37 = None 2025-03-04T20:51:41.2024804Z 2025-03-04T20:51:41.2025166Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:51:41.2025304Z getitem_38: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)] 2025-03-04T20:51:41.2025438Z mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_38; dy_1 = getitem_38 = None 2025-03-04T20:51:41.2025645Z getitem_39: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)]; ctr_y_1 = None 2025-03-04T20:51:41.2025804Z pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_39; mul_13 = getitem_39 = None 2025-03-04T20:51:41.2025868Z 2025-03-04T20:51:41.2026236Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:51:41.2026353Z exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3); dw_3 = None 2025-03-04T20:51:41.2026527Z getitem_40: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]; widths_1 = None 2025-03-04T20:51:41.2026665Z pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_40; exp_2 = getitem_40 = None 2025-03-04T20:51:41.2026748Z 2025-03-04T20:51:41.2027084Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:51:41.2027206Z exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3); dh_3 = None 2025-03-04T20:51:41.2027374Z getitem_41: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]; heights_1 = None 2025-03-04T20:51:41.2027533Z pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_41; exp_3 = getitem_41 = None 2025-03-04T20:51:41.2027594Z 2025-03-04T20:51:41.2027918Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:51:41.2028014Z mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1 2025-03-04T20:51:41.2028141Z x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16; mul_16 = None 2025-03-04T20:51:41.2028203Z 2025-03-04T20:51:41.2028521Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:51:41.2028616Z mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1 2025-03-04T20:51:41.2028739Z y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17; mul_17 = None 2025-03-04T20:51:41.2028801Z 2025-03-04T20:51:41.2029114Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:51:41.2029229Z mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1; pred_w_1 = None 2025-03-04T20:51:41.2029366Z x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18; pred_ctr_x_1 = mul_18 = None 2025-03-04T20:51:41.2029427Z 2025-03-04T20:51:41.2029740Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:51:41.2029884Z mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1; pred_h_1 = None 2025-03-04T20:51:41.2030016Z y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19; pred_ctr_y_1 = mul_19 = None 2025-03-04T20:51:41.2030102Z 2025-03-04T20:51:41.2030458Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:51:41.2030656Z pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1); x1_1 = y1_1 = x2_1 = y2_1 = None 2025-03-04T20:51:41.2030716Z 2025-03-04T20:51:41.2031060Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:51:41.2031219Z proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4)); pred_boxes_1 = None 2025-03-04T20:51:41.2031287Z 2025-03-04T20:51:41.2031671Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:51:41.2031847Z proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4); proposals_i_1 = None 2025-03-04T20:51:41.2031909Z 2025-03-04T20:51:41.2032336Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:51:41.2032541Z pred_anchor_deltas_i_5: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_4.reshape(-1, 4); pred_anchor_deltas_i_4 = None 2025-03-04T20:51:41.2032607Z 2025-03-04T20:51:41.2033044Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:51:41.2033198Z unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = tensor_2.unsqueeze(0); tensor_2 = None 2025-03-04T20:51:41.2033344Z expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1); unsqueeze_2 = None 2025-03-04T20:51:41.2033516Z anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4); expand_2 = None 2025-03-04T20:51:41.2033573Z 2025-03-04T20:51:41.2033940Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:51:41.2034102Z deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_5.float(); pred_anchor_deltas_i_5 = None 2025-03-04T20:51:41.2034169Z 2025-03-04T20:51:41.2034480Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:51:41.2034628Z boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32); anchors_i_2 = None 2025-03-04T20:51:41.2034688Z 2025-03-04T20:51:41.2035009Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:51:41.2035136Z getitem_42: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)] 2025-03-04T20:51:41.2035261Z getitem_43: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T20:51:41.2035403Z widths_2: "f32[67488][1]cpu" = getitem_42 - getitem_43; getitem_42 = getitem_43 = None 2025-03-04T20:51:41.2035469Z 2025-03-04T20:51:41.2035784Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:51:41.2035924Z getitem_44: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)] 2025-03-04T20:51:41.2036041Z getitem_45: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)] 2025-03-04T20:51:41.2036217Z heights_2: "f32[67488][1]cpu" = getitem_44 - getitem_45; getitem_44 = getitem_45 = None 2025-03-04T20:51:41.2036278Z 2025-03-04T20:51:41.2036585Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:51:41.2036706Z getitem_46: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T20:51:41.2036791Z mul_20: "f32[67488][1]cpu" = 0.5 * widths_2 2025-03-04T20:51:41.2036922Z ctr_x_2: "f32[67488][1]cpu" = getitem_46 + mul_20; getitem_46 = mul_20 = None 2025-03-04T20:51:41.2036981Z 2025-03-04T20:51:41.2037292Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:51:41.2037433Z getitem_47: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]; boxes_2 = None 2025-03-04T20:51:41.2037535Z mul_21: "f32[67488][1]cpu" = 0.5 * heights_2 2025-03-04T20:51:41.2037659Z ctr_y_2: "f32[67488][1]cpu" = getitem_47 + mul_21; getitem_47 = mul_21 = None 2025-03-04T20:51:41.2037727Z 2025-03-04T20:51:41.2038042Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:51:41.2038199Z getitem_48: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:51:41.2038307Z dx_2: "f32[67488, 1][1, 1]cpu" = getitem_48 / 1.0; getitem_48 = None 2025-03-04T20:51:41.2038376Z 2025-03-04T20:51:41.2038670Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:51:41.2038825Z getitem_49: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:51:41.2038933Z dy_2: "f32[67488, 1][1, 1]cpu" = getitem_49 / 1.0; getitem_49 = None 2025-03-04T20:51:41.2038999Z 2025-03-04T20:51:41.2039304Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:51:41.2039454Z getitem_50: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:51:41.2039558Z dw_4: "f32[67488, 1][1, 1]cpu" = getitem_50 / 1.0; getitem_50 = None 2025-03-04T20:51:41.2039623Z 2025-03-04T20:51:41.2039916Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:51:41.2040104Z getitem_51: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))]; deltas_2 = None 2025-03-04T20:51:41.2040207Z dh_4: "f32[67488, 1][1, 1]cpu" = getitem_51 / 1.0; getitem_51 = None 2025-03-04T20:51:41.2040281Z 2025-03-04T20:51:41.2040610Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:51:41.2040752Z dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356); dw_4 = None 2025-03-04T20:51:41.2040810Z 2025-03-04T20:51:41.2041150Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:51:41.2041282Z dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356); dh_4 = None 2025-03-04T20:51:41.2041350Z 2025-03-04T20:51:41.2041713Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:51:41.2041863Z getitem_52: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)] 2025-03-04T20:51:41.2041982Z mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_52; dx_2 = getitem_52 = None 2025-03-04T20:51:41.2042138Z getitem_53: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)]; ctr_x_2 = None 2025-03-04T20:51:41.2042278Z pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_53; mul_22 = getitem_53 = None 2025-03-04T20:51:41.2042336Z 2025-03-04T20:51:41.2042682Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:51:41.2042815Z getitem_54: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)] 2025-03-04T20:51:41.2042944Z mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_54; dy_2 = getitem_54 = None 2025-03-04T20:51:41.2043091Z getitem_55: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)]; ctr_y_2 = None 2025-03-04T20:51:41.2043229Z pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_55; mul_23 = getitem_55 = None 2025-03-04T20:51:41.2043290Z 2025-03-04T20:51:41.2043635Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:51:41.2043749Z exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5); dw_5 = None 2025-03-04T20:51:41.2043920Z getitem_56: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]; widths_2 = None 2025-03-04T20:51:41.2044046Z pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_56; exp_4 = getitem_56 = None 2025-03-04T20:51:41.2044112Z 2025-03-04T20:51:41.2044436Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:51:41.2044551Z exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5); dh_5 = None 2025-03-04T20:51:41.2044726Z getitem_57: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]; heights_2 = None 2025-03-04T20:51:41.2044857Z pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_57; exp_5 = getitem_57 = None 2025-03-04T20:51:41.2044915Z 2025-03-04T20:51:41.2045222Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:51:41.2045313Z mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2 2025-03-04T20:51:41.2045428Z x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26; mul_26 = None 2025-03-04T20:51:41.2045488Z 2025-03-04T20:51:41.2045800Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:51:41.2045888Z mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2 2025-03-04T20:51:41.2046005Z y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27; mul_27 = None 2025-03-04T20:51:41.2046064Z 2025-03-04T20:51:41.2046374Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:51:41.2046487Z mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2; pred_w_2 = None 2025-03-04T20:51:41.2046624Z x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28; pred_ctr_x_2 = mul_28 = None 2025-03-04T20:51:41.2046682Z 2025-03-04T20:51:41.2047002Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:51:41.2047112Z mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2; pred_h_2 = None 2025-03-04T20:51:41.2047261Z y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29; pred_ctr_y_2 = mul_29 = None 2025-03-04T20:51:41.2047319Z 2025-03-04T20:51:41.2047677Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:51:41.2047864Z pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1); x1_2 = y1_2 = x2_2 = y2_2 = None 2025-03-04T20:51:41.2047929Z 2025-03-04T20:51:41.2048264Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:51:41.2048431Z proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4)); pred_boxes_2 = None 2025-03-04T20:51:41.2048490Z 2025-03-04T20:51:41.2048887Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:51:41.2049073Z proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4); proposals_i_2 = None 2025-03-04T20:51:41.2049139Z 2025-03-04T20:51:41.2049532Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:51:41.2049743Z pred_anchor_deltas_i_7: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_6.reshape(-1, 4); pred_anchor_deltas_i_6 = None 2025-03-04T20:51:41.2049809Z 2025-03-04T20:51:41.2050243Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:51:41.2050396Z unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = tensor_3.unsqueeze(0); tensor_3 = None 2025-03-04T20:51:41.2050542Z expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1); unsqueeze_3 = None 2025-03-04T20:51:41.2050711Z anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4); expand_3 = None 2025-03-04T20:51:41.2050772Z 2025-03-04T20:51:41.2051143Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:51:41.2051307Z deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_7.float(); pred_anchor_deltas_i_7 = None 2025-03-04T20:51:41.2051373Z 2025-03-04T20:51:41.2051682Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:51:41.2051830Z boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32); anchors_i_3 = None 2025-03-04T20:51:41.2051889Z 2025-03-04T20:51:41.2052210Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:51:41.2052337Z getitem_58: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)] 2025-03-04T20:51:41.2052465Z getitem_59: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T20:51:41.2052610Z widths_3: "f32[16872][1]cpu" = getitem_58 - getitem_59; getitem_58 = getitem_59 = None 2025-03-04T20:51:41.2052676Z 2025-03-04T20:51:41.2053006Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:51:41.2053133Z getitem_60: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)] 2025-03-04T20:51:41.2053267Z getitem_61: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)] 2025-03-04T20:51:41.2053418Z heights_3: "f32[16872][1]cpu" = getitem_60 - getitem_61; getitem_60 = getitem_61 = None 2025-03-04T20:51:41.2053480Z 2025-03-04T20:51:41.2053798Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:51:41.2053915Z getitem_62: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T20:51:41.2054009Z mul_30: "f32[16872][1]cpu" = 0.5 * widths_3 2025-03-04T20:51:41.2054137Z ctr_x_3: "f32[16872][1]cpu" = getitem_62 + mul_30; getitem_62 = mul_30 = None 2025-03-04T20:51:41.2054203Z 2025-03-04T20:51:41.2054515Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:51:41.2054668Z getitem_63: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]; boxes_3 = None 2025-03-04T20:51:41.2054757Z mul_31: "f32[16872][1]cpu" = 0.5 * heights_3 2025-03-04T20:51:41.2054931Z ctr_y_3: "f32[16872][1]cpu" = getitem_63 + mul_31; getitem_63 = mul_31 = None 2025-03-04T20:51:41.2054992Z 2025-03-04T20:51:41.2055301Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:51:41.2055453Z getitem_64: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:51:41.2055571Z dx_3: "f32[16872, 1][1, 1]cpu" = getitem_64 / 1.0; getitem_64 = None 2025-03-04T20:51:41.2055630Z 2025-03-04T20:51:41.2055936Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:51:41.2056085Z getitem_65: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:51:41.2056203Z dy_3: "f32[16872, 1][1, 1]cpu" = getitem_65 / 1.0; getitem_65 = None 2025-03-04T20:51:41.2056279Z 2025-03-04T20:51:41.2056587Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:51:41.2056741Z getitem_66: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:51:41.2056848Z dw_6: "f32[16872, 1][1, 1]cpu" = getitem_66 / 1.0; getitem_66 = None 2025-03-04T20:51:41.2056915Z 2025-03-04T20:51:41.2057225Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:51:41.2057415Z getitem_67: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))]; deltas_3 = None 2025-03-04T20:51:41.2057522Z dh_6: "f32[16872, 1][1, 1]cpu" = getitem_67 / 1.0; getitem_67 = None 2025-03-04T20:51:41.2057588Z 2025-03-04T20:51:41.2057927Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:51:41.2058071Z dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356); dw_6 = None 2025-03-04T20:51:41.2058131Z 2025-03-04T20:51:41.2058472Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:51:41.2058621Z dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356); dh_6 = None 2025-03-04T20:51:41.2058688Z 2025-03-04T20:51:41.2059029Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:51:41.2059181Z getitem_68: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)] 2025-03-04T20:51:41.2059305Z mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_68; dx_3 = getitem_68 = None 2025-03-04T20:51:41.2059461Z getitem_69: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)]; ctr_x_3 = None 2025-03-04T20:51:41.2059597Z pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_69; mul_32 = getitem_69 = None 2025-03-04T20:51:41.2059663Z 2025-03-04T20:51:41.2060008Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:51:41.2060148Z getitem_70: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)] 2025-03-04T20:51:41.2060266Z mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_70; dy_3 = getitem_70 = None 2025-03-04T20:51:41.2060420Z getitem_71: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)]; ctr_y_3 = None 2025-03-04T20:51:41.2060570Z pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_71; mul_33 = getitem_71 = None 2025-03-04T20:51:41.2060639Z 2025-03-04T20:51:41.2060963Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:51:41.2061079Z exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7); dw_7 = None 2025-03-04T20:51:41.2061236Z getitem_72: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]; widths_3 = None 2025-03-04T20:51:41.2061373Z pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_72; exp_6 = getitem_72 = None 2025-03-04T20:51:41.2061432Z 2025-03-04T20:51:41.2061764Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:51:41.2061872Z exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7); dh_7 = None 2025-03-04T20:51:41.2062056Z getitem_73: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]; heights_3 = None 2025-03-04T20:51:41.2062183Z pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_73; exp_7 = getitem_73 = None 2025-03-04T20:51:41.2062251Z 2025-03-04T20:51:41.2062560Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:51:41.2062658Z mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3 2025-03-04T20:51:41.2062772Z x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36; mul_36 = None 2025-03-04T20:51:41.2062838Z 2025-03-04T20:51:41.2063144Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:51:41.2063241Z mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3 2025-03-04T20:51:41.2063363Z y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37; mul_37 = None 2025-03-04T20:51:41.2063422Z 2025-03-04T20:51:41.2063731Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:51:41.2063843Z mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3; pred_w_3 = None 2025-03-04T20:51:41.2063977Z x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38; pred_ctr_x_3 = mul_38 = None 2025-03-04T20:51:41.2064037Z 2025-03-04T20:51:41.2064364Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:51:41.2064491Z mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3; pred_h_3 = None 2025-03-04T20:51:41.2064625Z y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39; pred_ctr_y_3 = mul_39 = None 2025-03-04T20:51:41.2064685Z 2025-03-04T20:51:41.2065036Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:51:41.2065220Z pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1); x1_3 = y1_3 = x2_3 = y2_3 = None 2025-03-04T20:51:41.2065288Z 2025-03-04T20:51:41.2065687Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:51:41.2065863Z proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4)); pred_boxes_3 = None 2025-03-04T20:51:41.2065926Z 2025-03-04T20:51:41.2066327Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:51:41.2066523Z proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4); proposals_i_3 = None 2025-03-04T20:51:41.2066602Z 2025-03-04T20:51:41.2066994Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:51:41.2067202Z pred_anchor_deltas_i_9: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_8.reshape(-1, 4); pred_anchor_deltas_i_8 = None 2025-03-04T20:51:41.2067263Z 2025-03-04T20:51:41.2067701Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:51:41.2067849Z unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = tensor_4.unsqueeze(0); tensor_4 = None 2025-03-04T20:51:41.2068020Z expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1); unsqueeze_4 = None 2025-03-04T20:51:41.2068150Z anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4); expand_4 = None 2025-03-04T20:51:41.2068220Z 2025-03-04T20:51:41.2068586Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:51:41.2068757Z deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_9.float(); pred_anchor_deltas_i_9 = None 2025-03-04T20:51:41.2068817Z 2025-03-04T20:51:41.2069131Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:51:41.2069268Z boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32); anchors_i_4 = None 2025-03-04T20:51:41.2069336Z 2025-03-04T20:51:41.2069646Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:51:41.2069781Z getitem_74: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)] 2025-03-04T20:51:41.2069901Z getitem_75: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T20:51:41.2070050Z widths_4: "f32[4332][1]cpu" = getitem_74 - getitem_75; getitem_74 = getitem_75 = None 2025-03-04T20:51:41.2070120Z 2025-03-04T20:51:41.2070448Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:51:41.2070571Z getitem_76: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)] 2025-03-04T20:51:41.2070701Z getitem_77: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)] 2025-03-04T20:51:41.2070849Z heights_4: "f32[4332][1]cpu" = getitem_76 - getitem_77; getitem_76 = getitem_77 = None 2025-03-04T20:51:41.2070909Z 2025-03-04T20:51:41.2071217Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:51:41.2071332Z getitem_78: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T20:51:41.2071427Z mul_40: "f32[4332][1]cpu" = 0.5 * widths_4 2025-03-04T20:51:41.2071552Z ctr_x_4: "f32[4332][1]cpu" = getitem_78 + mul_40; getitem_78 = mul_40 = None 2025-03-04T20:51:41.2071621Z 2025-03-04T20:51:41.2071929Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:51:41.2072080Z getitem_79: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]; boxes_4 = None 2025-03-04T20:51:41.2072168Z mul_41: "f32[4332][1]cpu" = 0.5 * heights_4 2025-03-04T20:51:41.2072324Z ctr_y_4: "f32[4332][1]cpu" = getitem_79 + mul_41; getitem_79 = mul_41 = None 2025-03-04T20:51:41.2072385Z 2025-03-04T20:51:41.2072695Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:51:41.2072844Z getitem_80: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:51:41.2072957Z dx_4: "f32[4332, 1][1, 1]cpu" = getitem_80 / 1.0; getitem_80 = None 2025-03-04T20:51:41.2073018Z 2025-03-04T20:51:41.2073350Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:51:41.2073493Z getitem_81: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:51:41.2073621Z dy_4: "f32[4332, 1][1, 1]cpu" = getitem_81 / 1.0; getitem_81 = None 2025-03-04T20:51:41.2073680Z 2025-03-04T20:51:41.2073974Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:51:41.2074115Z getitem_82: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:51:41.2074227Z dw_8: "f32[4332, 1][1, 1]cpu" = getitem_82 / 1.0; getitem_82 = None 2025-03-04T20:51:41.2074285Z 2025-03-04T20:51:41.2074586Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:51:41.2074759Z getitem_83: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))]; deltas_4 = None 2025-03-04T20:51:41.2074869Z dh_8: "f32[4332, 1][1, 1]cpu" = getitem_83 / 1.0; getitem_83 = None 2025-03-04T20:51:41.2074927Z 2025-03-04T20:51:41.2075263Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:51:41.2075390Z dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356); dw_8 = None 2025-03-04T20:51:41.2075456Z 2025-03-04T20:51:41.2075776Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:51:41.2075925Z dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356); dh_8 = None 2025-03-04T20:51:41.2075983Z 2025-03-04T20:51:41.2076326Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:51:41.2076469Z getitem_84: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)] 2025-03-04T20:51:41.2076593Z mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_84; dx_4 = getitem_84 = None 2025-03-04T20:51:41.2076743Z getitem_85: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)]; ctr_x_4 = None 2025-03-04T20:51:41.2076873Z pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_85; mul_42 = getitem_85 = None 2025-03-04T20:51:41.2076938Z 2025-03-04T20:51:41.2077274Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:51:41.2077407Z getitem_86: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)] 2025-03-04T20:51:41.2077523Z mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_86; dy_4 = getitem_86 = None 2025-03-04T20:51:41.2077672Z getitem_87: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)]; ctr_y_4 = None 2025-03-04T20:51:41.2077816Z pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_87; mul_43 = getitem_87 = None 2025-03-04T20:51:41.2077881Z 2025-03-04T20:51:41.2078202Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:51:41.2078318Z exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9); dw_9 = None 2025-03-04T20:51:41.2078469Z getitem_88: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]; widths_4 = None 2025-03-04T20:51:41.2078600Z pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_88; exp_8 = getitem_88 = None 2025-03-04T20:51:41.2078660Z 2025-03-04T20:51:41.2078992Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:51:41.2079115Z exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9); dh_9 = None 2025-03-04T20:51:41.2079281Z getitem_89: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]; heights_4 = None 2025-03-04T20:51:41.2079405Z pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_89; exp_9 = getitem_89 = None 2025-03-04T20:51:41.2079472Z 2025-03-04T20:51:41.2079771Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:51:41.2079869Z mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4 2025-03-04T20:51:41.2079977Z x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46; mul_46 = None 2025-03-04T20:51:41.2080043Z 2025-03-04T20:51:41.2080341Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:51:41.2080439Z mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4 2025-03-04T20:51:41.2080546Z y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47; mul_47 = None 2025-03-04T20:51:41.2080611Z 2025-03-04T20:51:41.2080908Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:51:41.2081020Z mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4; pred_w_4 = None 2025-03-04T20:51:41.2081143Z x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48; pred_ctr_x_4 = mul_48 = None 2025-03-04T20:51:41.2081209Z 2025-03-04T20:51:41.2081517Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:51:41.2081646Z mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4; pred_h_4 = None 2025-03-04T20:51:41.2081767Z y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49; pred_ctr_y_4 = mul_49 = None 2025-03-04T20:51:41.2081835Z 2025-03-04T20:51:41.2082171Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:51:41.2082354Z pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1); x1_4 = y1_4 = x2_4 = y2_4 = None 2025-03-04T20:51:41.2082411Z 2025-03-04T20:51:41.2082741Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:51:41.2082894Z proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4)); pred_boxes_4 = None 2025-03-04T20:51:41.2082962Z 2025-03-04T20:51:41.2083330Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:51:41.2083514Z proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4); proposals_i_4 = None 2025-03-04T20:51:41.2083574Z 2025-03-04T20:51:41.2084046Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T20:51:41.2084179Z arange_10: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T20:51:41.2084238Z 2025-03-04T20:51:41.2084533Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:51:41.2084667Z batch_idx: "i64[4][1]cpu" = arange_10.to(device(type='cpu')); arange_10 = None 2025-03-04T20:51:41.2084735Z 2025-03-04T20:51:41.2085169Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:51:41.2085284Z topk = logits_i.topk(1000, dim = 1); logits_i = None 2025-03-04T20:51:41.2085381Z topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0] 2025-03-04T20:51:41.2085494Z topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1]; topk = None 2025-03-04T20:51:41.2085553Z 2025-03-04T20:51:41.2086007Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:51:41.2086135Z getitem_92: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T20:51:41.2086363Z topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_92, topk_idx)]; proposals_i_5 = getitem_92 = topk_idx = None 2025-03-04T20:51:41.2086424Z 2025-03-04T20:51:41.2086873Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:51:41.2087030Z full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:51:41.2087095Z 2025-03-04T20:51:41.2087394Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:51:41.2087519Z to_21: "i64[1000][1]cpu" = full.to(device(type='cpu')); full = None 2025-03-04T20:51:41.2087595Z 2025-03-04T20:51:41.2088040Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:51:41.2088150Z topk_1 = logits_i_1.topk(1000, dim = 1); logits_i_1 = None 2025-03-04T20:51:41.2088260Z topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0] 2025-03-04T20:51:41.2088371Z topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1]; topk_1 = None 2025-03-04T20:51:41.2088439Z 2025-03-04T20:51:41.2088885Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:51:41.2089018Z getitem_96: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T20:51:41.2089244Z topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_96, topk_idx_1)]; proposals_i_6 = getitem_96 = topk_idx_1 = None 2025-03-04T20:51:41.2089311Z 2025-03-04T20:51:41.2089770Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:51:41.2089937Z full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:51:41.2089998Z 2025-03-04T20:51:41.2090290Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:51:41.2090419Z to_22: "i64[1000][1]cpu" = full_1.to(device(type='cpu')); full_1 = None 2025-03-04T20:51:41.2090480Z 2025-03-04T20:51:41.2090914Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:51:41.2091040Z topk_2 = logits_i_2.topk(1000, dim = 1); logits_i_2 = None 2025-03-04T20:51:41.2091148Z topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0] 2025-03-04T20:51:41.2091257Z topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1]; topk_2 = None 2025-03-04T20:51:41.2091323Z 2025-03-04T20:51:41.2091759Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:51:41.2091893Z getitem_100: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T20:51:41.2092119Z topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_100, topk_idx_2)]; proposals_i_7 = getitem_100 = topk_idx_2 = None 2025-03-04T20:51:41.2092184Z 2025-03-04T20:51:41.2092629Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:51:41.2092794Z full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:51:41.2092852Z 2025-03-04T20:51:41.2093140Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:51:41.2093270Z to_23: "i64[1000][1]cpu" = full_2.to(device(type='cpu')); full_2 = None 2025-03-04T20:51:41.2093337Z 2025-03-04T20:51:41.2093755Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:51:41.2093892Z topk_3 = logits_i_3.topk(1000, dim = 1); logits_i_3 = None 2025-03-04T20:51:41.2093993Z topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0] 2025-03-04T20:51:41.2094107Z topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1]; topk_3 = None 2025-03-04T20:51:41.2094166Z 2025-03-04T20:51:41.2094612Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:51:41.2094736Z getitem_104: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T20:51:41.2094969Z topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_104, topk_idx_3)]; proposals_i_8 = getitem_104 = topk_idx_3 = None 2025-03-04T20:51:41.2095029Z 2025-03-04T20:51:41.2095490Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:51:41.2095646Z full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:51:41.2095714Z 2025-03-04T20:51:41.2095997Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:51:41.2096125Z to_24: "i64[1000][1]cpu" = full_3.to(device(type='cpu')); full_3 = None 2025-03-04T20:51:41.2096183Z 2025-03-04T20:51:41.2096606Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:51:41.2096722Z topk_4 = logits_i_4.topk(1000, dim = 1); logits_i_4 = None 2025-03-04T20:51:41.2096835Z topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0] 2025-03-04T20:51:41.2096951Z topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1]; topk_4 = None 2025-03-04T20:51:41.2097008Z 2025-03-04T20:51:41.2097455Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:51:41.2097610Z getitem_108: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T20:51:41.2097840Z topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_108, topk_idx_4)]; proposals_i_9 = getitem_108 = topk_idx_4 = None 2025-03-04T20:51:41.2097898Z 2025-03-04T20:51:41.2098343Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:51:41.2098497Z full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:51:41.2098574Z 2025-03-04T20:51:41.2098855Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:51:41.2098976Z to_25: "i64[1000][1]cpu" = full_4.to(device(type='cpu')); full_4 = None 2025-03-04T20:51:41.2099034Z 2025-03-04T20:51:41.2099326Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:51:41.2099692Z topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1); topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None 2025-03-04T20:51:41.2099786Z 2025-03-04T20:51:41.2100057Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:51:41.2100524Z topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1); topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None 2025-03-04T20:51:41.2100585Z 2025-03-04T20:51:41.2100870Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:51:41.2101068Z level_ids: "i64[5000][1]cpu" = torch.cat([to_21, to_22, to_23, to_24, to_25], 0); to_21 = to_22 = to_23 = to_24 = to_25 = level_ids = None 2025-03-04T20:51:41.2101136Z 2025-03-04T20:51:41.2101543Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T20:51:41.2101683Z getitem_110: "f32[5000, 4][4, 1]cpu" = topk_proposals[0]; topk_proposals = None 2025-03-04T20:51:41.2101749Z 2025-03-04T20:51:41.2102335Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:51:41.2102493Z tensor_5: "f32[5000, 4][4, 1]cpu" = getitem_110.to(torch.float32); getitem_110 = None 2025-03-04T20:51:41.2102554Z 2025-03-04T20:51:41.2102944Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T20:51:41.2103079Z scores_per_img: "f32[5000][1]cpu" = topk_scores[0]; topk_scores = None 2025-03-04T20:51:41.2103188Z 2025-03-04T20:51:41.2103695Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T20:51:41.2103840Z isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor_5); tensor_5 = None 2025-03-04T20:51:41.2103963Z all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:51:41.2104127Z isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T20:51:41.2104261Z valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T20:51:41.2104329Z 2025-03-04T20:51:41.2104720Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T20:51:41.2104857Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T20:51:41.2104917Z 2025-03-04T20:51:41.2105754Z 2025-03-04T20:51:41.2105856Z class GraphModule(torch.nn.Module): 2025-03-04T20:51:41.2226113Z def forward(self, L_stack0_tensor: "f32[4, 3, 1184, 1216][4319232, 1439744, 1216, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", 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L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", 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1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_: "f32[512, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_: "f32[2048, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_lateral_convs_0_parameters_weight_: "f32[256, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_0_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_0_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_0_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_1_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_1_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_1_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_2_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_2_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_2_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_3_parameters_weight_: "f32[256, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_3_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_3_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_3_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[3, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[3][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[12, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[12][1]cpu"): 2025-03-04T20:51:41.2227233Z l_stack0_tensor = L_stack0_tensor 2025-03-04T20:51:41.2227593Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.2227997Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2228409Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2228780Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2229158Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2229510Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.2229922Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2230321Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2230724Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2231096Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2231447Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.2231927Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2232393Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2232830Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2233203Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2233555Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.2233954Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2234363Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2234748Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2235136Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2235504Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:51:41.2235942Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2236362Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2236762Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2237156Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2237526Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.2237928Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2238334Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2238715Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2239095Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2239454Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.2239864Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2240269Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2240646Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2241027Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2241369Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.2241790Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2242201Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2242591Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2242968Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2243309Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.2243718Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2244134Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2244519Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2244888Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2245232Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.2245655Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2246044Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2246426Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2246794Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2247144Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.2247535Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2247948Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2248332Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2248722Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2249069Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.2249468Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2249867Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2250258Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2250635Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2250982Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.2251379Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2251780Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2252180Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2252553Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2252894Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.2253299Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2253703Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2254080Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2254468Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2254842Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:51:41.2255264Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2255681Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2256072Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2256465Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2256823Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.2257230Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2257626Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2258013Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2258405Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2258746Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.2259155Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2259553Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2259971Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2260403Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2260840Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.2261307Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2261799Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2262248Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2262683Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2263106Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.2263588Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2264067Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2264499Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2264919Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2265298Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.2265815Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2266259Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2266676Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2267081Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2267449Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.2267867Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2268346Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2268760Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2269172Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2269530Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.2269964Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2270387Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2270808Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2271212Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2271583Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.2272017Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2272461Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2272878Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2273285Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2273653Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.2274099Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2274515Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2274937Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2275353Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2275739Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.2276166Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2276580Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2276993Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2277360Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2277731Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.2278130Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2278528Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2278906Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2279298Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2279646Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.2280042Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2280444Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2280832Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2281214Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2281604Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:51:41.2282013Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2282449Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2282841Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2283237Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2283586Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.2284004Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2284407Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2284784Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2285162Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2285503Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.2285924Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2286324Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2286698Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2287074Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2287418Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.2287824Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2288228Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2288628Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2289003Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2289345Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.2289754Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2290152Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2290551Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2290919Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2291271Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.2291675Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2292084Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2292463Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2292830Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2293178Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.2293581Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2293983Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2294380Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2294747Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2295112Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.2295504Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2295905Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2296282Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2296674Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2297022Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.2297419Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2297821Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2298199Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2298590Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2298929Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.2299333Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2299737Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2300124Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2300507Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2300873Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.2301319Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2301725Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2302211Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2302610Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2302964Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.2303415Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2303819Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2304216Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2304592Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2304981Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.2305398Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2305855Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2306266Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2306680Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2307039Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.2307479Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2307902Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2308327Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2308722Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2309093Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.2309513Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2309941Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2310328Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2310723Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2311092Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.2311508Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2311935Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2312315Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2312709Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2313063Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.2313482Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2313901Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2314307Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2314700Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2315067Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.2315493Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2315918Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2316313Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2316728Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2317079Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.2317497Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2317889Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2318276Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2318667Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2319009Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.2319415Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2319812Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2320196Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2320567Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2320931Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.2321380Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2321774Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2322157Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2322527Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2322877Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.2323295Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2323695Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2324078Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2324445Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2324807Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.2325205Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2325603Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2325981Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2326357Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2326708Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.2327127Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2327529Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2327921Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2328294Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2328630Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.2329034Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2329449Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2329826Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2330201Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2330544Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.2330954Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2331364Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2331749Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2332127Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2332468Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.2332884Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2333278Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2333677Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2334062Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2334410Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.2334815Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2335208Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2335592Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2335982Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2336336Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.2336743Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2337150Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2337553Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2337928Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2338281Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.2338681Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2339097Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2339481Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2339875Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2340226Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.2340648Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2341053Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2341436Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2341818Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2342192Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.2342619Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2343041Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2343437Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2343834Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2344205Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.2344628Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2345042Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2345441Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2345984Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2346405Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.2346893Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2347367Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2347799Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2348219Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2348607Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.2349060Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2349548Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2349941Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2350337Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2350707Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.2351160Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2351568Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2351966Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2352354Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2352713Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.2353137Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2353570Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2353976Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2354382Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2354740Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.2355146Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2355566Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2355966Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2356357Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2356715Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.2357125Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2357540Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2357943Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2358342Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2358701Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.2359123Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2359540Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2359927Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2360327Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2360693Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.2361111Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2361517Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2361915Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2362303Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2362671Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.2363094Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2363493Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2363880Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2364268Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2364617Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.2365022Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2365415Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2365805Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2366173Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2366538Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.2366937Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2367353Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2367740Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2368111Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2368461Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.2368880Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2369299Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2369706Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2370126Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2370492Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.2370945Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2371372Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2371755Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2372139Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2372486Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.2372897Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2373329Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2373739Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2374118Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2374473Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.2374893Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2375304Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2375714Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2376105Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2376451Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.2376865Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2377290Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2377688Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2378079Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2378438Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.2378850Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2379256Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2379665Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2380051Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2380430Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.2380846Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2381253Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2381666Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2382085Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2382469Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.2382913Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2383354Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2383774Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2384189Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2384567Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.2385004Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2385460Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2385961Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2386415Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2386854Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.2387279Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2387695Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2388082Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2388481Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2388836Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.2389269Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2389679Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2390068Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2390450Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2390821Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.2391241Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2391652Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2392055Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2392448Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2392798Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.2393233Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2393640Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2394053Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2394434Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2394792Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.2395208Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2395629Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2396032Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2396418Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2396773Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.2397177Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2397595Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2397980Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2398347Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2398700Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.2399107Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2399508Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2399909Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2400299Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2400653Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.2401055Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2401470Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2401980Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2402409Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2402768Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.2403169Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2403572Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2403974Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2404351Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2404696Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.2405102Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2405507Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2405884Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2406282Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2406626Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.2407059Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2407460Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2407848Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2408226Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2408588Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.2408997Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2409395Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2409783Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2410158Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2410526Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.2410932Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2411329Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2411719Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2412094Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2412449Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.2412870Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2413282Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2413670Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2414040Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2414388Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.2414786Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2415203Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2415579Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2415954Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2416303Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.2416714Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2417114Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2417490Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2417865Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2418209Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.2418611Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2419026Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2419402Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2419793Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2420145Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:51:41.2420566Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2420973Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2421397Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2421786Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2422131Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.2422548Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2422960Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2423376Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2423768Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2424184Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.2424650Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2425119Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2425618Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2426085Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2426526Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.2427004Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2427462Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2427899Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2428306Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2428722Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.2429160Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2429616Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2430052Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2430474Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2430850Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.2431267Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2431678Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2432062Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2432457Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2432840Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.2433262Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.2433699Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.2434084Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.2434481Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.2434724Z l_self_modules_backbone_lateral_convs_0_parameters_weight_ = L_self_modules_backbone_lateral_convs_0_parameters_weight_ 2025-03-04T20:51:41.2434953Z l_self_modules_backbone_lateral_convs_0_parameters_bias_ = L_self_modules_backbone_lateral_convs_0_parameters_bias_ 2025-03-04T20:51:41.2435190Z l_self_modules_backbone_output_convs_0_parameters_weight_ = L_self_modules_backbone_output_convs_0_parameters_weight_ 2025-03-04T20:51:41.2435414Z l_self_modules_backbone_output_convs_0_parameters_bias_ = L_self_modules_backbone_output_convs_0_parameters_bias_ 2025-03-04T20:51:41.2435646Z l_self_modules_backbone_lateral_convs_1_parameters_weight_ = L_self_modules_backbone_lateral_convs_1_parameters_weight_ 2025-03-04T20:51:41.2435867Z l_self_modules_backbone_lateral_convs_1_parameters_bias_ = L_self_modules_backbone_lateral_convs_1_parameters_bias_ 2025-03-04T20:51:41.2436108Z l_self_modules_backbone_output_convs_1_parameters_weight_ = L_self_modules_backbone_output_convs_1_parameters_weight_ 2025-03-04T20:51:41.2436317Z l_self_modules_backbone_output_convs_1_parameters_bias_ = L_self_modules_backbone_output_convs_1_parameters_bias_ 2025-03-04T20:51:41.2436549Z l_self_modules_backbone_lateral_convs_2_parameters_weight_ = L_self_modules_backbone_lateral_convs_2_parameters_weight_ 2025-03-04T20:51:41.2436782Z l_self_modules_backbone_lateral_convs_2_parameters_bias_ = L_self_modules_backbone_lateral_convs_2_parameters_bias_ 2025-03-04T20:51:41.2437006Z l_self_modules_backbone_output_convs_2_parameters_weight_ = L_self_modules_backbone_output_convs_2_parameters_weight_ 2025-03-04T20:51:41.2437218Z l_self_modules_backbone_output_convs_2_parameters_bias_ = L_self_modules_backbone_output_convs_2_parameters_bias_ 2025-03-04T20:51:41.2437455Z l_self_modules_backbone_lateral_convs_3_parameters_weight_ = L_self_modules_backbone_lateral_convs_3_parameters_weight_ 2025-03-04T20:51:41.2437670Z l_self_modules_backbone_lateral_convs_3_parameters_bias_ = L_self_modules_backbone_lateral_convs_3_parameters_bias_ 2025-03-04T20:51:41.2437905Z l_self_modules_backbone_output_convs_3_parameters_weight_ = L_self_modules_backbone_output_convs_3_parameters_weight_ 2025-03-04T20:51:41.2438117Z l_self_modules_backbone_output_convs_3_parameters_bias_ = L_self_modules_backbone_output_convs_3_parameters_bias_ 2025-03-04T20:51:41.2438500Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T20:51:41.2438865Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ 2025-03-04T20:51:41.2439271Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ 2025-03-04T20:51:41.2439631Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ 2025-03-04T20:51:41.2439985Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ 2025-03-04T20:51:41.2440307Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T20:51:41.2440611Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T20:51:41.2440979Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T20:51:41.2441335Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T20:51:41.2441705Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T20:51:41.2442043Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T20:51:41.2442114Z 2025-03-04T20:51:41.2442394Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2442940Z x: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1); l_stack0_tensor = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.2443026Z 2025-03-04T20:51:41.2443304Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2445023Z x_1: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2445092Z 2025-03-04T20:51:41.2445376Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x) 2025-03-04T20:51:41.2445517Z x_2: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.relu_(x_1); x_1 = None 2025-03-04T20:51:41.2445577Z 2025-03-04T20:51:41.2445954Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1) 2025-03-04T20:51:41.2446184Z x_3: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1); x_2 = None 2025-03-04T20:51:41.2446265Z 2025-03-04T20:51:41.2446515Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2447000Z x_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.2447059Z 2025-03-04T20:51:41.2447330Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2449108Z x_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2449178Z 2025-03-04T20:51:41.2449467Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2449601Z out: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_5); x_5 = None 2025-03-04T20:51:41.2449687Z 2025-03-04T20:51:41.2449933Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2450418Z x_6: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.2450476Z 2025-03-04T20:51:41.2450743Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2452517Z x_7: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_6 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2452580Z 2025-03-04T20:51:41.2452870Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2453024Z out_1: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_7); x_7 = None 2025-03-04T20:51:41.2453092Z 2025-03-04T20:51:41.2453335Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2453834Z x_8: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_1 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.2453893Z 2025-03-04T20:51:41.2454161Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2455953Z x_9: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2456015Z 2025-03-04T20:51:41.2456271Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2456788Z x_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); x_3 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:51:41.2456847Z 2025-03-04T20:51:41.2457117Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2458939Z x_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_10 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2459009Z 2025-03-04T20:51:41.2459306Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.2459461Z x_9 += x_11; out_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_9; x_9 = x_11 = None 2025-03-04T20:51:41.2459527Z 2025-03-04T20:51:41.2459806Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2459960Z out_3: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_2); out_2 = None 2025-03-04T20:51:41.2460020Z 2025-03-04T20:51:41.2460274Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2460752Z x_12: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.2460819Z 2025-03-04T20:51:41.2461075Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2462851Z x_13: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_12 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2462935Z 2025-03-04T20:51:41.2463211Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2463356Z out_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_13); x_13 = None 2025-03-04T20:51:41.2463413Z 2025-03-04T20:51:41.2463662Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2464143Z x_14: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.2464218Z 2025-03-04T20:51:41.2464485Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2466448Z x_15: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_14 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2466545Z 2025-03-04T20:51:41.2466855Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2467002Z out_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_15); x_15 = None 2025-03-04T20:51:41.2467063Z 2025-03-04T20:51:41.2467323Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2467836Z x_16: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_5 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.2467907Z 2025-03-04T20:51:41.2468192Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2470051Z x_17: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_16 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2470138Z 2025-03-04T20:51:41.2470418Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.2470580Z x_17 += out_3; out_6: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_17; x_17 = out_3 = None 2025-03-04T20:51:41.2470640Z 2025-03-04T20:51:41.2470932Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2471079Z out_7: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_6); out_6 = None 2025-03-04T20:51:41.2471147Z 2025-03-04T20:51:41.2471399Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2471895Z x_18: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.2471962Z 2025-03-04T20:51:41.2472228Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2474068Z x_19: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_18 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2474152Z 2025-03-04T20:51:41.2474435Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2474579Z out_8: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_19); x_19 = None 2025-03-04T20:51:41.2474640Z 2025-03-04T20:51:41.2474943Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2475433Z x_20: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.2475500Z 2025-03-04T20:51:41.2475768Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2477579Z x_21: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_20 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2477665Z 2025-03-04T20:51:41.2477951Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2478092Z out_9: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_21); x_21 = None 2025-03-04T20:51:41.2478154Z 2025-03-04T20:51:41.2478408Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2478909Z x_22: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_9 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.2478992Z 2025-03-04T20:51:41.2479257Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2481078Z x_23: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_22 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2481146Z 2025-03-04T20:51:41.2481428Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.2481595Z x_23 += out_7; out_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_23; x_23 = out_7 = None 2025-03-04T20:51:41.2481655Z 2025-03-04T20:51:41.2481938Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2482085Z out_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_10); out_10 = None 2025-03-04T20:51:41.2482151Z 2025-03-04T20:51:41.2482394Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2482884Z x_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.2482959Z 2025-03-04T20:51:41.2483224Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2485024Z x_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2485085Z 2025-03-04T20:51:41.2485372Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2485509Z out_12: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_25); x_25 = None 2025-03-04T20:51:41.2485591Z 2025-03-04T20:51:41.2485834Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2486340Z x_26: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_12 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.2486407Z 2025-03-04T20:51:41.2486666Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2488441Z x_27: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_26 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2488510Z 2025-03-04T20:51:41.2488793Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2488937Z out_13: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_27); x_27 = None 2025-03-04T20:51:41.2488996Z 2025-03-04T20:51:41.2489249Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2489743Z x_28: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_13 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.2489828Z 2025-03-04T20:51:41.2490083Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2491841Z x_29: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_28 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2491909Z 2025-03-04T20:51:41.2492167Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2492664Z x_30: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:51:41.2492738Z 2025-03-04T20:51:41.2493003Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2494832Z x_31: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_30 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2494895Z 2025-03-04T20:51:41.2495174Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.2495317Z x_29 += x_31; out_14: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_29; x_29 = x_31 = None 2025-03-04T20:51:41.2495385Z 2025-03-04T20:51:41.2495663Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2495819Z out_15: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_14); out_14 = None 2025-03-04T20:51:41.2495895Z 2025-03-04T20:51:41.2496168Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2496686Z x_32: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.2496754Z 2025-03-04T20:51:41.2497031Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2498893Z x_33: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_32 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2498966Z 2025-03-04T20:51:41.2499272Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2499419Z out_16: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_33); x_33 = None 2025-03-04T20:51:41.2499481Z 2025-03-04T20:51:41.2499735Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2500241Z x_34: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_16 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.2500303Z 2025-03-04T20:51:41.2500574Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2502553Z x_35: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_34 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2502632Z 2025-03-04T20:51:41.2502930Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2503103Z out_17: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_35); x_35 = None 2025-03-04T20:51:41.2503173Z 2025-03-04T20:51:41.2503436Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2503974Z x_36: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_17 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.2504039Z 2025-03-04T20:51:41.2504321Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2506327Z x_37: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_36 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2506426Z 2025-03-04T20:51:41.2506731Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.2506884Z x_37 += out_15; out_18: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_37; x_37 = out_15 = None 2025-03-04T20:51:41.2506952Z 2025-03-04T20:51:41.2507233Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2507384Z out_19: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_18); out_18 = None 2025-03-04T20:51:41.2507446Z 2025-03-04T20:51:41.2507702Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2508207Z x_38: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.2508281Z 2025-03-04T20:51:41.2508544Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2510375Z x_39: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_38 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2510459Z 2025-03-04T20:51:41.2510739Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2510884Z out_20: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_39); x_39 = None 2025-03-04T20:51:41.2510944Z 2025-03-04T20:51:41.2511195Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2511689Z x_40: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_20 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.2511756Z 2025-03-04T20:51:41.2512014Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2513824Z x_41: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_40 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2513904Z 2025-03-04T20:51:41.2514181Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2514322Z out_21: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_41); x_41 = None 2025-03-04T20:51:41.2514381Z 2025-03-04T20:51:41.2514638Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2515149Z x_42: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_21 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.2515208Z 2025-03-04T20:51:41.2515469Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2517234Z x_43: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_42 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2517316Z 2025-03-04T20:51:41.2517596Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.2517746Z x_43 += out_19; out_22: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_43; x_43 = out_19 = None 2025-03-04T20:51:41.2517816Z 2025-03-04T20:51:41.2518099Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2518252Z out_23: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_22); out_22 = None 2025-03-04T20:51:41.2518312Z 2025-03-04T20:51:41.2518566Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2519059Z x_44: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.2519140Z 2025-03-04T20:51:41.2519400Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2521169Z x_45: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_44 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2521238Z 2025-03-04T20:51:41.2521529Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2521670Z out_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_45); x_45 = None 2025-03-04T20:51:41.2521730Z 2025-03-04T20:51:41.2521979Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2522457Z x_46: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.2522526Z 2025-03-04T20:51:41.2522805Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2524576Z x_47: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_46 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2524646Z 2025-03-04T20:51:41.2524924Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2525066Z out_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_47); x_47 = None 2025-03-04T20:51:41.2525123Z 2025-03-04T20:51:41.2525396Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2525878Z x_48: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_25 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.2525961Z 2025-03-04T20:51:41.2526227Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2528004Z x_49: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_48 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2528073Z 2025-03-04T20:51:41.2528344Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.2528495Z x_49 += out_23; out_26: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_49; x_49 = out_23 = None 2025-03-04T20:51:41.2528560Z 2025-03-04T20:51:41.2528836Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2528986Z out_27: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_26); out_26 = None 2025-03-04T20:51:41.2529045Z 2025-03-04T20:51:41.2529314Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2529787Z x_50: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.2529852Z 2025-03-04T20:51:41.2530112Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2531888Z x_51: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_50 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2531957Z 2025-03-04T20:51:41.2532231Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2532387Z out_28: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_51); x_51 = None 2025-03-04T20:51:41.2532447Z 2025-03-04T20:51:41.2532698Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2533169Z x_52: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_28 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.2533234Z 2025-03-04T20:51:41.2533492Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2535256Z x_53: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_52 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2535325Z 2025-03-04T20:51:41.2535600Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2535752Z out_29: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_53); x_53 = None 2025-03-04T20:51:41.2535810Z 2025-03-04T20:51:41.2536057Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2536535Z x_54: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_29 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.2536606Z 2025-03-04T20:51:41.2536863Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2538694Z x_55: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_54 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2538776Z 2025-03-04T20:51:41.2539025Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2539521Z x_56: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:51:41.2539581Z 2025-03-04T20:51:41.2539853Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2541726Z x_57: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_56 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2541792Z 2025-03-04T20:51:41.2542076Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.2542212Z x_55 += x_57; out_30: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_55; x_55 = x_57 = None 2025-03-04T20:51:41.2542294Z 2025-03-04T20:51:41.2542578Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2542726Z out_31: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_30); out_30 = None 2025-03-04T20:51:41.2542793Z 2025-03-04T20:51:41.2543042Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2543530Z x_58: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.2543591Z 2025-03-04T20:51:41.2543871Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2545845Z x_59: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_58 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2545941Z 2025-03-04T20:51:41.2546253Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2546391Z out_32: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_59); x_59 = None 2025-03-04T20:51:41.2546461Z 2025-03-04T20:51:41.2546720Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2547244Z x_60: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_32 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.2547304Z 2025-03-04T20:51:41.2547593Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2549395Z x_61: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_60 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2549488Z 2025-03-04T20:51:41.2549784Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2549915Z out_33: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_61); x_61 = None 2025-03-04T20:51:41.2549984Z 2025-03-04T20:51:41.2550235Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2550736Z x_62: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_33 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.2550800Z 2025-03-04T20:51:41.2551079Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2552916Z x_63: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_62 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2552993Z 2025-03-04T20:51:41.2553283Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.2553428Z x_63 += out_31; out_34: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_63; x_63 = out_31 = None 2025-03-04T20:51:41.2553497Z 2025-03-04T20:51:41.2553784Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2553935Z out_35: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_34); out_34 = None 2025-03-04T20:51:41.2553995Z 2025-03-04T20:51:41.2554271Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2554755Z x_64: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.2554823Z 2025-03-04T20:51:41.2555090Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2556871Z x_65: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_64 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2556953Z 2025-03-04T20:51:41.2557231Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2557366Z out_36: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_65); x_65 = None 2025-03-04T20:51:41.2557425Z 2025-03-04T20:51:41.2557676Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2558160Z x_66: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_36 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.2558218Z 2025-03-04T20:51:41.2558501Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2560256Z x_67: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_66 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2560343Z 2025-03-04T20:51:41.2560623Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2560761Z out_37: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_67); x_67 = None 2025-03-04T20:51:41.2560829Z 2025-03-04T20:51:41.2561071Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2561549Z x_68: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_37 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.2561608Z 2025-03-04T20:51:41.2561873Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2563614Z x_69: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_68 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2563699Z 2025-03-04T20:51:41.2563980Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.2564123Z x_69 += out_35; out_38: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_69; x_69 = out_35 = None 2025-03-04T20:51:41.2564189Z 2025-03-04T20:51:41.2564469Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2564614Z out_39: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_38); out_38 = None 2025-03-04T20:51:41.2564674Z 2025-03-04T20:51:41.2564943Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2565407Z x_70: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.2565491Z 2025-03-04T20:51:41.2565750Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2567526Z x_71: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_70 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2567597Z 2025-03-04T20:51:41.2567872Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2568007Z out_40: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_71); x_71 = None 2025-03-04T20:51:41.2568065Z 2025-03-04T20:51:41.2568319Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2568812Z x_72: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_40 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.2568894Z 2025-03-04T20:51:41.2569154Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2570914Z x_73: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_72 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2570980Z 2025-03-04T20:51:41.2571256Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2571403Z out_41: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_73); x_73 = None 2025-03-04T20:51:41.2571465Z 2025-03-04T20:51:41.2571715Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2572210Z x_74: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_41 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.2572269Z 2025-03-04T20:51:41.2572535Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2574309Z x_75: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_74 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2574379Z 2025-03-04T20:51:41.2574656Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.2574798Z x_75 += out_39; out_42: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_75; x_75 = out_39 = None 2025-03-04T20:51:41.2574865Z 2025-03-04T20:51:41.2575140Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2575298Z out_43: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_42); out_42 = None 2025-03-04T20:51:41.2575358Z 2025-03-04T20:51:41.2575608Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2576072Z x_76: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.2576137Z 2025-03-04T20:51:41.2576394Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2578178Z x_77: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_76 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2578285Z 2025-03-04T20:51:41.2578561Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2578695Z out_44: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_77); x_77 = None 2025-03-04T20:51:41.2578753Z 2025-03-04T20:51:41.2579000Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2579479Z x_78: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_44 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.2579546Z 2025-03-04T20:51:41.2579803Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2581576Z x_79: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_78 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2581659Z 2025-03-04T20:51:41.2581935Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2582069Z out_45: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_79); x_79 = None 2025-03-04T20:51:41.2582127Z 2025-03-04T20:51:41.2582378Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2582851Z x_80: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_45 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.2582919Z 2025-03-04T20:51:41.2583186Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2585006Z x_81: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_80 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2585094Z 2025-03-04T20:51:41.2585372Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.2585529Z x_81 += out_43; out_46: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_81; x_81 = out_43 = None 2025-03-04T20:51:41.2585652Z 2025-03-04T20:51:41.2585949Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2586090Z out_47: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_46); out_46 = None 2025-03-04T20:51:41.2586158Z 2025-03-04T20:51:41.2586417Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2586950Z x_82: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.2587021Z 2025-03-04T20:51:41.2587298Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2589142Z x_83: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_82 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2589228Z 2025-03-04T20:51:41.2589513Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2589654Z out_48: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_83); x_83 = None 2025-03-04T20:51:41.2589715Z 2025-03-04T20:51:41.2589972Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2590464Z x_84: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_48 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.2590530Z 2025-03-04T20:51:41.2590793Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2592646Z x_85: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_84 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2592729Z 2025-03-04T20:51:41.2593013Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2593152Z out_49: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_85); x_85 = None 2025-03-04T20:51:41.2593211Z 2025-03-04T20:51:41.2593484Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2593970Z x_86: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_49 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.2594038Z 2025-03-04T20:51:41.2594304Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2596115Z x_87: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_86 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2596200Z 2025-03-04T20:51:41.2596479Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.2596628Z x_87 += out_47; out_50: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_87; x_87 = out_47 = None 2025-03-04T20:51:41.2596688Z 2025-03-04T20:51:41.2596976Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2597112Z out_51: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_50); out_50 = None 2025-03-04T20:51:41.2597180Z 2025-03-04T20:51:41.2597425Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2597926Z x_88: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.2598008Z 2025-03-04T20:51:41.2598280Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2600111Z x_89: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_88 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2600177Z 2025-03-04T20:51:41.2600475Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2600604Z out_52: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_89); x_89 = None 2025-03-04T20:51:41.2600672Z 2025-03-04T20:51:41.2600924Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2601422Z x_90: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_52 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.2601504Z 2025-03-04T20:51:41.2601761Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2603674Z x_91: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_90 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2603749Z 2025-03-04T20:51:41.2604035Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2604172Z out_53: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_91); x_91 = None 2025-03-04T20:51:41.2604233Z 2025-03-04T20:51:41.2604518Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2605012Z x_92: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_53 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.2605105Z 2025-03-04T20:51:41.2605371Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2607196Z x_93: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_92 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2607267Z 2025-03-04T20:51:41.2607537Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.2607682Z x_93 += out_51; out_54: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_93; x_93 = out_51 = None 2025-03-04T20:51:41.2607743Z 2025-03-04T20:51:41.2608023Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2608159Z out_55: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_54); out_54 = None 2025-03-04T20:51:41.2608243Z 2025-03-04T20:51:41.2608487Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2608958Z x_94: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.2609016Z 2025-03-04T20:51:41.2609285Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2611049Z x_95: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_94 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2611113Z 2025-03-04T20:51:41.2611416Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2611544Z out_56: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_95); x_95 = None 2025-03-04T20:51:41.2611614Z 2025-03-04T20:51:41.2611860Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2612345Z x_96: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_56 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.2612403Z 2025-03-04T20:51:41.2612672Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2614465Z x_97: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_96 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2614528Z 2025-03-04T20:51:41.2614814Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2614957Z out_57: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_97); x_97 = None 2025-03-04T20:51:41.2615022Z 2025-03-04T20:51:41.2615264Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2615749Z x_98: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_57 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.2615813Z 2025-03-04T20:51:41.2616071Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2617868Z x_99: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_98 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2617951Z 2025-03-04T20:51:41.2618224Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.2618369Z x_99 += out_55; out_58: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_99; x_99 = out_55 = None 2025-03-04T20:51:41.2618427Z 2025-03-04T20:51:41.2618707Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2618841Z out_59: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_58); out_58 = None 2025-03-04T20:51:41.2618906Z 2025-03-04T20:51:41.2619148Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2619641Z x_100: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.2619701Z 2025-03-04T20:51:41.2619962Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2621716Z x_101: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_100 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2621798Z 2025-03-04T20:51:41.2622081Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2622215Z out_60: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_101); x_101 = None 2025-03-04T20:51:41.2622281Z 2025-03-04T20:51:41.2622525Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2623018Z x_102: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_60 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.2623079Z 2025-03-04T20:51:41.2623352Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2625194Z x_103: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_102 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2625272Z 2025-03-04T20:51:41.2625606Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2625747Z out_61: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_103); x_103 = None 2025-03-04T20:51:41.2625818Z 2025-03-04T20:51:41.2626068Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2626591Z x_104: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_61 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.2626652Z 2025-03-04T20:51:41.2626924Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2628763Z x_105: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_104 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2628842Z 2025-03-04T20:51:41.2629150Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.2629297Z x_105 += out_59; out_62: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_105; x_105 = out_59 = None 2025-03-04T20:51:41.2629362Z 2025-03-04T20:51:41.2629653Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2629800Z out_63: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_62); out_62 = None 2025-03-04T20:51:41.2629862Z 2025-03-04T20:51:41.2630126Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2630659Z x_106: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_63, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.2630719Z 2025-03-04T20:51:41.2631014Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2632821Z x_107: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_106 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2632890Z 2025-03-04T20:51:41.2633193Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2633326Z out_64: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_107); x_107 = None 2025-03-04T20:51:41.2633391Z 2025-03-04T20:51:41.2633642Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2634143Z x_108: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_64, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_64 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.2634203Z 2025-03-04T20:51:41.2634482Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2636424Z x_109: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_108, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_108 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2636528Z 2025-03-04T20:51:41.2636949Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2637161Z out_65: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_109); x_109 = None 2025-03-04T20:51:41.2637236Z 2025-03-04T20:51:41.2637495Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2638017Z x_110: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_65, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_65 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.2638104Z 2025-03-04T20:51:41.2638391Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2640247Z x_111: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_110, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_110 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2640312Z 2025-03-04T20:51:41.2640600Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.2640746Z x_111 += out_63; out_66: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_111; x_111 = out_63 = None 2025-03-04T20:51:41.2640815Z 2025-03-04T20:51:41.2641105Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2641252Z out_67: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_66); out_66 = None 2025-03-04T20:51:41.2641313Z 2025-03-04T20:51:41.2641584Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2642092Z x_112: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_67, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.2642163Z 2025-03-04T20:51:41.2642437Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2644215Z x_113: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_112, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_112 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2644299Z 2025-03-04T20:51:41.2644581Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2644733Z out_68: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_113); x_113 = None 2025-03-04T20:51:41.2644795Z 2025-03-04T20:51:41.2645048Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2645536Z x_114: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_68, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_68 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.2645598Z 2025-03-04T20:51:41.2645885Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2647753Z x_115: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_114, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_114 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2647827Z 2025-03-04T20:51:41.2648134Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2648276Z out_69: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_115); x_115 = None 2025-03-04T20:51:41.2648372Z 2025-03-04T20:51:41.2648639Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2649171Z x_116: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_69, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_69 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.2649236Z 2025-03-04T20:51:41.2649527Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2651303Z x_117: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_116, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_116 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2651387Z 2025-03-04T20:51:41.2651666Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.2651813Z x_117 += out_67; out_70: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_117; x_117 = out_67 = None 2025-03-04T20:51:41.2651880Z 2025-03-04T20:51:41.2652161Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2652306Z out_71: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_70); out_70 = None 2025-03-04T20:51:41.2652368Z 2025-03-04T20:51:41.2652626Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2653110Z x_118: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_71, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.2653179Z 2025-03-04T20:51:41.2653463Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2655286Z x_119: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_118, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_118 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2655371Z 2025-03-04T20:51:41.2655653Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2655794Z out_72: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_119); x_119 = None 2025-03-04T20:51:41.2655855Z 2025-03-04T20:51:41.2656109Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2656603Z x_120: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_72, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_72 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.2656671Z 2025-03-04T20:51:41.2656944Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2658765Z x_121: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_120, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_120 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2658850Z 2025-03-04T20:51:41.2659131Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2659271Z out_73: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_121); x_121 = None 2025-03-04T20:51:41.2659337Z 2025-03-04T20:51:41.2659582Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2660097Z x_122: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_73, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_73 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.2660158Z 2025-03-04T20:51:41.2660427Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2662263Z x_123: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_122, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_122 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2662349Z 2025-03-04T20:51:41.2662651Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.2662816Z x_123 += out_71; out_74: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_123; x_123 = out_71 = None 2025-03-04T20:51:41.2662884Z 2025-03-04T20:51:41.2663164Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2663309Z out_75: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_74); out_74 = None 2025-03-04T20:51:41.2663372Z 2025-03-04T20:51:41.2663639Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2664165Z x_124: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_75, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.2664237Z 2025-03-04T20:51:41.2664514Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2666498Z x_125: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_124, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_124 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2666576Z 2025-03-04T20:51:41.2666893Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2667035Z out_76: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_125); x_125 = None 2025-03-04T20:51:41.2667095Z 2025-03-04T20:51:41.2667350Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2667843Z x_126: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_76, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_76 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.2667912Z 2025-03-04T20:51:41.2668180Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2670011Z x_127: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_126, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_126 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2670083Z 2025-03-04T20:51:41.2670369Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2670508Z out_77: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_127); x_127 = None 2025-03-04T20:51:41.2670569Z 2025-03-04T20:51:41.2670828Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2671344Z x_128: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_77, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_77 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.2671433Z 2025-03-04T20:51:41.2671706Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2673526Z x_129: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_128, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_128 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2673596Z 2025-03-04T20:51:41.2673879Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.2674026Z x_129 += out_75; out_78: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_129; x_129 = out_75 = None 2025-03-04T20:51:41.2674094Z 2025-03-04T20:51:41.2674374Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2674518Z out_79: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_78); out_78 = None 2025-03-04T20:51:41.2674579Z 2025-03-04T20:51:41.2674831Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2675327Z x_130: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_79, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.2675393Z 2025-03-04T20:51:41.2675661Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2677470Z x_131: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_130, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_130 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2677539Z 2025-03-04T20:51:41.2677837Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2677989Z out_80: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_131); x_131 = None 2025-03-04T20:51:41.2678048Z 2025-03-04T20:51:41.2678304Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2678795Z x_132: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_80, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_80 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.2678860Z 2025-03-04T20:51:41.2679118Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2680886Z x_133: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_132, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_132 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2680955Z 2025-03-04T20:51:41.2681232Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2681369Z out_81: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_133); x_133 = None 2025-03-04T20:51:41.2681443Z 2025-03-04T20:51:41.2681691Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2682172Z x_134: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_81, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_81 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.2682239Z 2025-03-04T20:51:41.2682503Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2684334Z x_135: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_134, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_134 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2684417Z 2025-03-04T20:51:41.2684688Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.2684842Z x_135 += out_79; out_82: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_135; x_135 = out_79 = None 2025-03-04T20:51:41.2684902Z 2025-03-04T20:51:41.2685190Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2685328Z out_83: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_82); out_82 = None 2025-03-04T20:51:41.2685395Z 2025-03-04T20:51:41.2685644Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2686135Z x_136: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_83, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.2686216Z 2025-03-04T20:51:41.2686489Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2688260Z x_137: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_136, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_136 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2688340Z 2025-03-04T20:51:41.2688612Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2688746Z out_84: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_137); x_137 = None 2025-03-04T20:51:41.2688804Z 2025-03-04T20:51:41.2689050Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2689526Z x_138: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_84, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_84 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.2689593Z 2025-03-04T20:51:41.2689850Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2691620Z x_139: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_138, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_138 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2691700Z 2025-03-04T20:51:41.2691976Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2692114Z out_85: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_139); x_139 = None 2025-03-04T20:51:41.2692171Z 2025-03-04T20:51:41.2692418Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2692909Z x_140: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_85, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_85 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.2692978Z 2025-03-04T20:51:41.2693235Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2695041Z x_141: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_140, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_140 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2695124Z 2025-03-04T20:51:41.2695404Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.2695557Z x_141 += out_83; out_86: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_141; x_141 = out_83 = None 2025-03-04T20:51:41.2695619Z 2025-03-04T20:51:41.2695908Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2696051Z out_87: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_86); out_86 = None 2025-03-04T20:51:41.2696118Z 2025-03-04T20:51:41.2696365Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2696871Z x_142: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_87, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.2696933Z 2025-03-04T20:51:41.2697205Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2699035Z x_143: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_142, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_142 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2699098Z 2025-03-04T20:51:41.2699406Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2699540Z out_88: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_143); x_143 = None 2025-03-04T20:51:41.2699606Z 2025-03-04T20:51:41.2699855Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2700352Z x_144: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_88, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_88 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.2700420Z 2025-03-04T20:51:41.2700684Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2702691Z x_145: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_144, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_144 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2702776Z 2025-03-04T20:51:41.2703101Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2703256Z out_89: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_145); x_145 = None 2025-03-04T20:51:41.2703324Z 2025-03-04T20:51:41.2703612Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2704195Z x_146: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_89, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_89 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.2704298Z 2025-03-04T20:51:41.2704604Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2706703Z x_147: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_146, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_146 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2706798Z 2025-03-04T20:51:41.2707090Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.2707242Z x_147 += out_87; out_90: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_147; x_147 = out_87 = None 2025-03-04T20:51:41.2707303Z 2025-03-04T20:51:41.2707593Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2707730Z out_91: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_90); out_90 = None 2025-03-04T20:51:41.2707797Z 2025-03-04T20:51:41.2708046Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2708559Z x_148: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_91, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.2708621Z 2025-03-04T20:51:41.2708897Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2710715Z x_149: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_148, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_148 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2710777Z 2025-03-04T20:51:41.2711085Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2711232Z out_92: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_149); x_149 = None 2025-03-04T20:51:41.2711298Z 2025-03-04T20:51:41.2711550Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2712044Z x_150: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_92, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_92 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.2712114Z 2025-03-04T20:51:41.2712386Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2714253Z x_151: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_150, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_150 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2714320Z 2025-03-04T20:51:41.2714629Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2714773Z out_93: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_151); x_151 = None 2025-03-04T20:51:41.2714855Z 2025-03-04T20:51:41.2715127Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2715638Z x_152: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_93, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_93 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.2715708Z 2025-03-04T20:51:41.2715988Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2717924Z x_153: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_152, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_152 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2718010Z 2025-03-04T20:51:41.2718301Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.2718461Z x_153 += out_91; out_94: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_153; x_153 = out_91 = None 2025-03-04T20:51:41.2718523Z 2025-03-04T20:51:41.2718834Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2718977Z out_95: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_94); out_94 = None 2025-03-04T20:51:41.2719046Z 2025-03-04T20:51:41.2719317Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2719834Z x_154: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_95, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.2719900Z 2025-03-04T20:51:41.2720199Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2722103Z x_155: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_154, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_154 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2722184Z 2025-03-04T20:51:41.2722500Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2722639Z out_96: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_155); x_155 = None 2025-03-04T20:51:41.2722709Z 2025-03-04T20:51:41.2722985Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2723523Z x_156: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_96, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_96 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.2723588Z 2025-03-04T20:51:41.2723875Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2725812Z x_157: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_156, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_156 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2725908Z 2025-03-04T20:51:41.2726200Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2726332Z out_97: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_157); x_157 = None 2025-03-04T20:51:41.2726398Z 2025-03-04T20:51:41.2726645Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2727157Z x_158: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_97, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_97 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.2727227Z 2025-03-04T20:51:41.2727496Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2729392Z x_159: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_158, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_158 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2729482Z 2025-03-04T20:51:41.2729775Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.2729935Z x_159 += out_95; out_98: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_159; x_159 = out_95 = None 2025-03-04T20:51:41.2730000Z 2025-03-04T20:51:41.2730316Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2730457Z out_99: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_98); out_98 = None 2025-03-04T20:51:41.2730525Z 2025-03-04T20:51:41.2730776Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2731291Z x_160: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_99, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.2731354Z 2025-03-04T20:51:41.2731623Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2733454Z x_161: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_160, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_160 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2733528Z 2025-03-04T20:51:41.2733848Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2733998Z out_100: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_161); x_161 = None 2025-03-04T20:51:41.2734070Z 2025-03-04T20:51:41.2734332Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2734873Z x_162: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_100, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_100 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.2734939Z 2025-03-04T20:51:41.2735224Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2737179Z x_163: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_162, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_162 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2737247Z 2025-03-04T20:51:41.2737551Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2737696Z out_101: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_163); x_163 = None 2025-03-04T20:51:41.2737768Z 2025-03-04T20:51:41.2738030Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2738572Z x_164: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_101, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_101 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.2738650Z 2025-03-04T20:51:41.2738935Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2740875Z x_165: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_164, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_164 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2740944Z 2025-03-04T20:51:41.2741269Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.2741440Z x_165 += out_99; out_102: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_165; x_165 = out_99 = None 2025-03-04T20:51:41.2741515Z 2025-03-04T20:51:41.2741832Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2742001Z out_103: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_102); out_102 = None 2025-03-04T20:51:41.2742069Z 2025-03-04T20:51:41.2742357Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2742920Z x_166: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_103, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.2742987Z 2025-03-04T20:51:41.2743285Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2745283Z x_167: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_166, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_166 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2745361Z 2025-03-04T20:51:41.2745761Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2745972Z out_104: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_167); x_167 = None 2025-03-04T20:51:41.2746049Z 2025-03-04T20:51:41.2746334Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2746916Z x_168: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_104, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_104 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.2746986Z 2025-03-04T20:51:41.2747299Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2749208Z x_169: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_168, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_168 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2749285Z 2025-03-04T20:51:41.2749588Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2749729Z out_105: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_169); x_169 = None 2025-03-04T20:51:41.2749822Z 2025-03-04T20:51:41.2750085Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2750611Z x_170: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_105, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_105 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.2750674Z 2025-03-04T20:51:41.2750963Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2752910Z x_171: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_170, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_170 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2752991Z 2025-03-04T20:51:41.2753290Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.2753460Z x_171 += out_103; out_106: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_171; x_171 = out_103 = None 2025-03-04T20:51:41.2753531Z 2025-03-04T20:51:41.2753825Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2753988Z out_107: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_106); out_106 = None 2025-03-04T20:51:41.2754046Z 2025-03-04T20:51:41.2754303Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2754786Z x_172: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_107, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.2754854Z 2025-03-04T20:51:41.2755132Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2756960Z x_173: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_172, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_172 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2757045Z 2025-03-04T20:51:41.2757331Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2757475Z out_108: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_173); x_173 = None 2025-03-04T20:51:41.2757534Z 2025-03-04T20:51:41.2757791Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2758296Z x_174: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_108, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_108 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.2758360Z 2025-03-04T20:51:41.2758634Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2760475Z x_175: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_174, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_174 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2760561Z 2025-03-04T20:51:41.2760849Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2760984Z out_109: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_175); x_175 = None 2025-03-04T20:51:41.2761050Z 2025-03-04T20:51:41.2761308Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2761829Z x_176: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_109, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_109 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.2761892Z 2025-03-04T20:51:41.2762163Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2763966Z x_177: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_176, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_176 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2764051Z 2025-03-04T20:51:41.2764339Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.2764494Z x_177 += out_107; out_110: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_177; x_177 = out_107 = None 2025-03-04T20:51:41.2764562Z 2025-03-04T20:51:41.2764853Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2765002Z out_111: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_110); out_110 = None 2025-03-04T20:51:41.2765061Z 2025-03-04T20:51:41.2765318Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2765811Z x_178: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_111, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.2765879Z 2025-03-04T20:51:41.2766144Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2767973Z x_179: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_178, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_178 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2768043Z 2025-03-04T20:51:41.2768343Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2768487Z out_112: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_179); x_179 = None 2025-03-04T20:51:41.2768545Z 2025-03-04T20:51:41.2768800Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2769295Z x_180: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_112, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_112 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.2769363Z 2025-03-04T20:51:41.2769625Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2771450Z x_181: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_180, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_180 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2771518Z 2025-03-04T20:51:41.2771797Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2771932Z out_113: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_181); x_181 = None 2025-03-04T20:51:41.2771998Z 2025-03-04T20:51:41.2772239Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2772742Z x_182: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_113, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_113 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.2772819Z 2025-03-04T20:51:41.2773085Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2774910Z x_183: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_182, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_182 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2774983Z 2025-03-04T20:51:41.2775270Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.2775424Z x_183 += out_111; out_114: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_183; x_183 = out_111 = None 2025-03-04T20:51:41.2775492Z 2025-03-04T20:51:41.2775778Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2775926Z out_115: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_114); out_114 = None 2025-03-04T20:51:41.2775987Z 2025-03-04T20:51:41.2776243Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2776745Z x_184: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_115, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.2776812Z 2025-03-04T20:51:41.2777074Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2778891Z x_185: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_184, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_184 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2778958Z 2025-03-04T20:51:41.2779247Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2779397Z out_116: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_185); x_185 = None 2025-03-04T20:51:41.2779455Z 2025-03-04T20:51:41.2779710Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2780207Z x_186: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_116, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_116 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.2780277Z 2025-03-04T20:51:41.2780543Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2782397Z x_187: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_186, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_186 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2782468Z 2025-03-04T20:51:41.2782749Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2782890Z out_117: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_187); x_187 = None 2025-03-04T20:51:41.2782963Z 2025-03-04T20:51:41.2783221Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2783716Z x_188: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_117, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_117 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.2783783Z 2025-03-04T20:51:41.2784053Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2786030Z x_189: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_188, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_188 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2786125Z 2025-03-04T20:51:41.2786437Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.2786600Z x_189 += out_115; out_118: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_189; x_189 = out_115 = None 2025-03-04T20:51:41.2786670Z 2025-03-04T20:51:41.2786978Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2787133Z out_119: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_118); out_118 = None 2025-03-04T20:51:41.2787196Z 2025-03-04T20:51:41.2787477Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2787999Z x_190: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.2788072Z 2025-03-04T20:51:41.2788370Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2790304Z x_191: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_190, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_190 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2790424Z 2025-03-04T20:51:41.2790725Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2790874Z out_120: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_191); x_191 = None 2025-03-04T20:51:41.2790936Z 2025-03-04T20:51:41.2791209Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2791729Z x_192: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_120, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_120 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.2791803Z 2025-03-04T20:51:41.2792081Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2794035Z x_193: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_192, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_192 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2794123Z 2025-03-04T20:51:41.2794419Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2794564Z out_121: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_193); x_193 = None 2025-03-04T20:51:41.2794635Z 2025-03-04T20:51:41.2794881Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2795374Z x_194: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_121, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_121 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.2795449Z 2025-03-04T20:51:41.2795738Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2797648Z x_195: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_194, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_194 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2797736Z 2025-03-04T20:51:41.2797985Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2798489Z x_196: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:51:41.2798559Z 2025-03-04T20:51:41.2798834Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2800669Z x_197: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_196, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_196 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2800761Z 2025-03-04T20:51:41.2801036Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.2801193Z x_195 += x_197; out_122: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_195; x_195 = x_197 = None 2025-03-04T20:51:41.2801254Z 2025-03-04T20:51:41.2801544Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2801692Z out_123: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_122); out_122 = None 2025-03-04T20:51:41.2801758Z 2025-03-04T20:51:41.2802160Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2802656Z x_198: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_123, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.2802718Z 2025-03-04T20:51:41.2802982Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2804834Z x_199: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_198, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_198 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2804935Z 2025-03-04T20:51:41.2805243Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2805386Z out_124: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_199); x_199 = None 2025-03-04T20:51:41.2805457Z 2025-03-04T20:51:41.2805725Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2806252Z x_200: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_124, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_124 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.2806316Z 2025-03-04T20:51:41.2806623Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2808389Z x_201: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_200, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_200 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2808473Z 2025-03-04T20:51:41.2808758Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2808888Z out_125: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_201); x_201 = None 2025-03-04T20:51:41.2808968Z 2025-03-04T20:51:41.2809212Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2809696Z x_202: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_125, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_125 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.2809756Z 2025-03-04T20:51:41.2810022Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2811787Z x_203: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_202, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_202 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2811863Z 2025-03-04T20:51:41.2812142Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.2812293Z x_203 += out_123; out_126: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_203; x_203 = out_123 = None 2025-03-04T20:51:41.2812358Z 2025-03-04T20:51:41.2812632Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2812778Z out_127: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_126); out_126 = None 2025-03-04T20:51:41.2812836Z 2025-03-04T20:51:41.2813100Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2813571Z x_204: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_127, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.2813648Z 2025-03-04T20:51:41.2813909Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2815691Z x_205: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_204, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_204 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2815761Z 2025-03-04T20:51:41.2816049Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2816191Z out_128: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_205); x_205 = None 2025-03-04T20:51:41.2816256Z 2025-03-04T20:51:41.2816498Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2816983Z x_206: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_128, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_128 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.2817058Z 2025-03-04T20:51:41.2817324Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2819093Z x_207: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_206, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_206 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2819161Z 2025-03-04T20:51:41.2819446Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2819584Z out_129: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_207); x_207 = None 2025-03-04T20:51:41.2819650Z 2025-03-04T20:51:41.2819889Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2820392Z x_208: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_129, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_129 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.2820451Z 2025-03-04T20:51:41.2820718Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.2822540Z x_209: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_208, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_208 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.2822603Z 2025-03-04T20:51:41.2822885Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.2823039Z x_209 += out_127; out_130: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_209; x_209 = out_127 = None 2025-03-04T20:51:41.2823107Z 2025-03-04T20:51:41.2823385Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.2823552Z out_131: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_130); out_130 = None 2025-03-04T20:51:41.2823612Z 2025-03-04T20:51:41.2823875Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2824492Z x_210: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(out_131, l_self_modules_backbone_lateral_convs_0_parameters_weight_, l_self_modules_backbone_lateral_convs_0_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_131 = l_self_modules_backbone_lateral_convs_0_parameters_weight_ = l_self_modules_backbone_lateral_convs_0_parameters_bias_ = None 2025-03-04T20:51:41.2824563Z 2025-03-04T20:51:41.2824829Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2825429Z x_211: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_210, l_self_modules_backbone_output_convs_0_parameters_weight_, l_self_modules_backbone_output_convs_0_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_self_modules_backbone_output_convs_0_parameters_weight_ = l_self_modules_backbone_output_convs_0_parameters_bias_ = None 2025-03-04T20:51:41.2825499Z 2025-03-04T20:51:41.2825987Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest") 2025-03-04T20:51:41.2826319Z top_down_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.interpolate(x_210, scale_factor = 2.0, mode = 'nearest'); x_210 = None 2025-03-04T20:51:41.2826402Z 2025-03-04T20:51:41.2826687Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2827296Z x_212: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_lateral_convs_1_parameters_weight_, l_self_modules_backbone_lateral_convs_1_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_119 = l_self_modules_backbone_lateral_convs_1_parameters_weight_ = l_self_modules_backbone_lateral_convs_1_parameters_bias_ = None 2025-03-04T20:51:41.2827376Z 2025-03-04T20:51:41.2827757Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features 2025-03-04T20:51:41.2827963Z prev_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = x_212 + top_down_features; x_212 = top_down_features = None 2025-03-04T20:51:41.2828025Z 2025-03-04T20:51:41.2828300Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2828889Z x_213: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(prev_features, l_self_modules_backbone_output_convs_1_parameters_weight_, l_self_modules_backbone_output_convs_1_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_self_modules_backbone_output_convs_1_parameters_weight_ = l_self_modules_backbone_output_convs_1_parameters_bias_ = None 2025-03-04T20:51:41.2828957Z 2025-03-04T20:51:41.2829382Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest") 2025-03-04T20:51:41.2829704Z top_down_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.interpolate(prev_features, scale_factor = 2.0, mode = 'nearest'); prev_features = None 2025-03-04T20:51:41.2829771Z 2025-03-04T20:51:41.2830030Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2830642Z x_214: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_lateral_convs_2_parameters_weight_, l_self_modules_backbone_lateral_convs_2_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_27 = l_self_modules_backbone_lateral_convs_2_parameters_weight_ = l_self_modules_backbone_lateral_convs_2_parameters_bias_ = None 2025-03-04T20:51:41.2830705Z 2025-03-04T20:51:41.2831060Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features 2025-03-04T20:51:41.2831272Z prev_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = x_214 + top_down_features_1; x_214 = top_down_features_1 = None 2025-03-04T20:51:41.2831344Z 2025-03-04T20:51:41.2831599Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2832207Z x_215: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(prev_features_1, l_self_modules_backbone_output_convs_2_parameters_weight_, l_self_modules_backbone_output_convs_2_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_self_modules_backbone_output_convs_2_parameters_weight_ = l_self_modules_backbone_output_convs_2_parameters_bias_ = None 2025-03-04T20:51:41.2832279Z 2025-03-04T20:51:41.2832699Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest") 2025-03-04T20:51:41.2833035Z top_down_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.interpolate(prev_features_1, scale_factor = 2.0, mode = 'nearest'); prev_features_1 = None 2025-03-04T20:51:41.2833110Z 2025-03-04T20:51:41.2833365Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2833961Z x_216: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_lateral_convs_3_parameters_weight_, l_self_modules_backbone_lateral_convs_3_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_11 = l_self_modules_backbone_lateral_convs_3_parameters_weight_ = l_self_modules_backbone_lateral_convs_3_parameters_bias_ = None 2025-03-04T20:51:41.2834028Z 2025-03-04T20:51:41.2834372Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features 2025-03-04T20:51:41.2834585Z prev_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_216 + top_down_features_2; x_216 = top_down_features_2 = None 2025-03-04T20:51:41.2834645Z 2025-03-04T20:51:41.2834900Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2835536Z x_217: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(prev_features_2, l_self_modules_backbone_output_convs_3_parameters_weight_, l_self_modules_backbone_output_convs_3_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); prev_features_2 = l_self_modules_backbone_output_convs_3_parameters_weight_ = l_self_modules_backbone_output_convs_3_parameters_bias_ = None 2025-03-04T20:51:41.2835607Z 2025-03-04T20:51:41.2835969Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:200 in forward, code: return [F.max_pool2d(x, kernel_size=1, stride=2, padding=0)] 2025-03-04T20:51:41.2836184Z res: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.max_pool2d(x_211, kernel_size = 1, stride = 2, padding = 0) 2025-03-04T20:51:41.2836251Z 2025-03-04T20:51:41.2836692Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:51:41.2836868Z arange: "f32[304][1]cpu" = torch.arange(0.0, 1216, step = 4, dtype = torch.float32) 2025-03-04T20:51:41.2836929Z 2025-03-04T20:51:41.2837236Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:51:41.2837375Z shifts_x: "f32[304][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T20:51:41.2837444Z 2025-03-04T20:51:41.2837879Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:51:41.2838035Z arange_1: "f32[296][1]cpu" = torch.arange(0.0, 1184, step = 4, dtype = torch.float32) 2025-03-04T20:51:41.2838097Z 2025-03-04T20:51:41.2838396Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:51:41.2838532Z shifts_y: "f32[296][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T20:51:41.2838599Z 2025-03-04T20:51:41.2838978Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:51:41.2839177Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T20:51:41.2839282Z shift_y: "f32[296, 304][1, 0]cpu" = meshgrid[0] 2025-03-04T20:51:41.2839420Z shift_x: "f32[296, 304][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T20:51:41.2839480Z 2025-03-04T20:51:41.2839811Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:51:41.2839932Z shift_x_1: "f32[89984][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T20:51:41.2839997Z 2025-03-04T20:51:41.2840314Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:51:41.2840437Z shift_y_1: "f32[89984][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T20:51:41.2840496Z 2025-03-04T20:51:41.2840874Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:51:41.2841083Z shifts: "f32[89984, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T20:51:41.2841150Z 2025-03-04T20:51:41.2841574Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:51:41.2841706Z view: "f32[89984, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T20:51:41.2842124Z view_1: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T20:51:41.2842251Z add_3: "f32[89984, 3, 4][12, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T20:51:41.2842374Z x_218: "f32[269952, 4][4, 1]cpu" = add_3.reshape(-1, 4); add_3 = None 2025-03-04T20:51:41.2842434Z 2025-03-04T20:51:41.2842880Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:51:41.2843024Z arange_2: "f32[152][1]cpu" = torch.arange(0.0, 1216, step = 8, dtype = torch.float32) 2025-03-04T20:51:41.2843093Z 2025-03-04T20:51:41.2843381Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:51:41.2843524Z shifts_x_1: "f32[152][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T20:51:41.2843584Z 2025-03-04T20:51:41.2844016Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:51:41.2844158Z arange_3: "f32[148][1]cpu" = torch.arange(0.0, 1184, step = 8, dtype = torch.float32) 2025-03-04T20:51:41.2844225Z 2025-03-04T20:51:41.2844509Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:51:41.2844647Z shifts_y_1: "f32[148][1]cpu" = arange_3.to(device(type='cpu')); arange_3 = None 2025-03-04T20:51:41.2844706Z 2025-03-04T20:51:41.2845091Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:51:41.2845282Z meshgrid_1 = torch.functional.meshgrid(shifts_y_1, shifts_x_1); shifts_y_1 = shifts_x_1 = None 2025-03-04T20:51:41.2845404Z shift_y_2: "f32[148, 152][1, 0]cpu" = meshgrid_1[0] 2025-03-04T20:51:41.2845524Z shift_x_2: "f32[148, 152][0, 1]cpu" = meshgrid_1[1]; meshgrid_1 = None 2025-03-04T20:51:41.2845591Z 2025-03-04T20:51:41.2845908Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:51:41.2846037Z shift_x_3: "f32[22496][1]cpu" = shift_x_2.reshape(-1); shift_x_2 = None 2025-03-04T20:51:41.2846096Z 2025-03-04T20:51:41.2846416Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:51:41.2846538Z shift_y_3: "f32[22496][1]cpu" = shift_y_2.reshape(-1); shift_y_2 = None 2025-03-04T20:51:41.2846603Z 2025-03-04T20:51:41.2846969Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:51:41.2847209Z shifts_1: "f32[22496, 4][4, 1]cpu" = torch.stack((shift_x_3, shift_y_3, shift_x_3, shift_y_3), dim = 1); shift_x_3 = shift_y_3 = None 2025-03-04T20:51:41.2847270Z 2025-03-04T20:51:41.2847684Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:51:41.2847808Z view_2: "f32[22496, 1, 4][4, 4, 1]cpu" = shifts_1.view(-1, 1, 4); shifts_1 = None 2025-03-04T20:51:41.2848229Z view_3: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = None 2025-03-04T20:51:41.2848357Z add_4: "f32[22496, 3, 4][12, 4, 1]cpu" = view_2 + view_3; view_2 = view_3 = None 2025-03-04T20:51:41.2848466Z x_219: "f32[67488, 4][4, 1]cpu" = add_4.reshape(-1, 4); add_4 = None 2025-03-04T20:51:41.2848561Z 2025-03-04T20:51:41.2848981Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:51:41.2849129Z arange_4: "f32[76][1]cpu" = torch.arange(0.0, 1216, step = 16, dtype = torch.float32) 2025-03-04T20:51:41.2849188Z 2025-03-04T20:51:41.2849479Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:51:41.2849613Z shifts_x_2: "f32[76][1]cpu" = arange_4.to(device(type='cpu')); arange_4 = None 2025-03-04T20:51:41.2849680Z 2025-03-04T20:51:41.2850096Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:51:41.2850243Z arange_5: "f32[74][1]cpu" = torch.arange(0.0, 1184, step = 16, dtype = torch.float32) 2025-03-04T20:51:41.2850302Z 2025-03-04T20:51:41.2850592Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:51:41.2850722Z shifts_y_2: "f32[74][1]cpu" = arange_5.to(device(type='cpu')); arange_5 = None 2025-03-04T20:51:41.2850787Z 2025-03-04T20:51:41.2851165Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:51:41.2851360Z meshgrid_2 = torch.functional.meshgrid(shifts_y_2, shifts_x_2); shifts_y_2 = shifts_x_2 = None 2025-03-04T20:51:41.2851471Z shift_y_4: "f32[74, 76][1, 0]cpu" = meshgrid_2[0] 2025-03-04T20:51:41.2851593Z shift_x_4: "f32[74, 76][0, 1]cpu" = meshgrid_2[1]; meshgrid_2 = None 2025-03-04T20:51:41.2851653Z 2025-03-04T20:51:41.2851976Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:51:41.2852095Z shift_x_5: "f32[5624][1]cpu" = shift_x_4.reshape(-1); shift_x_4 = None 2025-03-04T20:51:41.2852159Z 2025-03-04T20:51:41.2852474Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:51:41.2852597Z shift_y_5: "f32[5624][1]cpu" = shift_y_4.reshape(-1); shift_y_4 = None 2025-03-04T20:51:41.2852656Z 2025-03-04T20:51:41.2853031Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:51:41.2853250Z shifts_2: "f32[5624, 4][4, 1]cpu" = torch.stack((shift_x_5, shift_y_5, shift_x_5, shift_y_5), dim = 1); shift_x_5 = shift_y_5 = None 2025-03-04T20:51:41.2853319Z 2025-03-04T20:51:41.2853726Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:51:41.2853856Z view_4: "f32[5624, 1, 4][4, 4, 1]cpu" = shifts_2.view(-1, 1, 4); shifts_2 = None 2025-03-04T20:51:41.2854265Z view_5: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = None 2025-03-04T20:51:41.2854392Z add_5: "f32[5624, 3, 4][12, 4, 1]cpu" = view_4 + view_5; view_4 = view_5 = None 2025-03-04T20:51:41.2854510Z x_220: "f32[16872, 4][4, 1]cpu" = add_5.reshape(-1, 4); add_5 = None 2025-03-04T20:51:41.2854584Z 2025-03-04T20:51:41.2855008Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:51:41.2855147Z arange_6: "f32[38][1]cpu" = torch.arange(0.0, 1216, step = 32, dtype = torch.float32) 2025-03-04T20:51:41.2855211Z 2025-03-04T20:51:41.2855498Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:51:41.2855635Z shifts_x_3: "f32[38][1]cpu" = arange_6.to(device(type='cpu')); arange_6 = None 2025-03-04T20:51:41.2855695Z 2025-03-04T20:51:41.2856119Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:51:41.2856258Z arange_7: "f32[37][1]cpu" = torch.arange(0.0, 1184, step = 32, dtype = torch.float32) 2025-03-04T20:51:41.2856325Z 2025-03-04T20:51:41.2856611Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:51:41.2856744Z shifts_y_3: "f32[37][1]cpu" = arange_7.to(device(type='cpu')); arange_7 = None 2025-03-04T20:51:41.2856803Z 2025-03-04T20:51:41.2857183Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:51:41.2857384Z meshgrid_3 = torch.functional.meshgrid(shifts_y_3, shifts_x_3); shifts_y_3 = shifts_x_3 = None 2025-03-04T20:51:41.2857485Z shift_y_6: "f32[37, 38][1, 0]cpu" = meshgrid_3[0] 2025-03-04T20:51:41.2857600Z shift_x_6: "f32[37, 38][0, 1]cpu" = meshgrid_3[1]; meshgrid_3 = None 2025-03-04T20:51:41.2857665Z 2025-03-04T20:51:41.2857984Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:51:41.2858113Z shift_x_7: "f32[1406][1]cpu" = shift_x_6.reshape(-1); shift_x_6 = None 2025-03-04T20:51:41.2858172Z 2025-03-04T20:51:41.2858496Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:51:41.2858613Z shift_y_7: "f32[1406][1]cpu" = shift_y_6.reshape(-1); shift_y_6 = None 2025-03-04T20:51:41.2858681Z 2025-03-04T20:51:41.2859058Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:51:41.2859299Z shifts_3: "f32[1406, 4][4, 1]cpu" = torch.stack((shift_x_7, shift_y_7, shift_x_7, shift_y_7), dim = 1); shift_x_7 = shift_y_7 = None 2025-03-04T20:51:41.2859360Z 2025-03-04T20:51:41.2859768Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:51:41.2859891Z view_6: "f32[1406, 1, 4][4, 4, 1]cpu" = shifts_3.view(-1, 1, 4); shifts_3 = None 2025-03-04T20:51:41.2860311Z view_7: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = None 2025-03-04T20:51:41.2860438Z add_6: "f32[1406, 3, 4][12, 4, 1]cpu" = view_6 + view_7; view_6 = view_7 = None 2025-03-04T20:51:41.2860568Z x_221: "f32[4218, 4][4, 1]cpu" = add_6.reshape(-1, 4); add_6 = None 2025-03-04T20:51:41.2860634Z 2025-03-04T20:51:41.2861057Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:51:41.2861204Z arange_8: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32) 2025-03-04T20:51:41.2861264Z 2025-03-04T20:51:41.2861562Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:51:41.2861694Z shifts_x_4: "f32[19][1]cpu" = arange_8.to(device(type='cpu')); arange_8 = None 2025-03-04T20:51:41.2861762Z 2025-03-04T20:51:41.2862189Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:51:41.2862334Z arange_9: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32) 2025-03-04T20:51:41.2862394Z 2025-03-04T20:51:41.2862688Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:51:41.2862817Z shifts_y_4: "f32[19][1]cpu" = arange_9.to(device(type='cpu')); arange_9 = None 2025-03-04T20:51:41.2862882Z 2025-03-04T20:51:41.2863273Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:51:41.2863491Z meshgrid_4 = torch.functional.meshgrid(shifts_y_4, shifts_x_4); shifts_y_4 = shifts_x_4 = None 2025-03-04T20:51:41.2863592Z shift_y_8: "f32[19, 19][1, 0]cpu" = meshgrid_4[0] 2025-03-04T20:51:41.2863720Z shift_x_8: "f32[19, 19][0, 1]cpu" = meshgrid_4[1]; meshgrid_4 = None 2025-03-04T20:51:41.2863785Z 2025-03-04T20:51:41.2864137Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:51:41.2864261Z shift_x_9: "f32[361][1]cpu" = shift_x_8.reshape(-1); shift_x_8 = None 2025-03-04T20:51:41.2864330Z 2025-03-04T20:51:41.2864671Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:51:41.2864803Z shift_y_9: "f32[361][1]cpu" = shift_y_8.reshape(-1); shift_y_8 = None 2025-03-04T20:51:41.2864868Z 2025-03-04T20:51:41.2865303Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:51:41.2865525Z shifts_4: "f32[361, 4][4, 1]cpu" = torch.stack((shift_x_9, shift_y_9, shift_x_9, shift_y_9), dim = 1); shift_x_9 = shift_y_9 = None 2025-03-04T20:51:41.2865674Z 2025-03-04T20:51:41.2866117Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:51:41.2866257Z view_8: "f32[361, 1, 4][4, 4, 1]cpu" = shifts_4.view(-1, 1, 4); shifts_4 = None 2025-03-04T20:51:41.2866701Z view_9: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = None 2025-03-04T20:51:41.2866835Z add_7: "f32[361, 3, 4][12, 4, 1]cpu" = view_8 + view_9; view_8 = view_9 = None 2025-03-04T20:51:41.2866990Z x_222: "f32[1083, 4][4, 1]cpu" = add_7.reshape(-1, 4); add_7 = None 2025-03-04T20:51:41.2867052Z 2025-03-04T20:51:41.2867359Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:51:41.2867487Z tensor: "f32[269952, 4][4, 1]cpu" = x_218.to(torch.float32); x_218 = None 2025-03-04T20:51:41.2867620Z tensor_1: "f32[67488, 4][4, 1]cpu" = x_219.to(torch.float32); x_219 = None 2025-03-04T20:51:41.2867743Z tensor_2: "f32[16872, 4][4, 1]cpu" = x_220.to(torch.float32); x_220 = None 2025-03-04T20:51:41.2867867Z tensor_3: "f32[4218, 4][4, 1]cpu" = x_221.to(torch.float32); x_221 = None 2025-03-04T20:51:41.2867982Z tensor_4: "f32[1083, 4][4, 1]cpu" = x_222.to(torch.float32); x_222 = None 2025-03-04T20:51:41.2887743Z 2025-03-04T20:51:41.2888126Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2888645Z x_223: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_217, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_217 = None 2025-03-04T20:51:41.2888717Z 2025-03-04T20:51:41.2889015Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:51:41.2889303Z x_224: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.relu(x_223, inplace = False); x_223 = None 2025-03-04T20:51:41.2889406Z 2025-03-04T20:51:41.2889790Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:51:41.2890304Z score: "f32[4, 3, 296, 304][269952, 89984, 304, 1]cpu" = torch.conv2d(x_224, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T20:51:41.2890367Z 2025-03-04T20:51:41.2890720Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:51:41.2891239Z x_233: "f32[4, 12, 296, 304][1079808, 89984, 304, 1]cpu" = torch.conv2d(x_224, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_224 = None 2025-03-04T20:51:41.2891300Z 2025-03-04T20:51:41.2891599Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2892076Z x_225: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(x_215, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_215 = None 2025-03-04T20:51:41.2892136Z 2025-03-04T20:51:41.2892408Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:51:41.2892599Z x_226: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.relu(x_225, inplace = False); x_225 = None 2025-03-04T20:51:41.2892658Z 2025-03-04T20:51:41.2893033Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:51:41.2893564Z score_1: "f32[4, 3, 148, 152][67488, 22496, 152, 1]cpu" = torch.conv2d(x_226, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T20:51:41.2893622Z 2025-03-04T20:51:41.2893975Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:51:41.2894480Z x_234: "f32[4, 12, 148, 152][269952, 22496, 152, 1]cpu" = torch.conv2d(x_226, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_226 = None 2025-03-04T20:51:41.2894546Z 2025-03-04T20:51:41.2894791Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2895255Z x_227: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(x_213, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_213 = None 2025-03-04T20:51:41.2895313Z 2025-03-04T20:51:41.2895601Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:51:41.2895778Z x_228: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.relu(x_227, inplace = False); x_227 = None 2025-03-04T20:51:41.2895856Z 2025-03-04T20:51:41.2896223Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:51:41.2896722Z score_2: "f32[4, 3, 74, 76][16872, 5624, 76, 1]cpu" = torch.conv2d(x_228, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T20:51:41.2896783Z 2025-03-04T20:51:41.2897131Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:51:41.2897631Z x_235: "f32[4, 12, 74, 76][67488, 5624, 76, 1]cpu" = torch.conv2d(x_228, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_228 = None 2025-03-04T20:51:41.2897690Z 2025-03-04T20:51:41.2897953Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2898405Z x_229: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_211, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_211 = None 2025-03-04T20:51:41.2898467Z 2025-03-04T20:51:41.2898732Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:51:41.2898910Z x_230: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.nn.functional.relu(x_229, inplace = False); x_229 = None 2025-03-04T20:51:41.2898968Z 2025-03-04T20:51:41.2899340Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:51:41.2899838Z score_3: "f32[4, 3, 37, 38][4218, 1406, 38, 1]cpu" = torch.conv2d(x_230, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T20:51:41.2899898Z 2025-03-04T20:51:41.2900242Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:51:41.2900743Z x_236: "f32[4, 12, 37, 38][16872, 1406, 38, 1]cpu" = torch.conv2d(x_230, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_230 = None 2025-03-04T20:51:41.2900813Z 2025-03-04T20:51:41.2901067Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.2901854Z x_231: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.conv2d(res, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); res = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T20:51:41.2902130Z 2025-03-04T20:51:41.2902423Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:51:41.2902654Z x_232: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.relu(x_231, inplace = False); x_231 = None 2025-03-04T20:51:41.2902727Z 2025-03-04T20:51:41.2903102Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:51:41.2903990Z score_4: "f32[4, 3, 19, 19][1083, 361, 19, 1]cpu" = torch.conv2d(x_232, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T20:51:41.2904062Z 2025-03-04T20:51:41.2904426Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:51:41.2905314Z x_237: "f32[4, 12, 19, 19][4332, 361, 19, 1]cpu" = torch.conv2d(x_232, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_232 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T20:51:41.2905383Z 2025-03-04T20:51:41.2905812Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T20:51:41.2905991Z permute: "f32[4, 296, 304, 3][269952, 304, 1, 89984]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T20:51:41.2906181Z logits_i: "f32[4, 269952][269952, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T20:51:41.2906350Z permute_1: "f32[4, 148, 152, 3][67488, 152, 1, 22496]cpu" = score_1.permute(0, 2, 3, 1); score_1 = None 2025-03-04T20:51:41.2906509Z logits_i_1: "f32[4, 67488][67488, 1]cpu" = permute_1.flatten(1); permute_1 = None 2025-03-04T20:51:41.2906669Z permute_2: "f32[4, 74, 76, 3][16872, 76, 1, 5624]cpu" = score_2.permute(0, 2, 3, 1); score_2 = None 2025-03-04T20:51:41.2906830Z logits_i_2: "f32[4, 16872][16872, 1]cpu" = permute_2.flatten(1); permute_2 = None 2025-03-04T20:51:41.2906975Z permute_3: "f32[4, 37, 38, 3][4218, 38, 1, 1406]cpu" = score_3.permute(0, 2, 3, 1); score_3 = None 2025-03-04T20:51:41.2907115Z logits_i_3: "f32[4, 4218][4218, 1]cpu" = permute_3.flatten(1); permute_3 = None 2025-03-04T20:51:41.2907265Z permute_4: "f32[4, 19, 19, 3][1083, 19, 1, 361]cpu" = score_4.permute(0, 2, 3, 1); score_4 = None 2025-03-04T20:51:41.2907394Z logits_i_4: "f32[4, 1083][1083, 1]cpu" = permute_4.flatten(1); permute_4 = None 2025-03-04T20:51:41.2907464Z 2025-03-04T20:51:41.2907893Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T20:51:41.2908079Z view_10: "f32[4, 3, 4, 296, 304][1079808, 359936, 89984, 304, 1]cpu" = x_233.view(4, -1, 4, 296, 304); x_233 = None 2025-03-04T20:51:41.2908262Z permute_5: "f32[4, 296, 304, 3, 4][1079808, 304, 1, 359936, 89984]cpu" = view_10.permute(0, 3, 4, 1, 2); view_10 = None 2025-03-04T20:51:41.2908469Z pred_anchor_deltas_i: "f32[4, 269952, 4][1079808, 4, 1]cpu" = permute_5.flatten(1, -2); permute_5 = None 2025-03-04T20:51:41.2908649Z view_11: "f32[4, 3, 4, 148, 152][269952, 89984, 22496, 152, 1]cpu" = x_234.view(4, -1, 4, 148, 152); x_234 = None 2025-03-04T20:51:41.2908830Z permute_6: "f32[4, 148, 152, 3, 4][269952, 152, 1, 89984, 22496]cpu" = view_11.permute(0, 3, 4, 1, 2); view_11 = None 2025-03-04T20:51:41.2909004Z pred_anchor_deltas_i_2: "f32[4, 67488, 4][269952, 4, 1]cpu" = permute_6.flatten(1, -2); permute_6 = None 2025-03-04T20:51:41.2909158Z view_12: "f32[4, 3, 4, 74, 76][67488, 22496, 5624, 76, 1]cpu" = x_235.view(4, -1, 4, 74, 76); x_235 = None 2025-03-04T20:51:41.2909321Z permute_7: "f32[4, 74, 76, 3, 4][67488, 76, 1, 22496, 5624]cpu" = view_12.permute(0, 3, 4, 1, 2); view_12 = None 2025-03-04T20:51:41.2909494Z pred_anchor_deltas_i_4: "f32[4, 16872, 4][67488, 4, 1]cpu" = permute_7.flatten(1, -2); permute_7 = None 2025-03-04T20:51:41.2909634Z view_13: "f32[4, 3, 4, 37, 38][16872, 5624, 1406, 38, 1]cpu" = x_236.view(4, -1, 4, 37, 38); x_236 = None 2025-03-04T20:51:41.2909799Z permute_8: "f32[4, 37, 38, 3, 4][16872, 38, 1, 5624, 1406]cpu" = view_13.permute(0, 3, 4, 1, 2); view_13 = None 2025-03-04T20:51:41.2909962Z pred_anchor_deltas_i_6: "f32[4, 4218, 4][16872, 4, 1]cpu" = permute_8.flatten(1, -2); permute_8 = None 2025-03-04T20:51:41.2910144Z view_14: "f32[4, 3, 4, 19, 19][4332, 1444, 361, 19, 1]cpu" = x_237.view(4, -1, 4, 19, 19); x_237 = None 2025-03-04T20:51:41.2910301Z permute_9: "f32[4, 19, 19, 3, 4][4332, 19, 1, 1444, 361]cpu" = view_14.permute(0, 3, 4, 1, 2); view_14 = None 2025-03-04T20:51:41.2910470Z pred_anchor_deltas_i_8: "f32[4, 1083, 4][4332, 4, 1]cpu" = permute_9.flatten(1, -2); permute_9 = None 2025-03-04T20:51:41.2910532Z 2025-03-04T20:51:41.2910963Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:51:41.2911169Z pred_anchor_deltas_i_1: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T20:51:41.2911237Z 2025-03-04T20:51:41.2911719Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:51:41.2911877Z unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T20:51:41.2912029Z expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T20:51:41.2912170Z anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T20:51:41.2912238Z 2025-03-04T20:51:41.2912633Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:51:41.2912812Z deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T20:51:41.2912873Z 2025-03-04T20:51:41.2913204Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:51:41.2913346Z boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T20:51:41.2913414Z 2025-03-04T20:51:41.2913741Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:51:41.2913881Z getitem_10: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:51:41.2914023Z getitem_11: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:51:41.2914182Z widths: "f32[1079808][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T20:51:41.2914268Z 2025-03-04T20:51:41.2914601Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:51:41.2914727Z getitem_12: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:51:41.2914855Z getitem_13: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:51:41.2915011Z heights: "f32[1079808][1]cpu" = getitem_12 - getitem_13; getitem_12 = getitem_13 = None 2025-03-04T20:51:41.2915079Z 2025-03-04T20:51:41.2915400Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:51:41.2915530Z getitem_14: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:51:41.2915617Z mul: "f32[1079808][1]cpu" = 0.5 * widths 2025-03-04T20:51:41.2915752Z ctr_x: "f32[1079808][1]cpu" = getitem_14 + mul; getitem_14 = mul = None 2025-03-04T20:51:41.2915812Z 2025-03-04T20:51:41.2916162Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:51:41.2916311Z getitem_15: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:51:41.2916404Z mul_1: "f32[1079808][1]cpu" = 0.5 * heights 2025-03-04T20:51:41.2916533Z ctr_y: "f32[1079808][1]cpu" = getitem_15 + mul_1; getitem_15 = mul_1 = None 2025-03-04T20:51:41.2916597Z 2025-03-04T20:51:41.2916944Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:51:41.2917107Z getitem_16: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:51:41.2917222Z dx: "f32[1079808, 1][1, 1]cpu" = getitem_16 / 1.0; getitem_16 = None 2025-03-04T20:51:41.2917288Z 2025-03-04T20:51:41.2917612Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:51:41.2917782Z getitem_17: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:51:41.2917887Z dy: "f32[1079808, 1][1, 1]cpu" = getitem_17 / 1.0; getitem_17 = None 2025-03-04T20:51:41.2917949Z 2025-03-04T20:51:41.2918235Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:51:41.2918387Z getitem_18: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:51:41.2918497Z dw: "f32[1079808, 1][1, 1]cpu" = getitem_18 / 1.0; getitem_18 = None 2025-03-04T20:51:41.2918556Z 2025-03-04T20:51:41.2918854Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:51:41.2919033Z getitem_19: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:51:41.2919141Z dh: "f32[1079808, 1][1, 1]cpu" = getitem_19 / 1.0; getitem_19 = None 2025-03-04T20:51:41.2919198Z 2025-03-04T20:51:41.2919530Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:51:41.2919666Z dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:51:41.2919746Z 2025-03-04T20:51:41.2920068Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:51:41.2920219Z dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:51:41.2920278Z 2025-03-04T20:51:41.2920619Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:51:41.2920749Z getitem_20: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:51:41.2920869Z mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_20; dx = getitem_20 = None 2025-03-04T20:51:41.2921012Z getitem_21: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:51:41.2921151Z pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_21; mul_2 = getitem_21 = None 2025-03-04T20:51:41.2921209Z 2025-03-04T20:51:41.2921550Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:51:41.2921696Z getitem_22: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:51:41.2921816Z mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_22; dy = getitem_22 = None 2025-03-04T20:51:41.2921958Z getitem_23: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:51:41.2922097Z pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_23; mul_3 = getitem_23 = None 2025-03-04T20:51:41.2922157Z 2025-03-04T20:51:41.2922486Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:51:41.2922600Z exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:51:41.2922763Z getitem_24: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:51:41.2922889Z pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_24; exp = getitem_24 = None 2025-03-04T20:51:41.2922969Z 2025-03-04T20:51:41.2923291Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:51:41.2923409Z exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:51:41.2923569Z getitem_25: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:51:41.2923705Z pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_25; exp_1 = getitem_25 = None 2025-03-04T20:51:41.2923765Z 2025-03-04T20:51:41.2924076Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:51:41.2924173Z mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T20:51:41.2924295Z x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:51:41.2924358Z 2025-03-04T20:51:41.2924666Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:51:41.2924754Z mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T20:51:41.2924869Z y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:51:41.2924928Z 2025-03-04T20:51:41.2925247Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:51:41.2925358Z mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:51:41.2925507Z x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:51:41.2925566Z 2025-03-04T20:51:41.2925870Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:51:41.2925977Z mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:51:41.2926106Z y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:51:41.2926165Z 2025-03-04T20:51:41.2926509Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:51:41.2926691Z pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:51:41.2926752Z 2025-03-04T20:51:41.2927083Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:51:41.2927242Z proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4)); pred_boxes = None 2025-03-04T20:51:41.2927322Z 2025-03-04T20:51:41.2927697Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:51:41.2927878Z proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T20:51:41.2927937Z 2025-03-04T20:51:41.2928335Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:51:41.2928537Z pred_anchor_deltas_i_3: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_2.reshape(-1, 4); pred_anchor_deltas_i_2 = None 2025-03-04T20:51:41.2928605Z 2025-03-04T20:51:41.2929024Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:51:41.2929197Z unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = tensor_1.unsqueeze(0); tensor_1 = None 2025-03-04T20:51:41.2929342Z expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1); unsqueeze_1 = None 2025-03-04T20:51:41.2929485Z anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4); expand_1 = None 2025-03-04T20:51:41.2929545Z 2025-03-04T20:51:41.2929914Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:51:41.2930082Z deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float(); pred_anchor_deltas_i_3 = None 2025-03-04T20:51:41.2930150Z 2025-03-04T20:51:41.2930454Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:51:41.2930604Z boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32); anchors_i_1 = None 2025-03-04T20:51:41.2930662Z 2025-03-04T20:51:41.2930971Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:51:41.2931097Z getitem_26: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)] 2025-03-04T20:51:41.2931242Z getitem_27: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T20:51:41.2931387Z widths_1: "f32[269952][1]cpu" = getitem_26 - getitem_27; getitem_26 = getitem_27 = None 2025-03-04T20:51:41.2931470Z 2025-03-04T20:51:41.2931786Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:51:41.2931914Z getitem_28: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)] 2025-03-04T20:51:41.2932038Z getitem_29: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)] 2025-03-04T20:51:41.2932408Z heights_1: "f32[269952][1]cpu" = getitem_28 - getitem_29; getitem_28 = getitem_29 = None 2025-03-04T20:51:41.2932688Z 2025-03-04T20:51:41.2933081Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:51:41.2933569Z getitem_30: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T20:51:41.2933854Z mul_10: "f32[269952][1]cpu" = 0.5 * widths_1 2025-03-04T20:51:41.2934130Z ctr_x_1: "f32[269952][1]cpu" = getitem_30 + mul_10; getitem_30 = mul_10 = None 2025-03-04T20:51:41.2934382Z 2025-03-04T20:51:41.2934807Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:51:41.2935331Z getitem_31: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]; boxes_1 = None 2025-03-04T20:51:41.2935628Z mul_11: "f32[269952][1]cpu" = 0.5 * heights_1 2025-03-04T20:51:41.2935903Z ctr_y_1: "f32[269952][1]cpu" = getitem_31 + mul_11; getitem_31 = mul_11 = None 2025-03-04T20:51:41.2936155Z 2025-03-04T20:51:41.2936550Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:51:41.2937066Z getitem_32: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:51:41.2937399Z dx_1: "f32[269952, 1][1, 1]cpu" = getitem_32 / 1.0; getitem_32 = None 2025-03-04T20:51:41.2937629Z 2025-03-04T20:51:41.2938084Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:51:41.2938595Z getitem_33: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:51:41.2938917Z dy_1: "f32[269952, 1][1, 1]cpu" = getitem_33 / 1.0; getitem_33 = None 2025-03-04T20:51:41.2939152Z 2025-03-04T20:51:41.2939556Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:51:41.2940084Z getitem_34: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:51:41.2940487Z dw_2: "f32[269952, 1][1, 1]cpu" = getitem_34 / 1.0; getitem_34 = None 2025-03-04T20:51:41.2940716Z 2025-03-04T20:51:41.2941100Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:51:41.2941644Z getitem_35: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))]; deltas_1 = None 2025-03-04T20:51:41.2941990Z dh_2: "f32[269952, 1][1, 1]cpu" = getitem_35 / 1.0; getitem_35 = None 2025-03-04T20:51:41.2942217Z 2025-03-04T20:51:41.2942640Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:51:41.2943194Z dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356); dw_2 = None 2025-03-04T20:51:41.2943464Z 2025-03-04T20:51:41.2943899Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:51:41.2944466Z dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356); dh_2 = None 2025-03-04T20:51:41.2944732Z 2025-03-04T20:51:41.2945181Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:51:41.2945884Z getitem_36: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)] 2025-03-04T20:51:41.2946248Z mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_36; dx_1 = getitem_36 = None 2025-03-04T20:51:41.2946627Z getitem_37: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)]; ctr_x_1 = None 2025-03-04T20:51:41.2947031Z pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_37; mul_12 = getitem_37 = None 2025-03-04T20:51:41.2947312Z 2025-03-04T20:51:41.2947758Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:51:41.2948336Z getitem_38: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)] 2025-03-04T20:51:41.2948654Z mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_38; dy_1 = getitem_38 = None 2025-03-04T20:51:41.2948991Z getitem_39: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)]; ctr_y_1 = None 2025-03-04T20:51:41.2949335Z pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_39; mul_13 = getitem_39 = None 2025-03-04T20:51:41.2949588Z 2025-03-04T20:51:41.2950005Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:51:41.2950502Z exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3); dw_3 = None 2025-03-04T20:51:41.2950832Z getitem_40: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]; widths_1 = None 2025-03-04T20:51:41.2951198Z pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_40; exp_2 = getitem_40 = None 2025-03-04T20:51:41.2951448Z 2025-03-04T20:51:41.2951865Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:51:41.2952359Z exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3); dh_3 = None 2025-03-04T20:51:41.2952690Z getitem_41: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]; heights_1 = None 2025-03-04T20:51:41.2953041Z pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_41; exp_3 = getitem_41 = None 2025-03-04T20:51:41.2953288Z 2025-03-04T20:51:41.2953678Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:51:41.2954142Z mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1 2025-03-04T20:51:41.2954415Z x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16; mul_16 = None 2025-03-04T20:51:41.2954656Z 2025-03-04T20:51:41.2955054Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:51:41.2955517Z mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1 2025-03-04T20:51:41.2955780Z y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17; mul_17 = None 2025-03-04T20:51:41.2956025Z 2025-03-04T20:51:41.2956421Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:51:41.2956899Z mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1; pred_w_1 = None 2025-03-04T20:51:41.2957223Z x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18; pred_ctr_x_1 = mul_18 = None 2025-03-04T20:51:41.2957473Z 2025-03-04T20:51:41.2957857Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:51:41.2958325Z mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1; pred_h_1 = None 2025-03-04T20:51:41.2958623Z y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19; pred_ctr_y_1 = mul_19 = None 2025-03-04T20:51:41.2958870Z 2025-03-04T20:51:41.2959299Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:51:41.2959892Z pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1); x1_1 = y1_1 = x2_1 = y2_1 = None 2025-03-04T20:51:41.2960183Z 2025-03-04T20:51:41.2960581Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:51:41.2961140Z proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4)); pred_boxes_1 = None 2025-03-04T20:51:41.2961421Z 2025-03-04T20:51:41.2961884Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:51:41.2962488Z proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4); proposals_i_1 = None 2025-03-04T20:51:41.2962776Z 2025-03-04T20:51:41.2963257Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:51:41.2963918Z pred_anchor_deltas_i_5: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_4.reshape(-1, 4); pred_anchor_deltas_i_4 = None 2025-03-04T20:51:41.2964252Z 2025-03-04T20:51:41.2964773Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:51:41.2965404Z unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = tensor_2.unsqueeze(0); tensor_2 = None 2025-03-04T20:51:41.2965766Z expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1); unsqueeze_2 = None 2025-03-04T20:51:41.2966113Z anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4); expand_2 = None 2025-03-04T20:51:41.2966371Z 2025-03-04T20:51:41.2966829Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:51:41.2967422Z deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_5.float(); pred_anchor_deltas_i_5 = None 2025-03-04T20:51:41.2967709Z 2025-03-04T20:51:41.2968107Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:51:41.2968622Z boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32); anchors_i_2 = None 2025-03-04T20:51:41.2968898Z 2025-03-04T20:51:41.2969285Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:51:41.2969788Z getitem_42: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)] 2025-03-04T20:51:41.2970085Z getitem_43: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T20:51:41.2970417Z widths_2: "f32[67488][1]cpu" = getitem_42 - getitem_43; getitem_42 = getitem_43 = None 2025-03-04T20:51:41.2970679Z 2025-03-04T20:51:41.2971074Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:51:41.2971560Z getitem_44: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)] 2025-03-04T20:51:41.2971853Z getitem_45: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)] 2025-03-04T20:51:41.2972177Z heights_2: "f32[67488][1]cpu" = getitem_44 - getitem_45; getitem_44 = getitem_45 = None 2025-03-04T20:51:41.2972445Z 2025-03-04T20:51:41.2972840Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:51:41.2973317Z getitem_46: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T20:51:41.2973578Z mul_20: "f32[67488][1]cpu" = 0.5 * widths_2 2025-03-04T20:51:41.2973851Z ctr_x_2: "f32[67488][1]cpu" = getitem_46 + mul_20; getitem_46 = mul_20 = None 2025-03-04T20:51:41.2974099Z 2025-03-04T20:51:41.2974507Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:51:41.2975017Z getitem_47: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]; boxes_2 = None 2025-03-04T20:51:41.2975315Z mul_21: "f32[67488][1]cpu" = 0.5 * heights_2 2025-03-04T20:51:41.2975586Z ctr_y_2: "f32[67488][1]cpu" = getitem_47 + mul_21; getitem_47 = mul_21 = None 2025-03-04T20:51:41.2975831Z 2025-03-04T20:51:41.2976229Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:51:41.2976726Z getitem_48: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:51:41.2977055Z dx_2: "f32[67488, 1][1, 1]cpu" = getitem_48 / 1.0; getitem_48 = None 2025-03-04T20:51:41.2977292Z 2025-03-04T20:51:41.2977678Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:51:41.2978183Z getitem_49: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:51:41.2978501Z dy_2: "f32[67488, 1][1, 1]cpu" = getitem_49 / 1.0; getitem_49 = None 2025-03-04T20:51:41.2978737Z 2025-03-04T20:51:41.2979123Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:51:41.2979624Z getitem_50: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:51:41.2979946Z dw_4: "f32[67488, 1][1, 1]cpu" = getitem_50 / 1.0; getitem_50 = None 2025-03-04T20:51:41.2980182Z 2025-03-04T20:51:41.2980571Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:51:41.2981107Z getitem_51: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))]; deltas_2 = None 2025-03-04T20:51:41.2981455Z dh_4: "f32[67488, 1][1, 1]cpu" = getitem_51 / 1.0; getitem_51 = None 2025-03-04T20:51:41.2981685Z 2025-03-04T20:51:41.2982129Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:51:41.2982653Z dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356); dw_4 = None 2025-03-04T20:51:41.2982926Z 2025-03-04T20:51:41.2983347Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:51:41.2983881Z dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356); dh_4 = None 2025-03-04T20:51:41.2984145Z 2025-03-04T20:51:41.2984592Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:51:41.2985152Z getitem_52: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)] 2025-03-04T20:51:41.2985476Z mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_52; dx_2 = getitem_52 = None 2025-03-04T20:51:41.2985918Z getitem_53: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)]; ctr_x_2 = None 2025-03-04T20:51:41.2986284Z pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_53; mul_22 = getitem_53 = None 2025-03-04T20:51:41.2986554Z 2025-03-04T20:51:41.2987024Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:51:41.2987557Z getitem_54: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)] 2025-03-04T20:51:41.2987868Z mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_54; dy_2 = getitem_54 = None 2025-03-04T20:51:41.2988194Z getitem_55: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)]; ctr_y_2 = None 2025-03-04T20:51:41.2988535Z pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_55; mul_23 = getitem_55 = None 2025-03-04T20:51:41.2988785Z 2025-03-04T20:51:41.2989195Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:51:41.2989693Z exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5); dw_5 = None 2025-03-04T20:51:41.2990011Z getitem_56: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]; widths_2 = None 2025-03-04T20:51:41.2990387Z pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_56; exp_4 = getitem_56 = None 2025-03-04T20:51:41.2990642Z 2025-03-04T20:51:41.2991054Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:51:41.2991545Z exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5); dh_5 = None 2025-03-04T20:51:41.2991876Z getitem_57: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]; heights_2 = None 2025-03-04T20:51:41.2992219Z pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_57; exp_5 = getitem_57 = None 2025-03-04T20:51:41.2992472Z 2025-03-04T20:51:41.2992866Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:51:41.2993324Z mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2 2025-03-04T20:51:41.2993590Z x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26; mul_26 = None 2025-03-04T20:51:41.2993822Z 2025-03-04T20:51:41.2994209Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:51:41.2994659Z mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2 2025-03-04T20:51:41.2994918Z y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27; mul_27 = None 2025-03-04T20:51:41.2995147Z 2025-03-04T20:51:41.2995547Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:51:41.2996030Z mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2; pred_w_2 = None 2025-03-04T20:51:41.2996328Z x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28; pred_ctr_x_2 = mul_28 = None 2025-03-04T20:51:41.2996572Z 2025-03-04T20:51:41.2996958Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:51:41.2997428Z mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2; pred_h_2 = None 2025-03-04T20:51:41.2997715Z y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29; pred_ctr_y_2 = mul_29 = None 2025-03-04T20:51:41.2997954Z 2025-03-04T20:51:41.2998376Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:51:41.2998951Z pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1); x1_2 = y1_2 = x2_2 = y2_2 = None 2025-03-04T20:51:41.2999244Z 2025-03-04T20:51:41.2999666Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:51:41.3000200Z proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4)); pred_boxes_2 = None 2025-03-04T20:51:41.3000468Z 2025-03-04T20:51:41.3000919Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:51:41.3001500Z proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4); proposals_i_2 = None 2025-03-04T20:51:41.3001778Z 2025-03-04T20:51:41.3002354Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:51:41.3003015Z pred_anchor_deltas_i_7: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_6.reshape(-1, 4); pred_anchor_deltas_i_6 = None 2025-03-04T20:51:41.3003379Z 2025-03-04T20:51:41.3003895Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:51:41.3004527Z unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = tensor_3.unsqueeze(0); tensor_3 = None 2025-03-04T20:51:41.3004876Z expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1); unsqueeze_3 = None 2025-03-04T20:51:41.3005214Z anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4); expand_3 = None 2025-03-04T20:51:41.3005470Z 2025-03-04T20:51:41.3005934Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:51:41.3006506Z deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_7.float(); pred_anchor_deltas_i_7 = None 2025-03-04T20:51:41.3006782Z 2025-03-04T20:51:41.3007174Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:51:41.3007675Z boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32); anchors_i_3 = None 2025-03-04T20:51:41.3007931Z 2025-03-04T20:51:41.3008351Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:51:41.3008855Z getitem_58: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)] 2025-03-04T20:51:41.3009182Z getitem_59: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T20:51:41.3009512Z widths_3: "f32[16872][1]cpu" = getitem_58 - getitem_59; getitem_58 = getitem_59 = None 2025-03-04T20:51:41.3009777Z 2025-03-04T20:51:41.3010176Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:51:41.3010674Z getitem_60: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)] 2025-03-04T20:51:41.3010974Z getitem_61: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)] 2025-03-04T20:51:41.3011298Z heights_3: "f32[16872][1]cpu" = getitem_60 - getitem_61; getitem_60 = getitem_61 = None 2025-03-04T20:51:41.3011563Z 2025-03-04T20:51:41.3011960Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:51:41.3012449Z getitem_62: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T20:51:41.3012715Z mul_30: "f32[16872][1]cpu" = 0.5 * widths_3 2025-03-04T20:51:41.3013012Z ctr_x_3: "f32[16872][1]cpu" = getitem_62 + mul_30; getitem_62 = mul_30 = None 2025-03-04T20:51:41.3013261Z 2025-03-04T20:51:41.3013661Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:51:41.3014172Z getitem_63: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]; boxes_3 = None 2025-03-04T20:51:41.3014463Z mul_31: "f32[16872][1]cpu" = 0.5 * heights_3 2025-03-04T20:51:41.3014735Z ctr_y_3: "f32[16872][1]cpu" = getitem_63 + mul_31; getitem_63 = mul_31 = None 2025-03-04T20:51:41.3014972Z 2025-03-04T20:51:41.3015371Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:51:41.3015876Z getitem_64: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:51:41.3016210Z dx_3: "f32[16872, 1][1, 1]cpu" = getitem_64 / 1.0; getitem_64 = None 2025-03-04T20:51:41.3016442Z 2025-03-04T20:51:41.3016835Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:51:41.3017348Z getitem_65: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:51:41.3017666Z dy_3: "f32[16872, 1][1, 1]cpu" = getitem_65 / 1.0; getitem_65 = None 2025-03-04T20:51:41.3017900Z 2025-03-04T20:51:41.3018289Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:51:41.3018858Z getitem_66: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:51:41.3019168Z dw_6: "f32[16872, 1][1, 1]cpu" = getitem_66 / 1.0; getitem_66 = None 2025-03-04T20:51:41.3019397Z 2025-03-04T20:51:41.3019779Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:51:41.3020310Z getitem_67: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))]; deltas_3 = None 2025-03-04T20:51:41.3020649Z dh_6: "f32[16872, 1][1, 1]cpu" = getitem_67 / 1.0; getitem_67 = None 2025-03-04T20:51:41.3020878Z 2025-03-04T20:51:41.3021313Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:51:41.3021831Z dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356); dw_6 = None 2025-03-04T20:51:41.3022096Z 2025-03-04T20:51:41.3022502Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:51:41.3023016Z dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356); dh_6 = None 2025-03-04T20:51:41.3023268Z 2025-03-04T20:51:41.3023698Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:51:41.3024229Z getitem_68: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)] 2025-03-04T20:51:41.3024545Z mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_68; dx_3 = getitem_68 = None 2025-03-04T20:51:41.3024877Z getitem_69: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)]; ctr_x_3 = None 2025-03-04T20:51:41.3025224Z pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_69; mul_32 = getitem_69 = None 2025-03-04T20:51:41.3025482Z 2025-03-04T20:51:41.3026018Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:51:41.3026560Z getitem_70: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)] 2025-03-04T20:51:41.3026874Z mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_70; dy_3 = getitem_70 = None 2025-03-04T20:51:41.3027200Z getitem_71: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)]; ctr_y_3 = None 2025-03-04T20:51:41.3027549Z pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_71; mul_33 = getitem_71 = None 2025-03-04T20:51:41.3027806Z 2025-03-04T20:51:41.3028236Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:51:41.3028726Z exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7); dw_7 = None 2025-03-04T20:51:41.3029065Z getitem_72: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]; widths_3 = None 2025-03-04T20:51:41.3029407Z pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_72; exp_6 = getitem_72 = None 2025-03-04T20:51:41.3029656Z 2025-03-04T20:51:41.3030062Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:51:41.3030546Z exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7); dh_7 = None 2025-03-04T20:51:41.3030866Z getitem_73: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]; heights_3 = None 2025-03-04T20:51:41.3031201Z pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_73; exp_7 = getitem_73 = None 2025-03-04T20:51:41.3031443Z 2025-03-04T20:51:41.3031824Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:51:41.3032276Z mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3 2025-03-04T20:51:41.3032531Z x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36; mul_36 = None 2025-03-04T20:51:41.3032756Z 2025-03-04T20:51:41.3033134Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:51:41.3033572Z mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3 2025-03-04T20:51:41.3033836Z y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37; mul_37 = None 2025-03-04T20:51:41.3034058Z 2025-03-04T20:51:41.3034435Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:51:41.3034918Z mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3; pred_w_3 = None 2025-03-04T20:51:41.3035217Z x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38; pred_ctr_x_3 = mul_38 = None 2025-03-04T20:51:41.3035462Z 2025-03-04T20:51:41.3035843Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:51:41.3036301Z mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3; pred_h_3 = None 2025-03-04T20:51:41.3036588Z y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39; pred_ctr_y_3 = mul_39 = None 2025-03-04T20:51:41.3036826Z 2025-03-04T20:51:41.3037246Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:51:41.3037815Z pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1); x1_3 = y1_3 = x2_3 = y2_3 = None 2025-03-04T20:51:41.3038105Z 2025-03-04T20:51:41.3038536Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:51:41.3039071Z proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4)); pred_boxes_3 = None 2025-03-04T20:51:41.3039341Z 2025-03-04T20:51:41.3039794Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:51:41.3040383Z proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4); proposals_i_3 = None 2025-03-04T20:51:41.3040669Z 2025-03-04T20:51:41.3041144Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:51:41.3041811Z pred_anchor_deltas_i_9: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_8.reshape(-1, 4); pred_anchor_deltas_i_8 = None 2025-03-04T20:51:41.3042119Z 2025-03-04T20:51:41.3042627Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:51:41.3043247Z unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = tensor_4.unsqueeze(0); tensor_4 = None 2025-03-04T20:51:41.3043585Z expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1); unsqueeze_4 = None 2025-03-04T20:51:41.3043917Z anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4); expand_4 = None 2025-03-04T20:51:41.3044165Z 2025-03-04T20:51:41.3044610Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:51:41.3045192Z deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_9.float(); pred_anchor_deltas_i_9 = None 2025-03-04T20:51:41.3045466Z 2025-03-04T20:51:41.3045853Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:51:41.3046350Z boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32); anchors_i_4 = None 2025-03-04T20:51:41.3046603Z 2025-03-04T20:51:41.3047015Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:51:41.3047511Z getitem_74: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)] 2025-03-04T20:51:41.3047804Z getitem_75: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T20:51:41.3048125Z widths_4: "f32[4332][1]cpu" = getitem_74 - getitem_75; getitem_74 = getitem_75 = None 2025-03-04T20:51:41.3048383Z 2025-03-04T20:51:41.3048773Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:51:41.3049252Z getitem_76: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)] 2025-03-04T20:51:41.3049539Z getitem_77: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)] 2025-03-04T20:51:41.3049855Z heights_4: "f32[4332][1]cpu" = getitem_76 - getitem_77; getitem_76 = getitem_77 = None 2025-03-04T20:51:41.3050111Z 2025-03-04T20:51:41.3050494Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:51:41.3050968Z getitem_78: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T20:51:41.3051227Z mul_40: "f32[4332][1]cpu" = 0.5 * widths_4 2025-03-04T20:51:41.3051502Z ctr_x_4: "f32[4332][1]cpu" = getitem_78 + mul_40; getitem_78 = mul_40 = None 2025-03-04T20:51:41.3051746Z 2025-03-04T20:51:41.3052135Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:51:41.3052632Z getitem_79: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]; boxes_4 = None 2025-03-04T20:51:41.3052912Z mul_41: "f32[4332][1]cpu" = 0.5 * heights_4 2025-03-04T20:51:41.3053168Z ctr_y_4: "f32[4332][1]cpu" = getitem_79 + mul_41; getitem_79 = mul_41 = None 2025-03-04T20:51:41.3053407Z 2025-03-04T20:51:41.3053794Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:51:41.3054302Z getitem_80: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:51:41.3054611Z dx_4: "f32[4332, 1][1, 1]cpu" = getitem_80 / 1.0; getitem_80 = None 2025-03-04T20:51:41.3054834Z 2025-03-04T20:51:41.3055222Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:51:41.3055733Z getitem_81: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:51:41.3056038Z dy_4: "f32[4332, 1][1, 1]cpu" = getitem_81 / 1.0; getitem_81 = None 2025-03-04T20:51:41.3056264Z 2025-03-04T20:51:41.3056638Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:51:41.3057120Z getitem_82: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:51:41.3057425Z dw_8: "f32[4332, 1][1, 1]cpu" = getitem_82 / 1.0; getitem_82 = None 2025-03-04T20:51:41.3057646Z 2025-03-04T20:51:41.3058025Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:51:41.3058542Z getitem_83: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))]; deltas_4 = None 2025-03-04T20:51:41.3058880Z dh_8: "f32[4332, 1][1, 1]cpu" = getitem_83 / 1.0; getitem_83 = None 2025-03-04T20:51:41.3059104Z 2025-03-04T20:51:41.3060093Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:51:41.3060653Z dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356); dw_8 = None 2025-03-04T20:51:41.3060902Z 2025-03-04T20:51:41.3061321Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:51:41.3061845Z dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356); dh_8 = None 2025-03-04T20:51:41.3062094Z 2025-03-04T20:51:41.3062522Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:51:41.3063057Z getitem_84: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)] 2025-03-04T20:51:41.3063368Z mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_84; dx_4 = getitem_84 = None 2025-03-04T20:51:41.3063699Z getitem_85: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)]; ctr_x_4 = None 2025-03-04T20:51:41.3064047Z pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_85; mul_42 = getitem_85 = None 2025-03-04T20:51:41.3064307Z 2025-03-04T20:51:41.3064778Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:51:41.3065321Z getitem_86: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)] 2025-03-04T20:51:41.3065714Z mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_86; dy_4 = getitem_86 = None 2025-03-04T20:51:41.3066057Z getitem_87: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)]; ctr_y_4 = None 2025-03-04T20:51:41.3066413Z pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_87; mul_43 = getitem_87 = None 2025-03-04T20:51:41.3066674Z 2025-03-04T20:51:41.3067108Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:51:41.3067647Z exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9); dw_9 = None 2025-03-04T20:51:41.3067970Z getitem_88: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]; widths_4 = None 2025-03-04T20:51:41.3068313Z pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_88; exp_8 = getitem_88 = None 2025-03-04T20:51:41.3068560Z 2025-03-04T20:51:41.3068977Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:51:41.3069475Z exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9); dh_9 = None 2025-03-04T20:51:41.3069805Z getitem_89: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]; heights_4 = None 2025-03-04T20:51:41.3070152Z pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_89; exp_9 = getitem_89 = None 2025-03-04T20:51:41.3070399Z 2025-03-04T20:51:41.3070802Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:51:41.3071269Z mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4 2025-03-04T20:51:41.3071530Z x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46; mul_46 = None 2025-03-04T20:51:41.3071760Z 2025-03-04T20:51:41.3072150Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:51:41.3072603Z mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4 2025-03-04T20:51:41.3072876Z y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47; mul_47 = None 2025-03-04T20:51:41.3073110Z 2025-03-04T20:51:41.3073499Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:51:41.3073985Z mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4; pred_w_4 = None 2025-03-04T20:51:41.3074279Z x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48; pred_ctr_x_4 = mul_48 = None 2025-03-04T20:51:41.3074527Z 2025-03-04T20:51:41.3074914Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:51:41.3075382Z mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4; pred_h_4 = None 2025-03-04T20:51:41.3075675Z y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49; pred_ctr_y_4 = mul_49 = None 2025-03-04T20:51:41.3075917Z 2025-03-04T20:51:41.3076347Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:51:41.3076927Z pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1); x1_4 = y1_4 = x2_4 = y2_4 = None 2025-03-04T20:51:41.3077224Z 2025-03-04T20:51:41.3077652Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:51:41.3078204Z proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4)); pred_boxes_4 = None 2025-03-04T20:51:41.3078491Z 2025-03-04T20:51:41.3078946Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:51:41.3079536Z proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4); proposals_i_4 = None 2025-03-04T20:51:41.3079814Z 2025-03-04T20:51:41.3080364Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T20:51:41.3081056Z arange_10: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T20:51:41.3081311Z 2025-03-04T20:51:41.3081678Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:51:41.3082154Z batch_idx: "i64[4][1]cpu" = arange_10.to(device(type='cpu')); arange_10 = None 2025-03-04T20:51:41.3082402Z 2025-03-04T20:51:41.3082909Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:51:41.3083490Z topk = logits_i.topk(1000, dim = 1); logits_i = None 2025-03-04T20:51:41.3083747Z topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0] 2025-03-04T20:51:41.3084009Z topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1]; topk = None 2025-03-04T20:51:41.3084231Z 2025-03-04T20:51:41.3084759Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:51:41.3085413Z getitem_92: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T20:51:41.3085818Z topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_92, topk_idx)]; proposals_i_5 = getitem_92 = topk_idx = None 2025-03-04T20:51:41.3086160Z 2025-03-04T20:51:41.3086698Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:51:41.3087409Z full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:51:41.3087681Z 2025-03-04T20:51:41.3088050Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:51:41.3088510Z to_21: "i64[1000][1]cpu" = full.to(device(type='cpu')); full = None 2025-03-04T20:51:41.3088738Z 2025-03-04T20:51:41.3089240Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:51:41.3089822Z topk_1 = logits_i_1.topk(1000, dim = 1); logits_i_1 = None 2025-03-04T20:51:41.3090089Z topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0] 2025-03-04T20:51:41.3090361Z topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1]; topk_1 = None 2025-03-04T20:51:41.3090594Z 2025-03-04T20:51:41.3091144Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:51:41.3091774Z getitem_96: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T20:51:41.3092184Z topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_96, topk_idx_1)]; proposals_i_6 = getitem_96 = topk_idx_1 = None 2025-03-04T20:51:41.3092520Z 2025-03-04T20:51:41.3093051Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:51:41.3093719Z full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:51:41.3094007Z 2025-03-04T20:51:41.3094377Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:51:41.3094841Z to_22: "i64[1000][1]cpu" = full_1.to(device(type='cpu')); full_1 = None 2025-03-04T20:51:41.3095083Z 2025-03-04T20:51:41.3095606Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:51:41.3096204Z topk_2 = logits_i_2.topk(1000, dim = 1); logits_i_2 = None 2025-03-04T20:51:41.3096471Z topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0] 2025-03-04T20:51:41.3096748Z topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1]; topk_2 = None 2025-03-04T20:51:41.3096985Z 2025-03-04T20:51:41.3097545Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:51:41.3098217Z getitem_100: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T20:51:41.3098654Z topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_100, topk_idx_2)]; proposals_i_7 = getitem_100 = topk_idx_2 = None 2025-03-04T20:51:41.3099010Z 2025-03-04T20:51:41.3099597Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:51:41.3100309Z full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:51:41.3100593Z 2025-03-04T20:51:41.3100997Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:51:41.3101492Z to_23: "i64[1000][1]cpu" = full_2.to(device(type='cpu')); full_2 = None 2025-03-04T20:51:41.3101744Z 2025-03-04T20:51:41.3102420Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:51:41.3103041Z topk_3 = logits_i_3.topk(1000, dim = 1); logits_i_3 = None 2025-03-04T20:51:41.3103321Z topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0] 2025-03-04T20:51:41.3103603Z topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1]; topk_3 = None 2025-03-04T20:51:41.3103845Z 2025-03-04T20:51:41.3104439Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:51:41.3105106Z getitem_104: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T20:51:41.3105582Z topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_104, topk_idx_3)]; proposals_i_8 = getitem_104 = topk_idx_3 = None 2025-03-04T20:51:41.3105973Z 2025-03-04T20:51:41.3106543Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:51:41.3107221Z full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:51:41.3107502Z 2025-03-04T20:51:41.3107885Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:51:41.3108397Z to_24: "i64[1000][1]cpu" = full_3.to(device(type='cpu')); full_3 = None 2025-03-04T20:51:41.3108639Z 2025-03-04T20:51:41.3109151Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:51:41.3109742Z topk_4 = logits_i_4.topk(1000, dim = 1); logits_i_4 = None 2025-03-04T20:51:41.3110004Z topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0] 2025-03-04T20:51:41.3110279Z topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1]; topk_4 = None 2025-03-04T20:51:41.3110509Z 2025-03-04T20:51:41.3111044Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:51:41.3111721Z getitem_108: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T20:51:41.3112169Z topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_108, topk_idx_4)]; proposals_i_9 = getitem_108 = topk_idx_4 = None 2025-03-04T20:51:41.3112511Z 2025-03-04T20:51:41.3113043Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:51:41.3113747Z full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:51:41.3114019Z 2025-03-04T20:51:41.3114413Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:51:41.3114875Z to_25: "i64[1000][1]cpu" = full_4.to(device(type='cpu')); full_4 = None 2025-03-04T20:51:41.3115110Z 2025-03-04T20:51:41.3115459Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:51:41.3116138Z topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1); topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None 2025-03-04T20:51:41.3116609Z 2025-03-04T20:51:41.3116964Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:51:41.3117755Z topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1); topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None 2025-03-04T20:51:41.3118348Z 2025-03-04T20:51:41.3118701Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:51:41.3119218Z level_ids: "i64[5000][1]cpu" = torch.cat([to_21, to_22, to_23, to_24, to_25], 0); to_21 = to_22 = to_23 = to_24 = to_25 = level_ids = None 2025-03-04T20:51:41.3119518Z 2025-03-04T20:51:41.3119980Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T20:51:41.3120542Z getitem_110: "f32[5000, 4][4, 1]cpu" = topk_proposals[0]; topk_proposals = None 2025-03-04T20:51:41.3120792Z 2025-03-04T20:51:41.3121167Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:51:41.3121667Z tensor_5: "f32[5000, 4][4, 1]cpu" = getitem_110.to(torch.float32); getitem_110 = None 2025-03-04T20:51:41.3121925Z 2025-03-04T20:51:41.3122370Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T20:51:41.3122917Z scores_per_img: "f32[5000][1]cpu" = topk_scores[0]; topk_scores = None 2025-03-04T20:51:41.3123161Z 2025-03-04T20:51:41.3123713Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T20:51:41.3124359Z isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor_5); tensor_5 = None 2025-03-04T20:51:41.3124658Z all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:51:41.3124979Z isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T20:51:41.3125310Z valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T20:51:41.3125553Z 2025-03-04T20:51:41.3125990Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T20:51:41.3126509Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T20:51:41.3126751Z 2025-03-04T20:51:41.3126838Z 2025-03-04T20:51:41.3126932Z class GraphModule(torch.nn.Module): 2025-03-04T20:51:41.3247314Z def forward(self, L_stack0_tensor: "f32[4, 3, 1184, 1216][4319232, 1439744, 1216, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_: 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L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_: 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L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_: 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1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_: "f32[512, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_: "f32[2048, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_lateral_convs_0_parameters_weight_: "f32[256, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_0_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_0_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_0_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_1_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_1_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_1_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_2_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_2_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_2_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_3_parameters_weight_: "f32[256, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_3_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_3_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_3_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[3, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[3][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[12, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[12][1]cpu"): 2025-03-04T20:51:41.3366945Z l_stack0_tensor = L_stack0_tensor 2025-03-04T20:51:41.3367414Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.3367884Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3368362Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3368799Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3369221Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3369617Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.3370047Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3370456Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3370837Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3371215Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3371571Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.3371995Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3372448Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3372843Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3373236Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3373599Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.3374013Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3374447Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3374837Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3375220Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3375581Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:51:41.3376006Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3376423Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3376832Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3377224Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3377571Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.3377990Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3378416Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3378813Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3379204Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3379561Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.3379991Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3380401Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3380808Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3381189Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3381575Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.3381986Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3382391Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3382789Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3383191Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3383557Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.3383966Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3384384Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3384784Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3385185Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3385589Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.3386034Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3386503Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3386933Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3387338Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3387722Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.3388153Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3388575Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3388969Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3389365Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3389716Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.3390164Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3390586Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3390981Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3391378Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3391762Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.3392187Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3392602Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3393005Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3393394Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3393749Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.3394172Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3394598Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3395006Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3395389Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3395765Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:51:41.3396197Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3396632Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3397040Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3397448Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3397810Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.3398221Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3398653Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3399047Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3399432Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3399794Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.3400206Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3400619Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3401040Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3401437Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3401787Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.3402271Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3402682Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3403061Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3403486Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3403847Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.3404270Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3404696Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3405115Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3405493Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3405836Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.3406242Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3406641Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3407020Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3407396Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3407762Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.3408195Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3408589Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3408976Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3409354Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3409698Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.3410127Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3410524Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3410909Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3411282Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3411652Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.3412058Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3412453Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3412834Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3413205Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3413555Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.3413971Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3414372Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3414775Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3415143Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3415493Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.3415890Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3416309Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3416688Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3417063Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3417411Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.3417809Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3418230Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3418608Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3418987Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3419329Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.3419744Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3420154Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3420556Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3420957Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3421331Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:51:41.3421763Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3422183Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3422597Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3423014Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3423365Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.3423786Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3424191Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3424602Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3424995Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3425372Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.3425853Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3426294Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3426722Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3427125Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3427484Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.3427914Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3428337Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3428747Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3429136Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3429525Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.3429950Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3430375Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3430783Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3431178Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3431562Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.3431977Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3432388Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3432788Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3433187Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3433558Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.3433991Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3434435Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3434836Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3435229Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3435591Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.3436016Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3436456Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3436847Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3437222Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3437562Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.3437985Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3438379Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3438763Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3439136Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3439479Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.3439882Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3440288Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3440675Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3441057Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3441407Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.3441815Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3442209Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3442612Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3442979Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3443328Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.3443724Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3444119Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3444587Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3444978Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3445588Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.3446065Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3446524Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3446910Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3447328Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3447727Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.3448169Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3448567Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3448953Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3449323Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3449690Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.3450099Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3450493Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3450877Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3451250Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3451616Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.3452015Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3452418Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3452799Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3453169Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3453519Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.3453935Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3454350Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3454728Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3455101Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3455451Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.3455851Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3456267Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3456647Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3457025Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3457372Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.3457800Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3458202Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3458580Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3458957Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3459305Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.3459718Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3460144Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3460530Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3460922Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3461265Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.3461668Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3462070Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3462479Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3462859Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3463217Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.3463637Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3464046Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3464455Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3464834Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3465189Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.3465648Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3466068Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3466459Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3466856Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3467232Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.3467641Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3468058Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3468447Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3468835Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3469213Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.3469622Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3470035Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3470423Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3470833Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3471185Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.3471608Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3472022Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3472411Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3472799Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3473150Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.3473585Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3474013Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3474402Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3474789Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3475140Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.3475560Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3475982Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3476374Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3476760Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3477115Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.3477863Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3478276Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3478676Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3479063Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3479431Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.3479854Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3480279Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3480667Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3481067Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3481430Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.3482533Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3482968Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3483399Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3483774Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3484128Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.3484532Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3484940Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3485345Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3485728Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3486082Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.3486498Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3486917Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3487309Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3487714Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3488086Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.3488509Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3488923Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3489322Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3489702Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3490064Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.3490490Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3490895Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3491294Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3491700Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3492056Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.3492487Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3492899Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3493300Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3493682Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3494060Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.3494478Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3494911Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3495296Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3495669Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3496026Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.3496449Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3496857Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3497242Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3497612Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3497981Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.3498382Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3498785Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3499170Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3499542Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3499895Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.3500296Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3500714Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3501107Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3501487Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3501839Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.3502328Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3502778Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3503172Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3503561Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3503917Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.3504336Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3504773Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3505165Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3505591Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3505955Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.3506384Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3506793Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3507218Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3507617Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3507965Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.3508371Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3508764Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3509474Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3509914Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3510280Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.3510696Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3511100Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3511504Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3511879Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3512233Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.3512637Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3513041Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3513427Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3513819Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3514169Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.3514588Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3514994Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3515374Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3515754Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3516139Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.3516543Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3516953Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3517332Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3517713Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3518073Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.3518478Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3518882Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3519261Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3519642Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3519987Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.3520417Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3520831Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3521217Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3521599Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3521961Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.3522394Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3522809Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3523201Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3523575Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3523931Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.3524360Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3524757Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3525148Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3525519Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3525877Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.3526289Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3526719Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3527118Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3527522Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3527886Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.3528369Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3528777Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3529178Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3529555Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3529916Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.3530320Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3530733Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3531126Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3531508Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3531862Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.3532263Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3532672Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3533050Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3533448Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3533813Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.3534229Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3534642Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3535030Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3535421Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3535822Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.3538027Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3538451Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3538871Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3539302Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3539684Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.3540149Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3540576Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3540997Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3541397Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3541764Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.3542187Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3542629Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3543039Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3543431Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3543799Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.3544249Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3544688Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3545169Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3545716Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3546132Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.3546605Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3547035Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3547436Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3547837Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3548206Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.3548641Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3549073Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3549501Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3549910Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3550282Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.3550723Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3551570Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3552052Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3552483Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3552841Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.3553277Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3553736Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3554149Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3554546Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3554915Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.3555352Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3555759Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3556157Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3556536Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3556922Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.3557335Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3557747Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3558147Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3558546Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3558916Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.3559359Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3559788Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3560200Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3560612Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3560973Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.3561384Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3561801Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3562192Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3562581Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3562932Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.3563369Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3563783Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3564166Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3564550Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3564915Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:51:41.3565367Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3565811Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3566221Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3566625Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3566999Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.3567415Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3567827Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3568219Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3568613Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3568965Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.3569385Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3569802Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3570211Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3570583Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3570935Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.3571341Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3571759Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3572156Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3572844Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3573244Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:51:41.3573669Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3574105Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3574505Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3574887Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3575248Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:51:41.3575662Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3576075Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3576464Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3576867Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3577218Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:51:41.3577617Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:51:41.3578019Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:51:41.3578410Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:51:41.3578817Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:51:41.3579048Z l_self_modules_backbone_lateral_convs_0_parameters_weight_ = L_self_modules_backbone_lateral_convs_0_parameters_weight_ 2025-03-04T20:51:41.3579291Z l_self_modules_backbone_lateral_convs_0_parameters_bias_ = L_self_modules_backbone_lateral_convs_0_parameters_bias_ 2025-03-04T20:51:41.3579515Z l_self_modules_backbone_output_convs_0_parameters_weight_ = L_self_modules_backbone_output_convs_0_parameters_weight_ 2025-03-04T20:51:41.3579737Z l_self_modules_backbone_output_convs_0_parameters_bias_ = L_self_modules_backbone_output_convs_0_parameters_bias_ 2025-03-04T20:51:41.3579968Z l_self_modules_backbone_lateral_convs_1_parameters_weight_ = L_self_modules_backbone_lateral_convs_1_parameters_weight_ 2025-03-04T20:51:41.3580199Z l_self_modules_backbone_lateral_convs_1_parameters_bias_ = L_self_modules_backbone_lateral_convs_1_parameters_bias_ 2025-03-04T20:51:41.3580428Z l_self_modules_backbone_output_convs_1_parameters_weight_ = L_self_modules_backbone_output_convs_1_parameters_weight_ 2025-03-04T20:51:41.3580639Z l_self_modules_backbone_output_convs_1_parameters_bias_ = L_self_modules_backbone_output_convs_1_parameters_bias_ 2025-03-04T20:51:41.3580867Z l_self_modules_backbone_lateral_convs_2_parameters_weight_ = L_self_modules_backbone_lateral_convs_2_parameters_weight_ 2025-03-04T20:51:41.3581082Z l_self_modules_backbone_lateral_convs_2_parameters_bias_ = L_self_modules_backbone_lateral_convs_2_parameters_bias_ 2025-03-04T20:51:41.3581307Z l_self_modules_backbone_output_convs_2_parameters_weight_ = L_self_modules_backbone_output_convs_2_parameters_weight_ 2025-03-04T20:51:41.3581518Z l_self_modules_backbone_output_convs_2_parameters_bias_ = L_self_modules_backbone_output_convs_2_parameters_bias_ 2025-03-04T20:51:41.3581750Z l_self_modules_backbone_lateral_convs_3_parameters_weight_ = L_self_modules_backbone_lateral_convs_3_parameters_weight_ 2025-03-04T20:51:41.3581969Z l_self_modules_backbone_lateral_convs_3_parameters_bias_ = L_self_modules_backbone_lateral_convs_3_parameters_bias_ 2025-03-04T20:51:41.3582203Z l_self_modules_backbone_output_convs_3_parameters_weight_ = L_self_modules_backbone_output_convs_3_parameters_weight_ 2025-03-04T20:51:41.3582418Z l_self_modules_backbone_output_convs_3_parameters_bias_ = L_self_modules_backbone_output_convs_3_parameters_bias_ 2025-03-04T20:51:41.3582795Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T20:51:41.3583185Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ 2025-03-04T20:51:41.3583561Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ 2025-03-04T20:51:41.3583941Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ 2025-03-04T20:51:41.3584303Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ 2025-03-04T20:51:41.3584656Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T20:51:41.3585014Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T20:51:41.3585406Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T20:51:41.3585867Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T20:51:41.3586259Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T20:51:41.3586630Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T20:51:41.3586724Z 2025-03-04T20:51:41.3587039Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3587618Z x: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1); l_stack0_tensor = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.3587691Z 2025-03-04T20:51:41.3587985Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3589815Z x_1: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3589894Z 2025-03-04T20:51:41.3590202Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x) 2025-03-04T20:51:41.3590372Z x_2: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.relu_(x_1); x_1 = None 2025-03-04T20:51:41.3590441Z 2025-03-04T20:51:41.3590834Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1) 2025-03-04T20:51:41.3591086Z x_3: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1); x_2 = None 2025-03-04T20:51:41.3591161Z 2025-03-04T20:51:41.3591437Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3591967Z x_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.3592036Z 2025-03-04T20:51:41.3592353Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3594315Z x_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3594399Z 2025-03-04T20:51:41.3594715Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3594861Z out: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_5); x_5 = None 2025-03-04T20:51:41.3594935Z 2025-03-04T20:51:41.3595206Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3595738Z x_6: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.3595808Z 2025-03-04T20:51:41.3596110Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3597944Z x_7: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_6 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3598029Z 2025-03-04T20:51:41.3598319Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3598452Z out_1: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_7); x_7 = None 2025-03-04T20:51:41.3598520Z 2025-03-04T20:51:41.3598776Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3599296Z x_8: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_1 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.3599366Z 2025-03-04T20:51:41.3599625Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3601413Z x_9: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3601491Z 2025-03-04T20:51:41.3601751Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3602349Z x_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); x_3 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:51:41.3602415Z 2025-03-04T20:51:41.3602683Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3604503Z x_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_10 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3604618Z 2025-03-04T20:51:41.3604908Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.3605057Z x_9 += x_11; out_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_9; x_9 = x_11 = None 2025-03-04T20:51:41.3605123Z 2025-03-04T20:51:41.3605408Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3605564Z out_3: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_2); out_2 = None 2025-03-04T20:51:41.3605628Z 2025-03-04T20:51:41.3605881Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3606399Z x_12: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.3606471Z 2025-03-04T20:51:41.3606773Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3608567Z x_13: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_12 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3608656Z 2025-03-04T20:51:41.3608936Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3609081Z out_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_13); x_13 = None 2025-03-04T20:51:41.3609139Z 2025-03-04T20:51:41.3609392Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3609876Z x_14: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.3609945Z 2025-03-04T20:51:41.3610204Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3611968Z x_15: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_14 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3612055Z 2025-03-04T20:51:41.3612335Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3612478Z out_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_15); x_15 = None 2025-03-04T20:51:41.3612538Z 2025-03-04T20:51:41.3612816Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3613322Z x_16: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_5 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.3613394Z 2025-03-04T20:51:41.3613655Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3615436Z x_17: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_16 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3615522Z 2025-03-04T20:51:41.3615806Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.3615979Z x_17 += out_3; out_6: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_17; x_17 = out_3 = None 2025-03-04T20:51:41.3616040Z 2025-03-04T20:51:41.3616323Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3616471Z out_7: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_6); out_6 = None 2025-03-04T20:51:41.3616533Z 2025-03-04T20:51:41.3616786Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3617263Z x_18: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.3617345Z 2025-03-04T20:51:41.3617607Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3619386Z x_19: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_18 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3619456Z 2025-03-04T20:51:41.3619736Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3619892Z out_8: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_19); x_19 = None 2025-03-04T20:51:41.3619957Z 2025-03-04T20:51:41.3620219Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3620715Z x_20: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.3620800Z 2025-03-04T20:51:41.3621068Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3622892Z x_21: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_20 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3622963Z 2025-03-04T20:51:41.3623253Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3623394Z out_9: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_21); x_21 = None 2025-03-04T20:51:41.3623454Z 2025-03-04T20:51:41.3623711Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3624228Z x_22: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_9 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.3624297Z 2025-03-04T20:51:41.3624560Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3626503Z x_23: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_22 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3626584Z 2025-03-04T20:51:41.3626895Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.3627059Z x_23 += out_7; out_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_23; x_23 = out_7 = None 2025-03-04T20:51:41.3627120Z 2025-03-04T20:51:41.3627411Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3627580Z out_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_10); out_10 = None 2025-03-04T20:51:41.3627650Z 2025-03-04T20:51:41.3627901Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3628408Z x_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.3628477Z 2025-03-04T20:51:41.3628745Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3630589Z x_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3630672Z 2025-03-04T20:51:41.3630965Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3631116Z out_12: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_25); x_25 = None 2025-03-04T20:51:41.3631177Z 2025-03-04T20:51:41.3631433Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3631939Z x_26: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_12 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.3632005Z 2025-03-04T20:51:41.3632271Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3634127Z x_27: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_26 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3634209Z 2025-03-04T20:51:41.3634496Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3634646Z out_13: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_27); x_27 = None 2025-03-04T20:51:41.3634706Z 2025-03-04T20:51:41.3634965Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3635469Z x_28: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_13 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.3635535Z 2025-03-04T20:51:41.3635798Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3637618Z x_29: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_28 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3637715Z 2025-03-04T20:51:41.3637967Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3638477Z x_30: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:51:41.3638539Z 2025-03-04T20:51:41.3638810Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3640687Z x_31: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_30 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3640751Z 2025-03-04T20:51:41.3641033Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.3641190Z x_29 += x_31; out_14: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_29; x_29 = x_31 = None 2025-03-04T20:51:41.3641258Z 2025-03-04T20:51:41.3641535Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3641693Z out_15: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_14); out_14 = None 2025-03-04T20:51:41.3641752Z 2025-03-04T20:51:41.3642007Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3642484Z x_32: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.3642553Z 2025-03-04T20:51:41.3642822Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3645081Z x_33: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_32 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3645190Z 2025-03-04T20:51:41.3645492Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3645642Z out_16: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_33); x_33 = None 2025-03-04T20:51:41.3645711Z 2025-03-04T20:51:41.3645965Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3646499Z x_34: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_16 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.3646608Z 2025-03-04T20:51:41.3646906Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3648762Z x_35: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_34 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3648851Z 2025-03-04T20:51:41.3649143Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3649281Z out_17: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_35); x_35 = None 2025-03-04T20:51:41.3649349Z 2025-03-04T20:51:41.3649598Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3650105Z x_36: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_17 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.3650177Z 2025-03-04T20:51:41.3650440Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3652264Z x_37: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_36 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3652352Z 2025-03-04T20:51:41.3652630Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.3652794Z x_37 += out_15; out_18: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_37; x_37 = out_15 = None 2025-03-04T20:51:41.3652856Z 2025-03-04T20:51:41.3653159Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3653321Z out_19: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_18); out_18 = None 2025-03-04T20:51:41.3653390Z 2025-03-04T20:51:41.3653633Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3654152Z x_38: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.3654220Z 2025-03-04T20:51:41.3654480Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3656259Z x_39: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_38 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3656350Z 2025-03-04T20:51:41.3656635Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3656778Z out_20: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_39); x_39 = None 2025-03-04T20:51:41.3656840Z 2025-03-04T20:51:41.3657096Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3657594Z x_40: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_20 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.3657710Z 2025-03-04T20:51:41.3657983Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3659818Z x_41: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_40 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3659887Z 2025-03-04T20:51:41.3660190Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3660334Z out_21: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_41); x_41 = None 2025-03-04T20:51:41.3660394Z 2025-03-04T20:51:41.3660665Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3661167Z x_42: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_21 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.3661237Z 2025-03-04T20:51:41.3661522Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3663339Z x_43: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_42 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3663409Z 2025-03-04T20:51:41.3663685Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.3663843Z x_43 += out_19; out_22: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_43; x_43 = out_19 = None 2025-03-04T20:51:41.3663906Z 2025-03-04T20:51:41.3664193Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3664338Z out_23: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_22); out_22 = None 2025-03-04T20:51:41.3664422Z 2025-03-04T20:51:41.3664677Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3665184Z x_44: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.3665249Z 2025-03-04T20:51:41.3665564Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3667415Z x_45: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_44 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3667483Z 2025-03-04T20:51:41.3667773Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3667911Z out_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_45); x_45 = None 2025-03-04T20:51:41.3667980Z 2025-03-04T20:51:41.3668246Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3668750Z x_46: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.3668819Z 2025-03-04T20:51:41.3669082Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3670893Z x_47: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_46 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3670964Z 2025-03-04T20:51:41.3671247Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3671407Z out_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_47); x_47 = None 2025-03-04T20:51:41.3671468Z 2025-03-04T20:51:41.3671729Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3672222Z x_48: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_25 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.3672289Z 2025-03-04T20:51:41.3672555Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3674381Z x_49: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_48 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3674454Z 2025-03-04T20:51:41.3674740Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.3674915Z x_49 += out_23; out_26: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_49; x_49 = out_23 = None 2025-03-04T20:51:41.3674975Z 2025-03-04T20:51:41.3675268Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3675415Z out_27: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_26); out_26 = None 2025-03-04T20:51:41.3675483Z 2025-03-04T20:51:41.3675732Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3676218Z x_50: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.3676280Z 2025-03-04T20:51:41.3676555Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3678322Z x_51: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_50 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3678400Z 2025-03-04T20:51:41.3678686Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3678817Z out_28: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_51); x_51 = None 2025-03-04T20:51:41.3678882Z 2025-03-04T20:51:41.3679125Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3679618Z x_52: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_28 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.3679680Z 2025-03-04T20:51:41.3679942Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3681741Z x_53: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_52 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3681820Z 2025-03-04T20:51:41.3682112Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3682241Z out_29: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_53); x_53 = None 2025-03-04T20:51:41.3682310Z 2025-03-04T20:51:41.3682559Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3683061Z x_54: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_29 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.3683129Z 2025-03-04T20:51:41.3683397Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3685165Z x_55: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_54 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3685249Z 2025-03-04T20:51:41.3685495Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3685978Z x_56: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:51:41.3686040Z 2025-03-04T20:51:41.3686315Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3688153Z x_57: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_56 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3688240Z 2025-03-04T20:51:41.3688526Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.3688666Z x_55 += x_57; out_30: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_55; x_55 = x_57 = None 2025-03-04T20:51:41.3688732Z 2025-03-04T20:51:41.3689015Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3689170Z out_31: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_30); out_30 = None 2025-03-04T20:51:41.3689231Z 2025-03-04T20:51:41.3689482Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3689962Z x_58: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.3690029Z 2025-03-04T20:51:41.3690295Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3692100Z x_59: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_58 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3692185Z 2025-03-04T20:51:41.3692469Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3692604Z out_32: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_59); x_59 = None 2025-03-04T20:51:41.3692667Z 2025-03-04T20:51:41.3692959Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3693467Z x_60: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_32 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.3693535Z 2025-03-04T20:51:41.3693799Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3695651Z x_61: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_60 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3695748Z 2025-03-04T20:51:41.3696076Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3696223Z out_33: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_61); x_61 = None 2025-03-04T20:51:41.3696287Z 2025-03-04T20:51:41.3696562Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3697085Z x_62: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_33 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.3697155Z 2025-03-04T20:51:41.3697444Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3699360Z x_63: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_62 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3699433Z 2025-03-04T20:51:41.3699731Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.3699895Z x_63 += out_31; out_34: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_63; x_63 = out_31 = None 2025-03-04T20:51:41.3699966Z 2025-03-04T20:51:41.3700252Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3700416Z out_35: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_34); out_34 = None 2025-03-04T20:51:41.3700478Z 2025-03-04T20:51:41.3700734Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3701242Z x_64: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.3701332Z 2025-03-04T20:51:41.3701612Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3703717Z x_65: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_64 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3703801Z 2025-03-04T20:51:41.3704124Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3704281Z out_36: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_65); x_65 = None 2025-03-04T20:51:41.3704349Z 2025-03-04T20:51:41.3704639Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3705212Z x_66: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_36 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.3705285Z 2025-03-04T20:51:41.3705611Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3707552Z x_67: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_66 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3707626Z 2025-03-04T20:51:41.3707958Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3708103Z out_37: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_67); x_67 = None 2025-03-04T20:51:41.3708172Z 2025-03-04T20:51:41.3708450Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3708967Z x_68: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_37 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.3709066Z 2025-03-04T20:51:41.3709345Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3711260Z x_69: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_68 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3711338Z 2025-03-04T20:51:41.3711634Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.3711793Z x_69 += out_35; out_38: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_69; x_69 = out_35 = None 2025-03-04T20:51:41.3711878Z 2025-03-04T20:51:41.3712181Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3712330Z out_39: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_38); out_38 = None 2025-03-04T20:51:41.3712401Z 2025-03-04T20:51:41.3712664Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3713175Z x_70: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.3713246Z 2025-03-04T20:51:41.3713526Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3715476Z x_71: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_70 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3715545Z 2025-03-04T20:51:41.3715865Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3716013Z out_40: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_71); x_71 = None 2025-03-04T20:51:41.3716071Z 2025-03-04T20:51:41.3716323Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3716791Z x_72: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_40 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.3716858Z 2025-03-04T20:51:41.3717118Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3718850Z x_73: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_72 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3718934Z 2025-03-04T20:51:41.3719213Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3719345Z out_41: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_73); x_73 = None 2025-03-04T20:51:41.3719404Z 2025-03-04T20:51:41.3719651Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3720127Z x_74: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_41 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.3720195Z 2025-03-04T20:51:41.3720448Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3722243Z x_75: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_74 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3722328Z 2025-03-04T20:51:41.3722600Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.3722747Z x_75 += out_39; out_42: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_75; x_75 = out_39 = None 2025-03-04T20:51:41.3722807Z 2025-03-04T20:51:41.3723087Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3723225Z out_43: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_42); out_42 = None 2025-03-04T20:51:41.3723289Z 2025-03-04T20:51:41.3723530Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3724010Z x_76: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.3724069Z 2025-03-04T20:51:41.3724332Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3726096Z x_77: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_76 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3726175Z 2025-03-04T20:51:41.3726458Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3726585Z out_44: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_77); x_77 = None 2025-03-04T20:51:41.3726653Z 2025-03-04T20:51:41.3726896Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3727399Z x_78: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_44 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.3727465Z 2025-03-04T20:51:41.3727738Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3729515Z x_79: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_78 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3729592Z 2025-03-04T20:51:41.3729880Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3730011Z out_45: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_79); x_79 = None 2025-03-04T20:51:41.3730071Z 2025-03-04T20:51:41.3730321Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3730798Z x_80: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_45 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.3730863Z 2025-03-04T20:51:41.3731123Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3732878Z x_81: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_80 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3732969Z 2025-03-04T20:51:41.3733249Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.3733410Z x_81 += out_43; out_46: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_81; x_81 = out_43 = None 2025-03-04T20:51:41.3733470Z 2025-03-04T20:51:41.3733767Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3733904Z out_47: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_46); out_46 = None 2025-03-04T20:51:41.3733973Z 2025-03-04T20:51:41.3734240Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3734721Z x_82: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.3734782Z 2025-03-04T20:51:41.3735054Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3736867Z x_83: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_82 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3736930Z 2025-03-04T20:51:41.3737225Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3737355Z out_48: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_83); x_83 = None 2025-03-04T20:51:41.3737423Z 2025-03-04T20:51:41.3737676Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3738167Z x_84: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_48 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.3738251Z 2025-03-04T20:51:41.3738525Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3740643Z x_85: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_84 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3740710Z 2025-03-04T20:51:41.3741007Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3741150Z out_49: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_85); x_85 = None 2025-03-04T20:51:41.3741223Z 2025-03-04T20:51:41.3741476Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3741974Z x_86: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_49 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.3742061Z 2025-03-04T20:51:41.3742330Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3744146Z x_87: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_86 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3744210Z 2025-03-04T20:51:41.3744500Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.3744650Z x_87 += out_47; out_50: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_87; x_87 = out_47 = None 2025-03-04T20:51:41.3744710Z 2025-03-04T20:51:41.3744998Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3745153Z out_51: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_50); out_50 = None 2025-03-04T20:51:41.3745220Z 2025-03-04T20:51:41.3745480Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3746045Z x_88: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.3746119Z 2025-03-04T20:51:41.3746430Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3748368Z x_89: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_88 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3748441Z 2025-03-04T20:51:41.3748735Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3748866Z out_52: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_89); x_89 = None 2025-03-04T20:51:41.3748952Z 2025-03-04T20:51:41.3749205Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3749693Z x_90: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_52 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.3749753Z 2025-03-04T20:51:41.3750026Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3751813Z x_91: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_90 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3751895Z 2025-03-04T20:51:41.3752191Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3752322Z out_53: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_91); x_91 = None 2025-03-04T20:51:41.3752389Z 2025-03-04T20:51:41.3752638Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3753139Z x_92: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_53 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.3753199Z 2025-03-04T20:51:41.3753481Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3755279Z x_93: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_92 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3755340Z 2025-03-04T20:51:41.3755617Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.3755776Z x_93 += out_51; out_54: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_93; x_93 = out_51 = None 2025-03-04T20:51:41.3755841Z 2025-03-04T20:51:41.3756117Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3756260Z out_55: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_54); out_54 = None 2025-03-04T20:51:41.3756319Z 2025-03-04T20:51:41.3756570Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3757038Z x_94: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.3757111Z 2025-03-04T20:51:41.3757378Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3759132Z x_95: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_94 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3759217Z 2025-03-04T20:51:41.3759497Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3759630Z out_56: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_95); x_95 = None 2025-03-04T20:51:41.3759696Z 2025-03-04T20:51:41.3759941Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3760442Z x_96: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_56 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.3760505Z 2025-03-04T20:51:41.3760772Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3762539Z x_97: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_96 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3762653Z 2025-03-04T20:51:41.3762944Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3763074Z out_57: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_97); x_97 = None 2025-03-04T20:51:41.3763140Z 2025-03-04T20:51:41.3763385Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3763877Z x_98: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_57 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.3763940Z 2025-03-04T20:51:41.3764212Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3765980Z x_99: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_98 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3766064Z 2025-03-04T20:51:41.3766342Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.3766478Z x_99 += out_55; out_58: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_99; x_99 = out_55 = None 2025-03-04T20:51:41.3766545Z 2025-03-04T20:51:41.3766820Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3766976Z out_59: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_58); out_58 = None 2025-03-04T20:51:41.3767037Z 2025-03-04T20:51:41.3767287Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3767772Z x_100: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.3767847Z 2025-03-04T20:51:41.3768103Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3769858Z x_101: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_100 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3769942Z 2025-03-04T20:51:41.3770222Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3770363Z out_60: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_101); x_101 = None 2025-03-04T20:51:41.3770421Z 2025-03-04T20:51:41.3770672Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3771150Z x_102: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_60 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.3771232Z 2025-03-04T20:51:41.3771496Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3773258Z x_103: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_102 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3773329Z 2025-03-04T20:51:41.3773627Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3773765Z out_61: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_103); x_103 = None 2025-03-04T20:51:41.3773824Z 2025-03-04T20:51:41.3774089Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3774587Z x_104: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_61 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.3774649Z 2025-03-04T20:51:41.3774923Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3776690Z x_105: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_104 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3776759Z 2025-03-04T20:51:41.3777043Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.3777188Z x_105 += out_59; out_62: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_105; x_105 = out_59 = None 2025-03-04T20:51:41.3777256Z 2025-03-04T20:51:41.3777532Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3777676Z out_63: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_62); out_62 = None 2025-03-04T20:51:41.3777751Z 2025-03-04T20:51:41.3778003Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3778476Z x_106: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_63, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.3778542Z 2025-03-04T20:51:41.3778805Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3780629Z x_107: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_106 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3780699Z 2025-03-04T20:51:41.3780985Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3781126Z out_64: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_107); x_107 = None 2025-03-04T20:51:41.3781188Z 2025-03-04T20:51:41.3781445Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3781948Z x_108: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_64, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_64 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.3782017Z 2025-03-04T20:51:41.3782284Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3784106Z x_109: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_108, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_108 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3784178Z 2025-03-04T20:51:41.3784463Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3784622Z out_65: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_109); x_109 = None 2025-03-04T20:51:41.3784682Z 2025-03-04T20:51:41.3784945Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3785432Z x_110: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_65, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_65 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.3785500Z 2025-03-04T20:51:41.3785827Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3787746Z x_111: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_110, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_110 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3787817Z 2025-03-04T20:51:41.3788097Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.3788268Z x_111 += out_63; out_66: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_111; x_111 = out_63 = None 2025-03-04T20:51:41.3788340Z 2025-03-04T20:51:41.3788626Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3788772Z out_67: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_66); out_66 = None 2025-03-04T20:51:41.3788833Z 2025-03-04T20:51:41.3789093Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3789584Z x_112: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_67, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.3789654Z 2025-03-04T20:51:41.3789923Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3791766Z x_113: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_112, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_112 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3791851Z 2025-03-04T20:51:41.3792135Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3792279Z out_68: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_113); x_113 = None 2025-03-04T20:51:41.3792340Z 2025-03-04T20:51:41.3792596Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3793083Z x_114: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_68, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_68 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.3793170Z 2025-03-04T20:51:41.3793433Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3795269Z x_115: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_114, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_114 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3795365Z 2025-03-04T20:51:41.3795651Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3795792Z out_69: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_115); x_115 = None 2025-03-04T20:51:41.3795852Z 2025-03-04T20:51:41.3796110Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3796603Z x_116: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_69, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_69 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.3796675Z 2025-03-04T20:51:41.3796940Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3798766Z x_117: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_116, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_116 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3798854Z 2025-03-04T20:51:41.3799128Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.3799283Z x_117 += out_67; out_70: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_117; x_117 = out_67 = None 2025-03-04T20:51:41.3799341Z 2025-03-04T20:51:41.3799628Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3799763Z out_71: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_70); out_70 = None 2025-03-04T20:51:41.3799843Z 2025-03-04T20:51:41.3800087Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3800579Z x_118: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_71, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.3800648Z 2025-03-04T20:51:41.3800908Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3802781Z x_119: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_118, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_118 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3802887Z 2025-03-04T20:51:41.3803172Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3803312Z out_72: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_119); x_119 = None 2025-03-04T20:51:41.3803372Z 2025-03-04T20:51:41.3803623Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3804105Z x_120: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_72, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_72 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.3804173Z 2025-03-04T20:51:41.3804438Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3806249Z x_121: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_120, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_120 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3806321Z 2025-03-04T20:51:41.3806627Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3806764Z out_73: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_121); x_121 = None 2025-03-04T20:51:41.3806824Z 2025-03-04T20:51:41.3807077Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3807579Z x_122: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_73, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_73 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.3807648Z 2025-03-04T20:51:41.3807906Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3809705Z x_123: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_122, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_122 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3809774Z 2025-03-04T20:51:41.3810044Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.3810194Z x_123 += out_71; out_74: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_123; x_123 = out_71 = None 2025-03-04T20:51:41.3810253Z 2025-03-04T20:51:41.3810537Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3810672Z out_75: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_74); out_74 = None 2025-03-04T20:51:41.3810738Z 2025-03-04T20:51:41.3810998Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3811479Z x_124: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_75, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.3811540Z 2025-03-04T20:51:41.3811807Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3813613Z x_125: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_124, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_124 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3813676Z 2025-03-04T20:51:41.3813963Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3814089Z out_76: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_125); x_125 = None 2025-03-04T20:51:41.3814159Z 2025-03-04T20:51:41.3814403Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3814898Z x_126: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_76, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_76 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.3814964Z 2025-03-04T20:51:41.3815221Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3817024Z x_127: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_126, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_126 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3817091Z 2025-03-04T20:51:41.3817367Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3817518Z out_77: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_127); x_127 = None 2025-03-04T20:51:41.3817578Z 2025-03-04T20:51:41.3817829Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3818309Z x_128: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_77, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_77 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.3818374Z 2025-03-04T20:51:41.3818631Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3820462Z x_129: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_128, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_128 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3820534Z 2025-03-04T20:51:41.3820815Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.3820972Z x_129 += out_75; out_78: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_129; x_129 = out_75 = None 2025-03-04T20:51:41.3821050Z 2025-03-04T20:51:41.3821345Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3821484Z out_79: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_78); out_78 = None 2025-03-04T20:51:41.3821551Z 2025-03-04T20:51:41.3821805Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3822299Z x_130: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_79, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.3822362Z 2025-03-04T20:51:41.3822638Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3824980Z x_131: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_130, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_130 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3825082Z 2025-03-04T20:51:41.3825420Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3825623Z out_80: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_131); x_131 = None 2025-03-04T20:51:41.3825703Z 2025-03-04T20:51:41.3825975Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3826519Z x_132: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_80, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_80 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.3826586Z 2025-03-04T20:51:41.3826894Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3828756Z x_133: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_132, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_132 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3828835Z 2025-03-04T20:51:41.3829130Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3829265Z out_81: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_133); x_133 = None 2025-03-04T20:51:41.3829333Z 2025-03-04T20:51:41.3829586Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3830094Z x_134: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_81, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_81 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.3830164Z 2025-03-04T20:51:41.3830463Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3832270Z x_135: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_134, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_134 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3832354Z 2025-03-04T20:51:41.3832632Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.3832787Z x_135 += out_79; out_82: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_135; x_135 = out_79 = None 2025-03-04T20:51:41.3832849Z 2025-03-04T20:51:41.3833139Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3833280Z out_83: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_82); out_82 = None 2025-03-04T20:51:41.3833365Z 2025-03-04T20:51:41.3833619Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3834126Z x_136: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_83, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.3834189Z 2025-03-04T20:51:41.3834463Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3836298Z x_137: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_136, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_136 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3836376Z 2025-03-04T20:51:41.3836669Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3836806Z out_84: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_137); x_137 = None 2025-03-04T20:51:41.3836873Z 2025-03-04T20:51:41.3837124Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3837627Z x_138: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_84, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_84 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.3837689Z 2025-03-04T20:51:41.3837960Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3839836Z x_139: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_138, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_138 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3839900Z 2025-03-04T20:51:41.3840210Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3840343Z out_85: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_139); x_139 = None 2025-03-04T20:51:41.3840412Z 2025-03-04T20:51:41.3840666Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3841190Z x_140: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_85, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_85 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.3841260Z 2025-03-04T20:51:41.3841525Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3843355Z x_141: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_140, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_140 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3843418Z 2025-03-04T20:51:41.3843709Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.3843863Z x_141 += out_83; out_86: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_141; x_141 = out_83 = None 2025-03-04T20:51:41.3843925Z 2025-03-04T20:51:41.3844232Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3844378Z out_87: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_86); out_86 = None 2025-03-04T20:51:41.3844451Z 2025-03-04T20:51:41.3844716Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3845259Z x_142: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_87, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.3845324Z 2025-03-04T20:51:41.3845612Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3847559Z x_143: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_142, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_142 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3847648Z 2025-03-04T20:51:41.3847950Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3848092Z out_88: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_143); x_143 = None 2025-03-04T20:51:41.3848163Z 2025-03-04T20:51:41.3848427Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3848974Z x_144: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_88, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_88 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.3849036Z 2025-03-04T20:51:41.3849324Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3851244Z x_145: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_144, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_144 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3851311Z 2025-03-04T20:51:41.3851627Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3851783Z out_89: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_145); x_145 = None 2025-03-04T20:51:41.3851853Z 2025-03-04T20:51:41.3852122Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3852654Z x_146: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_89, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_89 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.3852720Z 2025-03-04T20:51:41.3853007Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3854968Z x_147: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_146, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_146 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3855040Z 2025-03-04T20:51:41.3855348Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.3855502Z x_147 += out_87; out_90: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_147; x_147 = out_87 = None 2025-03-04T20:51:41.3855591Z 2025-03-04T20:51:41.3855890Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3856045Z out_91: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_90); out_90 = None 2025-03-04T20:51:41.3856109Z 2025-03-04T20:51:41.3856379Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3856897Z x_148: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_91, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.3856963Z 2025-03-04T20:51:41.3857249Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3859117Z x_149: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_148, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_148 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3859207Z 2025-03-04T20:51:41.3859500Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3859632Z out_92: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_149); x_149 = None 2025-03-04T20:51:41.3859701Z 2025-03-04T20:51:41.3859962Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3860463Z x_150: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_92, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_92 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.3860527Z 2025-03-04T20:51:41.3860814Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3862654Z x_151: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_150, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_150 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3862749Z 2025-03-04T20:51:41.3863041Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3863174Z out_93: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_151); x_151 = None 2025-03-04T20:51:41.3863243Z 2025-03-04T20:51:41.3863500Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3864015Z x_152: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_93, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_93 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.3864080Z 2025-03-04T20:51:41.3864366Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3866470Z x_153: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_152, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_152 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3866567Z 2025-03-04T20:51:41.3866892Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.3867053Z x_153 += out_91; out_94: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_153; x_153 = out_91 = None 2025-03-04T20:51:41.3867128Z 2025-03-04T20:51:41.3867442Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3867606Z out_95: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_94); out_94 = None 2025-03-04T20:51:41.3867677Z 2025-03-04T20:51:41.3867964Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3868487Z x_154: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_95, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.3868555Z 2025-03-04T20:51:41.3868810Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3870640Z x_155: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_154, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_154 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3870730Z 2025-03-04T20:51:41.3871020Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3871168Z out_96: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_155); x_155 = None 2025-03-04T20:51:41.3871227Z 2025-03-04T20:51:41.3871484Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3871987Z x_156: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_96, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_96 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.3872047Z 2025-03-04T20:51:41.3872308Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3874080Z x_157: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_156, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_156 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3874151Z 2025-03-04T20:51:41.3874453Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3874589Z out_97: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_157); x_157 = None 2025-03-04T20:51:41.3874657Z 2025-03-04T20:51:41.3874919Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3875417Z x_158: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_97, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_97 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.3875479Z 2025-03-04T20:51:41.3875747Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3877583Z x_159: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_158, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_158 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3877652Z 2025-03-04T20:51:41.3877941Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.3878087Z x_159 += out_95; out_98: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_159; x_159 = out_95 = None 2025-03-04T20:51:41.3878155Z 2025-03-04T20:51:41.3878439Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3878584Z out_99: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_98); out_98 = None 2025-03-04T20:51:41.3878644Z 2025-03-04T20:51:41.3878900Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3879399Z x_160: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_99, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.3879470Z 2025-03-04T20:51:41.3879740Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3881574Z x_161: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_160, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_160 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3881661Z 2025-03-04T20:51:41.3881951Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3882099Z out_100: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_161); x_161 = None 2025-03-04T20:51:41.3882163Z 2025-03-04T20:51:41.3882424Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3882931Z x_162: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_100, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_100 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.3883001Z 2025-03-04T20:51:41.3883266Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3885085Z x_163: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_162, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_162 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3885157Z 2025-03-04T20:51:41.3885444Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3885605Z out_101: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_163); x_163 = None 2025-03-04T20:51:41.3885666Z 2025-03-04T20:51:41.3885928Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3886444Z x_164: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_101, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_101 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.3886505Z 2025-03-04T20:51:41.3886782Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3888655Z x_165: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_164, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_164 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3888728Z 2025-03-04T20:51:41.3889010Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.3889163Z x_165 += out_99; out_102: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_165; x_165 = out_99 = None 2025-03-04T20:51:41.3889244Z 2025-03-04T20:51:41.3889528Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3889686Z out_103: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_102); out_102 = None 2025-03-04T20:51:41.3889747Z 2025-03-04T20:51:41.3890006Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3890491Z x_166: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_103, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.3890560Z 2025-03-04T20:51:41.3890826Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3892653Z x_167: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_166, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_166 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3892738Z 2025-03-04T20:51:41.3893016Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3893305Z out_104: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_167); x_167 = None 2025-03-04T20:51:41.3893374Z 2025-03-04T20:51:41.3893629Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3894121Z x_168: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_104, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_104 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.3894198Z 2025-03-04T20:51:41.3894483Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3896315Z x_169: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_168, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_168 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3896403Z 2025-03-04T20:51:41.3896688Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3896836Z out_105: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_169); x_169 = None 2025-03-04T20:51:41.3896896Z 2025-03-04T20:51:41.3897156Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3897659Z x_170: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_105, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_105 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.3897731Z 2025-03-04T20:51:41.3898005Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3900306Z x_171: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_170, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_170 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3900419Z 2025-03-04T20:51:41.3900746Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.3900921Z x_171 += out_103; out_106: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_171; x_171 = out_103 = None 2025-03-04T20:51:41.3900997Z 2025-03-04T20:51:41.3901327Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3901490Z out_107: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_106); out_106 = None 2025-03-04T20:51:41.3901556Z 2025-03-04T20:51:41.3901851Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3902497Z x_172: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_107, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.3902574Z 2025-03-04T20:51:41.3902854Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3904791Z x_173: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_172, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_172 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3904892Z 2025-03-04T20:51:41.3905194Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3905351Z out_108: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_173); x_173 = None 2025-03-04T20:51:41.3905418Z 2025-03-04T20:51:41.3905747Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3906312Z x_174: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_108, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_108 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.3906388Z 2025-03-04T20:51:41.3906696Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3908694Z x_175: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_174, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_174 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3908769Z 2025-03-04T20:51:41.3909105Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3909256Z out_109: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_175); x_175 = None 2025-03-04T20:51:41.3909321Z 2025-03-04T20:51:41.3909601Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3910153Z x_176: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_109, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_109 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.3910230Z 2025-03-04T20:51:41.3910515Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3912511Z x_177: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_176, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_176 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3912585Z 2025-03-04T20:51:41.3912889Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.3913064Z x_177 += out_107; out_110: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_177; x_177 = out_107 = None 2025-03-04T20:51:41.3913140Z 2025-03-04T20:51:41.3913425Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3913561Z out_111: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_110); out_110 = None 2025-03-04T20:51:41.3913627Z 2025-03-04T20:51:41.3913870Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3914378Z x_178: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_111, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.3914448Z 2025-03-04T20:51:41.3914713Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3916541Z x_179: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_178, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_178 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3916628Z 2025-03-04T20:51:41.3916909Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3917048Z out_112: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_179); x_179 = None 2025-03-04T20:51:41.3917108Z 2025-03-04T20:51:41.3917360Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3917858Z x_180: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_112, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_112 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.3917925Z 2025-03-04T20:51:41.3918185Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3919948Z x_181: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_180, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_180 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3920017Z 2025-03-04T20:51:41.3920296Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3920450Z out_113: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_181); x_181 = None 2025-03-04T20:51:41.3920510Z 2025-03-04T20:51:41.3920762Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3921243Z x_182: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_113, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_113 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.3921312Z 2025-03-04T20:51:41.3921569Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3923362Z x_183: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_182, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_182 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3923432Z 2025-03-04T20:51:41.3923708Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.3923866Z x_183 += out_111; out_114: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_183; x_183 = out_111 = None 2025-03-04T20:51:41.3923941Z 2025-03-04T20:51:41.3924227Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3924363Z out_115: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_114); out_114 = None 2025-03-04T20:51:41.3924430Z 2025-03-04T20:51:41.3924675Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3925158Z x_184: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_115, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.3925218Z 2025-03-04T20:51:41.3925486Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3927275Z x_185: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_184, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_184 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3927351Z 2025-03-04T20:51:41.3927643Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3927774Z out_116: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_185); x_185 = None 2025-03-04T20:51:41.3927842Z 2025-03-04T20:51:41.3928091Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3928575Z x_186: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_116, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_116 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.3928645Z 2025-03-04T20:51:41.3928923Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3930717Z x_187: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_186, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_186 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3930801Z 2025-03-04T20:51:41.3931080Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3931218Z out_117: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_187); x_187 = None 2025-03-04T20:51:41.3931276Z 2025-03-04T20:51:41.3931528Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3932020Z x_188: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_117, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_117 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.3932085Z 2025-03-04T20:51:41.3932344Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3934135Z x_189: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_188, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_188 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3934217Z 2025-03-04T20:51:41.3934497Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.3934654Z x_189 += out_115; out_118: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_189; x_189 = out_115 = None 2025-03-04T20:51:41.3934712Z 2025-03-04T20:51:41.3934997Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3935141Z out_119: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_118); out_118 = None 2025-03-04T20:51:41.3935209Z 2025-03-04T20:51:41.3935480Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3935987Z x_190: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.3936046Z 2025-03-04T20:51:41.3936313Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3938094Z x_191: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_190, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_190 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3938169Z 2025-03-04T20:51:41.3938460Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3938593Z out_120: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_191); x_191 = None 2025-03-04T20:51:41.3938659Z 2025-03-04T20:51:41.3938909Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3939394Z x_192: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_120, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_120 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.3939454Z 2025-03-04T20:51:41.3939720Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3941536Z x_193: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_192, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_192 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3941599Z 2025-03-04T20:51:41.3941911Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3942047Z out_121: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_193); x_193 = None 2025-03-04T20:51:41.3942113Z 2025-03-04T20:51:41.3942363Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3942882Z x_194: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_121, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_121 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.3942953Z 2025-03-04T20:51:41.3943223Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3945074Z x_195: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_194, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_194 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3945148Z 2025-03-04T20:51:41.3945415Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3945991Z x_196: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:51:41.3946061Z 2025-03-04T20:51:41.3946348Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3948250Z x_197: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_196, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_196 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3948346Z 2025-03-04T20:51:41.3948634Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.3948796Z x_195 += x_197; out_122: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_195; x_195 = x_197 = None 2025-03-04T20:51:41.3948865Z 2025-03-04T20:51:41.3949147Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3949296Z out_123: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_122); out_122 = None 2025-03-04T20:51:41.3949373Z 2025-03-04T20:51:41.3949633Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3950115Z x_198: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_123, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.3950204Z 2025-03-04T20:51:41.3950474Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3952275Z x_199: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_198, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_198 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3952346Z 2025-03-04T20:51:41.3952628Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3952771Z out_124: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_199); x_199 = None 2025-03-04T20:51:41.3952832Z 2025-03-04T20:51:41.3953087Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3953574Z x_200: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_124, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_124 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.3953659Z 2025-03-04T20:51:41.3953924Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3955746Z x_201: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_200, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_200 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3955820Z 2025-03-04T20:51:41.3956119Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3956257Z out_125: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_201); x_201 = None 2025-03-04T20:51:41.3956319Z 2025-03-04T20:51:41.3956573Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3957076Z x_202: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_125, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_125 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.3957153Z 2025-03-04T20:51:41.3957423Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3959222Z x_203: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_202, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_202 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3959291Z 2025-03-04T20:51:41.3959577Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.3959733Z x_203 += out_123; out_126: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_203; x_203 = out_123 = None 2025-03-04T20:51:41.3959815Z 2025-03-04T20:51:41.3960110Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3960257Z out_127: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_126); out_126 = None 2025-03-04T20:51:41.3960316Z 2025-03-04T20:51:41.3960567Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3961040Z x_204: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_127, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:51:41.3961105Z 2025-03-04T20:51:41.3961363Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3963152Z x_205: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_204, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_204 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3963225Z 2025-03-04T20:51:41.3963504Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3963665Z out_128: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_205); x_205 = None 2025-03-04T20:51:41.3963724Z 2025-03-04T20:51:41.3963969Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3964444Z x_206: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_128, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_128 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:51:41.3964513Z 2025-03-04T20:51:41.3964776Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3966601Z x_207: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_206, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_206 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3966685Z 2025-03-04T20:51:41.3966965Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3967101Z out_129: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_207); x_207 = None 2025-03-04T20:51:41.3967160Z 2025-03-04T20:51:41.3967413Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3967893Z x_208: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_129, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_129 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:51:41.3967959Z 2025-03-04T20:51:41.3968216Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:51:41.3970031Z x_209: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_208, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_208 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:51:41.3970119Z 2025-03-04T20:51:41.3970395Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:51:41.3970567Z x_209 += out_127; out_130: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_209; x_209 = out_127 = None 2025-03-04T20:51:41.3970627Z 2025-03-04T20:51:41.3970911Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:51:41.3971050Z out_131: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_130); out_130 = None 2025-03-04T20:51:41.3971115Z 2025-03-04T20:51:41.3971360Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3971945Z x_210: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(out_131, l_self_modules_backbone_lateral_convs_0_parameters_weight_, l_self_modules_backbone_lateral_convs_0_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_131 = l_self_modules_backbone_lateral_convs_0_parameters_weight_ = l_self_modules_backbone_lateral_convs_0_parameters_bias_ = None 2025-03-04T20:51:41.3972013Z 2025-03-04T20:51:41.3972256Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3972798Z x_211: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_210, l_self_modules_backbone_output_convs_0_parameters_weight_, l_self_modules_backbone_output_convs_0_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_self_modules_backbone_output_convs_0_parameters_weight_ = l_self_modules_backbone_output_convs_0_parameters_bias_ = None 2025-03-04T20:51:41.3972874Z 2025-03-04T20:51:41.3973282Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest") 2025-03-04T20:51:41.3973544Z top_down_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.interpolate(x_210, scale_factor = 2.0, mode = 'nearest'); x_210 = None 2025-03-04T20:51:41.3973612Z 2025-03-04T20:51:41.3973856Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3974421Z x_212: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_lateral_convs_1_parameters_weight_, l_self_modules_backbone_lateral_convs_1_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_119 = l_self_modules_backbone_lateral_convs_1_parameters_weight_ = l_self_modules_backbone_lateral_convs_1_parameters_bias_ = None 2025-03-04T20:51:41.3974491Z 2025-03-04T20:51:41.3974876Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features 2025-03-04T20:51:41.3975074Z prev_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = x_212 + top_down_features; x_212 = top_down_features = None 2025-03-04T20:51:41.3975136Z 2025-03-04T20:51:41.3975419Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3975994Z x_213: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(prev_features, l_self_modules_backbone_output_convs_1_parameters_weight_, l_self_modules_backbone_output_convs_1_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_self_modules_backbone_output_convs_1_parameters_weight_ = l_self_modules_backbone_output_convs_1_parameters_bias_ = None 2025-03-04T20:51:41.3976061Z 2025-03-04T20:51:41.3976480Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest") 2025-03-04T20:51:41.3976813Z top_down_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.interpolate(prev_features, scale_factor = 2.0, mode = 'nearest'); prev_features = None 2025-03-04T20:51:41.3976873Z 2025-03-04T20:51:41.3977129Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3977701Z x_214: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_lateral_convs_2_parameters_weight_, l_self_modules_backbone_lateral_convs_2_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_27 = l_self_modules_backbone_lateral_convs_2_parameters_weight_ = l_self_modules_backbone_lateral_convs_2_parameters_bias_ = None 2025-03-04T20:51:41.3977771Z 2025-03-04T20:51:41.3978116Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features 2025-03-04T20:51:41.3978328Z prev_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = x_214 + top_down_features_1; x_214 = top_down_features_1 = None 2025-03-04T20:51:41.3978395Z 2025-03-04T20:51:41.3978646Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3979229Z x_215: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(prev_features_1, l_self_modules_backbone_output_convs_2_parameters_weight_, l_self_modules_backbone_output_convs_2_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_self_modules_backbone_output_convs_2_parameters_weight_ = l_self_modules_backbone_output_convs_2_parameters_bias_ = None 2025-03-04T20:51:41.3979307Z 2025-03-04T20:51:41.3979712Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest") 2025-03-04T20:51:41.3980037Z top_down_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.interpolate(prev_features_1, scale_factor = 2.0, mode = 'nearest'); prev_features_1 = None 2025-03-04T20:51:41.3980105Z 2025-03-04T20:51:41.3980353Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3980936Z x_216: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_lateral_convs_3_parameters_weight_, l_self_modules_backbone_lateral_convs_3_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_11 = l_self_modules_backbone_lateral_convs_3_parameters_weight_ = l_self_modules_backbone_lateral_convs_3_parameters_bias_ = None 2025-03-04T20:51:41.3980999Z 2025-03-04T20:51:41.3981365Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features 2025-03-04T20:51:41.3981578Z prev_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_216 + top_down_features_2; x_216 = top_down_features_2 = None 2025-03-04T20:51:41.3981660Z 2025-03-04T20:51:41.3981908Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.3982542Z x_217: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(prev_features_2, l_self_modules_backbone_output_convs_3_parameters_weight_, l_self_modules_backbone_output_convs_3_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); prev_features_2 = l_self_modules_backbone_output_convs_3_parameters_weight_ = l_self_modules_backbone_output_convs_3_parameters_bias_ = None 2025-03-04T20:51:41.3982634Z 2025-03-04T20:51:41.3983014Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:200 in forward, code: return [F.max_pool2d(x, kernel_size=1, stride=2, padding=0)] 2025-03-04T20:51:41.3983242Z res: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.max_pool2d(x_211, kernel_size = 1, stride = 2, padding = 0) 2025-03-04T20:51:41.3983308Z 2025-03-04T20:51:41.3983775Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:51:41.3983929Z arange: "f32[304][1]cpu" = torch.arange(0.0, 1216, step = 4, dtype = torch.float32) 2025-03-04T20:51:41.3983997Z 2025-03-04T20:51:41.3984305Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:51:41.3984458Z shifts_x: "f32[304][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T20:51:41.3984522Z 2025-03-04T20:51:41.3984991Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:51:41.3985150Z arange_1: "f32[296][1]cpu" = torch.arange(0.0, 1184, step = 4, dtype = torch.float32) 2025-03-04T20:51:41.3985224Z 2025-03-04T20:51:41.3985582Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:51:41.3985771Z shifts_y: "f32[296][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T20:51:41.3985836Z 2025-03-04T20:51:41.3986254Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:51:41.3986444Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T20:51:41.3986554Z shift_y: "f32[296, 304][1, 0]cpu" = meshgrid[0] 2025-03-04T20:51:41.3986687Z shift_x: "f32[296, 304][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T20:51:41.3986757Z 2025-03-04T20:51:41.3987090Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:51:41.3987226Z shift_x_1: "f32[89984][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T20:51:41.3987288Z 2025-03-04T20:51:41.3987626Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:51:41.3987760Z shift_y_1: "f32[89984][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T20:51:41.3987830Z 2025-03-04T20:51:41.3988213Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:51:41.3988448Z shifts: "f32[89984, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T20:51:41.3988516Z 2025-03-04T20:51:41.3988927Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:51:41.3989057Z view: "f32[89984, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T20:51:41.3989488Z view_1: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T20:51:41.3989612Z add_3: "f32[89984, 3, 4][12, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T20:51:41.3989730Z x_218: "f32[269952, 4][4, 1]cpu" = add_3.reshape(-1, 4); add_3 = None 2025-03-04T20:51:41.3989796Z 2025-03-04T20:51:41.3990229Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:51:41.3990385Z arange_2: "f32[152][1]cpu" = torch.arange(0.0, 1216, step = 8, dtype = torch.float32) 2025-03-04T20:51:41.3990446Z 2025-03-04T20:51:41.3990744Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:51:41.3990896Z shifts_x_1: "f32[152][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T20:51:41.3990962Z 2025-03-04T20:51:41.3991381Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:51:41.3991526Z arange_3: "f32[148][1]cpu" = torch.arange(0.0, 1184, step = 8, dtype = torch.float32) 2025-03-04T20:51:41.3991586Z 2025-03-04T20:51:41.3991877Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:51:41.3992024Z shifts_y_1: "f32[148][1]cpu" = arange_3.to(device(type='cpu')); arange_3 = None 2025-03-04T20:51:41.3992092Z 2025-03-04T20:51:41.3992457Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:51:41.3992652Z meshgrid_1 = torch.functional.meshgrid(shifts_y_1, shifts_x_1); shifts_y_1 = shifts_x_1 = None 2025-03-04T20:51:41.3992751Z shift_y_2: "f32[148, 152][1, 0]cpu" = meshgrid_1[0] 2025-03-04T20:51:41.3992884Z shift_x_2: "f32[148, 152][0, 1]cpu" = meshgrid_1[1]; meshgrid_1 = None 2025-03-04T20:51:41.3992945Z 2025-03-04T20:51:41.3993279Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:51:41.3993409Z shift_x_3: "f32[22496][1]cpu" = shift_x_2.reshape(-1); shift_x_2 = None 2025-03-04T20:51:41.3993477Z 2025-03-04T20:51:41.3993818Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:51:41.3993953Z shift_y_3: "f32[22496][1]cpu" = shift_y_2.reshape(-1); shift_y_2 = None 2025-03-04T20:51:41.3994015Z 2025-03-04T20:51:41.3994426Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:51:41.3994654Z shifts_1: "f32[22496, 4][4, 1]cpu" = torch.stack((shift_x_3, shift_y_3, shift_x_3, shift_y_3), dim = 1); shift_x_3 = shift_y_3 = None 2025-03-04T20:51:41.3994716Z 2025-03-04T20:51:41.3995146Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:51:41.3995296Z view_2: "f32[22496, 1, 4][4, 4, 1]cpu" = shifts_1.view(-1, 1, 4); shifts_1 = None 2025-03-04T20:51:41.3995737Z view_3: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = None 2025-03-04T20:51:41.3995861Z add_4: "f32[22496, 3, 4][12, 4, 1]cpu" = view_2 + view_3; view_2 = view_3 = None 2025-03-04T20:51:41.3995994Z x_219: "f32[67488, 4][4, 1]cpu" = add_4.reshape(-1, 4); add_4 = None 2025-03-04T20:51:41.3996055Z 2025-03-04T20:51:41.3996489Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:51:41.3996650Z arange_4: "f32[76][1]cpu" = torch.arange(0.0, 1216, step = 16, dtype = torch.float32) 2025-03-04T20:51:41.3996720Z 2025-03-04T20:51:41.3997024Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:51:41.3997171Z shifts_x_2: "f32[76][1]cpu" = arange_4.to(device(type='cpu')); arange_4 = None 2025-03-04T20:51:41.3997232Z 2025-03-04T20:51:41.3997685Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:51:41.3997829Z arange_5: "f32[74][1]cpu" = torch.arange(0.0, 1184, step = 16, dtype = torch.float32) 2025-03-04T20:51:41.3997899Z 2025-03-04T20:51:41.3998199Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:51:41.3998668Z shifts_y_2: "f32[74][1]cpu" = arange_5.to(device(type='cpu')); arange_5 = None 2025-03-04T20:51:41.3998733Z 2025-03-04T20:51:41.3999131Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:51:41.3999328Z meshgrid_2 = torch.functional.meshgrid(shifts_y_2, shifts_x_2); shifts_y_2 = shifts_x_2 = None 2025-03-04T20:51:41.3999439Z shift_y_4: "f32[74, 76][1, 0]cpu" = meshgrid_2[0] 2025-03-04T20:51:41.3999559Z shift_x_4: "f32[74, 76][0, 1]cpu" = meshgrid_2[1]; meshgrid_2 = None 2025-03-04T20:51:41.3999630Z 2025-03-04T20:51:41.3999971Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:51:41.4000106Z shift_x_5: "f32[5624][1]cpu" = shift_x_4.reshape(-1); shift_x_4 = None 2025-03-04T20:51:41.4000169Z 2025-03-04T20:51:41.4000529Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:51:41.4000653Z shift_y_5: "f32[5624][1]cpu" = shift_y_4.reshape(-1); shift_y_4 = None 2025-03-04T20:51:41.4000725Z 2025-03-04T20:51:41.4001131Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:51:41.4001358Z shifts_2: "f32[5624, 4][4, 1]cpu" = torch.stack((shift_x_5, shift_y_5, shift_x_5, shift_y_5), dim = 1); shift_x_5 = shift_y_5 = None 2025-03-04T20:51:41.4001426Z 2025-03-04T20:51:41.4002419Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:51:41.4002615Z view_4: "f32[5624, 1, 4][4, 4, 1]cpu" = shifts_2.view(-1, 1, 4); shifts_2 = None 2025-03-04T20:51:41.4003050Z view_5: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = None 2025-03-04T20:51:41.4003184Z add_5: "f32[5624, 3, 4][12, 4, 1]cpu" = view_4 + view_5; view_4 = view_5 = None 2025-03-04T20:51:41.4003299Z x_220: "f32[16872, 4][4, 1]cpu" = add_5.reshape(-1, 4); add_5 = None 2025-03-04T20:51:41.4003372Z 2025-03-04T20:51:41.4003819Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:51:41.4003976Z arange_6: "f32[38][1]cpu" = torch.arange(0.0, 1216, step = 32, dtype = torch.float32) 2025-03-04T20:51:41.4004039Z 2025-03-04T20:51:41.4004350Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:51:41.4004489Z shifts_x_3: "f32[38][1]cpu" = arange_6.to(device(type='cpu')); arange_6 = None 2025-03-04T20:51:41.4004560Z 2025-03-04T20:51:41.4005002Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:51:41.4005158Z arange_7: "f32[37][1]cpu" = torch.arange(0.0, 1184, step = 32, dtype = torch.float32) 2025-03-04T20:51:41.4005220Z 2025-03-04T20:51:41.4005558Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:51:41.4005695Z shifts_y_3: "f32[37][1]cpu" = arange_7.to(device(type='cpu')); arange_7 = None 2025-03-04T20:51:41.4005763Z 2025-03-04T20:51:41.4006146Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:51:41.4006348Z meshgrid_3 = torch.functional.meshgrid(shifts_y_3, shifts_x_3); shifts_y_3 = shifts_x_3 = None 2025-03-04T20:51:41.4006449Z shift_y_6: "f32[37, 38][1, 0]cpu" = meshgrid_3[0] 2025-03-04T20:51:41.4006572Z shift_x_6: "f32[37, 38][0, 1]cpu" = meshgrid_3[1]; meshgrid_3 = None 2025-03-04T20:51:41.4006634Z 2025-03-04T20:51:41.4006985Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:51:41.4007104Z shift_x_7: "f32[1406][1]cpu" = shift_x_6.reshape(-1); shift_x_6 = None 2025-03-04T20:51:41.4007172Z 2025-03-04T20:51:41.4007514Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:51:41.4007641Z shift_y_7: "f32[1406][1]cpu" = shift_y_6.reshape(-1); shift_y_6 = None 2025-03-04T20:51:41.4007700Z 2025-03-04T20:51:41.4008105Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:51:41.4008319Z shifts_3: "f32[1406, 4][4, 1]cpu" = torch.stack((shift_x_7, shift_y_7, shift_x_7, shift_y_7), dim = 1); shift_x_7 = shift_y_7 = None 2025-03-04T20:51:41.4008382Z 2025-03-04T20:51:41.4008800Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:51:41.4008940Z view_6: "f32[1406, 1, 4][4, 4, 1]cpu" = shifts_3.view(-1, 1, 4); shifts_3 = None 2025-03-04T20:51:41.4009365Z view_7: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = None 2025-03-04T20:51:41.4009484Z add_6: "f32[1406, 3, 4][12, 4, 1]cpu" = view_6 + view_7; view_6 = view_7 = None 2025-03-04T20:51:41.4009602Z x_221: "f32[4218, 4][4, 1]cpu" = add_6.reshape(-1, 4); add_6 = None 2025-03-04T20:51:41.4009663Z 2025-03-04T20:51:41.4010101Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:51:41.4010247Z arange_8: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32) 2025-03-04T20:51:41.4010316Z 2025-03-04T20:51:41.4010609Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:51:41.4010747Z shifts_x_4: "f32[19][1]cpu" = arange_8.to(device(type='cpu')); arange_8 = None 2025-03-04T20:51:41.4010808Z 2025-03-04T20:51:41.4011263Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:51:41.4011408Z arange_9: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32) 2025-03-04T20:51:41.4011495Z 2025-03-04T20:51:41.4011805Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:51:41.4011949Z shifts_y_4: "f32[19][1]cpu" = arange_9.to(device(type='cpu')); arange_9 = None 2025-03-04T20:51:41.4012010Z 2025-03-04T20:51:41.4012409Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:51:41.4012606Z meshgrid_4 = torch.functional.meshgrid(shifts_y_4, shifts_x_4); shifts_y_4 = shifts_x_4 = None 2025-03-04T20:51:41.4012713Z shift_y_8: "f32[19, 19][1, 0]cpu" = meshgrid_4[0] 2025-03-04T20:51:41.4012830Z shift_x_8: "f32[19, 19][0, 1]cpu" = meshgrid_4[1]; meshgrid_4 = None 2025-03-04T20:51:41.4012900Z 2025-03-04T20:51:41.4013244Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:51:41.4013379Z shift_x_9: "f32[361][1]cpu" = shift_x_8.reshape(-1); shift_x_8 = None 2025-03-04T20:51:41.4013465Z 2025-03-04T20:51:41.4013819Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:51:41.4013940Z shift_y_9: "f32[361][1]cpu" = shift_y_8.reshape(-1); shift_y_8 = None 2025-03-04T20:51:41.4014009Z 2025-03-04T20:51:41.4014427Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:51:41.4014649Z shifts_4: "f32[361, 4][4, 1]cpu" = torch.stack((shift_x_9, shift_y_9, shift_x_9, shift_y_9), dim = 1); shift_x_9 = shift_y_9 = None 2025-03-04T20:51:41.4014719Z 2025-03-04T20:51:41.4015156Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:51:41.4015304Z view_8: "f32[361, 1, 4][4, 4, 1]cpu" = shifts_4.view(-1, 1, 4); shifts_4 = None 2025-03-04T20:51:41.4015752Z view_9: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = None 2025-03-04T20:51:41.4015878Z add_7: "f32[361, 3, 4][12, 4, 1]cpu" = view_8 + view_9; view_8 = view_9 = None 2025-03-04T20:51:41.4015992Z x_222: "f32[1083, 4][4, 1]cpu" = add_7.reshape(-1, 4); add_7 = None 2025-03-04T20:51:41.4016063Z 2025-03-04T20:51:41.4016384Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:51:41.4016521Z tensor: "f32[269952, 4][4, 1]cpu" = x_218.to(torch.float32); x_218 = None 2025-03-04T20:51:41.4016651Z tensor_1: "f32[67488, 4][4, 1]cpu" = x_219.to(torch.float32); x_219 = None 2025-03-04T20:51:41.4016779Z tensor_2: "f32[16872, 4][4, 1]cpu" = x_220.to(torch.float32); x_220 = None 2025-03-04T20:51:41.4016900Z tensor_3: "f32[4218, 4][4, 1]cpu" = x_221.to(torch.float32); x_221 = None 2025-03-04T20:51:41.4017025Z tensor_4: "f32[1083, 4][4, 1]cpu" = x_222.to(torch.float32); x_222 = None 2025-03-04T20:51:41.4017090Z 2025-03-04T20:51:41.4017360Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.4017883Z x_223: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_217, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_217 = None 2025-03-04T20:51:41.4017972Z 2025-03-04T20:51:41.4018268Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:51:41.4018474Z x_224: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.relu(x_223, inplace = False); x_223 = None 2025-03-04T20:51:41.4018536Z 2025-03-04T20:51:41.4018951Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:51:41.4019478Z score: "f32[4, 3, 296, 304][269952, 89984, 304, 1]cpu" = torch.conv2d(x_224, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T20:51:41.4019547Z 2025-03-04T20:51:41.4019916Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:51:41.4020459Z x_233: "f32[4, 12, 296, 304][1079808, 89984, 304, 1]cpu" = torch.conv2d(x_224, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_224 = None 2025-03-04T20:51:41.4020528Z 2025-03-04T20:51:41.4020781Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.4021268Z x_225: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(x_215, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_215 = None 2025-03-04T20:51:41.4021347Z 2025-03-04T20:51:41.4021630Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:51:41.4021819Z x_226: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.relu(x_225, inplace = False); x_225 = None 2025-03-04T20:51:41.4021887Z 2025-03-04T20:51:41.4022263Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:51:41.4022785Z score_1: "f32[4, 3, 148, 152][67488, 22496, 152, 1]cpu" = torch.conv2d(x_226, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T20:51:41.4022848Z 2025-03-04T20:51:41.4023215Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:51:41.4023748Z x_234: "f32[4, 12, 148, 152][269952, 22496, 152, 1]cpu" = torch.conv2d(x_226, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_226 = None 2025-03-04T20:51:41.4023807Z 2025-03-04T20:51:41.4024064Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.4024546Z x_227: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(x_213, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_213 = None 2025-03-04T20:51:41.4024629Z 2025-03-04T20:51:41.4024925Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:51:41.4025114Z x_228: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.relu(x_227, inplace = False); x_227 = None 2025-03-04T20:51:41.4025175Z 2025-03-04T20:51:41.4025602Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:51:41.4026133Z score_2: "f32[4, 3, 74, 76][16872, 5624, 76, 1]cpu" = torch.conv2d(x_228, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T20:51:41.4026212Z 2025-03-04T20:51:41.4026609Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:51:41.4027158Z x_235: "f32[4, 12, 74, 76][67488, 5624, 76, 1]cpu" = torch.conv2d(x_228, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_228 = None 2025-03-04T20:51:41.4027229Z 2025-03-04T20:51:41.4027476Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.4027944Z x_229: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_211, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_211 = None 2025-03-04T20:51:41.4028022Z 2025-03-04T20:51:41.4028296Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:51:41.4028472Z x_230: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.nn.functional.relu(x_229, inplace = False); x_229 = None 2025-03-04T20:51:41.4028538Z 2025-03-04T20:51:41.4028907Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:51:41.4029403Z score_3: "f32[4, 3, 37, 38][4218, 1406, 38, 1]cpu" = torch.conv2d(x_230, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T20:51:41.4029463Z 2025-03-04T20:51:41.4029817Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:51:41.4030310Z x_236: "f32[4, 12, 37, 38][16872, 1406, 38, 1]cpu" = torch.conv2d(x_230, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_230 = None 2025-03-04T20:51:41.4030376Z 2025-03-04T20:51:41.4030628Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:51:41.4031387Z x_231: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.conv2d(res, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); res = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T20:51:41.4031455Z 2025-03-04T20:51:41.4031720Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:51:41.4031896Z x_232: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.relu(x_231, inplace = False); x_231 = None 2025-03-04T20:51:41.4031954Z 2025-03-04T20:51:41.4032323Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:51:41.4033177Z score_4: "f32[4, 3, 19, 19][1083, 361, 19, 1]cpu" = torch.conv2d(x_232, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T20:51:41.4033245Z 2025-03-04T20:51:41.4033604Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:51:41.4034404Z x_237: "f32[4, 12, 19, 19][4332, 361, 19, 1]cpu" = torch.conv2d(x_232, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_232 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T20:51:41.4034487Z 2025-03-04T20:51:41.4034826Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T20:51:41.4034998Z permute: "f32[4, 296, 304, 3][269952, 304, 1, 89984]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T20:51:41.4035139Z logits_i: "f32[4, 269952][269952, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T20:51:41.4035306Z permute_1: "f32[4, 148, 152, 3][67488, 152, 1, 22496]cpu" = score_1.permute(0, 2, 3, 1); score_1 = None 2025-03-04T20:51:41.4035450Z logits_i_1: "f32[4, 67488][67488, 1]cpu" = permute_1.flatten(1); permute_1 = None 2025-03-04T20:51:41.4035610Z permute_2: "f32[4, 74, 76, 3][16872, 76, 1, 5624]cpu" = score_2.permute(0, 2, 3, 1); score_2 = None 2025-03-04T20:51:41.4035749Z logits_i_2: "f32[4, 16872][16872, 1]cpu" = permute_2.flatten(1); permute_2 = None 2025-03-04T20:51:41.4035912Z permute_3: "f32[4, 37, 38, 3][4218, 38, 1, 1406]cpu" = score_3.permute(0, 2, 3, 1); score_3 = None 2025-03-04T20:51:41.4036039Z logits_i_3: "f32[4, 4218][4218, 1]cpu" = permute_3.flatten(1); permute_3 = None 2025-03-04T20:51:41.4036189Z permute_4: "f32[4, 19, 19, 3][1083, 19, 1, 361]cpu" = score_4.permute(0, 2, 3, 1); score_4 = None 2025-03-04T20:51:41.4036315Z logits_i_4: "f32[4, 1083][1083, 1]cpu" = permute_4.flatten(1); permute_4 = None 2025-03-04T20:51:41.4036385Z 2025-03-04T20:51:41.4036798Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T20:51:41.4036998Z view_10: "f32[4, 3, 4, 296, 304][1079808, 359936, 89984, 304, 1]cpu" = x_233.view(4, -1, 4, 296, 304); x_233 = None 2025-03-04T20:51:41.4037177Z permute_5: "f32[4, 296, 304, 3, 4][1079808, 304, 1, 359936, 89984]cpu" = view_10.permute(0, 3, 4, 1, 2); view_10 = None 2025-03-04T20:51:41.4037356Z pred_anchor_deltas_i: "f32[4, 269952, 4][1079808, 4, 1]cpu" = permute_5.flatten(1, -2); permute_5 = None 2025-03-04T20:51:41.4037521Z view_11: "f32[4, 3, 4, 148, 152][269952, 89984, 22496, 152, 1]cpu" = x_234.view(4, -1, 4, 148, 152); x_234 = None 2025-03-04T20:51:41.4037689Z permute_6: "f32[4, 148, 152, 3, 4][269952, 152, 1, 89984, 22496]cpu" = view_11.permute(0, 3, 4, 1, 2); view_11 = None 2025-03-04T20:51:41.4037862Z pred_anchor_deltas_i_2: "f32[4, 67488, 4][269952, 4, 1]cpu" = permute_6.flatten(1, -2); permute_6 = None 2025-03-04T20:51:41.4038007Z view_12: "f32[4, 3, 4, 74, 76][67488, 22496, 5624, 76, 1]cpu" = x_235.view(4, -1, 4, 74, 76); x_235 = None 2025-03-04T20:51:41.4038171Z permute_7: "f32[4, 74, 76, 3, 4][67488, 76, 1, 22496, 5624]cpu" = view_12.permute(0, 3, 4, 1, 2); view_12 = None 2025-03-04T20:51:41.4038348Z pred_anchor_deltas_i_4: "f32[4, 16872, 4][67488, 4, 1]cpu" = permute_7.flatten(1, -2); permute_7 = None 2025-03-04T20:51:41.4038490Z view_13: "f32[4, 3, 4, 37, 38][16872, 5624, 1406, 38, 1]cpu" = x_236.view(4, -1, 4, 37, 38); x_236 = None 2025-03-04T20:51:41.4038656Z permute_8: "f32[4, 37, 38, 3, 4][16872, 38, 1, 5624, 1406]cpu" = view_13.permute(0, 3, 4, 1, 2); view_13 = None 2025-03-04T20:51:41.4038824Z pred_anchor_deltas_i_6: "f32[4, 4218, 4][16872, 4, 1]cpu" = permute_8.flatten(1, -2); permute_8 = None 2025-03-04T20:51:41.4038955Z view_14: "f32[4, 3, 4, 19, 19][4332, 1444, 361, 19, 1]cpu" = x_237.view(4, -1, 4, 19, 19); x_237 = None 2025-03-04T20:51:41.4039114Z permute_9: "f32[4, 19, 19, 3, 4][4332, 19, 1, 1444, 361]cpu" = view_14.permute(0, 3, 4, 1, 2); view_14 = None 2025-03-04T20:51:41.4039270Z pred_anchor_deltas_i_8: "f32[4, 1083, 4][4332, 4, 1]cpu" = permute_9.flatten(1, -2); permute_9 = None 2025-03-04T20:51:41.4039352Z 2025-03-04T20:51:41.4039755Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:51:41.4039961Z pred_anchor_deltas_i_1: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T20:51:41.4040022Z 2025-03-04T20:51:41.4040454Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:51:41.4040606Z unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T20:51:41.4040756Z expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T20:51:41.4040889Z anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T20:51:41.4040957Z 2025-03-04T20:51:41.4041323Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:51:41.4041495Z deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T20:51:41.4041554Z 2025-03-04T20:51:41.4041866Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:51:41.4042008Z boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T20:51:41.4042083Z 2025-03-04T20:51:41.4042400Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:51:41.4042530Z getitem_10: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:51:41.4042660Z getitem_11: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:51:41.4042807Z widths: "f32[1079808][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T20:51:41.4042873Z 2025-03-04T20:51:41.4043189Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:51:41.4043320Z getitem_12: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:51:41.4043438Z getitem_13: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:51:41.4043594Z heights: "f32[1079808][1]cpu" = getitem_12 - getitem_13; getitem_12 = getitem_13 = None 2025-03-04T20:51:41.4043654Z 2025-03-04T20:51:41.4043981Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:51:41.4044101Z getitem_14: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:51:41.4044192Z mul: "f32[1079808][1]cpu" = 0.5 * widths 2025-03-04T20:51:41.4044330Z ctr_x: "f32[1079808][1]cpu" = getitem_14 + mul; getitem_14 = mul = None 2025-03-04T20:51:41.4044398Z 2025-03-04T20:51:41.4044713Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:51:41.4044867Z getitem_15: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:51:41.4044965Z mul_1: "f32[1079808][1]cpu" = 0.5 * heights 2025-03-04T20:51:41.4045095Z ctr_y: "f32[1079808][1]cpu" = getitem_15 + mul_1; getitem_15 = mul_1 = None 2025-03-04T20:51:41.4045173Z 2025-03-04T20:51:41.4045512Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:51:41.4045669Z getitem_16: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:51:41.4045790Z dx: "f32[1079808, 1][1, 1]cpu" = getitem_16 / 1.0; getitem_16 = None 2025-03-04T20:51:41.4045852Z 2025-03-04T20:51:41.4046158Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:51:41.4046313Z getitem_17: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:51:41.4046436Z dy: "f32[1079808, 1][1, 1]cpu" = getitem_17 / 1.0; getitem_17 = None 2025-03-04T20:51:41.4046497Z 2025-03-04T20:51:41.4046809Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:51:41.4046959Z getitem_18: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:51:41.4047075Z dw: "f32[1079808, 1][1, 1]cpu" = getitem_18 / 1.0; getitem_18 = None 2025-03-04T20:51:41.4047134Z 2025-03-04T20:51:41.4047446Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:51:41.4047628Z getitem_19: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:51:41.4047742Z dh: "f32[1079808, 1][1, 1]cpu" = getitem_19 / 1.0; getitem_19 = None 2025-03-04T20:51:41.4047819Z 2025-03-04T20:51:41.4048164Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:51:41.4048303Z dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:51:41.4048370Z 2025-03-04T20:51:41.4048698Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:51:41.4048839Z dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:51:41.4048906Z 2025-03-04T20:51:41.4049247Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:51:41.4049391Z getitem_20: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:51:41.4049514Z mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_20; dx = getitem_20 = None 2025-03-04T20:51:41.4049689Z getitem_21: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:51:41.4049830Z pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_21; mul_2 = getitem_21 = None 2025-03-04T20:51:41.4049897Z 2025-03-04T20:51:41.4050255Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:51:41.4050402Z getitem_22: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:51:41.4050523Z mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_22; dy = getitem_22 = None 2025-03-04T20:51:41.4050680Z getitem_23: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:51:41.4050817Z pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_23; mul_3 = getitem_23 = None 2025-03-04T20:51:41.4050900Z 2025-03-04T20:51:41.4051236Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:51:41.4051359Z exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:51:41.4051520Z getitem_24: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:51:41.4051656Z pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_24; exp = getitem_24 = None 2025-03-04T20:51:41.4051716Z 2025-03-04T20:51:41.4052059Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:51:41.4052176Z exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:51:41.4052348Z getitem_25: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:51:41.4052482Z pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_25; exp_1 = getitem_25 = None 2025-03-04T20:51:41.4052550Z 2025-03-04T20:51:41.4052865Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:51:41.4052969Z mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T20:51:41.4053085Z x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:51:41.4053153Z 2025-03-04T20:51:41.4053466Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:51:41.4053582Z mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T20:51:41.4053694Z y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:51:41.4053763Z 2025-03-04T20:51:41.4054086Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:51:41.4054207Z mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:51:41.4054333Z x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:51:41.4054401Z 2025-03-04T20:51:41.4054703Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:51:41.4054819Z mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:51:41.4054943Z y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:51:41.4055012Z 2025-03-04T20:51:41.4055373Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:51:41.4055564Z pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:51:41.4055625Z 2025-03-04T20:51:41.4055980Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:51:41.4056142Z proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4)); pred_boxes = None 2025-03-04T20:51:41.4056209Z 2025-03-04T20:51:41.4056590Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:51:41.4056774Z proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T20:51:41.4056857Z 2025-03-04T20:51:41.4057270Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:51:41.4057485Z pred_anchor_deltas_i_3: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_2.reshape(-1, 4); pred_anchor_deltas_i_2 = None 2025-03-04T20:51:41.4057546Z 2025-03-04T20:51:41.4057993Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:51:41.4058144Z unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = tensor_1.unsqueeze(0); tensor_1 = None 2025-03-04T20:51:41.4058299Z expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1); unsqueeze_1 = None 2025-03-04T20:51:41.4058438Z anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4); expand_1 = None 2025-03-04T20:51:41.4058506Z 2025-03-04T20:51:41.4058877Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:51:41.4059052Z deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float(); pred_anchor_deltas_i_3 = None 2025-03-04T20:51:41.4059112Z 2025-03-04T20:51:41.4059434Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:51:41.4059577Z boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32); anchors_i_1 = None 2025-03-04T20:51:41.4059660Z 2025-03-04T20:51:41.4059978Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:51:41.4060121Z getitem_26: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)] 2025-03-04T20:51:41.4060247Z getitem_27: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T20:51:41.4060403Z widths_1: "f32[269952][1]cpu" = getitem_26 - getitem_27; getitem_26 = getitem_27 = None 2025-03-04T20:51:41.4060464Z 2025-03-04T20:51:41.4060793Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:51:41.4060917Z getitem_28: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)] 2025-03-04T20:51:41.4061045Z getitem_29: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)] 2025-03-04T20:51:41.4061196Z heights_1: "f32[269952][1]cpu" = getitem_28 - getitem_29; getitem_28 = getitem_29 = None 2025-03-04T20:51:41.4061265Z 2025-03-04T20:51:41.4061597Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:51:41.4061727Z getitem_30: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T20:51:41.4061815Z mul_10: "f32[269952][1]cpu" = 0.5 * widths_1 2025-03-04T20:51:41.4061965Z ctr_x_1: "f32[269952][1]cpu" = getitem_30 + mul_10; getitem_30 = mul_10 = None 2025-03-04T20:51:41.4062027Z 2025-03-04T20:51:41.4062347Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:51:41.4062496Z getitem_31: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]; boxes_1 = None 2025-03-04T20:51:41.4062599Z mul_11: "f32[269952][1]cpu" = 0.5 * heights_1 2025-03-04T20:51:41.4062729Z ctr_y_1: "f32[269952][1]cpu" = getitem_31 + mul_11; getitem_31 = mul_11 = None 2025-03-04T20:51:41.4062817Z 2025-03-04T20:51:41.4063139Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:51:41.4063303Z getitem_32: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:51:41.4063419Z dx_1: "f32[269952, 1][1, 1]cpu" = getitem_32 / 1.0; getitem_32 = None 2025-03-04T20:51:41.4063488Z 2025-03-04T20:51:41.4063800Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:51:41.4063954Z getitem_33: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:51:41.4064074Z dy_1: "f32[269952, 1][1, 1]cpu" = getitem_33 / 1.0; getitem_33 = None 2025-03-04T20:51:41.4064138Z 2025-03-04T20:51:41.4064448Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:51:41.4064601Z getitem_34: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:51:41.4064718Z dw_2: "f32[269952, 1][1, 1]cpu" = getitem_34 / 1.0; getitem_34 = None 2025-03-04T20:51:41.4064780Z 2025-03-04T20:51:41.4065093Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:51:41.4065281Z getitem_35: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))]; deltas_1 = None 2025-03-04T20:51:41.4065415Z dh_2: "f32[269952, 1][1, 1]cpu" = getitem_35 / 1.0; getitem_35 = None 2025-03-04T20:51:41.4065477Z 2025-03-04T20:51:41.4065915Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:51:41.4066064Z dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356); dw_2 = None 2025-03-04T20:51:41.4066133Z 2025-03-04T20:51:41.4066474Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:51:41.4066617Z dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356); dh_2 = None 2025-03-04T20:51:41.4066679Z 2025-03-04T20:51:41.4067042Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:51:41.4067177Z getitem_36: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)] 2025-03-04T20:51:41.4067311Z mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_36; dx_1 = getitem_36 = None 2025-03-04T20:51:41.4067486Z getitem_37: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)]; ctr_x_1 = None 2025-03-04T20:51:41.4067638Z pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_37; mul_12 = getitem_37 = None 2025-03-04T20:51:41.4067700Z 2025-03-04T20:51:41.4068067Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:51:41.4068204Z getitem_38: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)] 2025-03-04T20:51:41.4068337Z mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_38; dy_1 = getitem_38 = None 2025-03-04T20:51:41.4068491Z getitem_39: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)]; ctr_y_1 = None 2025-03-04T20:51:41.4068658Z pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_39; mul_13 = getitem_39 = None 2025-03-04T20:51:41.4068721Z 2025-03-04T20:51:41.4069049Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:51:41.4069158Z exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3); dw_3 = None 2025-03-04T20:51:41.4069325Z getitem_40: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]; widths_1 = None 2025-03-04T20:51:41.4069468Z pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_40; exp_2 = getitem_40 = None 2025-03-04T20:51:41.4069527Z 2025-03-04T20:51:41.4069860Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:51:41.4069970Z exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3); dh_3 = None 2025-03-04T20:51:41.4070144Z getitem_41: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]; heights_1 = None 2025-03-04T20:51:41.4070274Z pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_41; exp_3 = getitem_41 = None 2025-03-04T20:51:41.4070337Z 2025-03-04T20:51:41.4070648Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:51:41.4070751Z mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1 2025-03-04T20:51:41.4070865Z x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16; mul_16 = None 2025-03-04T20:51:41.4070933Z 2025-03-04T20:51:41.4071239Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:51:41.4071378Z mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1 2025-03-04T20:51:41.4071494Z y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17; mul_17 = None 2025-03-04T20:51:41.4071571Z 2025-03-04T20:51:41.4071866Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:51:41.4071984Z mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1; pred_w_1 = None 2025-03-04T20:51:41.4072111Z x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18; pred_ctr_x_1 = mul_18 = None 2025-03-04T20:51:41.4072178Z 2025-03-04T20:51:41.4072468Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:51:41.4072583Z mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1; pred_h_1 = None 2025-03-04T20:51:41.4072709Z y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19; pred_ctr_y_1 = mul_19 = None 2025-03-04T20:51:41.4072778Z 2025-03-04T20:51:41.4073139Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:51:41.4073339Z pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1); x1_1 = y1_1 = x2_1 = y2_1 = None 2025-03-04T20:51:41.4073399Z 2025-03-04T20:51:41.4073753Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:51:41.4073918Z proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4)); pred_boxes_1 = None 2025-03-04T20:51:41.4073988Z 2025-03-04T20:51:41.4074371Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:51:41.4074569Z proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4); proposals_i_1 = None 2025-03-04T20:51:41.4074629Z 2025-03-04T20:51:41.4075036Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:51:41.4075242Z pred_anchor_deltas_i_5: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_4.reshape(-1, 4); pred_anchor_deltas_i_4 = None 2025-03-04T20:51:41.4075310Z 2025-03-04T20:51:41.4075744Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:51:41.4075911Z unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = tensor_2.unsqueeze(0); tensor_2 = None 2025-03-04T20:51:41.4076061Z expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1); unsqueeze_2 = None 2025-03-04T20:51:41.4076200Z anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4); expand_2 = None 2025-03-04T20:51:41.4076258Z 2025-03-04T20:51:41.4076629Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:51:41.4076797Z deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_5.float(); pred_anchor_deltas_i_5 = None 2025-03-04T20:51:41.4076858Z 2025-03-04T20:51:41.4077176Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:51:41.4077334Z boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32); anchors_i_2 = None 2025-03-04T20:51:41.4077402Z 2025-03-04T20:51:41.4077717Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:51:41.4077849Z getitem_42: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)] 2025-03-04T20:51:41.4077970Z getitem_43: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T20:51:41.4078123Z widths_2: "f32[67488][1]cpu" = getitem_42 - getitem_43; getitem_42 = getitem_43 = None 2025-03-04T20:51:41.4078184Z 2025-03-04T20:51:41.4078508Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:51:41.4078629Z getitem_44: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)] 2025-03-04T20:51:41.4078754Z getitem_45: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)] 2025-03-04T20:51:41.4078918Z heights_2: "f32[67488][1]cpu" = getitem_44 - getitem_45; getitem_44 = getitem_45 = None 2025-03-04T20:51:41.4078986Z 2025-03-04T20:51:41.4079291Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:51:41.4079416Z getitem_46: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T20:51:41.4079527Z mul_20: "f32[67488][1]cpu" = 0.5 * widths_2 2025-03-04T20:51:41.4079665Z ctr_x_2: "f32[67488][1]cpu" = getitem_46 + mul_20; getitem_46 = mul_20 = None 2025-03-04T20:51:41.4079727Z 2025-03-04T20:51:41.4080046Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:51:41.4080191Z getitem_47: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]; boxes_2 = None 2025-03-04T20:51:41.4080304Z mul_21: "f32[67488][1]cpu" = 0.5 * heights_2 2025-03-04T20:51:41.4080433Z ctr_y_2: "f32[67488][1]cpu" = getitem_47 + mul_21; getitem_47 = mul_21 = None 2025-03-04T20:51:41.4080501Z 2025-03-04T20:51:41.4080803Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:51:41.4080960Z getitem_48: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:51:41.4081070Z dx_2: "f32[67488, 1][1, 1]cpu" = getitem_48 / 1.0; getitem_48 = None 2025-03-04T20:51:41.4081138Z 2025-03-04T20:51:41.4081435Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:51:41.4081591Z getitem_49: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:51:41.4081702Z dy_2: "f32[67488, 1][1, 1]cpu" = getitem_49 / 1.0; getitem_49 = None 2025-03-04T20:51:41.4081773Z 2025-03-04T20:51:41.4082069Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:51:41.4082224Z getitem_50: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:51:41.4082333Z dw_4: "f32[67488, 1][1, 1]cpu" = getitem_50 / 1.0; getitem_50 = None 2025-03-04T20:51:41.4082402Z 2025-03-04T20:51:41.4082700Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:51:41.4082887Z getitem_51: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))]; deltas_2 = None 2025-03-04T20:51:41.4083019Z dh_4: "f32[67488, 1][1, 1]cpu" = getitem_51 / 1.0; getitem_51 = None 2025-03-04T20:51:41.4083081Z 2025-03-04T20:51:41.4083420Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:51:41.4083556Z dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356); dw_4 = None 2025-03-04T20:51:41.4083625Z 2025-03-04T20:51:41.4083954Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:51:41.4084094Z dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356); dh_4 = None 2025-03-04T20:51:41.4084154Z 2025-03-04T20:51:41.4084510Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:51:41.4084645Z getitem_52: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)] 2025-03-04T20:51:41.4084789Z mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_52; dx_2 = getitem_52 = None 2025-03-04T20:51:41.4084944Z getitem_53: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)]; ctr_x_2 = None 2025-03-04T20:51:41.4085086Z pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_53; mul_22 = getitem_53 = None 2025-03-04T20:51:41.4085146Z 2025-03-04T20:51:41.4085517Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:51:41.4085652Z getitem_54: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)] 2025-03-04T20:51:41.4085779Z mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_54; dy_2 = getitem_54 = None 2025-03-04T20:51:41.4085928Z getitem_55: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)]; ctr_y_2 = None 2025-03-04T20:51:41.4086084Z pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_55; mul_23 = getitem_55 = None 2025-03-04T20:51:41.4086145Z 2025-03-04T20:51:41.4086482Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:51:41.4086595Z exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5); dw_5 = None 2025-03-04T20:51:41.4086759Z getitem_56: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]; widths_2 = None 2025-03-04T20:51:41.4086891Z pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_56; exp_4 = getitem_56 = None 2025-03-04T20:51:41.4086959Z 2025-03-04T20:51:41.4087295Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:51:41.4087415Z exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5); dh_5 = None 2025-03-04T20:51:41.4087577Z getitem_57: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]; heights_2 = None 2025-03-04T20:51:41.4087714Z pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_57; exp_5 = getitem_57 = None 2025-03-04T20:51:41.4087774Z 2025-03-04T20:51:41.4088093Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:51:41.4088189Z mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2 2025-03-04T20:51:41.4088311Z x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26; mul_26 = None 2025-03-04T20:51:41.4088389Z 2025-03-04T20:51:41.4088703Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:51:41.4088795Z mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2 2025-03-04T20:51:41.4089367Z y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27; mul_27 = None 2025-03-04T20:51:41.4089440Z 2025-03-04T20:51:41.4089790Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:51:41.4089913Z mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2; pred_w_2 = None 2025-03-04T20:51:41.4090055Z x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28; pred_ctr_x_2 = mul_28 = None 2025-03-04T20:51:41.4090116Z 2025-03-04T20:51:41.4090434Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:51:41.4090544Z mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2; pred_h_2 = None 2025-03-04T20:51:41.4090677Z y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29; pred_ctr_y_2 = mul_29 = None 2025-03-04T20:51:41.4090758Z 2025-03-04T20:51:41.4091110Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:51:41.4091309Z pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1); x1_2 = y1_2 = x2_2 = y2_2 = None 2025-03-04T20:51:41.4091387Z 2025-03-04T20:51:41.4091730Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:51:41.4091891Z proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4)); pred_boxes_2 = None 2025-03-04T20:51:41.4091963Z 2025-03-04T20:51:41.4092348Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:51:41.4092540Z proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4); proposals_i_2 = None 2025-03-04T20:51:41.4092599Z 2025-03-04T20:51:41.4092997Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:51:41.4093194Z pred_anchor_deltas_i_7: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_6.reshape(-1, 4); pred_anchor_deltas_i_6 = None 2025-03-04T20:51:41.4093261Z 2025-03-04T20:51:41.4093684Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:51:41.4093831Z unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = tensor_3.unsqueeze(0); tensor_3 = None 2025-03-04T20:51:41.4093977Z expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1); unsqueeze_3 = None 2025-03-04T20:51:41.4094114Z anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4); expand_3 = None 2025-03-04T20:51:41.4094174Z 2025-03-04T20:51:41.4094545Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:51:41.4094704Z deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_7.float(); pred_anchor_deltas_i_7 = None 2025-03-04T20:51:41.4094771Z 2025-03-04T20:51:41.4095074Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:51:41.4095235Z boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32); anchors_i_3 = None 2025-03-04T20:51:41.4095297Z 2025-03-04T20:51:41.4095610Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:51:41.4095733Z getitem_58: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)] 2025-03-04T20:51:41.4095858Z getitem_59: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T20:51:41.4096002Z widths_3: "f32[16872][1]cpu" = getitem_58 - getitem_59; getitem_58 = getitem_59 = None 2025-03-04T20:51:41.4096071Z 2025-03-04T20:51:41.4096381Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:51:41.4096508Z getitem_60: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)] 2025-03-04T20:51:41.4096622Z getitem_61: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)] 2025-03-04T20:51:41.4096788Z heights_3: "f32[16872][1]cpu" = getitem_60 - getitem_61; getitem_60 = getitem_61 = None 2025-03-04T20:51:41.4096851Z 2025-03-04T20:51:41.4097160Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:51:41.4097288Z getitem_62: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T20:51:41.4097382Z mul_30: "f32[16872][1]cpu" = 0.5 * widths_3 2025-03-04T20:51:41.4097507Z ctr_x_3: "f32[16872][1]cpu" = getitem_62 + mul_30; getitem_62 = mul_30 = None 2025-03-04T20:51:41.4097576Z 2025-03-04T20:51:41.4097879Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:51:41.4098028Z getitem_63: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]; boxes_3 = None 2025-03-04T20:51:41.4098131Z mul_31: "f32[16872][1]cpu" = 0.5 * heights_3 2025-03-04T20:51:41.4098263Z ctr_y_3: "f32[16872][1]cpu" = getitem_63 + mul_31; getitem_63 = mul_31 = None 2025-03-04T20:51:41.4098322Z 2025-03-04T20:51:41.4098626Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:51:41.4098780Z getitem_64: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:51:41.4098885Z dx_3: "f32[16872, 1][1, 1]cpu" = getitem_64 / 1.0; getitem_64 = None 2025-03-04T20:51:41.4098953Z 2025-03-04T20:51:41.4099248Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:51:41.4099398Z getitem_65: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:51:41.4099502Z dy_3: "f32[16872, 1][1, 1]cpu" = getitem_65 / 1.0; getitem_65 = None 2025-03-04T20:51:41.4099566Z 2025-03-04T20:51:41.4099861Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:51:41.4100016Z getitem_66: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:51:41.4100122Z dw_6: "f32[16872, 1][1, 1]cpu" = getitem_66 / 1.0; getitem_66 = None 2025-03-04T20:51:41.4100190Z 2025-03-04T20:51:41.4100494Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:51:41.4100699Z getitem_67: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))]; deltas_3 = None 2025-03-04T20:51:41.4100808Z dh_6: "f32[16872, 1][1, 1]cpu" = getitem_67 / 1.0; getitem_67 = None 2025-03-04T20:51:41.4100876Z 2025-03-04T20:51:41.4101214Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:51:41.4101358Z dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356); dw_6 = None 2025-03-04T20:51:41.4101417Z 2025-03-04T20:51:41.4101760Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:51:41.4101974Z dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356); dh_6 = None 2025-03-04T20:51:41.4102052Z 2025-03-04T20:51:41.4102404Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:51:41.4102598Z getitem_68: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)] 2025-03-04T20:51:41.4102726Z mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_68; dx_3 = getitem_68 = None 2025-03-04T20:51:41.4102891Z getitem_69: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)]; ctr_x_3 = None 2025-03-04T20:51:41.4103055Z pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_69; mul_32 = getitem_69 = None 2025-03-04T20:51:41.4103128Z 2025-03-04T20:51:41.4103491Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:51:41.4103640Z getitem_70: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)] 2025-03-04T20:51:41.4103766Z mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_70; dy_3 = getitem_70 = None 2025-03-04T20:51:41.4103953Z getitem_71: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)]; ctr_y_3 = None 2025-03-04T20:51:41.4104112Z pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_71; mul_33 = getitem_71 = None 2025-03-04T20:51:41.4104177Z 2025-03-04T20:51:41.4104528Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:51:41.4104645Z exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7); dw_7 = None 2025-03-04T20:51:41.4104813Z getitem_72: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]; widths_3 = None 2025-03-04T20:51:41.4104949Z pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_72; exp_6 = getitem_72 = None 2025-03-04T20:51:41.4105020Z 2025-03-04T20:51:41.4105358Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:51:41.4105484Z exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7); dh_7 = None 2025-03-04T20:51:41.4105695Z getitem_73: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]; heights_3 = None 2025-03-04T20:51:41.4105842Z pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_73; exp_7 = getitem_73 = None 2025-03-04T20:51:41.4105905Z 2025-03-04T20:51:41.4106235Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:51:41.4106331Z mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3 2025-03-04T20:51:41.4106453Z x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36; mul_36 = None 2025-03-04T20:51:41.4106559Z 2025-03-04T20:51:41.4106878Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:51:41.4106975Z mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3 2025-03-04T20:51:41.4107100Z y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37; mul_37 = None 2025-03-04T20:51:41.4107163Z 2025-03-04T20:51:41.4107485Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:51:41.4107597Z mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3; pred_w_3 = None 2025-03-04T20:51:41.4107736Z x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38; pred_ctr_x_3 = mul_38 = None 2025-03-04T20:51:41.4107797Z 2025-03-04T20:51:41.4108114Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:51:41.4108228Z mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3; pred_h_3 = None 2025-03-04T20:51:41.4108383Z y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39; pred_ctr_y_3 = mul_39 = None 2025-03-04T20:51:41.4108446Z 2025-03-04T20:51:41.4108808Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:51:41.4109014Z pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1); x1_3 = y1_3 = x2_3 = y2_3 = None 2025-03-04T20:51:41.4109084Z 2025-03-04T20:51:41.4109422Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:51:41.4109597Z proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4)); pred_boxes_3 = None 2025-03-04T20:51:41.4109659Z 2025-03-04T20:51:41.4110078Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:51:41.4110249Z proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4); proposals_i_3 = None 2025-03-04T20:51:41.4110320Z 2025-03-04T20:51:41.4110733Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:51:41.4110950Z pred_anchor_deltas_i_9: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_8.reshape(-1, 4); pred_anchor_deltas_i_8 = None 2025-03-04T20:51:41.4111011Z 2025-03-04T20:51:41.4111465Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:51:41.4111623Z unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = tensor_4.unsqueeze(0); tensor_4 = None 2025-03-04T20:51:41.4111774Z expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1); unsqueeze_4 = None 2025-03-04T20:51:41.4111915Z anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4); expand_4 = None 2025-03-04T20:51:41.4111977Z 2025-03-04T20:51:41.4112364Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:51:41.4112534Z deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_9.float(); pred_anchor_deltas_i_9 = None 2025-03-04T20:51:41.4112619Z 2025-03-04T20:51:41.4112940Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:51:41.4113092Z boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32); anchors_i_4 = None 2025-03-04T20:51:41.4113153Z 2025-03-04T20:51:41.4113484Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:51:41.4113614Z getitem_74: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)] 2025-03-04T20:51:41.4113748Z getitem_75: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T20:51:41.4113897Z widths_4: "f32[4332][1]cpu" = getitem_74 - getitem_75; getitem_74 = getitem_75 = None 2025-03-04T20:51:41.4113965Z 2025-03-04T20:51:41.4114300Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:51:41.4114431Z getitem_76: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)] 2025-03-04T20:51:41.4114569Z getitem_77: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)] 2025-03-04T20:51:41.4114725Z heights_4: "f32[4332][1]cpu" = getitem_76 - getitem_77; getitem_76 = getitem_77 = None 2025-03-04T20:51:41.4114789Z 2025-03-04T20:51:41.4115109Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:51:41.4115249Z getitem_78: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T20:51:41.4115344Z mul_40: "f32[4332][1]cpu" = 0.5 * widths_4 2025-03-04T20:51:41.4115475Z ctr_x_4: "f32[4332][1]cpu" = getitem_78 + mul_40; getitem_78 = mul_40 = None 2025-03-04T20:51:41.4115545Z 2025-03-04T20:51:41.4115871Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:51:41.4116035Z getitem_79: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]; boxes_4 = None 2025-03-04T20:51:41.4116121Z mul_41: "f32[4332][1]cpu" = 0.5 * heights_4 2025-03-04T20:51:41.4116249Z ctr_y_4: "f32[4332][1]cpu" = getitem_79 + mul_41; getitem_79 = mul_41 = None 2025-03-04T20:51:41.4116308Z 2025-03-04T20:51:41.4116612Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:51:41.4116758Z getitem_80: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:51:41.4116873Z dx_4: "f32[4332, 1][1, 1]cpu" = getitem_80 / 1.0; getitem_80 = None 2025-03-04T20:51:41.4116933Z 2025-03-04T20:51:41.4117233Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:51:41.4117378Z getitem_81: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:51:41.4117493Z dy_4: "f32[4332, 1][1, 1]cpu" = getitem_81 / 1.0; getitem_81 = None 2025-03-04T20:51:41.4117552Z 2025-03-04T20:51:41.4117850Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:51:41.4117992Z getitem_82: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:51:41.4118103Z dw_8: "f32[4332, 1][1, 1]cpu" = getitem_82 / 1.0; getitem_82 = None 2025-03-04T20:51:41.4118163Z 2025-03-04T20:51:41.4118463Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:51:41.4118666Z getitem_83: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))]; deltas_4 = None 2025-03-04T20:51:41.4118771Z dh_8: "f32[4332, 1][1, 1]cpu" = getitem_83 / 1.0; getitem_83 = None 2025-03-04T20:51:41.4118840Z 2025-03-04T20:51:41.4119167Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:51:41.4119304Z dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356); dw_8 = None 2025-03-04T20:51:41.4119367Z 2025-03-04T20:51:41.4119702Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:51:41.4119832Z dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356); dh_8 = None 2025-03-04T20:51:41.4119900Z 2025-03-04T20:51:41.4120252Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:51:41.4120402Z getitem_84: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)] 2025-03-04T20:51:41.4120521Z mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_84; dx_4 = getitem_84 = None 2025-03-04T20:51:41.4120671Z getitem_85: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)]; ctr_x_4 = None 2025-03-04T20:51:41.4120818Z pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_85; mul_42 = getitem_85 = None 2025-03-04T20:51:41.4120886Z 2025-03-04T20:51:41.4121236Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:51:41.4121372Z getitem_86: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)] 2025-03-04T20:51:41.4121487Z mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_86; dy_4 = getitem_86 = None 2025-03-04T20:51:41.4121652Z getitem_87: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)]; ctr_y_4 = None 2025-03-04T20:51:41.4121780Z pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_87; mul_43 = getitem_87 = None 2025-03-04T20:51:41.4121845Z 2025-03-04T20:51:41.4122168Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:51:41.4122284Z exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9); dw_9 = None 2025-03-04T20:51:41.4122434Z getitem_88: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]; widths_4 = None 2025-03-04T20:51:41.4122566Z pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_88; exp_8 = getitem_88 = None 2025-03-04T20:51:41.4122627Z 2025-03-04T20:51:41.4122955Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:51:41.4123061Z exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9); dh_9 = None 2025-03-04T20:51:41.4123226Z getitem_89: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]; heights_4 = None 2025-03-04T20:51:41.4123351Z pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_89; exp_9 = getitem_89 = None 2025-03-04T20:51:41.4123419Z 2025-03-04T20:51:41.4123716Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:51:41.4123815Z mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4 2025-03-04T20:51:41.4123946Z x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46; mul_46 = None 2025-03-04T20:51:41.4124012Z 2025-03-04T20:51:41.4124312Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:51:41.4124408Z mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4 2025-03-04T20:51:41.4124516Z y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47; mul_47 = None 2025-03-04T20:51:41.4124585Z 2025-03-04T20:51:41.4124880Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:51:41.4125002Z mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4; pred_w_4 = None 2025-03-04T20:51:41.4125128Z x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48; pred_ctr_x_4 = mul_48 = None 2025-03-04T20:51:41.4125196Z 2025-03-04T20:51:41.4125510Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:51:41.4125625Z mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4; pred_h_4 = None 2025-03-04T20:51:41.4125759Z y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49; pred_ctr_y_4 = mul_49 = None 2025-03-04T20:51:41.4125827Z 2025-03-04T20:51:41.4126168Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:51:41.4126370Z pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1); x1_4 = y1_4 = x2_4 = y2_4 = None 2025-03-04T20:51:41.4126436Z 2025-03-04T20:51:41.4126757Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:51:41.4126914Z proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4)); pred_boxes_4 = None 2025-03-04T20:51:41.4126987Z 2025-03-04T20:51:41.4127372Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:51:41.4127541Z proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4); proposals_i_4 = None 2025-03-04T20:51:41.4127605Z 2025-03-04T20:51:41.4128089Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T20:51:41.4128231Z arange_10: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T20:51:41.4128291Z 2025-03-04T20:51:41.4128603Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:51:41.4128739Z batch_idx: "i64[4][1]cpu" = arange_10.to(device(type='cpu')); arange_10 = None 2025-03-04T20:51:41.4128803Z 2025-03-04T20:51:41.4129229Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:51:41.4129344Z topk = logits_i.topk(1000, dim = 1); logits_i = None 2025-03-04T20:51:41.4129443Z topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0] 2025-03-04T20:51:41.4129558Z topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1]; topk = None 2025-03-04T20:51:41.4129618Z 2025-03-04T20:51:41.4130076Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:51:41.4130218Z getitem_92: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T20:51:41.4130449Z topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_92, topk_idx)]; proposals_i_5 = getitem_92 = topk_idx = None 2025-03-04T20:51:41.4130508Z 2025-03-04T20:51:41.4130964Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:51:41.4131123Z full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:51:41.4131189Z 2025-03-04T20:51:41.4131476Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:51:41.4131604Z to_21: "i64[1000][1]cpu" = full.to(device(type='cpu')); full = None 2025-03-04T20:51:41.4131666Z 2025-03-04T20:51:41.4132104Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:51:41.4132219Z topk_1 = logits_i_1.topk(1000, dim = 1); logits_i_1 = None 2025-03-04T20:51:41.4132328Z topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0] 2025-03-04T20:51:41.4132453Z topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1]; topk_1 = None 2025-03-04T20:51:41.4132520Z 2025-03-04T20:51:41.4132964Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:51:41.4133096Z getitem_96: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T20:51:41.4133346Z topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_96, topk_idx_1)]; proposals_i_6 = getitem_96 = topk_idx_1 = None 2025-03-04T20:51:41.4133406Z 2025-03-04T20:51:41.4133857Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:51:41.4134018Z full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:51:41.4134087Z 2025-03-04T20:51:41.4134381Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:51:41.4134511Z to_22: "i64[1000][1]cpu" = full_1.to(device(type='cpu')); full_1 = None 2025-03-04T20:51:41.4134574Z 2025-03-04T20:51:41.4135022Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:51:41.4135142Z topk_2 = logits_i_2.topk(1000, dim = 1); logits_i_2 = None 2025-03-04T20:51:41.4135255Z topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0] 2025-03-04T20:51:41.4135371Z topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1]; topk_2 = None 2025-03-04T20:51:41.4135444Z 2025-03-04T20:51:41.4135907Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:51:41.4136044Z getitem_100: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T20:51:41.4136290Z topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_100, topk_idx_2)]; proposals_i_7 = getitem_100 = topk_idx_2 = None 2025-03-04T20:51:41.4136358Z 2025-03-04T20:51:41.4136856Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:51:41.4137023Z full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:51:41.4137085Z 2025-03-04T20:51:41.4137383Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:51:41.4137503Z to_23: "i64[1000][1]cpu" = full_2.to(device(type='cpu')); full_2 = None 2025-03-04T20:51:41.4137574Z 2025-03-04T20:51:41.4137999Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:51:41.4138134Z topk_3 = logits_i_3.topk(1000, dim = 1); logits_i_3 = None 2025-03-04T20:51:41.4138238Z topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0] 2025-03-04T20:51:41.4138354Z topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1]; topk_3 = None 2025-03-04T20:51:41.4138416Z 2025-03-04T20:51:41.4138890Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:51:41.4139025Z getitem_104: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T20:51:41.4139261Z topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_104, topk_idx_3)]; proposals_i_8 = getitem_104 = topk_idx_3 = None 2025-03-04T20:51:41.4139349Z 2025-03-04T20:51:41.4139806Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:51:41.4139975Z full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:51:41.4140035Z 2025-03-04T20:51:41.4140339Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:51:41.4140460Z to_24: "i64[1000][1]cpu" = full_3.to(device(type='cpu')); full_3 = None 2025-03-04T20:51:41.4140527Z 2025-03-04T20:51:41.4140962Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:51:41.4141083Z topk_4 = logits_i_4.topk(1000, dim = 1); logits_i_4 = None 2025-03-04T20:51:41.4141185Z topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0] 2025-03-04T20:51:41.4141303Z topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1]; topk_4 = None 2025-03-04T20:51:41.4141363Z 2025-03-04T20:51:41.4141827Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:51:41.4141992Z getitem_108: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T20:51:41.4142239Z topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_108, topk_idx_4)]; proposals_i_9 = getitem_108 = topk_idx_4 = None 2025-03-04T20:51:41.4142319Z 2025-03-04T20:51:41.4142792Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:51:41.4142954Z full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:51:41.4143023Z 2025-03-04T20:51:41.4143320Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:51:41.4143450Z to_25: "i64[1000][1]cpu" = full_4.to(device(type='cpu')); full_4 = None 2025-03-04T20:51:41.4143513Z 2025-03-04T20:51:41.4143809Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:51:41.4144223Z topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1); topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None 2025-03-04T20:51:41.4144296Z 2025-03-04T20:51:41.4144588Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:51:41.4145095Z topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1); topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None 2025-03-04T20:51:41.4145166Z 2025-03-04T20:51:41.4145454Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:51:41.4145769Z level_ids: "i64[5000][1]cpu" = torch.cat([to_21, to_22, to_23, to_24, to_25], 0); to_21 = to_22 = to_23 = to_24 = to_25 = level_ids = None 2025-03-04T20:51:41.4145873Z 2025-03-04T20:51:41.4146295Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T20:51:41.4146443Z getitem_110: "f32[5000, 4][4, 1]cpu" = topk_proposals[0]; topk_proposals = None 2025-03-04T20:51:41.4146515Z 2025-03-04T20:51:41.4146837Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:51:41.4146998Z tensor_5: "f32[5000, 4][4, 1]cpu" = getitem_110.to(torch.float32); getitem_110 = None 2025-03-04T20:51:41.4147065Z 2025-03-04T20:51:41.4147483Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T20:51:41.4147618Z scores_per_img: "f32[5000][1]cpu" = topk_scores[0]; topk_scores = None 2025-03-04T20:51:41.4147689Z 2025-03-04T20:51:41.4148173Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T20:51:41.4148317Z isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor_5); tensor_5 = None 2025-03-04T20:51:41.4148438Z all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:51:41.4148597Z isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T20:51:41.4148725Z valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T20:51:41.4148815Z 2025-03-04T20:51:41.4149183Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T20:51:41.4149309Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T20:51:41.4149372Z 2025-03-04T20:52:01.2088886Z 2025-03-04T20:52:01.2094674Z class GraphModule(torch.nn.Module): 2025-03-04T20:52:01.2102745Z def forward(self, L_features_p2_: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu", L_features_p3_: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu", L_features_p4_: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu", L_features_p5_: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu", L_features_p6_: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_1_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_2_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_3_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_4_: "f32[3, 4][4, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_bias_: "f32[256][1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[3, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[3][1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[12, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[12][1]cpu"): 2025-03-04T20:52:01.2105928Z l_features_p2_ = L_features_p2_ 2025-03-04T20:52:01.2106163Z l_features_p3_ = L_features_p3_ 2025-03-04T20:52:01.2106377Z l_features_p4_ = L_features_p4_ 2025-03-04T20:52:01.2106586Z l_features_p5_ = L_features_p5_ 2025-03-04T20:52:01.2106796Z l_features_p6_ = L_features_p6_ 2025-03-04T20:52:01.2107257Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T20:52:01.2107836Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_1_ 2025-03-04T20:52:01.2108413Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_2_ 2025-03-04T20:52:01.2109224Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_3_ 2025-03-04T20:52:01.2109813Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_4_ 2025-03-04T20:52:01.2110361Z l_self_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T20:52:01.2110856Z l_self_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T20:52:01.2111396Z l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T20:52:01.2111965Z l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T20:52:01.2112526Z l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T20:52:01.2113086Z l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T20:52:01.2113457Z 2025-03-04T20:52:01.2114427Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:52:01.2115167Z arange: "f32[304][1]cpu" = torch.arange(0.0, 1216, step = 4, dtype = torch.float32) 2025-03-04T20:52:01.2115453Z 2025-03-04T20:52:01.2115843Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:52:01.2116329Z shifts_x: "f32[304][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T20:52:01.2116579Z 2025-03-04T20:52:01.2117101Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:52:01.2117725Z arange_1: "f32[296][1]cpu" = torch.arange(0.0, 1184, step = 4, dtype = torch.float32) 2025-03-04T20:52:01.2117986Z 2025-03-04T20:52:01.2118390Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:52:01.2118888Z shifts_y: "f32[296][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T20:52:01.2119402Z 2025-03-04T20:52:01.2119920Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:52:01.2120523Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T20:52:01.2120850Z shift_y: "f32[296, 304][1, 0]cpu" = meshgrid[0] 2025-03-04T20:52:01.2121116Z shift_x: "f32[296, 304][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T20:52:01.2121478Z 2025-03-04T20:52:01.2121904Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:52:01.2122446Z shift_x_1: "f32[89984][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T20:52:01.2122681Z 2025-03-04T20:52:01.2123079Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:52:01.2123561Z shift_y_1: "f32[89984][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T20:52:01.2123796Z 2025-03-04T20:52:01.2126045Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:52:01.2126744Z shifts: "f32[89984, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T20:52:01.2127074Z 2025-03-04T20:52:01.2127575Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:52:01.2128163Z view: "f32[89984, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T20:52:01.2128656Z view_1: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T20:52:01.2129133Z add: "f32[89984, 3, 4][12, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T20:52:01.2129883Z x: "f32[269952, 4][4, 1]cpu" = add.reshape(-1, 4); add = None 2025-03-04T20:52:01.2130135Z 2025-03-04T20:52:01.2130666Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:52:01.2131384Z arange_2: "f32[152][1]cpu" = torch.arange(0.0, 1216, step = 8, dtype = torch.float32) 2025-03-04T20:52:01.2131648Z 2025-03-04T20:52:01.2132023Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:52:01.2132500Z shifts_x_1: "f32[152][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T20:52:01.2132756Z 2025-03-04T20:52:01.2133267Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:52:01.2133888Z arange_3: "f32[148][1]cpu" = torch.arange(0.0, 1184, step = 8, dtype = torch.float32) 2025-03-04T20:52:01.2134146Z 2025-03-04T20:52:01.2135059Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:52:01.2136150Z shifts_y_1: "f32[148][1]cpu" = arange_3.to(device(type='cpu')); arange_3 = None 2025-03-04T20:52:01.2136503Z 2025-03-04T20:52:01.2137107Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:52:01.2137959Z meshgrid_1 = torch.functional.meshgrid(shifts_y_1, shifts_x_1); shifts_y_1 = shifts_x_1 = None 2025-03-04T20:52:01.2138354Z shift_y_2: "f32[148, 152][1, 0]cpu" = meshgrid_1[0] 2025-03-04T20:52:01.2138632Z shift_x_2: "f32[148, 152][0, 1]cpu" = meshgrid_1[1]; meshgrid_1 = None 2025-03-04T20:52:01.2138886Z 2025-03-04T20:52:01.2139311Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:52:01.2139871Z shift_x_3: "f32[22496][1]cpu" = shift_x_2.reshape(-1); shift_x_2 = None 2025-03-04T20:52:01.2140120Z 2025-03-04T20:52:01.2140544Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:52:01.2141068Z shift_y_3: "f32[22496][1]cpu" = shift_y_2.reshape(-1); shift_y_2 = None 2025-03-04T20:52:01.2141325Z 2025-03-04T20:52:01.2141807Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:52:01.2142466Z shifts_1: "f32[22496, 4][4, 1]cpu" = torch.stack((shift_x_3, shift_y_3, shift_x_3, shift_y_3), dim = 1); shift_x_3 = shift_y_3 = None 2025-03-04T20:52:01.2142790Z 2025-03-04T20:52:01.2143297Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:52:01.2143903Z view_2: "f32[22496, 1, 4][4, 4, 1]cpu" = shifts_1.view(-1, 1, 4); shifts_1 = None 2025-03-04T20:52:01.2144414Z view_3: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_ = None 2025-03-04T20:52:01.2144907Z add_1: "f32[22496, 3, 4][12, 4, 1]cpu" = view_2 + view_3; view_2 = view_3 = None 2025-03-04T20:52:01.2145201Z x_1: "f32[67488, 4][4, 1]cpu" = add_1.reshape(-1, 4); add_1 = None 2025-03-04T20:52:01.2145429Z 2025-03-04T20:52:01.2145999Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:52:01.2146674Z arange_4: "f32[76][1]cpu" = torch.arange(0.0, 1216, step = 16, dtype = torch.float32) 2025-03-04T20:52:01.2146940Z 2025-03-04T20:52:01.2147324Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:52:01.2147828Z shifts_x_2: "f32[76][1]cpu" = arange_4.to(device(type='cpu')); arange_4 = None 2025-03-04T20:52:01.2148105Z 2025-03-04T20:52:01.2148635Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:52:01.2149284Z arange_5: "f32[74][1]cpu" = torch.arange(0.0, 1184, step = 16, dtype = torch.float32) 2025-03-04T20:52:01.2149554Z 2025-03-04T20:52:01.2149967Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:52:01.2150471Z shifts_y_2: "f32[74][1]cpu" = arange_5.to(device(type='cpu')); arange_5 = None 2025-03-04T20:52:01.2150731Z 2025-03-04T20:52:01.2151183Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:52:01.2151855Z meshgrid_2 = torch.functional.meshgrid(shifts_y_2, shifts_x_2); shifts_y_2 = shifts_x_2 = None 2025-03-04T20:52:01.2152207Z shift_y_4: "f32[74, 76][1, 0]cpu" = meshgrid_2[0] 2025-03-04T20:52:01.2152484Z shift_x_4: "f32[74, 76][0, 1]cpu" = meshgrid_2[1]; meshgrid_2 = None 2025-03-04T20:52:01.2152726Z 2025-03-04T20:52:01.2153149Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:52:01.2153712Z shift_x_5: "f32[5624][1]cpu" = shift_x_4.reshape(-1); shift_x_4 = None 2025-03-04T20:52:01.2154077Z 2025-03-04T20:52:01.2154588Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:52:01.2155104Z shift_y_5: "f32[5624][1]cpu" = shift_y_4.reshape(-1); shift_y_4 = None 2025-03-04T20:52:01.2155355Z 2025-03-04T20:52:01.2155834Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:52:01.2156496Z shifts_2: "f32[5624, 4][4, 1]cpu" = torch.stack((shift_x_5, shift_y_5, shift_x_5, shift_y_5), dim = 1); shift_x_5 = shift_y_5 = None 2025-03-04T20:52:01.2156836Z 2025-03-04T20:52:01.2157374Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:52:01.2157981Z view_4: "f32[5624, 1, 4][4, 4, 1]cpu" = shifts_2.view(-1, 1, 4); shifts_2 = None 2025-03-04T20:52:01.2158496Z view_5: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_ = None 2025-03-04T20:52:01.2159002Z add_2: "f32[5624, 3, 4][12, 4, 1]cpu" = view_4 + view_5; view_4 = view_5 = None 2025-03-04T20:52:01.2159300Z x_2: "f32[16872, 4][4, 1]cpu" = add_2.reshape(-1, 4); add_2 = None 2025-03-04T20:52:01.2159538Z 2025-03-04T20:52:01.2160100Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:52:01.2160748Z arange_6: "f32[38][1]cpu" = torch.arange(0.0, 1216, step = 32, dtype = torch.float32) 2025-03-04T20:52:01.2161016Z 2025-03-04T20:52:01.2161403Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:52:01.2161900Z shifts_x_3: "f32[38][1]cpu" = arange_6.to(device(type='cpu')); arange_6 = None 2025-03-04T20:52:01.2162156Z 2025-03-04T20:52:01.2162684Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:52:01.2163329Z arange_7: "f32[37][1]cpu" = torch.arange(0.0, 1184, step = 32, dtype = torch.float32) 2025-03-04T20:52:01.2163593Z 2025-03-04T20:52:01.2163997Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:52:01.2164495Z shifts_y_3: "f32[37][1]cpu" = arange_7.to(device(type='cpu')); arange_7 = None 2025-03-04T20:52:01.2164748Z 2025-03-04T20:52:01.2165214Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:52:01.2165864Z meshgrid_3 = torch.functional.meshgrid(shifts_y_3, shifts_x_3); shifts_y_3 = shifts_x_3 = None 2025-03-04T20:52:01.2166218Z shift_y_6: "f32[37, 38][1, 0]cpu" = meshgrid_3[0] 2025-03-04T20:52:01.2166496Z shift_x_6: "f32[37, 38][0, 1]cpu" = meshgrid_3[1]; meshgrid_3 = None 2025-03-04T20:52:01.2166745Z 2025-03-04T20:52:01.2167176Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:52:01.2167716Z shift_x_7: "f32[1406][1]cpu" = shift_x_6.reshape(-1); shift_x_6 = None 2025-03-04T20:52:01.2167970Z 2025-03-04T20:52:01.2168377Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:52:01.2168886Z shift_y_7: "f32[1406][1]cpu" = shift_y_6.reshape(-1); shift_y_6 = None 2025-03-04T20:52:01.2169127Z 2025-03-04T20:52:01.2169592Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:52:01.2170240Z shifts_3: "f32[1406, 4][4, 1]cpu" = torch.stack((shift_x_7, shift_y_7, shift_x_7, shift_y_7), dim = 1); shift_x_7 = shift_y_7 = None 2025-03-04T20:52:01.2170566Z 2025-03-04T20:52:01.2171071Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:52:01.2171675Z view_6: "f32[1406, 1, 4][4, 4, 1]cpu" = shifts_3.view(-1, 1, 4); shifts_3 = None 2025-03-04T20:52:01.2172173Z view_7: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_ = None 2025-03-04T20:52:01.2172661Z add_3: "f32[1406, 3, 4][12, 4, 1]cpu" = view_6 + view_7; view_6 = view_7 = None 2025-03-04T20:52:01.2172947Z x_3: "f32[4218, 4][4, 1]cpu" = add_3.reshape(-1, 4); add_3 = None 2025-03-04T20:52:01.2173174Z 2025-03-04T20:52:01.2173722Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:52:01.2174356Z arange_8: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32) 2025-03-04T20:52:01.2174618Z 2025-03-04T20:52:01.2175000Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:52:01.2175488Z shifts_x_4: "f32[19][1]cpu" = arange_8.to(device(type='cpu')); arange_8 = None 2025-03-04T20:52:01.2175742Z 2025-03-04T20:52:01.2176263Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:52:01.2176891Z arange_9: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32) 2025-03-04T20:52:01.2177150Z 2025-03-04T20:52:01.2177550Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:52:01.2178033Z shifts_y_4: "f32[19][1]cpu" = arange_9.to(device(type='cpu')); arange_9 = None 2025-03-04T20:52:01.2178292Z 2025-03-04T20:52:01.2178760Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:52:01.2179399Z meshgrid_4 = torch.functional.meshgrid(shifts_y_4, shifts_x_4); shifts_y_4 = shifts_x_4 = None 2025-03-04T20:52:01.2179733Z shift_y_8: "f32[19, 19][1, 0]cpu" = meshgrid_4[0] 2025-03-04T20:52:01.2180000Z shift_x_8: "f32[19, 19][0, 1]cpu" = meshgrid_4[1]; meshgrid_4 = None 2025-03-04T20:52:01.2180233Z 2025-03-04T20:52:01.2180645Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:52:01.2181171Z shift_x_9: "f32[361][1]cpu" = shift_x_8.reshape(-1); shift_x_8 = None 2025-03-04T20:52:01.2181408Z 2025-03-04T20:52:01.2181810Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:52:01.2182311Z shift_y_9: "f32[361][1]cpu" = shift_y_8.reshape(-1); shift_y_8 = None 2025-03-04T20:52:01.2182550Z 2025-03-04T20:52:01.2183015Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:52:01.2183659Z shifts_4: "f32[361, 4][4, 1]cpu" = torch.stack((shift_x_9, shift_y_9, shift_x_9, shift_y_9), dim = 1); shift_x_9 = shift_y_9 = None 2025-03-04T20:52:01.2183985Z 2025-03-04T20:52:01.2184484Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:52:01.2185080Z view_8: "f32[361, 1, 4][4, 4, 1]cpu" = shifts_4.view(-1, 1, 4); shifts_4 = None 2025-03-04T20:52:01.2185569Z view_9: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_ = None 2025-03-04T20:52:01.2186055Z add_4: "f32[361, 3, 4][12, 4, 1]cpu" = view_8 + view_9; view_8 = view_9 = None 2025-03-04T20:52:01.2186342Z x_4: "f32[1083, 4][4, 1]cpu" = add_4.reshape(-1, 4); add_4 = None 2025-03-04T20:52:01.2186572Z 2025-03-04T20:52:01.2186986Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:52:01.2187472Z tensor: "f32[269952, 4][4, 1]cpu" = x.to(torch.float32); x = None 2025-03-04T20:52:01.2187771Z tensor_1: "f32[67488, 4][4, 1]cpu" = x_1.to(torch.float32); x_1 = None 2025-03-04T20:52:01.2188070Z tensor_2: "f32[16872, 4][4, 1]cpu" = x_2.to(torch.float32); x_2 = None 2025-03-04T20:52:01.2188363Z tensor_3: "f32[4218, 4][4, 1]cpu" = x_3.to(torch.float32); x_3 = None 2025-03-04T20:52:01.2188649Z tensor_4: "f32[1083, 4][4, 1]cpu" = x_4.to(torch.float32); x_4 = None 2025-03-04T20:52:01.2188884Z 2025-03-04T20:52:01.2189227Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:52:01.2190000Z x_5: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(l_features_p2_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_p2_ = None 2025-03-04T20:52:01.2190537Z 2025-03-04T20:52:01.2190921Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:52:01.2191445Z x_6: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.relu(x_5, inplace = False); x_5 = None 2025-03-04T20:52:01.2191746Z 2025-03-04T20:52:01.2192253Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:52:01.2193122Z score: "f32[4, 3, 296, 304][269952, 89984, 304, 1]cpu" = torch.conv2d(x_6, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T20:52:01.2193662Z 2025-03-04T20:52:01.2194229Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:52:01.2195134Z x_15: "f32[4, 12, 296, 304][1079808, 89984, 304, 1]cpu" = torch.conv2d(x_6, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_6 = None 2025-03-04T20:52:01.2195681Z 2025-03-04T20:52:01.2196033Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:52:01.2196788Z x_7: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(l_features_p3_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_p3_ = None 2025-03-04T20:52:01.2197331Z 2025-03-04T20:52:01.2197709Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:52:01.2198248Z x_8: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.relu(x_7, inplace = False); x_7 = None 2025-03-04T20:52:01.2198559Z 2025-03-04T20:52:01.2199052Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:52:01.2199929Z score_1: "f32[4, 3, 148, 152][67488, 22496, 152, 1]cpu" = torch.conv2d(x_8, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T20:52:01.2200468Z 2025-03-04T20:52:01.2200928Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:52:01.2201795Z x_16: "f32[4, 12, 148, 152][269952, 22496, 152, 1]cpu" = torch.conv2d(x_8, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_8 = None 2025-03-04T20:52:01.2218429Z 2025-03-04T20:52:01.2218856Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:52:01.2219613Z x_9: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(l_features_p4_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_p4_ = None 2025-03-04T20:52:01.2220135Z 2025-03-04T20:52:01.2220504Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:52:01.2221014Z x_10: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.relu(x_9, inplace = False); x_9 = None 2025-03-04T20:52:01.2221467Z 2025-03-04T20:52:01.2221962Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:52:01.2222845Z score_2: "f32[4, 3, 74, 76][16872, 5624, 76, 1]cpu" = torch.conv2d(x_10, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T20:52:01.2223353Z 2025-03-04T20:52:01.2223800Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:52:01.2224595Z x_17: "f32[4, 12, 74, 76][67488, 5624, 76, 1]cpu" = torch.conv2d(x_10, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_10 = None 2025-03-04T20:52:01.2225131Z 2025-03-04T20:52:01.2225472Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:52:01.2226166Z x_11: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(l_features_p5_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_p5_ = None 2025-03-04T20:52:01.2226666Z 2025-03-04T20:52:01.2227023Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:52:01.2227527Z x_12: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.nn.functional.relu(x_11, inplace = False); x_11 = None 2025-03-04T20:52:01.2227811Z 2025-03-04T20:52:01.2228265Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:52:01.2229071Z score_3: "f32[4, 3, 37, 38][4218, 1406, 38, 1]cpu" = torch.conv2d(x_12, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T20:52:01.2229566Z 2025-03-04T20:52:01.2230002Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:52:01.2230782Z x_18: "f32[4, 12, 37, 38][16872, 1406, 38, 1]cpu" = torch.conv2d(x_12, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_12 = None 2025-03-04T20:52:01.2231309Z 2025-03-04T20:52:01.2231641Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:52:01.2232508Z x_13: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.conv2d(l_features_p6_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_p6_ = l_self_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T20:52:01.2233174Z 2025-03-04T20:52:01.2233515Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:52:01.2234074Z x_14: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.relu(x_13, inplace = False); x_13 = None 2025-03-04T20:52:01.2234377Z 2025-03-04T20:52:01.2234881Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:52:01.2235981Z score_4: "f32[4, 3, 19, 19][1083, 361, 19, 1]cpu" = torch.conv2d(x_14, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T20:52:01.2236717Z 2025-03-04T20:52:01.2237173Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:52:01.2238207Z x_19: "f32[4, 12, 19, 19][4332, 361, 19, 1]cpu" = torch.conv2d(x_14, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_14 = l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T20:52:01.2238961Z 2025-03-04T20:52:01.2239398Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T20:52:01.2239970Z permute: "f32[4, 296, 304, 3][269952, 304, 1, 89984]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T20:52:01.2240337Z logits_i: "f32[4, 269952][269952, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T20:52:01.2240701Z permute_1: "f32[4, 148, 152, 3][67488, 152, 1, 22496]cpu" = score_1.permute(0, 2, 3, 1); score_1 = None 2025-03-04T20:52:01.2241071Z logits_i_1: "f32[4, 67488][67488, 1]cpu" = permute_1.flatten(1); permute_1 = None 2025-03-04T20:52:01.2241434Z permute_2: "f32[4, 74, 76, 3][16872, 76, 1, 5624]cpu" = score_2.permute(0, 2, 3, 1); score_2 = None 2025-03-04T20:52:01.2241783Z logits_i_2: "f32[4, 16872][16872, 1]cpu" = permute_2.flatten(1); permute_2 = None 2025-03-04T20:52:01.2242130Z permute_3: "f32[4, 37, 38, 3][4218, 38, 1, 1406]cpu" = score_3.permute(0, 2, 3, 1); score_3 = None 2025-03-04T20:52:01.2242474Z logits_i_3: "f32[4, 4218][4218, 1]cpu" = permute_3.flatten(1); permute_3 = None 2025-03-04T20:52:01.2242817Z permute_4: "f32[4, 19, 19, 3][1083, 19, 1, 361]cpu" = score_4.permute(0, 2, 3, 1); score_4 = None 2025-03-04T20:52:01.2243156Z logits_i_4: "f32[4, 1083][1083, 1]cpu" = permute_4.flatten(1); permute_4 = None 2025-03-04T20:52:01.2243413Z 2025-03-04T20:52:01.2243941Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T20:52:01.2244620Z view_10: "f32[4, 3, 4, 296, 304][1079808, 359936, 89984, 304, 1]cpu" = x_15.view(4, -1, 4, 296, 304); x_15 = None 2025-03-04T20:52:01.2245037Z permute_5: "f32[4, 296, 304, 3, 4][1079808, 304, 1, 359936, 89984]cpu" = view_10.permute(0, 3, 4, 1, 2); view_10 = None 2025-03-04T20:52:01.2245433Z pred_anchor_deltas_i: "f32[4, 269952, 4][1079808, 4, 1]cpu" = permute_5.flatten(1, -2); permute_5 = None 2025-03-04T20:52:01.2245809Z view_11: "f32[4, 3, 4, 148, 152][269952, 89984, 22496, 152, 1]cpu" = x_16.view(4, -1, 4, 148, 152); x_16 = None 2025-03-04T20:52:01.2246183Z permute_6: "f32[4, 148, 152, 3, 4][269952, 152, 1, 89984, 22496]cpu" = view_11.permute(0, 3, 4, 1, 2); view_11 = None 2025-03-04T20:52:01.2246568Z pred_anchor_deltas_i_2: "f32[4, 67488, 4][269952, 4, 1]cpu" = permute_6.flatten(1, -2); permute_6 = None 2025-03-04T20:52:01.2246932Z view_12: "f32[4, 3, 4, 74, 76][67488, 22496, 5624, 76, 1]cpu" = x_17.view(4, -1, 4, 74, 76); x_17 = None 2025-03-04T20:52:01.2247308Z permute_7: "f32[4, 74, 76, 3, 4][67488, 76, 1, 22496, 5624]cpu" = view_12.permute(0, 3, 4, 1, 2); view_12 = None 2025-03-04T20:52:01.2247690Z pred_anchor_deltas_i_4: "f32[4, 16872, 4][67488, 4, 1]cpu" = permute_7.flatten(1, -2); permute_7 = None 2025-03-04T20:52:01.2248058Z view_13: "f32[4, 3, 4, 37, 38][16872, 5624, 1406, 38, 1]cpu" = x_18.view(4, -1, 4, 37, 38); x_18 = None 2025-03-04T20:52:01.2248417Z permute_8: "f32[4, 37, 38, 3, 4][16872, 38, 1, 5624, 1406]cpu" = view_13.permute(0, 3, 4, 1, 2); view_13 = None 2025-03-04T20:52:01.2248783Z pred_anchor_deltas_i_6: "f32[4, 4218, 4][16872, 4, 1]cpu" = permute_8.flatten(1, -2); permute_8 = None 2025-03-04T20:52:01.2249129Z view_14: "f32[4, 3, 4, 19, 19][4332, 1444, 361, 19, 1]cpu" = x_19.view(4, -1, 4, 19, 19); x_19 = None 2025-03-04T20:52:01.2249464Z permute_9: "f32[4, 19, 19, 3, 4][4332, 19, 1, 1444, 361]cpu" = view_14.permute(0, 3, 4, 1, 2); view_14 = None 2025-03-04T20:52:01.2249845Z pred_anchor_deltas_i_8: "f32[4, 1083, 4][4332, 4, 1]cpu" = permute_9.flatten(1, -2); permute_9 = None 2025-03-04T20:52:01.2250117Z 2025-03-04T20:52:01.2250611Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:52:01.2251267Z pred_anchor_deltas_i_1: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T20:52:01.2251580Z 2025-03-04T20:52:01.2252092Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:52:01.2252727Z unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T20:52:01.2253076Z expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T20:52:01.2253412Z anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T20:52:01.2253657Z 2025-03-04T20:52:01.2254114Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:52:01.2254696Z deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T20:52:01.2254974Z 2025-03-04T20:52:01.2255369Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:52:01.2255886Z boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T20:52:01.2256138Z 2025-03-04T20:52:01.2256532Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:52:01.2257022Z getitem_10: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:52:01.2257327Z getitem_11: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:52:01.2257653Z widths: "f32[1079808][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T20:52:01.2257912Z 2025-03-04T20:52:01.2258307Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:52:01.2258789Z getitem_12: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:52:01.2259082Z getitem_13: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:52:01.2259399Z heights: "f32[1079808][1]cpu" = getitem_12 - getitem_13; getitem_12 = getitem_13 = None 2025-03-04T20:52:01.2259661Z 2025-03-04T20:52:01.2260066Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:52:01.2260540Z getitem_14: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:52:01.2260802Z mul: "f32[1079808][1]cpu" = 0.5 * widths 2025-03-04T20:52:01.2261075Z ctr_x: "f32[1079808][1]cpu" = getitem_14 + mul; getitem_14 = mul = None 2025-03-04T20:52:01.2261310Z 2025-03-04T20:52:01.2261702Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:52:01.2262203Z getitem_15: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:52:01.2262487Z mul_1: "f32[1079808][1]cpu" = 0.5 * heights 2025-03-04T20:52:01.2262770Z ctr_y: "f32[1079808][1]cpu" = getitem_15 + mul_1; getitem_15 = mul_1 = None 2025-03-04T20:52:01.2263013Z 2025-03-04T20:52:01.2263849Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:52:01.2264619Z getitem_16: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:52:01.2265006Z dx: "f32[1079808, 1][1, 1]cpu" = getitem_16 / 1.0; getitem_16 = None 2025-03-04T20:52:01.2265256Z 2025-03-04T20:52:01.2265695Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:52:01.2266200Z getitem_17: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:52:01.2266517Z dy: "f32[1079808, 1][1, 1]cpu" = getitem_17 / 1.0; getitem_17 = None 2025-03-04T20:52:01.2266744Z 2025-03-04T20:52:01.2267128Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:52:01.2267626Z getitem_18: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:52:01.2267935Z dw: "f32[1079808, 1][1, 1]cpu" = getitem_18 / 1.0; getitem_18 = None 2025-03-04T20:52:01.2268161Z 2025-03-04T20:52:01.2268549Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:52:01.2269090Z getitem_19: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:52:01.2269559Z dh: "f32[1079808, 1][1, 1]cpu" = getitem_19 / 1.0; getitem_19 = None 2025-03-04T20:52:01.2269792Z 2025-03-04T20:52:01.2270224Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:52:01.2270771Z dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:52:01.2271030Z 2025-03-04T20:52:01.2271466Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:52:01.2271989Z dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:52:01.2272236Z 2025-03-04T20:52:01.2272662Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:52:01.2273192Z getitem_20: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:52:01.2273560Z mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_20; dx = getitem_20 = None 2025-03-04T20:52:01.2273895Z getitem_21: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:52:01.2274480Z pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_21; mul_2 = getitem_21 = None 2025-03-04T20:52:01.2274854Z 2025-03-04T20:52:01.2275550Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:52:01.2276077Z getitem_22: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:52:01.2276383Z mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_22; dy = getitem_22 = None 2025-03-04T20:52:01.2276706Z getitem_23: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:52:01.2277068Z pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_23; mul_3 = getitem_23 = None 2025-03-04T20:52:01.2277323Z 2025-03-04T20:52:01.2277731Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:52:01.2278217Z exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:52:01.2278541Z getitem_24: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:52:01.2278875Z pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_24; exp = getitem_24 = None 2025-03-04T20:52:01.2279117Z 2025-03-04T20:52:01.2279521Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:52:01.2280008Z exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:52:01.2280337Z getitem_25: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:52:01.2280680Z pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_25; exp_1 = getitem_25 = None 2025-03-04T20:52:01.2280926Z 2025-03-04T20:52:01.2281313Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:52:01.2281766Z mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T20:52:01.2282021Z x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:52:01.2282247Z 2025-03-04T20:52:01.2282630Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:52:01.2283095Z mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T20:52:01.2283345Z y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:52:01.2283571Z 2025-03-04T20:52:01.2283951Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:52:01.2284411Z mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:52:01.2284701Z x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:52:01.2284942Z 2025-03-04T20:52:01.2285322Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:52:01.2285782Z mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:52:01.2286067Z y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:52:01.2286303Z 2025-03-04T20:52:01.2286769Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:52:01.2287335Z pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:52:01.2287615Z 2025-03-04T20:52:01.2288039Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:52:01.2288572Z proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4)); pred_boxes = None 2025-03-04T20:52:01.2288845Z 2025-03-04T20:52:01.2291889Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:52:01.2293775Z proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T20:52:01.2294190Z 2025-03-04T20:52:01.2294949Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:52:01.2297140Z pred_anchor_deltas_i_3: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_2.reshape(-1, 4); pred_anchor_deltas_i_2 = None 2025-03-04T20:52:01.2297524Z 2025-03-04T20:52:01.2298301Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:52:01.2299022Z unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = tensor_1.unsqueeze(0); tensor_1 = None 2025-03-04T20:52:01.2299415Z expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1); unsqueeze_1 = None 2025-03-04T20:52:01.2299816Z anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4); expand_1 = None 2025-03-04T20:52:01.2300100Z 2025-03-04T20:52:01.2300604Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:52:01.2301251Z deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float(); pred_anchor_deltas_i_3 = None 2025-03-04T20:52:01.2301563Z 2025-03-04T20:52:01.2302121Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:52:01.2302851Z boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32); anchors_i_1 = None 2025-03-04T20:52:01.2303142Z 2025-03-04T20:52:01.2303586Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:52:01.2304174Z getitem_26: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)] 2025-03-04T20:52:01.2304504Z getitem_27: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T20:52:01.2304852Z widths_1: "f32[269952][1]cpu" = getitem_26 - getitem_27; getitem_26 = getitem_27 = None 2025-03-04T20:52:01.2305132Z 2025-03-04T20:52:01.2305557Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:52:01.2306080Z getitem_28: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)] 2025-03-04T20:52:01.2306395Z getitem_29: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)] 2025-03-04T20:52:01.2306734Z heights_1: "f32[269952][1]cpu" = getitem_28 - getitem_29; getitem_28 = getitem_29 = None 2025-03-04T20:52:01.2307012Z 2025-03-04T20:52:01.2307482Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:52:01.2308003Z getitem_30: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T20:52:01.2308291Z mul_10: "f32[269952][1]cpu" = 0.5 * widths_1 2025-03-04T20:52:01.2308616Z ctr_x_1: "f32[269952][1]cpu" = getitem_30 + mul_10; getitem_30 = mul_10 = None 2025-03-04T20:52:01.2308877Z 2025-03-04T20:52:01.2309305Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:52:01.2309824Z getitem_31: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]; boxes_1 = None 2025-03-04T20:52:01.2310122Z mul_11: "f32[269952][1]cpu" = 0.5 * heights_1 2025-03-04T20:52:01.2310437Z ctr_y_1: "f32[269952][1]cpu" = getitem_31 + mul_11; getitem_31 = mul_11 = None 2025-03-04T20:52:01.2310686Z 2025-03-04T20:52:01.2311099Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:52:01.2311623Z getitem_32: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:52:01.2311952Z dx_1: "f32[269952, 1][1, 1]cpu" = getitem_32 / 1.0; getitem_32 = None 2025-03-04T20:52:01.2312188Z 2025-03-04T20:52:01.2312581Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:52:01.2313105Z getitem_33: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:52:01.2313427Z dy_1: "f32[269952, 1][1, 1]cpu" = getitem_33 / 1.0; getitem_33 = None 2025-03-04T20:52:01.2313659Z 2025-03-04T20:52:01.2314146Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:52:01.2314671Z getitem_34: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:52:01.2314996Z dw_2: "f32[269952, 1][1, 1]cpu" = getitem_34 / 1.0; getitem_34 = None 2025-03-04T20:52:01.2315232Z 2025-03-04T20:52:01.2315638Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:52:01.2316178Z getitem_35: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))]; deltas_1 = None 2025-03-04T20:52:01.2316576Z dh_2: "f32[269952, 1][1, 1]cpu" = getitem_35 / 1.0; getitem_35 = None 2025-03-04T20:52:01.2316804Z 2025-03-04T20:52:01.2317225Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:52:01.2317752Z dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356); dw_2 = None 2025-03-04T20:52:01.2318003Z 2025-03-04T20:52:01.2318409Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:52:01.2318931Z dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356); dh_2 = None 2025-03-04T20:52:01.2319174Z 2025-03-04T20:52:01.2319602Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:52:01.2320136Z getitem_36: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)] 2025-03-04T20:52:01.2320474Z mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_36; dx_1 = getitem_36 = None 2025-03-04T20:52:01.2320808Z getitem_37: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)]; ctr_x_1 = None 2025-03-04T20:52:01.2321158Z pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_37; mul_12 = getitem_37 = None 2025-03-04T20:52:01.2321413Z 2025-03-04T20:52:01.2321862Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:52:01.2322394Z getitem_38: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)] 2025-03-04T20:52:01.2322709Z mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_38; dy_1 = getitem_38 = None 2025-03-04T20:52:01.2323035Z getitem_39: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)]; ctr_y_1 = None 2025-03-04T20:52:01.2323402Z pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_39; mul_13 = getitem_39 = None 2025-03-04T20:52:01.2323657Z 2025-03-04T20:52:01.2324071Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:52:01.2324584Z exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3); dw_3 = None 2025-03-04T20:52:01.2324908Z getitem_40: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]; widths_1 = None 2025-03-04T20:52:01.2325253Z pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_40; exp_2 = getitem_40 = None 2025-03-04T20:52:01.2325500Z 2025-03-04T20:52:01.2325919Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:52:01.2326417Z exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3); dh_3 = None 2025-03-04T20:52:01.2326751Z getitem_41: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]; heights_1 = None 2025-03-04T20:52:01.2327103Z pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_41; exp_3 = getitem_41 = None 2025-03-04T20:52:01.2327354Z 2025-03-04T20:52:01.2327742Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:52:01.2328191Z mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1 2025-03-04T20:52:01.2328447Z x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16; mul_16 = None 2025-03-04T20:52:01.2328675Z 2025-03-04T20:52:01.2329122Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:52:01.2329584Z mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1 2025-03-04T20:52:01.2329847Z y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17; mul_17 = None 2025-03-04T20:52:01.2330080Z 2025-03-04T20:52:01.2330471Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:52:01.2330993Z mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1; pred_w_1 = None 2025-03-04T20:52:01.2331289Z x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18; pred_ctr_x_1 = mul_18 = None 2025-03-04T20:52:01.2331530Z 2025-03-04T20:52:01.2331911Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:52:01.2332373Z mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1; pred_h_1 = None 2025-03-04T20:52:01.2332664Z y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19; pred_ctr_y_1 = mul_19 = None 2025-03-04T20:52:01.2332923Z 2025-03-04T20:52:01.2333345Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:52:01.2333922Z pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1); x1_1 = y1_1 = x2_1 = y2_1 = None 2025-03-04T20:52:01.2334231Z 2025-03-04T20:52:01.2334649Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:52:01.2335195Z proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4)); pred_boxes_1 = None 2025-03-04T20:52:01.2335474Z 2025-03-04T20:52:01.2335941Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:52:01.2336563Z proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4); proposals_i_1 = None 2025-03-04T20:52:01.2336845Z 2025-03-04T20:52:01.2337311Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:52:01.2337961Z pred_anchor_deltas_i_5: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_4.reshape(-1, 4); pred_anchor_deltas_i_4 = None 2025-03-04T20:52:01.2338268Z 2025-03-04T20:52:01.2338768Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:52:01.2339397Z unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = tensor_2.unsqueeze(0); tensor_2 = None 2025-03-04T20:52:01.2339749Z expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1); unsqueeze_2 = None 2025-03-04T20:52:01.2340091Z anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4); expand_2 = None 2025-03-04T20:52:01.2340346Z 2025-03-04T20:52:01.2340804Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:52:01.2341406Z deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_5.float(); pred_anchor_deltas_i_5 = None 2025-03-04T20:52:01.2341678Z 2025-03-04T20:52:01.2342060Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:52:01.2342687Z boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32); anchors_i_2 = None 2025-03-04T20:52:01.2342951Z 2025-03-04T20:52:01.2343350Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:52:01.2343845Z getitem_42: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)] 2025-03-04T20:52:01.2344154Z getitem_43: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T20:52:01.2344484Z widths_2: "f32[67488][1]cpu" = getitem_42 - getitem_43; getitem_42 = getitem_43 = None 2025-03-04T20:52:01.2344754Z 2025-03-04T20:52:01.2345162Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:52:01.2345655Z getitem_44: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)] 2025-03-04T20:52:01.2345947Z getitem_45: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)] 2025-03-04T20:52:01.2346287Z heights_2: "f32[67488][1]cpu" = getitem_44 - getitem_45; getitem_44 = getitem_45 = None 2025-03-04T20:52:01.2346553Z 2025-03-04T20:52:01.2346947Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:52:01.2347446Z getitem_46: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T20:52:01.2347716Z mul_20: "f32[67488][1]cpu" = 0.5 * widths_2 2025-03-04T20:52:01.2347992Z ctr_x_2: "f32[67488][1]cpu" = getitem_46 + mul_20; getitem_46 = mul_20 = None 2025-03-04T20:52:01.2348244Z 2025-03-04T20:52:01.2348657Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:52:01.2349192Z getitem_47: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]; boxes_2 = None 2025-03-04T20:52:01.2349502Z mul_21: "f32[67488][1]cpu" = 0.5 * heights_2 2025-03-04T20:52:01.2349769Z ctr_y_2: "f32[67488][1]cpu" = getitem_47 + mul_21; getitem_47 = mul_21 = None 2025-03-04T20:52:01.2350011Z 2025-03-04T20:52:01.2350397Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:52:01.2350902Z getitem_48: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:52:01.2351215Z dx_2: "f32[67488, 1][1, 1]cpu" = getitem_48 / 1.0; getitem_48 = None 2025-03-04T20:52:01.2351439Z 2025-03-04T20:52:01.2351817Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:52:01.2352317Z getitem_49: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:52:01.2352629Z dy_2: "f32[67488, 1][1, 1]cpu" = getitem_49 / 1.0; getitem_49 = None 2025-03-04T20:52:01.2352853Z 2025-03-04T20:52:01.2353232Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:52:01.2353729Z getitem_50: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:52:01.2354124Z dw_4: "f32[67488, 1][1, 1]cpu" = getitem_50 / 1.0; getitem_50 = None 2025-03-04T20:52:01.2354356Z 2025-03-04T20:52:01.2354753Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:52:01.2355322Z getitem_51: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))]; deltas_2 = None 2025-03-04T20:52:01.2355675Z dh_4: "f32[67488, 1][1, 1]cpu" = getitem_51 / 1.0; getitem_51 = None 2025-03-04T20:52:01.2355900Z 2025-03-04T20:52:01.2356318Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:52:01.2356839Z dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356); dw_4 = None 2025-03-04T20:52:01.2357094Z 2025-03-04T20:52:01.2357502Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:52:01.2358026Z dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356); dh_4 = None 2025-03-04T20:52:01.2358270Z 2025-03-04T20:52:01.2358696Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:52:01.2359247Z getitem_52: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)] 2025-03-04T20:52:01.2359575Z mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_52; dx_2 = getitem_52 = None 2025-03-04T20:52:01.2359909Z getitem_53: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)]; ctr_x_2 = None 2025-03-04T20:52:01.2360275Z pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_53; mul_22 = getitem_53 = None 2025-03-04T20:52:01.2360532Z 2025-03-04T20:52:01.2360965Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:52:01.2361500Z getitem_54: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)] 2025-03-04T20:52:01.2361811Z mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_54; dy_2 = getitem_54 = None 2025-03-04T20:52:01.2362157Z getitem_55: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)]; ctr_y_2 = None 2025-03-04T20:52:01.2362503Z pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_55; mul_23 = getitem_55 = None 2025-03-04T20:52:01.2362763Z 2025-03-04T20:52:01.2363180Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:52:01.2363682Z exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5); dw_5 = None 2025-03-04T20:52:01.2364004Z getitem_56: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]; widths_2 = None 2025-03-04T20:52:01.2364350Z pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_56; exp_4 = getitem_56 = None 2025-03-04T20:52:01.2364604Z 2025-03-04T20:52:01.2365022Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:52:01.2365522Z exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5); dh_5 = None 2025-03-04T20:52:01.2365842Z getitem_57: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]; heights_2 = None 2025-03-04T20:52:01.2366189Z pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_57; exp_5 = getitem_57 = None 2025-03-04T20:52:01.2366437Z 2025-03-04T20:52:01.2366839Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:52:01.2367299Z mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2 2025-03-04T20:52:01.2367558Z x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26; mul_26 = None 2025-03-04T20:52:01.2367819Z 2025-03-04T20:52:01.2368199Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:52:01.2368653Z mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2 2025-03-04T20:52:01.2368906Z y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27; mul_27 = None 2025-03-04T20:52:01.2369134Z 2025-03-04T20:52:01.2369516Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:52:01.2369988Z mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2; pred_w_2 = None 2025-03-04T20:52:01.2370283Z x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28; pred_ctr_x_2 = mul_28 = None 2025-03-04T20:52:01.2370525Z 2025-03-04T20:52:01.2370905Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:52:01.2371370Z mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2; pred_h_2 = None 2025-03-04T20:52:01.2371674Z y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29; pred_ctr_y_2 = mul_29 = None 2025-03-04T20:52:01.2371919Z 2025-03-04T20:52:01.2372347Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:52:01.2372940Z pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1); x1_2 = y1_2 = x2_2 = y2_2 = None 2025-03-04T20:52:01.2373231Z 2025-03-04T20:52:01.2373641Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:52:01.2374176Z proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4)); pred_boxes_2 = None 2025-03-04T20:52:01.2374456Z 2025-03-04T20:52:01.2374939Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:52:01.2375540Z proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4); proposals_i_2 = None 2025-03-04T20:52:01.2375814Z 2025-03-04T20:52:01.2376283Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:52:01.2376925Z pred_anchor_deltas_i_7: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_6.reshape(-1, 4); pred_anchor_deltas_i_6 = None 2025-03-04T20:52:01.2377239Z 2025-03-04T20:52:01.2377743Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:52:01.2378365Z unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = tensor_3.unsqueeze(0); tensor_3 = None 2025-03-04T20:52:01.2378709Z expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1); unsqueeze_3 = None 2025-03-04T20:52:01.2379042Z anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4); expand_3 = None 2025-03-04T20:52:01.2379289Z 2025-03-04T20:52:01.2379734Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:52:01.2380312Z deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_7.float(); pred_anchor_deltas_i_7 = None 2025-03-04T20:52:01.2380587Z 2025-03-04T20:52:01.2380993Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:52:01.2381498Z boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32); anchors_i_3 = None 2025-03-04T20:52:01.2381760Z 2025-03-04T20:52:01.2382151Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:52:01.2382646Z getitem_58: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)] 2025-03-04T20:52:01.2382948Z getitem_59: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T20:52:01.2383276Z widths_3: "f32[16872][1]cpu" = getitem_58 - getitem_59; getitem_58 = getitem_59 = None 2025-03-04T20:52:01.2383539Z 2025-03-04T20:52:01.2383936Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:52:01.2384420Z getitem_60: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)] 2025-03-04T20:52:01.2384733Z getitem_61: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)] 2025-03-04T20:52:01.2385052Z heights_3: "f32[16872][1]cpu" = getitem_60 - getitem_61; getitem_60 = getitem_61 = None 2025-03-04T20:52:01.2385310Z 2025-03-04T20:52:01.2385697Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:52:01.2386204Z getitem_62: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T20:52:01.2386465Z mul_30: "f32[16872][1]cpu" = 0.5 * widths_3 2025-03-04T20:52:01.2386727Z ctr_x_3: "f32[16872][1]cpu" = getitem_62 + mul_30; getitem_62 = mul_30 = None 2025-03-04T20:52:01.2386967Z 2025-03-04T20:52:01.2387351Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:52:01.2387867Z getitem_63: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]; boxes_3 = None 2025-03-04T20:52:01.2388154Z mul_31: "f32[16872][1]cpu" = 0.5 * heights_3 2025-03-04T20:52:01.2388416Z ctr_y_3: "f32[16872][1]cpu" = getitem_63 + mul_31; getitem_63 = mul_31 = None 2025-03-04T20:52:01.2388655Z 2025-03-04T20:52:01.2389036Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:52:01.2389528Z getitem_64: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:52:01.2389838Z dx_3: "f32[16872, 1][1, 1]cpu" = getitem_64 / 1.0; getitem_64 = None 2025-03-04T20:52:01.2390068Z 2025-03-04T20:52:01.2390443Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:52:01.2390932Z getitem_65: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:52:01.2391239Z dy_3: "f32[16872, 1][1, 1]cpu" = getitem_65 / 1.0; getitem_65 = None 2025-03-04T20:52:01.2391457Z 2025-03-04T20:52:01.2391828Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:52:01.2392315Z getitem_66: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:52:01.2392620Z dw_6: "f32[16872, 1][1, 1]cpu" = getitem_66 / 1.0; getitem_66 = None 2025-03-04T20:52:01.2392844Z 2025-03-04T20:52:01.2393220Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:52:01.2393759Z getitem_67: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))]; deltas_3 = None 2025-03-04T20:52:01.2394191Z dh_6: "f32[16872, 1][1, 1]cpu" = getitem_67 / 1.0; getitem_67 = None 2025-03-04T20:52:01.2394423Z 2025-03-04T20:52:01.2394852Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:52:01.2395388Z dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356); dw_6 = None 2025-03-04T20:52:01.2395639Z 2025-03-04T20:52:01.2396038Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:52:01.2396550Z dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356); dh_6 = None 2025-03-04T20:52:01.2396800Z 2025-03-04T20:52:01.2397239Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:52:01.2397759Z getitem_68: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)] 2025-03-04T20:52:01.2398067Z mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_68; dx_3 = getitem_68 = None 2025-03-04T20:52:01.2398387Z getitem_69: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)]; ctr_x_3 = None 2025-03-04T20:52:01.2398739Z pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_69; mul_32 = getitem_69 = None 2025-03-04T20:52:01.2398994Z 2025-03-04T20:52:01.2399415Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:52:01.2399935Z getitem_70: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)] 2025-03-04T20:52:01.2400258Z mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_70; dy_3 = getitem_70 = None 2025-03-04T20:52:01.2400577Z getitem_71: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)]; ctr_y_3 = None 2025-03-04T20:52:01.2400910Z pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_71; mul_33 = getitem_71 = None 2025-03-04T20:52:01.2401160Z 2025-03-04T20:52:01.2401572Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:52:01.2402246Z exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7); dw_7 = None 2025-03-04T20:52:01.2402571Z getitem_72: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]; widths_3 = None 2025-03-04T20:52:01.2402933Z pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_72; exp_6 = getitem_72 = None 2025-03-04T20:52:01.2403194Z 2025-03-04T20:52:01.2403625Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:52:01.2404136Z exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7); dh_7 = None 2025-03-04T20:52:01.2404481Z getitem_73: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]; heights_3 = None 2025-03-04T20:52:01.2404839Z pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_73; exp_7 = getitem_73 = None 2025-03-04T20:52:01.2405100Z 2025-03-04T20:52:01.2405507Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:52:01.2405986Z mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3 2025-03-04T20:52:01.2406298Z x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36; mul_36 = None 2025-03-04T20:52:01.2406538Z 2025-03-04T20:52:01.2406936Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:52:01.2407394Z mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3 2025-03-04T20:52:01.2407658Z y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37; mul_37 = None 2025-03-04T20:52:01.2407896Z 2025-03-04T20:52:01.2408293Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:52:01.2408775Z mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3; pred_w_3 = None 2025-03-04T20:52:01.2409074Z x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38; pred_ctr_x_3 = mul_38 = None 2025-03-04T20:52:01.2409327Z 2025-03-04T20:52:01.2409715Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:52:01.2410219Z mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3; pred_h_3 = None 2025-03-04T20:52:01.2410513Z y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39; pred_ctr_y_3 = mul_39 = None 2025-03-04T20:52:01.2410769Z 2025-03-04T20:52:01.2411217Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:52:01.2411845Z pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1); x1_3 = y1_3 = x2_3 = y2_3 = None 2025-03-04T20:52:01.2412137Z 2025-03-04T20:52:01.2412552Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:52:01.2413096Z proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4)); pred_boxes_3 = None 2025-03-04T20:52:01.2413392Z 2025-03-04T20:52:01.2413854Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:52:01.2414453Z proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4); proposals_i_3 = None 2025-03-04T20:52:01.2414734Z 2025-03-04T20:52:01.2415214Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:52:01.2415870Z pred_anchor_deltas_i_9: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_8.reshape(-1, 4); pred_anchor_deltas_i_8 = None 2025-03-04T20:52:01.2416190Z 2025-03-04T20:52:01.2416708Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:52:01.2417320Z unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = tensor_4.unsqueeze(0); tensor_4 = None 2025-03-04T20:52:01.2417650Z expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1); unsqueeze_4 = None 2025-03-04T20:52:01.2417970Z anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4); expand_4 = None 2025-03-04T20:52:01.2418207Z 2025-03-04T20:52:01.2418647Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:52:01.2419211Z deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_9.float(); pred_anchor_deltas_i_9 = None 2025-03-04T20:52:01.2419501Z 2025-03-04T20:52:01.2419890Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:52:01.2420387Z boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32); anchors_i_4 = None 2025-03-04T20:52:01.2420637Z 2025-03-04T20:52:01.2421027Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:52:01.2421507Z getitem_74: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)] 2025-03-04T20:52:01.2421793Z getitem_75: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T20:52:01.2422102Z widths_4: "f32[4332][1]cpu" = getitem_74 - getitem_75; getitem_74 = getitem_75 = None 2025-03-04T20:52:01.2422348Z 2025-03-04T20:52:01.2422740Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:52:01.2423216Z getitem_76: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)] 2025-03-04T20:52:01.2423516Z getitem_77: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)] 2025-03-04T20:52:01.2423824Z heights_4: "f32[4332][1]cpu" = getitem_76 - getitem_77; getitem_76 = getitem_77 = None 2025-03-04T20:52:01.2424083Z 2025-03-04T20:52:01.2424494Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:52:01.2424968Z getitem_78: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T20:52:01.2425223Z mul_40: "f32[4332][1]cpu" = 0.5 * widths_4 2025-03-04T20:52:01.2425485Z ctr_x_4: "f32[4332][1]cpu" = getitem_78 + mul_40; getitem_78 = mul_40 = None 2025-03-04T20:52:01.2425729Z 2025-03-04T20:52:01.2426117Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:52:01.2426642Z getitem_79: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]; boxes_4 = None 2025-03-04T20:52:01.2426929Z mul_41: "f32[4332][1]cpu" = 0.5 * heights_4 2025-03-04T20:52:01.2427190Z ctr_y_4: "f32[4332][1]cpu" = getitem_79 + mul_41; getitem_79 = mul_41 = None 2025-03-04T20:52:01.2427429Z 2025-03-04T20:52:01.2427815Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:52:01.2428318Z getitem_80: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:52:01.2428639Z dx_4: "f32[4332, 1][1, 1]cpu" = getitem_80 / 1.0; getitem_80 = None 2025-03-04T20:52:01.2428861Z 2025-03-04T20:52:01.2429232Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:52:01.2429721Z getitem_81: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:52:01.2430023Z dy_4: "f32[4332, 1][1, 1]cpu" = getitem_81 / 1.0; getitem_81 = None 2025-03-04T20:52:01.2430240Z 2025-03-04T20:52:01.2430607Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:52:01.2431092Z getitem_82: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:52:01.2431391Z dw_8: "f32[4332, 1][1, 1]cpu" = getitem_82 / 1.0; getitem_82 = None 2025-03-04T20:52:01.2431608Z 2025-03-04T20:52:01.2431981Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:52:01.2432516Z getitem_83: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))]; deltas_4 = None 2025-03-04T20:52:01.2432844Z dh_8: "f32[4332, 1][1, 1]cpu" = getitem_83 / 1.0; getitem_83 = None 2025-03-04T20:52:01.2433064Z 2025-03-04T20:52:01.2433478Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:52:01.2434439Z dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356); dw_8 = None 2025-03-04T20:52:01.2434726Z 2025-03-04T20:52:01.2435163Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:52:01.2435692Z dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356); dh_8 = None 2025-03-04T20:52:01.2435943Z 2025-03-04T20:52:01.2436399Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:52:01.2436934Z getitem_84: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)] 2025-03-04T20:52:01.2437251Z mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_84; dx_4 = getitem_84 = None 2025-03-04T20:52:01.2437595Z getitem_85: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)]; ctr_x_4 = None 2025-03-04T20:52:01.2437944Z pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_85; mul_42 = getitem_85 = None 2025-03-04T20:52:01.2438197Z 2025-03-04T20:52:01.2438626Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:52:01.2439159Z getitem_86: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)] 2025-03-04T20:52:01.2439488Z mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_86; dy_4 = getitem_86 = None 2025-03-04T20:52:01.2439806Z getitem_87: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)]; ctr_y_4 = None 2025-03-04T20:52:01.2440142Z pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_87; mul_43 = getitem_87 = None 2025-03-04T20:52:01.2440398Z 2025-03-04T20:52:01.2440825Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:52:01.2441330Z exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9); dw_9 = None 2025-03-04T20:52:01.2441658Z getitem_88: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]; widths_4 = None 2025-03-04T20:52:01.2442009Z pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_88; exp_8 = getitem_88 = None 2025-03-04T20:52:01.2442274Z 2025-03-04T20:52:01.2442690Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:52:01.2443181Z exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9); dh_9 = None 2025-03-04T20:52:01.2443502Z getitem_89: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]; heights_4 = None 2025-03-04T20:52:01.2443850Z pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_89; exp_9 = getitem_89 = None 2025-03-04T20:52:01.2444097Z 2025-03-04T20:52:01.2444495Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:52:01.2444956Z mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4 2025-03-04T20:52:01.2445236Z x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46; mul_46 = None 2025-03-04T20:52:01.2445467Z 2025-03-04T20:52:01.2445866Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:52:01.2446323Z mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4 2025-03-04T20:52:01.2446582Z y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47; mul_47 = None 2025-03-04T20:52:01.2446808Z 2025-03-04T20:52:01.2447200Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:52:01.2447684Z mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4; pred_w_4 = None 2025-03-04T20:52:01.2447977Z x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48; pred_ctr_x_4 = mul_48 = None 2025-03-04T20:52:01.2448223Z 2025-03-04T20:52:01.2448614Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:52:01.2449101Z mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4; pred_h_4 = None 2025-03-04T20:52:01.2449391Z y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49; pred_ctr_y_4 = mul_49 = None 2025-03-04T20:52:01.2449634Z 2025-03-04T20:52:01.2450064Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:52:01.2450673Z pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1); x1_4 = y1_4 = x2_4 = y2_4 = None 2025-03-04T20:52:01.2450969Z 2025-03-04T20:52:01.2451393Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:52:01.2451942Z proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4)); pred_boxes_4 = None 2025-03-04T20:52:01.2452234Z 2025-03-04T20:52:01.2452703Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:52:01.2453307Z proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4); proposals_i_4 = None 2025-03-04T20:52:01.2453594Z 2025-03-04T20:52:01.2454169Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T20:52:01.2454856Z arange_10: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T20:52:01.2455104Z 2025-03-04T20:52:01.2455484Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:52:01.2455977Z batch_idx: "i64[4][1]cpu" = arange_10.to(device(type='cpu')); arange_10 = None 2025-03-04T20:52:01.2456232Z 2025-03-04T20:52:01.2456753Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:52:01.2457352Z topk = logits_i.topk(1000, dim = 1); logits_i = None 2025-03-04T20:52:01.2457616Z topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0] 2025-03-04T20:52:01.2457883Z topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1]; topk = None 2025-03-04T20:52:01.2458108Z 2025-03-04T20:52:01.2458650Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:52:01.2459314Z getitem_92: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T20:52:01.2459727Z topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_92, topk_idx)]; proposals_i_5 = getitem_92 = topk_idx = None 2025-03-04T20:52:01.2460061Z 2025-03-04T20:52:01.2460606Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:52:01.2461276Z full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:52:01.2461550Z 2025-03-04T20:52:01.2461926Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:52:01.2462397Z to_21: "i64[1000][1]cpu" = full.to(device(type='cpu')); full = None 2025-03-04T20:52:01.2462632Z 2025-03-04T20:52:01.2463164Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:52:01.2463761Z topk_1 = logits_i_1.topk(1000, dim = 1); logits_i_1 = None 2025-03-04T20:52:01.2464046Z topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0] 2025-03-04T20:52:01.2464320Z topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1]; topk_1 = None 2025-03-04T20:52:01.2464555Z 2025-03-04T20:52:01.2465114Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:52:01.2465772Z getitem_96: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T20:52:01.2466207Z topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_96, topk_idx_1)]; proposals_i_6 = getitem_96 = topk_idx_1 = None 2025-03-04T20:52:01.2466554Z 2025-03-04T20:52:01.2467102Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:52:01.2467788Z full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:52:01.2468076Z 2025-03-04T20:52:01.2468466Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:52:01.2468948Z to_22: "i64[1000][1]cpu" = full_1.to(device(type='cpu')); full_1 = None 2025-03-04T20:52:01.2469195Z 2025-03-04T20:52:01.2469727Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:52:01.2470339Z topk_2 = logits_i_2.topk(1000, dim = 1); logits_i_2 = None 2025-03-04T20:52:01.2470615Z topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0] 2025-03-04T20:52:01.2470895Z topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1]; topk_2 = None 2025-03-04T20:52:01.2471133Z 2025-03-04T20:52:01.2471689Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:52:01.2472373Z getitem_100: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T20:52:01.2472809Z topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_100, topk_idx_2)]; proposals_i_7 = getitem_100 = topk_idx_2 = None 2025-03-04T20:52:01.2473167Z 2025-03-04T20:52:01.2473723Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:52:01.2474498Z full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:52:01.2474790Z 2025-03-04T20:52:01.2475183Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:52:01.2475676Z to_23: "i64[1000][1]cpu" = full_2.to(device(type='cpu')); full_2 = None 2025-03-04T20:52:01.2475926Z 2025-03-04T20:52:01.2476492Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:52:01.2477104Z topk_3 = logits_i_3.topk(1000, dim = 1); logits_i_3 = None 2025-03-04T20:52:01.2477378Z topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0] 2025-03-04T20:52:01.2477654Z topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1]; topk_3 = None 2025-03-04T20:52:01.2477892Z 2025-03-04T20:52:01.2478463Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:52:01.2479120Z getitem_104: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T20:52:01.2479555Z topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_104, topk_idx_3)]; proposals_i_8 = getitem_104 = topk_idx_3 = None 2025-03-04T20:52:01.2479934Z 2025-03-04T20:52:01.2480457Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:52:01.2481102Z full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:52:01.2481370Z 2025-03-04T20:52:01.2481727Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:52:01.2482192Z to_24: "i64[1000][1]cpu" = full_3.to(device(type='cpu')); full_3 = None 2025-03-04T20:52:01.2482416Z 2025-03-04T20:52:01.2482914Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:52:01.2483490Z topk_4 = logits_i_4.topk(1000, dim = 1); logits_i_4 = None 2025-03-04T20:52:01.2483752Z topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0] 2025-03-04T20:52:01.2484016Z topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1]; topk_4 = None 2025-03-04T20:52:01.2484237Z 2025-03-04T20:52:01.2484758Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:52:01.2485407Z getitem_108: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T20:52:01.2485866Z topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_108, topk_idx_4)]; proposals_i_9 = getitem_108 = topk_idx_4 = None 2025-03-04T20:52:01.2486204Z 2025-03-04T20:52:01.2486727Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:52:01.2487376Z full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:52:01.2487649Z 2025-03-04T20:52:01.2488019Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:52:01.2488476Z to_25: "i64[1000][1]cpu" = full_4.to(device(type='cpu')); full_4 = None 2025-03-04T20:52:01.2488716Z 2025-03-04T20:52:01.2489079Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:52:01.2489807Z topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1); topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None 2025-03-04T20:52:01.2490295Z 2025-03-04T20:52:01.2490648Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:52:01.2491430Z topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1); topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None 2025-03-04T20:52:01.2491988Z 2025-03-04T20:52:01.2492339Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:52:01.2492855Z level_ids: "i64[5000][1]cpu" = torch.cat([to_21, to_22, to_23, to_24, to_25], 0); to_21 = to_22 = to_23 = to_24 = to_25 = level_ids = None 2025-03-04T20:52:01.2493179Z 2025-03-04T20:52:01.2493643Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T20:52:01.2494210Z getitem_110: "f32[5000, 4][4, 1]cpu" = topk_proposals[0]; topk_proposals = None 2025-03-04T20:52:01.2494464Z 2025-03-04T20:52:01.2494835Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:52:01.2495319Z tensor_5: "f32[5000, 4][4, 1]cpu" = getitem_110.to(torch.float32); getitem_110 = None 2025-03-04T20:52:01.2495572Z 2025-03-04T20:52:01.2496031Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T20:52:01.2496665Z scores_per_img: "f32[5000][1]cpu" = topk_scores[0]; topk_scores = None 2025-03-04T20:52:01.2496904Z 2025-03-04T20:52:01.2497456Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T20:52:01.2498109Z isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor_5); tensor_5 = None 2025-03-04T20:52:01.2498407Z all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:52:01.2498722Z isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T20:52:01.2499066Z valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T20:52:01.2499307Z 2025-03-04T20:52:01.2499751Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T20:52:01.2500269Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T20:52:01.2500493Z 2025-03-04T20:52:01.2500741Z 2025-03-04T20:52:01.2500832Z class GraphModule(torch.nn.Module): 2025-03-04T20:52:01.2503192Z def forward(self, L_features_p2_: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu", L_features_p3_: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu", L_features_p4_: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu", L_features_p5_: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu", L_features_p6_: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_1_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_2_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_3_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_4_: "f32[3, 4][4, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_bias_: "f32[256][1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[3, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[3][1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[12, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[12][1]cpu"): 2025-03-04T20:52:01.2505527Z l_features_p2_ = L_features_p2_ 2025-03-04T20:52:01.2505750Z l_features_p3_ = L_features_p3_ 2025-03-04T20:52:01.2505966Z l_features_p4_ = L_features_p4_ 2025-03-04T20:52:01.2506176Z l_features_p5_ = L_features_p5_ 2025-03-04T20:52:01.2506406Z l_features_p6_ = L_features_p6_ 2025-03-04T20:52:01.2506792Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T20:52:01.2507352Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_1_ 2025-03-04T20:52:01.2507917Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_2_ 2025-03-04T20:52:01.2508473Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_3_ 2025-03-04T20:52:01.2509024Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_4_ 2025-03-04T20:52:01.2509551Z l_self_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T20:52:01.2510041Z l_self_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T20:52:01.2510585Z l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T20:52:01.2511182Z l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T20:52:01.2511750Z l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T20:52:01.2512302Z l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T20:52:01.2512704Z 2025-03-04T20:52:01.2513255Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:52:01.2513960Z arange: "f32[304][1]cpu" = torch.arange(0.0, 1216, step = 4, dtype = torch.float32) 2025-03-04T20:52:01.2514240Z 2025-03-04T20:52:01.2514616Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:52:01.2515104Z shifts_x: "f32[304][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T20:52:01.2515349Z 2025-03-04T20:52:01.2515856Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:52:01.2516474Z arange_1: "f32[296][1]cpu" = torch.arange(0.0, 1184, step = 4, dtype = torch.float32) 2025-03-04T20:52:01.2516735Z 2025-03-04T20:52:01.2517121Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:52:01.2517600Z shifts_y: "f32[296][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T20:52:01.2517851Z 2025-03-04T20:52:01.2518319Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:52:01.2518914Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T20:52:01.2519237Z shift_y: "f32[296, 304][1, 0]cpu" = meshgrid[0] 2025-03-04T20:52:01.2519498Z shift_x: "f32[296, 304][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T20:52:01.2519728Z 2025-03-04T20:52:01.2520133Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:52:01.2520643Z shift_x_1: "f32[89984][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T20:52:01.2520879Z 2025-03-04T20:52:01.2521275Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:52:01.2521764Z shift_y_1: "f32[89984][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T20:52:01.2521993Z 2025-03-04T20:52:01.2522444Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:52:01.2523074Z shifts: "f32[89984, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T20:52:01.2523388Z 2025-03-04T20:52:01.2523876Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:52:01.2524446Z view: "f32[89984, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T20:52:01.2524932Z view_1: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T20:52:01.2525402Z add: "f32[89984, 3, 4][12, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T20:52:01.2525683Z x: "f32[269952, 4][4, 1]cpu" = add.reshape(-1, 4); add = None 2025-03-04T20:52:01.2525931Z 2025-03-04T20:52:01.2526436Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:52:01.2527057Z arange_2: "f32[152][1]cpu" = torch.arange(0.0, 1216, step = 8, dtype = torch.float32) 2025-03-04T20:52:01.2527316Z 2025-03-04T20:52:01.2527688Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:52:01.2528172Z shifts_x_1: "f32[152][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T20:52:01.2528427Z 2025-03-04T20:52:01.2528937Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:52:01.2529553Z arange_3: "f32[148][1]cpu" = torch.arange(0.0, 1184, step = 8, dtype = torch.float32) 2025-03-04T20:52:01.2529812Z 2025-03-04T20:52:01.2530207Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:52:01.2530683Z shifts_y_1: "f32[148][1]cpu" = arange_3.to(device(type='cpu')); arange_3 = None 2025-03-04T20:52:01.2530933Z 2025-03-04T20:52:01.2531397Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:52:01.2532008Z meshgrid_1 = torch.functional.meshgrid(shifts_y_1, shifts_x_1); shifts_y_1 = shifts_x_1 = None 2025-03-04T20:52:01.2532351Z shift_y_2: "f32[148, 152][1, 0]cpu" = meshgrid_1[0] 2025-03-04T20:52:01.2532619Z shift_x_2: "f32[148, 152][0, 1]cpu" = meshgrid_1[1]; meshgrid_1 = None 2025-03-04T20:52:01.2532857Z 2025-03-04T20:52:01.2533268Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:52:01.2533793Z shift_x_3: "f32[22496][1]cpu" = shift_x_2.reshape(-1); shift_x_2 = None 2025-03-04T20:52:01.2534037Z 2025-03-04T20:52:01.2534446Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:52:01.2534952Z shift_y_3: "f32[22496][1]cpu" = shift_y_2.reshape(-1); shift_y_2 = None 2025-03-04T20:52:01.2535193Z 2025-03-04T20:52:01.2535654Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:52:01.2536293Z shifts_1: "f32[22496, 4][4, 1]cpu" = torch.stack((shift_x_3, shift_y_3, shift_x_3, shift_y_3), dim = 1); shift_x_3 = shift_y_3 = None 2025-03-04T20:52:01.2536620Z 2025-03-04T20:52:01.2537117Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:52:01.2537713Z view_2: "f32[22496, 1, 4][4, 4, 1]cpu" = shifts_1.view(-1, 1, 4); shifts_1 = None 2025-03-04T20:52:01.2538206Z view_3: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_ = None 2025-03-04T20:52:01.2538692Z add_1: "f32[22496, 3, 4][12, 4, 1]cpu" = view_2 + view_3; view_2 = view_3 = None 2025-03-04T20:52:01.2538990Z x_1: "f32[67488, 4][4, 1]cpu" = add_1.reshape(-1, 4); add_1 = None 2025-03-04T20:52:01.2539237Z 2025-03-04T20:52:01.2539739Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:52:01.2540356Z arange_4: "f32[76][1]cpu" = torch.arange(0.0, 1216, step = 16, dtype = torch.float32) 2025-03-04T20:52:01.2540610Z 2025-03-04T20:52:01.2540973Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:52:01.2541460Z shifts_x_2: "f32[76][1]cpu" = arange_4.to(device(type='cpu')); arange_4 = None 2025-03-04T20:52:01.2541701Z 2025-03-04T20:52:01.2542203Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:52:01.2542814Z arange_5: "f32[74][1]cpu" = torch.arange(0.0, 1184, step = 16, dtype = torch.float32) 2025-03-04T20:52:01.2543066Z 2025-03-04T20:52:01.2543453Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:52:01.2543929Z shifts_y_2: "f32[74][1]cpu" = arange_5.to(device(type='cpu')); arange_5 = None 2025-03-04T20:52:01.2544176Z 2025-03-04T20:52:01.2544641Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:52:01.2545245Z meshgrid_2 = torch.functional.meshgrid(shifts_y_2, shifts_x_2); shifts_y_2 = shifts_x_2 = None 2025-03-04T20:52:01.2545584Z shift_y_4: "f32[74, 76][1, 0]cpu" = meshgrid_2[0] 2025-03-04T20:52:01.2545848Z shift_x_4: "f32[74, 76][0, 1]cpu" = meshgrid_2[1]; meshgrid_2 = None 2025-03-04T20:52:01.2546082Z 2025-03-04T20:52:01.2546484Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:52:01.2547092Z shift_x_5: "f32[5624][1]cpu" = shift_x_4.reshape(-1); shift_x_4 = None 2025-03-04T20:52:01.2547330Z 2025-03-04T20:52:01.2547730Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:52:01.2548223Z shift_y_5: "f32[5624][1]cpu" = shift_y_4.reshape(-1); shift_y_4 = None 2025-03-04T20:52:01.2548459Z 2025-03-04T20:52:01.2548915Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:52:01.2549549Z shifts_2: "f32[5624, 4][4, 1]cpu" = torch.stack((shift_x_5, shift_y_5, shift_x_5, shift_y_5), dim = 1); shift_x_5 = shift_y_5 = None 2025-03-04T20:52:01.2549872Z 2025-03-04T20:52:01.2550360Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:52:01.2550939Z view_4: "f32[5624, 1, 4][4, 4, 1]cpu" = shifts_2.view(-1, 1, 4); shifts_2 = None 2025-03-04T20:52:01.2551424Z view_5: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_ = None 2025-03-04T20:52:01.2551898Z add_2: "f32[5624, 3, 4][12, 4, 1]cpu" = view_4 + view_5; view_4 = view_5 = None 2025-03-04T20:52:01.2552183Z x_2: "f32[16872, 4][4, 1]cpu" = add_2.reshape(-1, 4); add_2 = None 2025-03-04T20:52:01.2552434Z 2025-03-04T20:52:01.2552947Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:52:01.2553574Z arange_6: "f32[38][1]cpu" = torch.arange(0.0, 1216, step = 32, dtype = torch.float32) 2025-03-04T20:52:01.2553827Z 2025-03-04T20:52:01.2554272Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:52:01.2554766Z shifts_x_3: "f32[38][1]cpu" = arange_6.to(device(type='cpu')); arange_6 = None 2025-03-04T20:52:01.2555021Z 2025-03-04T20:52:01.2555532Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:52:01.2556144Z arange_7: "f32[37][1]cpu" = torch.arange(0.0, 1184, step = 32, dtype = torch.float32) 2025-03-04T20:52:01.2556402Z 2025-03-04T20:52:01.2556786Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:52:01.2557259Z shifts_y_3: "f32[37][1]cpu" = arange_7.to(device(type='cpu')); arange_7 = None 2025-03-04T20:52:01.2557503Z 2025-03-04T20:52:01.2557882Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:52:01.2558077Z meshgrid_3 = torch.functional.meshgrid(shifts_y_3, shifts_x_3); shifts_y_3 = shifts_x_3 = None 2025-03-04T20:52:01.2558169Z shift_y_6: "f32[37, 38][1, 0]cpu" = meshgrid_3[0] 2025-03-04T20:52:01.2558288Z shift_x_6: "f32[37, 38][0, 1]cpu" = meshgrid_3[1]; meshgrid_3 = None 2025-03-04T20:52:01.2558349Z 2025-03-04T20:52:01.2558678Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:52:01.2559845Z shift_x_7: "f32[1406][1]cpu" = shift_x_6.reshape(-1); shift_x_6 = None 2025-03-04T20:52:01.2559912Z 2025-03-04T20:52:01.2560229Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:52:01.2560355Z shift_y_7: "f32[1406][1]cpu" = shift_y_6.reshape(-1); shift_y_6 = None 2025-03-04T20:52:01.2560415Z 2025-03-04T20:52:01.2560800Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:52:01.2561015Z shifts_3: "f32[1406, 4][4, 1]cpu" = torch.stack((shift_x_7, shift_y_7, shift_x_7, shift_y_7), dim = 1); shift_x_7 = shift_y_7 = None 2025-03-04T20:52:01.2561077Z 2025-03-04T20:52:01.2561492Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:52:01.2561612Z view_6: "f32[1406, 1, 4][4, 4, 1]cpu" = shifts_3.view(-1, 1, 4); shifts_3 = None 2025-03-04T20:52:01.2561924Z view_7: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_ = None 2025-03-04T20:52:01.2562040Z add_3: "f32[1406, 3, 4][12, 4, 1]cpu" = view_6 + view_7; view_6 = view_7 = None 2025-03-04T20:52:01.2562153Z x_3: "f32[4218, 4][4, 1]cpu" = add_3.reshape(-1, 4); add_3 = None 2025-03-04T20:52:01.2562229Z 2025-03-04T20:52:01.2562655Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:52:01.2562797Z arange_8: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32) 2025-03-04T20:52:01.2562862Z 2025-03-04T20:52:01.2563145Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:52:01.2563284Z shifts_x_4: "f32[19][1]cpu" = arange_8.to(device(type='cpu')); arange_8 = None 2025-03-04T20:52:01.2563343Z 2025-03-04T20:52:01.2563766Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:52:01.2563904Z arange_9: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32) 2025-03-04T20:52:01.2563971Z 2025-03-04T20:52:01.2564274Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:52:01.2564411Z shifts_y_4: "f32[19][1]cpu" = arange_9.to(device(type='cpu')); arange_9 = None 2025-03-04T20:52:01.2564467Z 2025-03-04T20:52:01.2564856Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:52:01.2565040Z meshgrid_4 = torch.functional.meshgrid(shifts_y_4, shifts_x_4); shifts_y_4 = shifts_x_4 = None 2025-03-04T20:52:01.2565141Z shift_y_8: "f32[19, 19][1, 0]cpu" = meshgrid_4[0] 2025-03-04T20:52:01.2565255Z shift_x_8: "f32[19, 19][0, 1]cpu" = meshgrid_4[1]; meshgrid_4 = None 2025-03-04T20:52:01.2565323Z 2025-03-04T20:52:01.2565642Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:52:01.2565800Z shift_x_9: "f32[361][1]cpu" = shift_x_8.reshape(-1); shift_x_8 = None 2025-03-04T20:52:01.2565861Z 2025-03-04T20:52:01.2566188Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:52:01.2566306Z shift_y_9: "f32[361][1]cpu" = shift_y_8.reshape(-1); shift_y_8 = None 2025-03-04T20:52:01.2566374Z 2025-03-04T20:52:01.2566743Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:52:01.2566956Z shifts_4: "f32[361, 4][4, 1]cpu" = torch.stack((shift_x_9, shift_y_9, shift_x_9, shift_y_9), dim = 1); shift_x_9 = shift_y_9 = None 2025-03-04T20:52:01.2567026Z 2025-03-04T20:52:01.2567428Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:52:01.2567556Z view_8: "f32[361, 1, 4][4, 4, 1]cpu" = shifts_4.view(-1, 1, 4); shifts_4 = None 2025-03-04T20:52:01.2567857Z view_9: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_ = None 2025-03-04T20:52:01.2567979Z add_4: "f32[361, 3, 4][12, 4, 1]cpu" = view_8 + view_9; view_8 = view_9 = None 2025-03-04T20:52:01.2568085Z x_4: "f32[1083, 4][4, 1]cpu" = add_4.reshape(-1, 4); add_4 = None 2025-03-04T20:52:01.2568174Z 2025-03-04T20:52:01.2568470Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:52:01.2568594Z tensor: "f32[269952, 4][4, 1]cpu" = x.to(torch.float32); x = None 2025-03-04T20:52:01.2568714Z tensor_1: "f32[67488, 4][4, 1]cpu" = x_1.to(torch.float32); x_1 = None 2025-03-04T20:52:01.2568835Z tensor_2: "f32[16872, 4][4, 1]cpu" = x_2.to(torch.float32); x_2 = None 2025-03-04T20:52:01.2568946Z tensor_3: "f32[4218, 4][4, 1]cpu" = x_3.to(torch.float32); x_3 = None 2025-03-04T20:52:01.2569065Z tensor_4: "f32[1083, 4][4, 1]cpu" = x_4.to(torch.float32); x_4 = None 2025-03-04T20:52:01.2569124Z 2025-03-04T20:52:01.2569383Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:52:01.2569796Z x_5: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(l_features_p2_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_p2_ = None 2025-03-04T20:52:01.2569868Z 2025-03-04T20:52:01.2570155Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:52:01.2570351Z x_6: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.relu(x_5, inplace = False); x_5 = None 2025-03-04T20:52:01.2570409Z 2025-03-04T20:52:01.2570814Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:52:01.2571216Z score: "f32[4, 3, 296, 304][269952, 89984, 304, 1]cpu" = torch.conv2d(x_6, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T20:52:01.2571285Z 2025-03-04T20:52:01.2571641Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:52:01.2572067Z x_15: "f32[4, 12, 296, 304][1079808, 89984, 304, 1]cpu" = torch.conv2d(x_6, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_6 = None 2025-03-04T20:52:01.2572135Z 2025-03-04T20:52:01.2572388Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:52:01.2572791Z x_7: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(l_features_p3_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_p3_ = None 2025-03-04T20:52:01.2572853Z 2025-03-04T20:52:01.2573129Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:52:01.2573310Z x_8: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.relu(x_7, inplace = False); x_7 = None 2025-03-04T20:52:01.2573375Z 2025-03-04T20:52:01.2573742Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:52:01.2574148Z score_1: "f32[4, 3, 148, 152][67488, 22496, 152, 1]cpu" = torch.conv2d(x_8, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T20:52:01.2574223Z 2025-03-04T20:52:01.2574581Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:52:01.2574978Z x_16: "f32[4, 12, 148, 152][269952, 22496, 152, 1]cpu" = torch.conv2d(x_8, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_8 = None 2025-03-04T20:52:01.2575047Z 2025-03-04T20:52:01.2575297Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:52:01.2575690Z x_9: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(l_features_p4_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_p4_ = None 2025-03-04T20:52:01.2575758Z 2025-03-04T20:52:01.2576023Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:52:01.2576218Z x_10: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.relu(x_9, inplace = False); x_9 = None 2025-03-04T20:52:01.2576279Z 2025-03-04T20:52:01.2576647Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:52:01.2577071Z score_2: "f32[4, 3, 74, 76][16872, 5624, 76, 1]cpu" = torch.conv2d(x_10, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T20:52:01.2577138Z 2025-03-04T20:52:01.2577487Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:52:01.2577875Z x_17: "f32[4, 12, 74, 76][67488, 5624, 76, 1]cpu" = torch.conv2d(x_10, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_10 = None 2025-03-04T20:52:01.2577952Z 2025-03-04T20:52:01.2578205Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:52:01.2578591Z x_11: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(l_features_p5_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_p5_ = None 2025-03-04T20:52:01.2578657Z 2025-03-04T20:52:01.2578922Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:52:01.2579102Z x_12: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.nn.functional.relu(x_11, inplace = False); x_11 = None 2025-03-04T20:52:01.2579163Z 2025-03-04T20:52:01.2579539Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:52:01.2579925Z score_3: "f32[4, 3, 37, 38][4218, 1406, 38, 1]cpu" = torch.conv2d(x_12, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T20:52:01.2579991Z 2025-03-04T20:52:01.2580346Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:52:01.2580726Z x_18: "f32[4, 12, 37, 38][16872, 1406, 38, 1]cpu" = torch.conv2d(x_12, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_12 = None 2025-03-04T20:52:01.2580812Z 2025-03-04T20:52:01.2581059Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:52:01.2581628Z x_13: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.conv2d(l_features_p6_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_p6_ = l_self_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T20:52:01.2581688Z 2025-03-04T20:52:01.2581958Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:52:01.2582124Z x_14: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.relu(x_13, inplace = False); x_13 = None 2025-03-04T20:52:01.2582193Z 2025-03-04T20:52:01.2582573Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:52:01.2583213Z score_4: "f32[4, 3, 19, 19][1083, 361, 19, 1]cpu" = torch.conv2d(x_14, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T20:52:01.2583282Z 2025-03-04T20:52:01.2583632Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:52:01.2584217Z x_19: "f32[4, 12, 19, 19][4332, 361, 19, 1]cpu" = torch.conv2d(x_14, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_14 = l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T20:52:01.2584293Z 2025-03-04T20:52:01.2584632Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T20:52:01.2584793Z permute: "f32[4, 296, 304, 3][269952, 304, 1, 89984]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T20:52:01.2584939Z logits_i: "f32[4, 269952][269952, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T20:52:01.2585096Z permute_1: "f32[4, 148, 152, 3][67488, 152, 1, 22496]cpu" = score_1.permute(0, 2, 3, 1); score_1 = None 2025-03-04T20:52:01.2585242Z logits_i_1: "f32[4, 67488][67488, 1]cpu" = permute_1.flatten(1); permute_1 = None 2025-03-04T20:52:01.2585392Z permute_2: "f32[4, 74, 76, 3][16872, 76, 1, 5624]cpu" = score_2.permute(0, 2, 3, 1); score_2 = None 2025-03-04T20:52:01.2585532Z logits_i_2: "f32[4, 16872][16872, 1]cpu" = permute_2.flatten(1); permute_2 = None 2025-03-04T20:52:01.2585674Z permute_3: "f32[4, 37, 38, 3][4218, 38, 1, 1406]cpu" = score_3.permute(0, 2, 3, 1); score_3 = None 2025-03-04T20:52:01.2585810Z logits_i_3: "f32[4, 4218][4218, 1]cpu" = permute_3.flatten(1); permute_3 = None 2025-03-04T20:52:01.2585948Z permute_4: "f32[4, 19, 19, 3][1083, 19, 1, 361]cpu" = score_4.permute(0, 2, 3, 1); score_4 = None 2025-03-04T20:52:01.2586079Z logits_i_4: "f32[4, 1083][1083, 1]cpu" = permute_4.flatten(1); permute_4 = None 2025-03-04T20:52:01.2586159Z 2025-03-04T20:52:01.2586582Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T20:52:01.2586751Z view_10: "f32[4, 3, 4, 296, 304][1079808, 359936, 89984, 304, 1]cpu" = x_15.view(4, -1, 4, 296, 304); x_15 = None 2025-03-04T20:52:01.2586937Z permute_5: "f32[4, 296, 304, 3, 4][1079808, 304, 1, 359936, 89984]cpu" = view_10.permute(0, 3, 4, 1, 2); view_10 = None 2025-03-04T20:52:01.2587110Z pred_anchor_deltas_i: "f32[4, 269952, 4][1079808, 4, 1]cpu" = permute_5.flatten(1, -2); permute_5 = None 2025-03-04T20:52:01.2587272Z view_11: "f32[4, 3, 4, 148, 152][269952, 89984, 22496, 152, 1]cpu" = x_16.view(4, -1, 4, 148, 152); x_16 = None 2025-03-04T20:52:01.2587447Z permute_6: "f32[4, 148, 152, 3, 4][269952, 152, 1, 89984, 22496]cpu" = view_11.permute(0, 3, 4, 1, 2); view_11 = None 2025-03-04T20:52:01.2587615Z pred_anchor_deltas_i_2: "f32[4, 67488, 4][269952, 4, 1]cpu" = permute_6.flatten(1, -2); permute_6 = None 2025-03-04T20:52:01.2587760Z view_12: "f32[4, 3, 4, 74, 76][67488, 22496, 5624, 76, 1]cpu" = x_17.view(4, -1, 4, 74, 76); x_17 = None 2025-03-04T20:52:01.2587935Z permute_7: "f32[4, 74, 76, 3, 4][67488, 76, 1, 22496, 5624]cpu" = view_12.permute(0, 3, 4, 1, 2); view_12 = None 2025-03-04T20:52:01.2588106Z pred_anchor_deltas_i_4: "f32[4, 16872, 4][67488, 4, 1]cpu" = permute_7.flatten(1, -2); permute_7 = None 2025-03-04T20:52:01.2588267Z view_13: "f32[4, 3, 4, 37, 38][16872, 5624, 1406, 38, 1]cpu" = x_18.view(4, -1, 4, 37, 38); x_18 = None 2025-03-04T20:52:01.2588430Z permute_8: "f32[4, 37, 38, 3, 4][16872, 38, 1, 5624, 1406]cpu" = view_13.permute(0, 3, 4, 1, 2); view_13 = None 2025-03-04T20:52:01.2588590Z pred_anchor_deltas_i_6: "f32[4, 4218, 4][16872, 4, 1]cpu" = permute_8.flatten(1, -2); permute_8 = None 2025-03-04T20:52:01.2588734Z view_14: "f32[4, 3, 4, 19, 19][4332, 1444, 361, 19, 1]cpu" = x_19.view(4, -1, 4, 19, 19); x_19 = None 2025-03-04T20:52:01.2588888Z permute_9: "f32[4, 19, 19, 3, 4][4332, 19, 1, 1444, 361]cpu" = view_14.permute(0, 3, 4, 1, 2); view_14 = None 2025-03-04T20:52:01.2589071Z pred_anchor_deltas_i_8: "f32[4, 1083, 4][4332, 4, 1]cpu" = permute_9.flatten(1, -2); permute_9 = None 2025-03-04T20:52:01.2589130Z 2025-03-04T20:52:01.2589535Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:52:01.2589732Z pred_anchor_deltas_i_1: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T20:52:01.2589800Z 2025-03-04T20:52:01.2590226Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:52:01.2590380Z unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T20:52:01.2590525Z expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T20:52:01.2590667Z anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T20:52:01.2590728Z 2025-03-04T20:52:01.2591122Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:52:01.2591297Z deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T20:52:01.2591367Z 2025-03-04T20:52:01.2591687Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:52:01.2591857Z boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T20:52:01.2591921Z 2025-03-04T20:52:01.2592255Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:52:01.2592394Z getitem_10: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:52:01.2592523Z getitem_11: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:52:01.2592683Z widths: "f32[1079808][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T20:52:01.2592745Z 2025-03-04T20:52:01.2593087Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:52:01.2593213Z getitem_12: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:52:01.2593338Z getitem_13: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:52:01.2593506Z heights: "f32[1079808][1]cpu" = getitem_12 - getitem_13; getitem_12 = getitem_13 = None 2025-03-04T20:52:01.2593574Z 2025-03-04T20:52:01.2593890Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:52:01.2594091Z getitem_14: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:52:01.2594205Z mul: "f32[1079808][1]cpu" = 0.5 * widths 2025-03-04T20:52:01.2594350Z ctr_x: "f32[1079808][1]cpu" = getitem_14 + mul; getitem_14 = mul = None 2025-03-04T20:52:01.2594415Z 2025-03-04T20:52:01.2594763Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:52:01.2594929Z getitem_15: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:52:01.2595050Z mul_1: "f32[1079808][1]cpu" = 0.5 * heights 2025-03-04T20:52:01.2595183Z ctr_y: "f32[1079808][1]cpu" = getitem_15 + mul_1; getitem_15 = mul_1 = None 2025-03-04T20:52:01.2595254Z 2025-03-04T20:52:01.2595577Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:52:01.2595746Z getitem_16: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:52:01.2595863Z dx: "f32[1079808, 1][1, 1]cpu" = getitem_16 / 1.0; getitem_16 = None 2025-03-04T20:52:01.2595931Z 2025-03-04T20:52:01.2596242Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:52:01.2596406Z getitem_17: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:52:01.2596520Z dy: "f32[1079808, 1][1, 1]cpu" = getitem_17 / 1.0; getitem_17 = None 2025-03-04T20:52:01.2596594Z 2025-03-04T20:52:01.2596900Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:52:01.2597062Z getitem_18: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:52:01.2597175Z dw: "f32[1079808, 1][1, 1]cpu" = getitem_18 / 1.0; getitem_18 = None 2025-03-04T20:52:01.2597245Z 2025-03-04T20:52:01.2597560Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:52:01.2597757Z getitem_19: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:52:01.2597889Z dh: "f32[1079808, 1][1, 1]cpu" = getitem_19 / 1.0; getitem_19 = None 2025-03-04T20:52:01.2597962Z 2025-03-04T20:52:01.2598320Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:52:01.2598474Z dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:52:01.2598539Z 2025-03-04T20:52:01.2598896Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:52:01.2599043Z dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:52:01.2599108Z 2025-03-04T20:52:01.2599480Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:52:01.2599627Z getitem_20: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:52:01.2599784Z mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_20; dx = getitem_20 = None 2025-03-04T20:52:01.2599938Z getitem_21: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:52:01.2600087Z pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_21; mul_2 = getitem_21 = None 2025-03-04T20:52:01.2600150Z 2025-03-04T20:52:01.2600539Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:52:01.2600680Z getitem_22: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:52:01.2600811Z mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_22; dy = getitem_22 = None 2025-03-04T20:52:01.2600963Z getitem_23: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:52:01.2601127Z pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_23; mul_3 = getitem_23 = None 2025-03-04T20:52:01.2601189Z 2025-03-04T20:52:01.2601542Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:52:01.2601661Z exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:52:01.2601832Z getitem_24: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:52:01.2602148Z pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_24; exp = getitem_24 = None 2025-03-04T20:52:01.2602261Z 2025-03-04T20:52:01.2602738Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:52:01.2602868Z exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:52:01.2603033Z getitem_25: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:52:01.2603174Z pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_25; exp_1 = getitem_25 = None 2025-03-04T20:52:01.2603236Z 2025-03-04T20:52:01.2603564Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:52:01.2603660Z mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T20:52:01.2603786Z x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:52:01.2603849Z 2025-03-04T20:52:01.2604227Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:52:01.2604322Z mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T20:52:01.2604444Z y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:52:01.2604506Z 2025-03-04T20:52:01.2604825Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:52:01.2604936Z mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:52:01.2605077Z x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:52:01.2605139Z 2025-03-04T20:52:01.2605454Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:52:01.2605568Z mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:52:01.2605702Z y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:52:01.2605766Z 2025-03-04T20:52:01.2606159Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:52:01.2606337Z pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:52:01.2606406Z 2025-03-04T20:52:01.2606765Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:52:01.2606930Z proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4)); pred_boxes = None 2025-03-04T20:52:01.2606991Z 2025-03-04T20:52:01.2607371Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:52:01.2607566Z proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T20:52:01.2607632Z 2025-03-04T20:52:01.2608026Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:52:01.2608228Z pred_anchor_deltas_i_3: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_2.reshape(-1, 4); pred_anchor_deltas_i_2 = None 2025-03-04T20:52:01.2608296Z 2025-03-04T20:52:01.2608714Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:52:01.2608869Z unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = tensor_1.unsqueeze(0); tensor_1 = None 2025-03-04T20:52:01.2609016Z expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1); unsqueeze_1 = None 2025-03-04T20:52:01.2609158Z anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4); expand_1 = None 2025-03-04T20:52:01.2609218Z 2025-03-04T20:52:01.2609587Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:52:01.2609753Z deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float(); pred_anchor_deltas_i_3 = None 2025-03-04T20:52:01.2609820Z 2025-03-04T20:52:01.2610120Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:52:01.2610286Z boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32); anchors_i_1 = None 2025-03-04T20:52:01.2610344Z 2025-03-04T20:52:01.2610656Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:52:01.2610782Z getitem_26: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)] 2025-03-04T20:52:01.2610910Z getitem_27: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T20:52:01.2611055Z widths_1: "f32[269952][1]cpu" = getitem_26 - getitem_27; getitem_26 = getitem_27 = None 2025-03-04T20:52:01.2611120Z 2025-03-04T20:52:01.2611428Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:52:01.2611555Z getitem_28: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)] 2025-03-04T20:52:01.2611672Z getitem_29: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)] 2025-03-04T20:52:01.2611851Z heights_1: "f32[269952][1]cpu" = getitem_28 - getitem_29; getitem_28 = getitem_29 = None 2025-03-04T20:52:01.2611912Z 2025-03-04T20:52:01.2612220Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:52:01.2612335Z getitem_30: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T20:52:01.2612446Z mul_10: "f32[269952][1]cpu" = 0.5 * widths_1 2025-03-04T20:52:01.2612574Z ctr_x_1: "f32[269952][1]cpu" = getitem_30 + mul_10; getitem_30 = mul_10 = None 2025-03-04T20:52:01.2612641Z 2025-03-04T20:52:01.2612945Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:52:01.2613096Z getitem_31: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]; boxes_1 = None 2025-03-04T20:52:01.2613202Z mul_11: "f32[269952][1]cpu" = 0.5 * heights_1 2025-03-04T20:52:01.2613333Z ctr_y_1: "f32[269952][1]cpu" = getitem_31 + mul_11; getitem_31 = mul_11 = None 2025-03-04T20:52:01.2613392Z 2025-03-04T20:52:01.2613697Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:52:01.2613845Z getitem_32: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:52:01.2613964Z dx_1: "f32[269952, 1][1, 1]cpu" = getitem_32 / 1.0; getitem_32 = None 2025-03-04T20:52:01.2614023Z 2025-03-04T20:52:01.2614320Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:52:01.2614473Z getitem_33: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:52:01.2614581Z dy_1: "f32[269952, 1][1, 1]cpu" = getitem_33 / 1.0; getitem_33 = None 2025-03-04T20:52:01.2614648Z 2025-03-04T20:52:01.2614938Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:52:01.2615088Z getitem_34: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:52:01.2615195Z dw_2: "f32[269952, 1][1, 1]cpu" = getitem_34 / 1.0; getitem_34 = None 2025-03-04T20:52:01.2615259Z 2025-03-04T20:52:01.2615556Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:52:01.2615760Z getitem_35: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))]; deltas_1 = None 2025-03-04T20:52:01.2615864Z dh_2: "f32[269952, 1][1, 1]cpu" = getitem_35 / 1.0; getitem_35 = None 2025-03-04T20:52:01.2615931Z 2025-03-04T20:52:01.2616258Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:52:01.2616401Z dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356); dw_2 = None 2025-03-04T20:52:01.2616462Z 2025-03-04T20:52:01.2616790Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:52:01.2616918Z dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356); dh_2 = None 2025-03-04T20:52:01.2616986Z 2025-03-04T20:52:01.2617321Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:52:01.2617476Z getitem_36: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)] 2025-03-04T20:52:01.2617597Z mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_36; dx_1 = getitem_36 = None 2025-03-04T20:52:01.2617753Z getitem_37: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)]; ctr_x_1 = None 2025-03-04T20:52:01.2617887Z pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_37; mul_12 = getitem_37 = None 2025-03-04T20:52:01.2617970Z 2025-03-04T20:52:01.2618312Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:52:01.2618450Z getitem_38: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)] 2025-03-04T20:52:01.2618569Z mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_38; dy_1 = getitem_38 = None 2025-03-04T20:52:01.2618745Z getitem_39: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)]; ctr_y_1 = None 2025-03-04T20:52:01.2618879Z pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_39; mul_13 = getitem_39 = None 2025-03-04T20:52:01.2618948Z 2025-03-04T20:52:01.2619269Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:52:01.2619388Z exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3); dw_3 = None 2025-03-04T20:52:01.2619541Z getitem_40: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]; widths_1 = None 2025-03-04T20:52:01.2619678Z pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_40; exp_2 = getitem_40 = None 2025-03-04T20:52:01.2619739Z 2025-03-04T20:52:01.2620065Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:52:01.2620183Z exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3); dh_3 = None 2025-03-04T20:52:01.2620344Z getitem_41: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]; heights_1 = None 2025-03-04T20:52:01.2620477Z pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_41; exp_3 = getitem_41 = None 2025-03-04T20:52:01.2620538Z 2025-03-04T20:52:01.2620852Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:52:01.2620946Z mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1 2025-03-04T20:52:01.2621067Z x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16; mul_16 = None 2025-03-04T20:52:01.2621143Z 2025-03-04T20:52:01.2621452Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:52:01.2621544Z mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1 2025-03-04T20:52:01.2621662Z y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17; mul_17 = None 2025-03-04T20:52:01.2621721Z 2025-03-04T20:52:01.2622024Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:52:01.2622137Z mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1; pred_w_1 = None 2025-03-04T20:52:01.2622274Z x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18; pred_ctr_x_1 = mul_18 = None 2025-03-04T20:52:01.2622333Z 2025-03-04T20:52:01.2622638Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:52:01.2622749Z mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1; pred_h_1 = None 2025-03-04T20:52:01.2622901Z y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19; pred_ctr_y_1 = mul_19 = None 2025-03-04T20:52:01.2622962Z 2025-03-04T20:52:01.2623308Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:52:01.2623511Z pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1); x1_1 = y1_1 = x2_1 = y2_1 = None 2025-03-04T20:52:01.2623578Z 2025-03-04T20:52:01.2623903Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:52:01.2624069Z proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4)); pred_boxes_1 = None 2025-03-04T20:52:01.2624127Z 2025-03-04T20:52:01.2624507Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:52:01.2624697Z proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4); proposals_i_1 = None 2025-03-04T20:52:01.2624764Z 2025-03-04T20:52:01.2625155Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:52:01.2625363Z pred_anchor_deltas_i_5: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_4.reshape(-1, 4); pred_anchor_deltas_i_4 = None 2025-03-04T20:52:01.2625423Z 2025-03-04T20:52:01.2625850Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:52:01.2625997Z unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = tensor_2.unsqueeze(0); tensor_2 = None 2025-03-04T20:52:01.2626149Z expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1); unsqueeze_2 = None 2025-03-04T20:52:01.2626279Z anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4); expand_2 = None 2025-03-04T20:52:01.2626345Z 2025-03-04T20:52:01.2626703Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:52:01.2626874Z deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_5.float(); pred_anchor_deltas_i_5 = None 2025-03-04T20:52:01.2626934Z 2025-03-04T20:52:01.2627262Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:52:01.2627406Z boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32); anchors_i_2 = None 2025-03-04T20:52:01.2627466Z 2025-03-04T20:52:01.2627775Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:52:01.2627898Z getitem_42: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)] 2025-03-04T20:52:01.2628026Z getitem_43: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T20:52:01.2628171Z widths_2: "f32[67488][1]cpu" = getitem_42 - getitem_43; getitem_42 = getitem_43 = None 2025-03-04T20:52:01.2628239Z 2025-03-04T20:52:01.2628559Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:52:01.2628696Z getitem_44: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)] 2025-03-04T20:52:01.2628834Z getitem_45: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)] 2025-03-04T20:52:01.2628986Z heights_2: "f32[67488][1]cpu" = getitem_44 - getitem_45; getitem_44 = getitem_45 = None 2025-03-04T20:52:01.2629046Z 2025-03-04T20:52:01.2629358Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:52:01.2629492Z getitem_46: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T20:52:01.2629586Z mul_20: "f32[67488][1]cpu" = 0.5 * widths_2 2025-03-04T20:52:01.2629712Z ctr_x_2: "f32[67488][1]cpu" = getitem_46 + mul_20; getitem_46 = mul_20 = None 2025-03-04T20:52:01.2629783Z 2025-03-04T20:52:01.2630099Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:52:01.2630273Z getitem_47: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]; boxes_2 = None 2025-03-04T20:52:01.2630364Z mul_21: "f32[67488][1]cpu" = 0.5 * heights_2 2025-03-04T20:52:01.2630496Z ctr_y_2: "f32[67488][1]cpu" = getitem_47 + mul_21; getitem_47 = mul_21 = None 2025-03-04T20:52:01.2630565Z 2025-03-04T20:52:01.2630881Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:52:01.2631033Z getitem_48: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:52:01.2631150Z dx_2: "f32[67488, 1][1, 1]cpu" = getitem_48 / 1.0; getitem_48 = None 2025-03-04T20:52:01.2631211Z 2025-03-04T20:52:01.2631521Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:52:01.2631678Z getitem_49: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:52:01.2631794Z dy_2: "f32[67488, 1][1, 1]cpu" = getitem_49 / 1.0; getitem_49 = None 2025-03-04T20:52:01.2631853Z 2025-03-04T20:52:01.2632163Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:52:01.2632310Z getitem_50: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:52:01.2632422Z dw_4: "f32[67488, 1][1, 1]cpu" = getitem_50 / 1.0; getitem_50 = None 2025-03-04T20:52:01.2632482Z 2025-03-04T20:52:01.2632795Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:52:01.2633005Z getitem_51: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))]; deltas_2 = None 2025-03-04T20:52:01.2633121Z dh_4: "f32[67488, 1][1, 1]cpu" = getitem_51 / 1.0; getitem_51 = None 2025-03-04T20:52:01.2633184Z 2025-03-04T20:52:01.2633532Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:52:01.2633675Z dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356); dw_4 = None 2025-03-04T20:52:01.2633738Z 2025-03-04T20:52:01.2634162Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:52:01.2634302Z dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356); dh_4 = None 2025-03-04T20:52:01.2634374Z 2025-03-04T20:52:01.2634718Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:52:01.2634880Z getitem_52: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)] 2025-03-04T20:52:01.2635003Z mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_52; dx_2 = getitem_52 = None 2025-03-04T20:52:01.2635164Z getitem_53: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)]; ctr_x_2 = None 2025-03-04T20:52:01.2635323Z pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_53; mul_22 = getitem_53 = None 2025-03-04T20:52:01.2635393Z 2025-03-04T20:52:01.2635740Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:52:01.2635882Z getitem_54: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)] 2025-03-04T20:52:01.2636001Z mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_54; dy_2 = getitem_54 = None 2025-03-04T20:52:01.2636175Z getitem_55: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)]; ctr_y_2 = None 2025-03-04T20:52:01.2636311Z pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_55; mul_23 = getitem_55 = None 2025-03-04T20:52:01.2636378Z 2025-03-04T20:52:01.2636707Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:52:01.2636825Z exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5); dw_5 = None 2025-03-04T20:52:01.2636979Z getitem_56: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]; widths_2 = None 2025-03-04T20:52:01.2637118Z pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_56; exp_4 = getitem_56 = None 2025-03-04T20:52:01.2637178Z 2025-03-04T20:52:01.2637513Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:52:01.2637625Z exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5); dh_5 = None 2025-03-04T20:52:01.2637789Z getitem_57: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]; heights_2 = None 2025-03-04T20:52:01.2637916Z pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_57; exp_5 = getitem_57 = None 2025-03-04T20:52:01.2637985Z 2025-03-04T20:52:01.2638294Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:52:01.2638396Z mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2 2025-03-04T20:52:01.2638528Z x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26; mul_26 = None 2025-03-04T20:52:01.2638596Z 2025-03-04T20:52:01.2638903Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:52:01.2639005Z mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2 2025-03-04T20:52:01.2639115Z y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27; mul_27 = None 2025-03-04T20:52:01.2639182Z 2025-03-04T20:52:01.2639485Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:52:01.2639606Z mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2; pred_w_2 = None 2025-03-04T20:52:01.2639736Z x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28; pred_ctr_x_2 = mul_28 = None 2025-03-04T20:52:01.2639804Z 2025-03-04T20:52:01.2640110Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:52:01.2640231Z mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2; pred_h_2 = None 2025-03-04T20:52:01.2640373Z y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29; pred_ctr_y_2 = mul_29 = None 2025-03-04T20:52:01.2640443Z 2025-03-04T20:52:01.2640789Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:52:01.2640998Z pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1); x1_2 = y1_2 = x2_2 = y2_2 = None 2025-03-04T20:52:01.2641063Z 2025-03-04T20:52:01.2641406Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:52:01.2641573Z proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4)); pred_boxes_2 = None 2025-03-04T20:52:01.2641652Z 2025-03-04T20:52:01.2642048Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:52:01.2642220Z proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4); proposals_i_2 = None 2025-03-04T20:52:01.2642290Z 2025-03-04T20:52:01.2642698Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:52:01.2642909Z pred_anchor_deltas_i_7: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_6.reshape(-1, 4); pred_anchor_deltas_i_6 = None 2025-03-04T20:52:01.2642973Z 2025-03-04T20:52:01.2643415Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:52:01.2643562Z unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = tensor_3.unsqueeze(0); tensor_3 = None 2025-03-04T20:52:01.2643713Z expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1); unsqueeze_3 = None 2025-03-04T20:52:01.2643845Z anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4); expand_3 = None 2025-03-04T20:52:01.2643916Z 2025-03-04T20:52:01.2644292Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:52:01.2644460Z deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_7.float(); pred_anchor_deltas_i_7 = None 2025-03-04T20:52:01.2644539Z 2025-03-04T20:52:01.2644847Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:52:01.2644986Z boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32); anchors_i_3 = None 2025-03-04T20:52:01.2645051Z 2025-03-04T20:52:01.2645357Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:52:01.2645489Z getitem_58: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)] 2025-03-04T20:52:01.2645608Z getitem_59: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T20:52:01.2645758Z widths_3: "f32[16872][1]cpu" = getitem_58 - getitem_59; getitem_58 = getitem_59 = None 2025-03-04T20:52:01.2645819Z 2025-03-04T20:52:01.2646139Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:52:01.2646259Z getitem_60: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)] 2025-03-04T20:52:01.2646397Z getitem_61: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)] 2025-03-04T20:52:01.2646541Z heights_3: "f32[16872][1]cpu" = getitem_60 - getitem_61; getitem_60 = getitem_61 = None 2025-03-04T20:52:01.2646608Z 2025-03-04T20:52:01.2647013Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:52:01.2647139Z getitem_62: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T20:52:01.2647225Z mul_30: "f32[16872][1]cpu" = 0.5 * widths_3 2025-03-04T20:52:01.2647358Z ctr_x_3: "f32[16872][1]cpu" = getitem_62 + mul_30; getitem_62 = mul_30 = None 2025-03-04T20:52:01.2647419Z 2025-03-04T20:52:01.2647731Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:52:01.2647888Z getitem_63: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]; boxes_3 = None 2025-03-04T20:52:01.2647983Z mul_31: "f32[16872][1]cpu" = 0.5 * heights_3 2025-03-04T20:52:01.2648106Z ctr_y_3: "f32[16872][1]cpu" = getitem_63 + mul_31; getitem_63 = mul_31 = None 2025-03-04T20:52:01.2648176Z 2025-03-04T20:52:01.2648480Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:52:01.2648638Z getitem_64: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:52:01.2648756Z dx_3: "f32[16872, 1][1, 1]cpu" = getitem_64 / 1.0; getitem_64 = None 2025-03-04T20:52:01.2648821Z 2025-03-04T20:52:01.2649124Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:52:01.2649276Z getitem_65: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:52:01.2649395Z dy_3: "f32[16872, 1][1, 1]cpu" = getitem_65 / 1.0; getitem_65 = None 2025-03-04T20:52:01.2649457Z 2025-03-04T20:52:01.2649764Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:52:01.2649912Z getitem_66: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:52:01.2650024Z dw_6: "f32[16872, 1][1, 1]cpu" = getitem_66 / 1.0; getitem_66 = None 2025-03-04T20:52:01.2650085Z 2025-03-04T20:52:01.2650392Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:52:01.2650592Z getitem_67: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))]; deltas_3 = None 2025-03-04T20:52:01.2650703Z dh_6: "f32[16872, 1][1, 1]cpu" = getitem_67 / 1.0; getitem_67 = None 2025-03-04T20:52:01.2650761Z 2025-03-04T20:52:01.2651093Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:52:01.2651226Z dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356); dw_6 = None 2025-03-04T20:52:01.2651289Z 2025-03-04T20:52:01.2651611Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:52:01.2651748Z dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356); dh_6 = None 2025-03-04T20:52:01.2651806Z 2025-03-04T20:52:01.2652163Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:52:01.2652292Z getitem_68: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)] 2025-03-04T20:52:01.2652417Z mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_68; dx_3 = getitem_68 = None 2025-03-04T20:52:01.2652576Z getitem_69: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)]; ctr_x_3 = None 2025-03-04T20:52:01.2652717Z pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_69; mul_32 = getitem_69 = None 2025-03-04T20:52:01.2652777Z 2025-03-04T20:52:01.2653121Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:52:01.2653252Z getitem_70: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)] 2025-03-04T20:52:01.2653397Z mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_70; dy_3 = getitem_70 = None 2025-03-04T20:52:01.2653539Z getitem_71: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)]; ctr_y_3 = None 2025-03-04T20:52:01.2653674Z pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_71; mul_33 = getitem_71 = None 2025-03-04T20:52:01.2653734Z 2025-03-04T20:52:01.2654062Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:52:01.2654173Z exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7); dw_7 = None 2025-03-04T20:52:01.2654325Z getitem_72: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]; widths_3 = None 2025-03-04T20:52:01.2654461Z pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_72; exp_6 = getitem_72 = None 2025-03-04T20:52:01.2654523Z 2025-03-04T20:52:01.2654853Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:52:01.2654956Z exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7); dh_7 = None 2025-03-04T20:52:01.2655119Z getitem_73: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]; heights_3 = None 2025-03-04T20:52:01.2655245Z pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_73; exp_7 = getitem_73 = None 2025-03-04T20:52:01.2655311Z 2025-03-04T20:52:01.2655615Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:52:01.2655732Z mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3 2025-03-04T20:52:01.2655893Z x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36; mul_36 = None 2025-03-04T20:52:01.2655982Z 2025-03-04T20:52:01.2656460Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:52:01.2656563Z mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3 2025-03-04T20:52:01.2656672Z y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37; mul_37 = None 2025-03-04T20:52:01.2656739Z 2025-03-04T20:52:01.2657038Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:52:01.2657156Z mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3; pred_w_3 = None 2025-03-04T20:52:01.2657281Z x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38; pred_ctr_x_3 = mul_38 = None 2025-03-04T20:52:01.2657348Z 2025-03-04T20:52:01.2657643Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:52:01.2657782Z mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3; pred_h_3 = None 2025-03-04T20:52:01.2657907Z y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39; pred_ctr_y_3 = mul_39 = None 2025-03-04T20:52:01.2657973Z 2025-03-04T20:52:01.2658324Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:52:01.2658511Z pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1); x1_3 = y1_3 = x2_3 = y2_3 = None 2025-03-04T20:52:01.2658570Z 2025-03-04T20:52:01.2658898Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:52:01.2659055Z proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4)); pred_boxes_3 = None 2025-03-04T20:52:01.2659136Z 2025-03-04T20:52:01.2659518Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:52:01.2659692Z proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4); proposals_i_3 = None 2025-03-04T20:52:01.2659751Z 2025-03-04T20:52:01.2660153Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:52:01.2660352Z pred_anchor_deltas_i_9: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_8.reshape(-1, 4); pred_anchor_deltas_i_8 = None 2025-03-04T20:52:01.2660423Z 2025-03-04T20:52:01.2660849Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:52:01.2661002Z unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = tensor_4.unsqueeze(0); tensor_4 = None 2025-03-04T20:52:01.2661147Z expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1); unsqueeze_4 = None 2025-03-04T20:52:01.2661285Z anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4); expand_4 = None 2025-03-04T20:52:01.2661344Z 2025-03-04T20:52:01.2661715Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:52:01.2661882Z deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_9.float(); pred_anchor_deltas_i_9 = None 2025-03-04T20:52:01.2661960Z 2025-03-04T20:52:01.2662268Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:52:01.2662406Z boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32); anchors_i_4 = None 2025-03-04T20:52:01.2662473Z 2025-03-04T20:52:01.2662777Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:52:01.2662907Z getitem_74: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)] 2025-03-04T20:52:01.2663024Z getitem_75: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T20:52:01.2663172Z widths_4: "f32[4332][1]cpu" = getitem_74 - getitem_75; getitem_74 = getitem_75 = None 2025-03-04T20:52:01.2663233Z 2025-03-04T20:52:01.2663548Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:52:01.2663683Z getitem_76: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)] 2025-03-04T20:52:01.2663806Z getitem_77: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)] 2025-03-04T20:52:01.2663945Z heights_4: "f32[4332][1]cpu" = getitem_76 - getitem_77; getitem_76 = getitem_77 = None 2025-03-04T20:52:01.2664011Z 2025-03-04T20:52:01.2664330Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:52:01.2664452Z getitem_78: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T20:52:01.2664537Z mul_40: "f32[4332][1]cpu" = 0.5 * widths_4 2025-03-04T20:52:01.2664665Z ctr_x_4: "f32[4332][1]cpu" = getitem_78 + mul_40; getitem_78 = mul_40 = None 2025-03-04T20:52:01.2664724Z 2025-03-04T20:52:01.2665035Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:52:01.2665193Z getitem_79: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]; boxes_4 = None 2025-03-04T20:52:01.2665281Z mul_41: "f32[4332][1]cpu" = 0.5 * heights_4 2025-03-04T20:52:01.2665402Z ctr_y_4: "f32[4332][1]cpu" = getitem_79 + mul_41; getitem_79 = mul_41 = None 2025-03-04T20:52:01.2665469Z 2025-03-04T20:52:01.2665763Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:52:01.2665913Z getitem_80: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:52:01.2666019Z dx_4: "f32[4332, 1][1, 1]cpu" = getitem_80 / 1.0; getitem_80 = None 2025-03-04T20:52:01.2666082Z 2025-03-04T20:52:01.2666377Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:52:01.2666531Z getitem_81: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:52:01.2666636Z dy_4: "f32[4332, 1][1, 1]cpu" = getitem_81 / 1.0; getitem_81 = None 2025-03-04T20:52:01.2666704Z 2025-03-04T20:52:01.2667146Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:52:01.2667335Z getitem_82: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:52:01.2667437Z dw_8: "f32[4332, 1][1, 1]cpu" = getitem_82 / 1.0; getitem_82 = None 2025-03-04T20:52:01.2667530Z 2025-03-04T20:52:01.2667999Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:52:01.2668194Z getitem_83: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))]; deltas_4 = None 2025-03-04T20:52:01.2668308Z dh_8: "f32[4332, 1][1, 1]cpu" = getitem_83 / 1.0; getitem_83 = None 2025-03-04T20:52:01.2668369Z 2025-03-04T20:52:01.2668720Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:52:01.2668854Z dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356); dw_8 = None 2025-03-04T20:52:01.2668922Z 2025-03-04T20:52:01.2669260Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:52:01.2669399Z dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356); dh_8 = None 2025-03-04T20:52:01.2669461Z 2025-03-04T20:52:01.2669847Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:52:01.2669978Z getitem_84: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)] 2025-03-04T20:52:01.2670100Z mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_84; dx_4 = getitem_84 = None 2025-03-04T20:52:01.2670259Z getitem_85: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)]; ctr_x_4 = None 2025-03-04T20:52:01.2670400Z pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_85; mul_42 = getitem_85 = None 2025-03-04T20:52:01.2670461Z 2025-03-04T20:52:01.2670821Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:52:01.2670954Z getitem_86: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)] 2025-03-04T20:52:01.2671101Z mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_86; dy_4 = getitem_86 = None 2025-03-04T20:52:01.2671245Z getitem_87: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)]; ctr_y_4 = None 2025-03-04T20:52:01.2671383Z pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_87; mul_43 = getitem_87 = None 2025-03-04T20:52:01.2671443Z 2025-03-04T20:52:01.2671788Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:52:01.2671897Z exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9); dw_9 = None 2025-03-04T20:52:01.2672062Z getitem_88: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]; widths_4 = None 2025-03-04T20:52:01.2672192Z pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_88; exp_8 = getitem_88 = None 2025-03-04T20:52:01.2672262Z 2025-03-04T20:52:01.2672598Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:52:01.2672713Z exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9); dh_9 = None 2025-03-04T20:52:01.2672877Z getitem_89: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]; heights_4 = None 2025-03-04T20:52:01.2673010Z pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_89; exp_9 = getitem_89 = None 2025-03-04T20:52:01.2673071Z 2025-03-04T20:52:01.2673389Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:52:01.2673501Z mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4 2025-03-04T20:52:01.2673620Z x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46; mul_46 = None 2025-03-04T20:52:01.2673683Z 2025-03-04T20:52:01.2674091Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:52:01.2674190Z mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4 2025-03-04T20:52:01.2674311Z y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47; mul_47 = None 2025-03-04T20:52:01.2674384Z 2025-03-04T20:52:01.2674711Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:52:01.2674823Z mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4; pred_w_4 = None 2025-03-04T20:52:01.2674960Z x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48; pred_ctr_x_4 = mul_48 = None 2025-03-04T20:52:01.2675023Z 2025-03-04T20:52:01.2675343Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:52:01.2675478Z mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4; pred_h_4 = None 2025-03-04T20:52:01.2675615Z y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49; pred_ctr_y_4 = mul_49 = None 2025-03-04T20:52:01.2675676Z 2025-03-04T20:52:01.2676062Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:52:01.2676244Z pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1); x1_4 = y1_4 = x2_4 = y2_4 = None 2025-03-04T20:52:01.2676313Z 2025-03-04T20:52:01.2676645Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:52:01.2676863Z proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4)); pred_boxes_4 = None 2025-03-04T20:52:01.2676933Z 2025-03-04T20:52:01.2677319Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:52:01.2677496Z proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4); proposals_i_4 = None 2025-03-04T20:52:01.2677560Z 2025-03-04T20:52:01.2678055Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T20:52:01.2678187Z arange_10: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T20:52:01.2678285Z 2025-03-04T20:52:01.2678745Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:52:01.2678959Z batch_idx: "i64[4][1]cpu" = arange_10.to(device(type='cpu')); arange_10 = None 2025-03-04T20:52:01.2679022Z 2025-03-04T20:52:01.2679470Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:52:01.2679583Z topk = logits_i.topk(1000, dim = 1); logits_i = None 2025-03-04T20:52:01.2679689Z topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0] 2025-03-04T20:52:01.2679799Z topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1]; topk = None 2025-03-04T20:52:01.2679865Z 2025-03-04T20:52:01.2680352Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:52:01.2680492Z getitem_92: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T20:52:01.2680721Z topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_92, topk_idx)]; proposals_i_5 = getitem_92 = topk_idx = None 2025-03-04T20:52:01.2680788Z 2025-03-04T20:52:01.2681248Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:52:01.2681419Z full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:52:01.2681481Z 2025-03-04T20:52:01.2681784Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:52:01.2681919Z to_21: "i64[1000][1]cpu" = full.to(device(type='cpu')); full = None 2025-03-04T20:52:01.2681988Z 2025-03-04T20:52:01.2682420Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:52:01.2682540Z topk_1 = logits_i_1.topk(1000, dim = 1); logits_i_1 = None 2025-03-04T20:52:01.2682660Z topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0] 2025-03-04T20:52:01.2682781Z topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1]; topk_1 = None 2025-03-04T20:52:01.2682840Z 2025-03-04T20:52:01.2683301Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:52:01.2683457Z getitem_96: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T20:52:01.2683688Z topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_96, topk_idx_1)]; proposals_i_6 = getitem_96 = topk_idx_1 = None 2025-03-04T20:52:01.2683753Z 2025-03-04T20:52:01.2684200Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:52:01.2684364Z full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:52:01.2684422Z 2025-03-04T20:52:01.2684716Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:52:01.2684836Z to_22: "i64[1000][1]cpu" = full_1.to(device(type='cpu')); full_1 = None 2025-03-04T20:52:01.2684904Z 2025-03-04T20:52:01.2685327Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:52:01.2685440Z topk_2 = logits_i_2.topk(1000, dim = 1); logits_i_2 = None 2025-03-04T20:52:01.2685540Z topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0] 2025-03-04T20:52:01.2685656Z topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1]; topk_2 = None 2025-03-04T20:52:01.2685715Z 2025-03-04T20:52:01.2686166Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:52:01.2686315Z getitem_100: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T20:52:01.2686553Z topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_100, topk_idx_2)]; proposals_i_7 = getitem_100 = topk_idx_2 = None 2025-03-04T20:52:01.2686611Z 2025-03-04T20:52:01.2687061Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:52:01.2687220Z full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:52:01.2687287Z 2025-03-04T20:52:01.2687573Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:52:01.2687703Z to_23: "i64[1000][1]cpu" = full_2.to(device(type='cpu')); full_2 = None 2025-03-04T20:52:01.2687764Z 2025-03-04T20:52:01.2688221Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:52:01.2688332Z topk_3 = logits_i_3.topk(1000, dim = 1); logits_i_3 = None 2025-03-04T20:52:01.2688441Z topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0] 2025-03-04T20:52:01.2688570Z topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1]; topk_3 = None 2025-03-04T20:52:01.2688640Z 2025-03-04T20:52:01.2689093Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:52:01.2689231Z getitem_104: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T20:52:01.2689467Z topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_104, topk_idx_3)]; proposals_i_8 = getitem_104 = topk_idx_3 = None 2025-03-04T20:52:01.2689546Z 2025-03-04T20:52:01.2690006Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:52:01.2690174Z full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:52:01.2690239Z 2025-03-04T20:52:01.2690540Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:52:01.2690662Z to_24: "i64[1000][1]cpu" = full_3.to(device(type='cpu')); full_3 = None 2025-03-04T20:52:01.2690722Z 2025-03-04T20:52:01.2691149Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:52:01.2691255Z topk_4 = logits_i_4.topk(1000, dim = 1); logits_i_4 = None 2025-03-04T20:52:01.2691362Z topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0] 2025-03-04T20:52:01.2691471Z topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1]; topk_4 = None 2025-03-04T20:52:01.2691537Z 2025-03-04T20:52:01.2691978Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:52:01.2692140Z getitem_108: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T20:52:01.2692381Z topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_108, topk_idx_4)]; proposals_i_9 = getitem_108 = topk_idx_4 = None 2025-03-04T20:52:01.2692448Z 2025-03-04T20:52:01.2692892Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:52:01.2693053Z full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:52:01.2693112Z 2025-03-04T20:52:01.2693405Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:52:01.2693522Z to_25: "i64[1000][1]cpu" = full_4.to(device(type='cpu')); full_4 = None 2025-03-04T20:52:01.2693591Z 2025-03-04T20:52:01.2693864Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:52:01.2694254Z topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1); topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None 2025-03-04T20:52:01.2694315Z 2025-03-04T20:52:01.2694598Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:52:01.2695081Z topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1); topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None 2025-03-04T20:52:01.2695144Z 2025-03-04T20:52:01.2695427Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:52:01.2695646Z level_ids: "i64[5000][1]cpu" = torch.cat([to_21, to_22, to_23, to_24, to_25], 0); to_21 = to_22 = to_23 = to_24 = to_25 = level_ids = None 2025-03-04T20:52:01.2695713Z 2025-03-04T20:52:01.2696098Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T20:52:01.2696242Z getitem_110: "f32[5000, 4][4, 1]cpu" = topk_proposals[0]; topk_proposals = None 2025-03-04T20:52:01.2696301Z 2025-03-04T20:52:01.2696604Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:52:01.2696746Z tensor_5: "f32[5000, 4][4, 1]cpu" = getitem_110.to(torch.float32); getitem_110 = None 2025-03-04T20:52:01.2696815Z 2025-03-04T20:52:01.2697191Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T20:52:01.2697329Z scores_per_img: "f32[5000][1]cpu" = topk_scores[0]; topk_scores = None 2025-03-04T20:52:01.2697389Z 2025-03-04T20:52:01.2697876Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T20:52:01.2698008Z isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor_5); tensor_5 = None 2025-03-04T20:52:01.2698135Z all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:52:01.2698285Z isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T20:52:01.2698437Z valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T20:52:01.2698499Z 2025-03-04T20:52:01.2698883Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T20:52:01.2698995Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T20:52:01.2699062Z 2025-03-04T20:52:05.9699408Z 2025-03-04T20:52:05.9700228Z class GraphModule(torch.nn.Module): 2025-03-04T20:52:05.9702430Z def forward(self, L_pred_anchor_deltas_0_: "f32[4, 269952, 4][1079808, 4, 1]cpu", L_anchors_0_tensor: "f32[269952, 4][4, 1]cpu", L_pred_anchor_deltas_1_: "f32[4, 67488, 4][269952, 4, 1]cpu", L_anchors_1_tensor: "f32[67488, 4][4, 1]cpu", L_pred_anchor_deltas_2_: "f32[4, 16872, 4][67488, 4, 1]cpu", L_anchors_2_tensor: "f32[16872, 4][4, 1]cpu", L_pred_anchor_deltas_3_: "f32[4, 4218, 4][16872, 4, 1]cpu", L_anchors_3_tensor: "f32[4218, 4][4, 1]cpu", L_pred_anchor_deltas_4_: "f32[4, 1083, 4][4332, 4, 1]cpu", L_anchors_4_tensor: "f32[1083, 4][4, 1]cpu", L_pred_objectness_logits_0_: "f32[4, 269952][269952, 1]cpu", L_pred_objectness_logits_1_: "f32[4, 67488][67488, 1]cpu", L_pred_objectness_logits_2_: "f32[4, 16872][16872, 1]cpu", L_pred_objectness_logits_3_: "f32[4, 4218][4218, 1]cpu", L_pred_objectness_logits_4_: "f32[4, 1083][1083, 1]cpu"): 2025-03-04T20:52:05.9703836Z l_pred_anchor_deltas_0_ = L_pred_anchor_deltas_0_ 2025-03-04T20:52:05.9704083Z l_anchors_0_tensor = L_anchors_0_tensor 2025-03-04T20:52:05.9704383Z l_pred_anchor_deltas_1_ = L_pred_anchor_deltas_1_ 2025-03-04T20:52:05.9704619Z l_anchors_1_tensor = L_anchors_1_tensor 2025-03-04T20:52:05.9704845Z l_pred_anchor_deltas_2_ = L_pred_anchor_deltas_2_ 2025-03-04T20:52:05.9705072Z l_anchors_2_tensor = L_anchors_2_tensor 2025-03-04T20:52:05.9705307Z l_pred_anchor_deltas_3_ = L_pred_anchor_deltas_3_ 2025-03-04T20:52:05.9705543Z l_anchors_3_tensor = L_anchors_3_tensor 2025-03-04T20:52:05.9705833Z l_pred_anchor_deltas_4_ = L_pred_anchor_deltas_4_ 2025-03-04T20:52:05.9706058Z l_anchors_4_tensor = L_anchors_4_tensor 2025-03-04T20:52:05.9706301Z l_pred_objectness_logits_0_ = L_pred_objectness_logits_0_ 2025-03-04T20:52:05.9706577Z l_pred_objectness_logits_1_ = L_pred_objectness_logits_1_ 2025-03-04T20:52:05.9706858Z l_pred_objectness_logits_2_ = L_pred_objectness_logits_2_ 2025-03-04T20:52:05.9707158Z l_pred_objectness_logits_3_ = L_pred_objectness_logits_3_ 2025-03-04T20:52:05.9707545Z l_pred_objectness_logits_4_ = L_pred_objectness_logits_4_ 2025-03-04T20:52:05.9707852Z 2025-03-04T20:52:05.9708615Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:52:05.9709465Z pred_anchor_deltas_i: "f32[1079808, 4][4, 1]cpu" = l_pred_anchor_deltas_0_.reshape(-1, 4); l_pred_anchor_deltas_0_ = None 2025-03-04T20:52:05.9709810Z 2025-03-04T20:52:05.9710433Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:52:05.9711450Z unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = l_anchors_0_tensor.unsqueeze(0); l_anchors_0_tensor = None 2025-03-04T20:52:05.9712070Z expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T20:52:05.9712498Z anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T20:52:05.9712755Z 2025-03-04T20:52:05.9713327Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:52:05.9714035Z deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.float(); pred_anchor_deltas_i = None 2025-03-04T20:52:05.9714337Z 2025-03-04T20:52:05.9714771Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:52:05.9715351Z boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T20:52:05.9715626Z 2025-03-04T20:52:05.9716056Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:52:05.9716587Z getitem: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:52:05.9716909Z getitem_1: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:52:05.9717250Z widths: "f32[1079808][1]cpu" = getitem - getitem_1; getitem = getitem_1 = None 2025-03-04T20:52:05.9717524Z 2025-03-04T20:52:05.9717980Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:52:05.9718509Z getitem_2: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:52:05.9718825Z getitem_3: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:52:05.9719186Z heights: "f32[1079808][1]cpu" = getitem_2 - getitem_3; getitem_2 = getitem_3 = None 2025-03-04T20:52:05.9719466Z 2025-03-04T20:52:05.9719984Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:52:05.9720500Z getitem_4: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:52:05.9720780Z mul: "f32[1079808][1]cpu" = 0.5 * widths 2025-03-04T20:52:05.9721055Z ctr_x: "f32[1079808][1]cpu" = getitem_4 + mul; getitem_4 = mul = None 2025-03-04T20:52:05.9721330Z 2025-03-04T20:52:05.9721753Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:52:05.9722303Z getitem_5: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:52:05.9722610Z mul_1: "f32[1079808][1]cpu" = 0.5 * heights 2025-03-04T20:52:05.9722872Z ctr_y: "f32[1079808][1]cpu" = getitem_5 + mul_1; getitem_5 = mul_1 = None 2025-03-04T20:52:05.9723110Z 2025-03-04T20:52:05.9723540Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:52:05.9724047Z getitem_6: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:52:05.9724363Z dx: "f32[1079808, 1][1, 1]cpu" = getitem_6 / 1.0; getitem_6 = None 2025-03-04T20:52:05.9724590Z 2025-03-04T20:52:05.9724971Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:52:05.9725465Z getitem_7: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:52:05.9725776Z dy: "f32[1079808, 1][1, 1]cpu" = getitem_7 / 1.0; getitem_7 = None 2025-03-04T20:52:05.9725997Z 2025-03-04T20:52:05.9726370Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:52:05.9726862Z getitem_8: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:52:05.9727193Z dw: "f32[1079808, 1][1, 1]cpu" = getitem_8 / 1.0; getitem_8 = None 2025-03-04T20:52:05.9727413Z 2025-03-04T20:52:05.9727788Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:52:05.9728313Z getitem_9: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:52:05.9728643Z dh: "f32[1079808, 1][1, 1]cpu" = getitem_9 / 1.0; getitem_9 = None 2025-03-04T20:52:05.9728862Z 2025-03-04T20:52:05.9729276Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:52:05.9729805Z dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:52:05.9730055Z 2025-03-04T20:52:05.9730463Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:52:05.9730974Z dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:52:05.9732195Z 2025-03-04T20:52:05.9732630Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:52:05.9733161Z getitem_10: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:52:05.9733491Z mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_10; dx = getitem_10 = None 2025-03-04T20:52:05.9733819Z getitem_11: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:52:05.9734159Z pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_11; mul_2 = getitem_11 = None 2025-03-04T20:52:05.9734411Z 2025-03-04T20:52:05.9734838Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:52:05.9735396Z getitem_12: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:52:05.9735703Z mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_12; dy = getitem_12 = None 2025-03-04T20:52:05.9736022Z getitem_13: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:52:05.9736360Z pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_13; mul_3 = getitem_13 = None 2025-03-04T20:52:05.9736611Z 2025-03-04T20:52:05.9737016Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:52:05.9737505Z exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:52:05.9737827Z getitem_14: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:52:05.9738170Z pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_14; exp = getitem_14 = None 2025-03-04T20:52:05.9738414Z 2025-03-04T20:52:05.9738825Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:52:05.9739314Z exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:52:05.9739645Z getitem_15: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:52:05.9739992Z pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_15; exp_1 = getitem_15 = None 2025-03-04T20:52:05.9740241Z 2025-03-04T20:52:05.9740632Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:52:05.9741106Z mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T20:52:05.9741367Z x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:52:05.9741594Z 2025-03-04T20:52:05.9741983Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:52:05.9742428Z mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T20:52:05.9742710Z y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:52:05.9742937Z 2025-03-04T20:52:05.9743311Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:52:05.9743774Z mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:52:05.9744064Z x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:52:05.9744307Z 2025-03-04T20:52:05.9744707Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:52:05.9745171Z mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:52:05.9745456Z y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:52:05.9745696Z 2025-03-04T20:52:05.9746137Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:52:05.9746817Z pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:52:05.9747121Z 2025-03-04T20:52:05.9747533Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:52:05.9748135Z proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4)); pred_boxes = None 2025-03-04T20:52:05.9748421Z 2025-03-04T20:52:05.9748923Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:52:05.9749548Z proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T20:52:05.9749842Z 2025-03-04T20:52:05.9750326Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:52:05.9750994Z pred_anchor_deltas_i_1: "f32[269952, 4][4, 1]cpu" = l_pred_anchor_deltas_1_.reshape(-1, 4); l_pred_anchor_deltas_1_ = None 2025-03-04T20:52:05.9751320Z 2025-03-04T20:52:05.9751836Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:52:05.9752506Z unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = l_anchors_1_tensor.unsqueeze(0); l_anchors_1_tensor = None 2025-03-04T20:52:05.9752895Z expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1); unsqueeze_1 = None 2025-03-04T20:52:05.9753292Z anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4); expand_1 = None 2025-03-04T20:52:05.9753562Z 2025-03-04T20:52:05.9754028Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:52:05.9754653Z deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T20:52:05.9754941Z 2025-03-04T20:52:05.9755337Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:52:05.9755854Z boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32); anchors_i_1 = None 2025-03-04T20:52:05.9756121Z 2025-03-04T20:52:05.9756521Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:52:05.9757029Z getitem_16: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)] 2025-03-04T20:52:05.9757343Z getitem_17: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T20:52:05.9757682Z widths_1: "f32[269952][1]cpu" = getitem_16 - getitem_17; getitem_16 = getitem_17 = None 2025-03-04T20:52:05.9757954Z 2025-03-04T20:52:05.9758382Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:52:05.9758885Z getitem_18: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)] 2025-03-04T20:52:05.9759188Z getitem_19: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)] 2025-03-04T20:52:05.9759537Z heights_1: "f32[269952][1]cpu" = getitem_18 - getitem_19; getitem_18 = getitem_19 = None 2025-03-04T20:52:05.9759805Z 2025-03-04T20:52:05.9760195Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:52:05.9760696Z getitem_20: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T20:52:05.9760961Z mul_10: "f32[269952][1]cpu" = 0.5 * widths_1 2025-03-04T20:52:05.9761234Z ctr_x_1: "f32[269952][1]cpu" = getitem_20 + mul_10; getitem_20 = mul_10 = None 2025-03-04T20:52:05.9761504Z 2025-03-04T20:52:05.9761909Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:52:05.9762425Z getitem_21: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]; boxes_1 = None 2025-03-04T20:52:05.9762721Z mul_11: "f32[269952][1]cpu" = 0.5 * heights_1 2025-03-04T20:52:05.9762994Z ctr_y_1: "f32[269952][1]cpu" = getitem_21 + mul_11; getitem_21 = mul_11 = None 2025-03-04T20:52:05.9763241Z 2025-03-04T20:52:05.9763649Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:52:05.9764157Z getitem_22: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:52:05.9764478Z dx_1: "f32[269952, 1][1, 1]cpu" = getitem_22 / 1.0; getitem_22 = None 2025-03-04T20:52:05.9764711Z 2025-03-04T20:52:05.9765090Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:52:05.9765587Z getitem_23: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:52:05.9765901Z dy_1: "f32[269952, 1][1, 1]cpu" = getitem_23 / 1.0; getitem_23 = None 2025-03-04T20:52:05.9766141Z 2025-03-04T20:52:05.9766510Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:52:05.9766995Z getitem_24: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:52:05.9767321Z dw_2: "f32[269952, 1][1, 1]cpu" = getitem_24 / 1.0; getitem_24 = None 2025-03-04T20:52:05.9767540Z 2025-03-04T20:52:05.9767917Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:52:05.9768438Z getitem_25: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))]; deltas_1 = None 2025-03-04T20:52:05.9768777Z dh_2: "f32[269952, 1][1, 1]cpu" = getitem_25 / 1.0; getitem_25 = None 2025-03-04T20:52:05.9768998Z 2025-03-04T20:52:05.9769414Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:52:05.9769930Z dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356); dw_2 = None 2025-03-04T20:52:05.9770178Z 2025-03-04T20:52:05.9770587Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:52:05.9771116Z dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356); dh_2 = None 2025-03-04T20:52:05.9771367Z 2025-03-04T20:52:05.9771784Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:52:05.9772329Z getitem_26: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)] 2025-03-04T20:52:05.9772644Z mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_26; dx_1 = getitem_26 = None 2025-03-04T20:52:05.9772976Z getitem_27: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)]; ctr_x_1 = None 2025-03-04T20:52:05.9773319Z pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_27; mul_12 = getitem_27 = None 2025-03-04T20:52:05.9773579Z 2025-03-04T20:52:05.9774002Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:52:05.9774547Z getitem_28: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)] 2025-03-04T20:52:05.9774855Z mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_28; dy_1 = getitem_28 = None 2025-03-04T20:52:05.9775176Z getitem_29: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)]; ctr_y_1 = None 2025-03-04T20:52:05.9775519Z pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_29; mul_13 = getitem_29 = None 2025-03-04T20:52:05.9775772Z 2025-03-04T20:52:05.9776178Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:52:05.9776668Z exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3); dw_3 = None 2025-03-04T20:52:05.9776988Z getitem_30: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]; widths_1 = None 2025-03-04T20:52:05.9777333Z pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_30; exp_2 = getitem_30 = None 2025-03-04T20:52:05.9777580Z 2025-03-04T20:52:05.9777990Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:52:05.9778481Z exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3); dh_3 = None 2025-03-04T20:52:05.9778809Z getitem_31: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]; heights_1 = None 2025-03-04T20:52:05.9779156Z pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_31; exp_3 = getitem_31 = None 2025-03-04T20:52:05.9779420Z 2025-03-04T20:52:05.9779798Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:52:05.9780250Z mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1 2025-03-04T20:52:05.9780509Z x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16; mul_16 = None 2025-03-04T20:52:05.9780735Z 2025-03-04T20:52:05.9781118Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:52:05.9781560Z mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1 2025-03-04T20:52:05.9781811Z y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17; mul_17 = None 2025-03-04T20:52:05.9782039Z 2025-03-04T20:52:05.9782413Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:52:05.9782881Z mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1; pred_w_1 = None 2025-03-04T20:52:05.9783178Z x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18; pred_ctr_x_1 = mul_18 = None 2025-03-04T20:52:05.9783434Z 2025-03-04T20:52:05.9783817Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:52:05.9784282Z mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1; pred_h_1 = None 2025-03-04T20:52:05.9784689Z y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19; pred_ctr_y_1 = mul_19 = None 2025-03-04T20:52:05.9784932Z 2025-03-04T20:52:05.9785355Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:52:05.9785929Z pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1); x1_1 = y1_1 = x2_1 = y2_1 = None 2025-03-04T20:52:05.9786226Z 2025-03-04T20:52:05.9786666Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:52:05.9787223Z proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4)); pred_boxes_1 = None 2025-03-04T20:52:05.9787505Z 2025-03-04T20:52:05.9787985Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:52:05.9788602Z proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4); proposals_i_1 = None 2025-03-04T20:52:05.9788894Z 2025-03-04T20:52:05.9789379Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:52:05.9790051Z pred_anchor_deltas_i_2: "f32[67488, 4][4, 1]cpu" = l_pred_anchor_deltas_2_.reshape(-1, 4); l_pred_anchor_deltas_2_ = None 2025-03-04T20:52:05.9790384Z 2025-03-04T20:52:05.9790904Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:52:05.9791589Z unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = l_anchors_2_tensor.unsqueeze(0); l_anchors_2_tensor = None 2025-03-04T20:52:05.9791982Z expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1); unsqueeze_2 = None 2025-03-04T20:52:05.9792330Z anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4); expand_2 = None 2025-03-04T20:52:05.9792605Z 2025-03-04T20:52:05.9793060Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:52:05.9793745Z deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_2.float(); pred_anchor_deltas_i_2 = None 2025-03-04T20:52:05.9794032Z 2025-03-04T20:52:05.9794427Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:52:05.9794939Z boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32); anchors_i_2 = None 2025-03-04T20:52:05.9795202Z 2025-03-04T20:52:05.9795598Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:52:05.9796133Z getitem_32: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)] 2025-03-04T20:52:05.9796441Z getitem_33: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T20:52:05.9796769Z widths_2: "f32[67488][1]cpu" = getitem_32 - getitem_33; getitem_32 = getitem_33 = None 2025-03-04T20:52:05.9797056Z 2025-03-04T20:52:05.9797455Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:52:05.9797950Z getitem_34: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)] 2025-03-04T20:52:05.9798287Z getitem_35: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)] 2025-03-04T20:52:05.9798612Z heights_2: "f32[67488][1]cpu" = getitem_34 - getitem_35; getitem_34 = getitem_35 = None 2025-03-04T20:52:05.9798877Z 2025-03-04T20:52:05.9799270Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:52:05.9799763Z getitem_36: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T20:52:05.9800041Z mul_20: "f32[67488][1]cpu" = 0.5 * widths_2 2025-03-04T20:52:05.9800315Z ctr_x_2: "f32[67488][1]cpu" = getitem_36 + mul_20; getitem_36 = mul_20 = None 2025-03-04T20:52:05.9800565Z 2025-03-04T20:52:05.9800962Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:52:05.9801486Z getitem_37: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]; boxes_2 = None 2025-03-04T20:52:05.9801788Z mul_21: "f32[67488][1]cpu" = 0.5 * heights_2 2025-03-04T20:52:05.9802237Z ctr_y_2: "f32[67488][1]cpu" = getitem_37 + mul_21; getitem_37 = mul_21 = None 2025-03-04T20:52:05.9802493Z 2025-03-04T20:52:05.9802899Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:52:05.9803421Z getitem_38: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:52:05.9803750Z dx_2: "f32[67488, 1][1, 1]cpu" = getitem_38 / 1.0; getitem_38 = None 2025-03-04T20:52:05.9803987Z 2025-03-04T20:52:05.9804376Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:52:05.9804888Z getitem_39: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:52:05.9805206Z dy_2: "f32[67488, 1][1, 1]cpu" = getitem_39 / 1.0; getitem_39 = None 2025-03-04T20:52:05.9805436Z 2025-03-04T20:52:05.9805824Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:52:05.9806387Z getitem_40: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:52:05.9806695Z dw_4: "f32[67488, 1][1, 1]cpu" = getitem_40 / 1.0; getitem_40 = None 2025-03-04T20:52:05.9806917Z 2025-03-04T20:52:05.9807292Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:52:05.9807816Z getitem_41: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))]; deltas_2 = None 2025-03-04T20:52:05.9808152Z dh_4: "f32[67488, 1][1, 1]cpu" = getitem_41 / 1.0; getitem_41 = None 2025-03-04T20:52:05.9808390Z 2025-03-04T20:52:05.9808799Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:52:05.9809315Z dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356); dw_4 = None 2025-03-04T20:52:05.9809561Z 2025-03-04T20:52:05.9809990Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:52:05.9810498Z dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356); dh_4 = None 2025-03-04T20:52:05.9810748Z 2025-03-04T20:52:05.9811192Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:52:05.9811716Z getitem_42: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)] 2025-03-04T20:52:05.9812027Z mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_42; dx_2 = getitem_42 = None 2025-03-04T20:52:05.9812356Z getitem_43: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)]; ctr_x_2 = None 2025-03-04T20:52:05.9812700Z pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_43; mul_22 = getitem_43 = None 2025-03-04T20:52:05.9812986Z 2025-03-04T20:52:05.9813409Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:52:05.9813931Z getitem_44: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)] 2025-03-04T20:52:05.9814240Z mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_44; dy_2 = getitem_44 = None 2025-03-04T20:52:05.9814559Z getitem_45: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)]; ctr_y_2 = None 2025-03-04T20:52:05.9814896Z pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_45; mul_23 = getitem_45 = None 2025-03-04T20:52:05.9815145Z 2025-03-04T20:52:05.9815551Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:52:05.9816047Z exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5); dw_5 = None 2025-03-04T20:52:05.9816366Z getitem_46: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]; widths_2 = None 2025-03-04T20:52:05.9816708Z pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_46; exp_4 = getitem_46 = None 2025-03-04T20:52:05.9816956Z 2025-03-04T20:52:05.9817367Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:52:05.9817854Z exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5); dh_5 = None 2025-03-04T20:52:05.9818175Z getitem_47: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]; heights_2 = None 2025-03-04T20:52:05.9818537Z pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_47; exp_5 = getitem_47 = None 2025-03-04T20:52:05.9818781Z 2025-03-04T20:52:05.9819159Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:52:05.9819613Z mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2 2025-03-04T20:52:05.9819872Z x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26; mul_26 = None 2025-03-04T20:52:05.9820101Z 2025-03-04T20:52:05.9820486Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:52:05.9820927Z mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2 2025-03-04T20:52:05.9821180Z y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27; mul_27 = None 2025-03-04T20:52:05.9821414Z 2025-03-04T20:52:05.9821789Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:52:05.9822255Z mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2; pred_w_2 = None 2025-03-04T20:52:05.9822563Z x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28; pred_ctr_x_2 = mul_28 = None 2025-03-04T20:52:05.9822803Z 2025-03-04T20:52:05.9823179Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:52:05.9823652Z mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2; pred_h_2 = None 2025-03-04T20:52:05.9823940Z y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29; pred_ctr_y_2 = mul_29 = None 2025-03-04T20:52:05.9824181Z 2025-03-04T20:52:05.9824607Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:52:05.9825197Z pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1); x1_2 = y1_2 = x2_2 = y2_2 = None 2025-03-04T20:52:05.9825513Z 2025-03-04T20:52:05.9825944Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:52:05.9826507Z proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4)); pred_boxes_2 = None 2025-03-04T20:52:05.9826788Z 2025-03-04T20:52:05.9827267Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:52:05.9827884Z proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4); proposals_i_2 = None 2025-03-04T20:52:05.9828184Z 2025-03-04T20:52:05.9828681Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:52:05.9829361Z pred_anchor_deltas_i_3: "f32[16872, 4][4, 1]cpu" = l_pred_anchor_deltas_3_.reshape(-1, 4); l_pred_anchor_deltas_3_ = None 2025-03-04T20:52:05.9829693Z 2025-03-04T20:52:05.9830228Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:52:05.9830914Z unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = l_anchors_3_tensor.unsqueeze(0); l_anchors_3_tensor = None 2025-03-04T20:52:05.9831312Z expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1); unsqueeze_3 = None 2025-03-04T20:52:05.9831658Z anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4); expand_3 = None 2025-03-04T20:52:05.9831943Z 2025-03-04T20:52:05.9832406Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:52:05.9833011Z deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float(); pred_anchor_deltas_i_3 = None 2025-03-04T20:52:05.9833365Z 2025-03-04T20:52:05.9833770Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:52:05.9834297Z boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32); anchors_i_3 = None 2025-03-04T20:52:05.9834567Z 2025-03-04T20:52:05.9834976Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:52:05.9835488Z getitem_48: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)] 2025-03-04T20:52:05.9835805Z getitem_49: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T20:52:05.9836168Z widths_3: "f32[16872][1]cpu" = getitem_48 - getitem_49; getitem_48 = getitem_49 = None 2025-03-04T20:52:05.9836454Z 2025-03-04T20:52:05.9836877Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:52:05.9837415Z getitem_50: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)] 2025-03-04T20:52:05.9837721Z getitem_51: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)] 2025-03-04T20:52:05.9838059Z heights_3: "f32[16872][1]cpu" = getitem_50 - getitem_51; getitem_50 = getitem_51 = None 2025-03-04T20:52:05.9838331Z 2025-03-04T20:52:05.9838741Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:52:05.9839266Z getitem_52: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T20:52:05.9839533Z mul_30: "f32[16872][1]cpu" = 0.5 * widths_3 2025-03-04T20:52:05.9839815Z ctr_x_3: "f32[16872][1]cpu" = getitem_52 + mul_30; getitem_52 = mul_30 = None 2025-03-04T20:52:05.9840077Z 2025-03-04T20:52:05.9840485Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:52:05.9841028Z getitem_53: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]; boxes_3 = None 2025-03-04T20:52:05.9841315Z mul_31: "f32[16872][1]cpu" = 0.5 * heights_3 2025-03-04T20:52:05.9841579Z ctr_y_3: "f32[16872][1]cpu" = getitem_53 + mul_31; getitem_53 = mul_31 = None 2025-03-04T20:52:05.9841826Z 2025-03-04T20:52:05.9842217Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:52:05.9842719Z getitem_54: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:52:05.9843032Z dx_3: "f32[16872, 1][1, 1]cpu" = getitem_54 / 1.0; getitem_54 = None 2025-03-04T20:52:05.9843258Z 2025-03-04T20:52:05.9843632Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:52:05.9844123Z getitem_55: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:52:05.9844430Z dy_3: "f32[16872, 1][1, 1]cpu" = getitem_55 / 1.0; getitem_55 = None 2025-03-04T20:52:05.9844655Z 2025-03-04T20:52:05.9845028Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:52:05.9845540Z getitem_56: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:52:05.9845852Z dw_6: "f32[16872, 1][1, 1]cpu" = getitem_56 / 1.0; getitem_56 = None 2025-03-04T20:52:05.9846076Z 2025-03-04T20:52:05.9846451Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:52:05.9846975Z getitem_57: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))]; deltas_3 = None 2025-03-04T20:52:05.9847312Z dh_6: "f32[16872, 1][1, 1]cpu" = getitem_57 / 1.0; getitem_57 = None 2025-03-04T20:52:05.9847534Z 2025-03-04T20:52:05.9847943Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:52:05.9848459Z dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356); dw_6 = None 2025-03-04T20:52:05.9848709Z 2025-03-04T20:52:05.9849144Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:52:05.9849649Z dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356); dh_6 = None 2025-03-04T20:52:05.9849893Z 2025-03-04T20:52:05.9850324Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:52:05.9850845Z getitem_58: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)] 2025-03-04T20:52:05.9851156Z mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_58; dx_3 = getitem_58 = None 2025-03-04T20:52:05.9851487Z getitem_59: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)]; ctr_x_3 = None 2025-03-04T20:52:05.9851845Z pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_59; mul_32 = getitem_59 = None 2025-03-04T20:52:05.9852101Z 2025-03-04T20:52:05.9852525Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:52:05.9853049Z getitem_60: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)] 2025-03-04T20:52:05.9853356Z mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_60; dy_3 = getitem_60 = None 2025-03-04T20:52:05.9853673Z getitem_61: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)]; ctr_y_3 = None 2025-03-04T20:52:05.9854007Z pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_61; mul_33 = getitem_61 = None 2025-03-04T20:52:05.9854259Z 2025-03-04T20:52:05.9854665Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:52:05.9855154Z exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7); dw_7 = None 2025-03-04T20:52:05.9855468Z getitem_62: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]; widths_3 = None 2025-03-04T20:52:05.9855807Z pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_62; exp_6 = getitem_62 = None 2025-03-04T20:52:05.9856051Z 2025-03-04T20:52:05.9856456Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:52:05.9856937Z exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7); dh_7 = None 2025-03-04T20:52:05.9857254Z getitem_63: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]; heights_3 = None 2025-03-04T20:52:05.9857611Z pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_63; exp_7 = getitem_63 = None 2025-03-04T20:52:05.9857860Z 2025-03-04T20:52:05.9858247Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:52:05.9858702Z mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3 2025-03-04T20:52:05.9858965Z x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36; mul_36 = None 2025-03-04T20:52:05.9859200Z 2025-03-04T20:52:05.9859595Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:52:05.9860056Z mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3 2025-03-04T20:52:05.9860316Z y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37; mul_37 = None 2025-03-04T20:52:05.9860549Z 2025-03-04T20:52:05.9860934Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:52:05.9861425Z mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3; pred_w_3 = None 2025-03-04T20:52:05.9861725Z x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38; pred_ctr_x_3 = mul_38 = None 2025-03-04T20:52:05.9861975Z 2025-03-04T20:52:05.9862373Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:52:05.9862837Z mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3; pred_h_3 = None 2025-03-04T20:52:05.9863125Z y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39; pred_ctr_y_3 = mul_39 = None 2025-03-04T20:52:05.9863366Z 2025-03-04T20:52:05.9863781Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:52:05.9864375Z pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1); x1_3 = y1_3 = x2_3 = y2_3 = None 2025-03-04T20:52:05.9864669Z 2025-03-04T20:52:05.9865067Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:52:05.9865601Z proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4)); pred_boxes_3 = None 2025-03-04T20:52:05.9865874Z 2025-03-04T20:52:05.9866328Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:52:05.9866910Z proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4); proposals_i_3 = None 2025-03-04T20:52:05.9867187Z 2025-03-04T20:52:05.9867653Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:52:05.9868295Z pred_anchor_deltas_i_4: "f32[4332, 4][4, 1]cpu" = l_pred_anchor_deltas_4_.reshape(-1, 4); l_pred_anchor_deltas_4_ = None 2025-03-04T20:52:05.9868609Z 2025-03-04T20:52:05.9869113Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:52:05.9869762Z unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = l_anchors_4_tensor.unsqueeze(0); l_anchors_4_tensor = None 2025-03-04T20:52:05.9870139Z expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1); unsqueeze_4 = None 2025-03-04T20:52:05.9870503Z anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4); expand_4 = None 2025-03-04T20:52:05.9870765Z 2025-03-04T20:52:05.9871232Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:52:05.9871839Z deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_4.float(); pred_anchor_deltas_i_4 = None 2025-03-04T20:52:05.9872123Z 2025-03-04T20:52:05.9872521Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:52:05.9873036Z boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32); anchors_i_4 = None 2025-03-04T20:52:05.9873381Z 2025-03-04T20:52:05.9873809Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:52:05.9874328Z getitem_64: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)] 2025-03-04T20:52:05.9874663Z getitem_65: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T20:52:05.9875011Z widths_4: "f32[4332][1]cpu" = getitem_64 - getitem_65; getitem_64 = getitem_65 = None 2025-03-04T20:52:05.9875275Z 2025-03-04T20:52:05.9875682Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:52:05.9876195Z getitem_66: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)] 2025-03-04T20:52:05.9876491Z getitem_67: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)] 2025-03-04T20:52:05.9876810Z heights_4: "f32[4332][1]cpu" = getitem_66 - getitem_67; getitem_66 = getitem_67 = None 2025-03-04T20:52:05.9877076Z 2025-03-04T20:52:05.9877463Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:52:05.9877974Z getitem_68: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T20:52:05.9878241Z mul_40: "f32[4332][1]cpu" = 0.5 * widths_4 2025-03-04T20:52:05.9878513Z ctr_x_4: "f32[4332][1]cpu" = getitem_68 + mul_40; getitem_68 = mul_40 = None 2025-03-04T20:52:05.9878758Z 2025-03-04T20:52:05.9879158Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:52:05.9879677Z getitem_69: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]; boxes_4 = None 2025-03-04T20:52:05.9879966Z mul_41: "f32[4332][1]cpu" = 0.5 * heights_4 2025-03-04T20:52:05.9880235Z ctr_y_4: "f32[4332][1]cpu" = getitem_69 + mul_41; getitem_69 = mul_41 = None 2025-03-04T20:52:05.9880477Z 2025-03-04T20:52:05.9880871Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:52:05.9881373Z getitem_70: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:52:05.9881688Z dx_4: "f32[4332, 1][1, 1]cpu" = getitem_70 / 1.0; getitem_70 = None 2025-03-04T20:52:05.9881912Z 2025-03-04T20:52:05.9882293Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:52:05.9882798Z getitem_71: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:52:05.9883110Z dy_4: "f32[4332, 1][1, 1]cpu" = getitem_71 / 1.0; getitem_71 = None 2025-03-04T20:52:05.9883360Z 2025-03-04T20:52:05.9883748Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:52:05.9884265Z getitem_72: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:52:05.9884583Z dw_8: "f32[4332, 1][1, 1]cpu" = getitem_72 / 1.0; getitem_72 = None 2025-03-04T20:52:05.9884817Z 2025-03-04T20:52:05.9885206Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:52:05.9885753Z getitem_73: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))]; deltas_4 = None 2025-03-04T20:52:05.9886100Z dh_8: "f32[4332, 1][1, 1]cpu" = getitem_73 / 1.0; getitem_73 = None 2025-03-04T20:52:05.9886333Z 2025-03-04T20:52:05.9886760Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:52:05.9887299Z dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356); dw_8 = None 2025-03-04T20:52:05.9887579Z 2025-03-04T20:52:05.9887994Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:52:05.9888512Z dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356); dh_8 = None 2025-03-04T20:52:05.9888764Z 2025-03-04T20:52:05.9889209Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:52:05.9889740Z getitem_74: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)] 2025-03-04T20:52:05.9890057Z mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_74; dx_4 = getitem_74 = None 2025-03-04T20:52:05.9890373Z getitem_75: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)]; ctr_x_4 = None 2025-03-04T20:52:05.9890728Z pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_75; mul_42 = getitem_75 = None 2025-03-04T20:52:05.9890979Z 2025-03-04T20:52:05.9891404Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:52:05.9891935Z getitem_76: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)] 2025-03-04T20:52:05.9892244Z mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_76; dy_4 = getitem_76 = None 2025-03-04T20:52:05.9892563Z getitem_77: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)]; ctr_y_4 = None 2025-03-04T20:52:05.9892896Z pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_77; mul_43 = getitem_77 = None 2025-03-04T20:52:05.9893151Z 2025-03-04T20:52:05.9893563Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:52:05.9894055Z exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9); dw_9 = None 2025-03-04T20:52:05.9894373Z getitem_78: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]; widths_4 = None 2025-03-04T20:52:05.9894713Z pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_78; exp_8 = getitem_78 = None 2025-03-04T20:52:05.9894963Z 2025-03-04T20:52:05.9895373Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:52:05.9895864Z exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9); dh_9 = None 2025-03-04T20:52:05.9896211Z getitem_79: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]; heights_4 = None 2025-03-04T20:52:05.9896542Z pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_79; exp_9 = getitem_79 = None 2025-03-04T20:52:05.9896785Z 2025-03-04T20:52:05.9897177Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:52:05.9897652Z mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4 2025-03-04T20:52:05.9897905Z x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46; mul_46 = None 2025-03-04T20:52:05.9898128Z 2025-03-04T20:52:05.9898509Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:52:05.9898951Z mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4 2025-03-04T20:52:05.9899203Z y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47; mul_47 = None 2025-03-04T20:52:05.9899426Z 2025-03-04T20:52:05.9899806Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:52:05.9900288Z mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4; pred_w_4 = None 2025-03-04T20:52:05.9900580Z x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48; pred_ctr_x_4 = mul_48 = None 2025-03-04T20:52:05.9900825Z 2025-03-04T20:52:05.9901229Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:52:05.9901694Z mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4; pred_h_4 = None 2025-03-04T20:52:05.9902142Z y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49; pred_ctr_y_4 = mul_49 = None 2025-03-04T20:52:05.9902394Z 2025-03-04T20:52:05.9902819Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:52:05.9903443Z pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1); x1_4 = y1_4 = x2_4 = y2_4 = None 2025-03-04T20:52:05.9903737Z 2025-03-04T20:52:05.9904142Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:52:05.9904682Z proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4)); pred_boxes_4 = None 2025-03-04T20:52:05.9904957Z 2025-03-04T20:52:05.9905418Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:52:05.9906015Z proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4); proposals_i_4 = None 2025-03-04T20:52:05.9906298Z 2025-03-04T20:52:05.9906860Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T20:52:05.9907528Z arange: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T20:52:05.9907765Z 2025-03-04T20:52:05.9908139Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:52:05.9908615Z batch_idx: "i64[4][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T20:52:05.9908856Z 2025-03-04T20:52:05.9909381Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:52:05.9910083Z topk = l_pred_objectness_logits_0_.topk(1000, dim = 1); l_pred_objectness_logits_0_ = None 2025-03-04T20:52:05.9910438Z topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0] 2025-03-04T20:52:05.9910704Z topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1]; topk = None 2025-03-04T20:52:05.9910931Z 2025-03-04T20:52:05.9911480Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:52:05.9912186Z getitem_82: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T20:52:05.9912605Z topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_82, topk_idx)]; proposals_i_5 = getitem_82 = topk_idx = None 2025-03-04T20:52:05.9912951Z 2025-03-04T20:52:05.9913614Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:52:05.9914403Z full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:52:05.9914686Z 2025-03-04T20:52:05.9915067Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:52:05.9915563Z to_6: "i64[1000][1]cpu" = full.to(device(type='cpu')); full = None 2025-03-04T20:52:05.9915797Z 2025-03-04T20:52:05.9916307Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:52:05.9916964Z topk_1 = l_pred_objectness_logits_1_.topk(1000, dim = 1); l_pred_objectness_logits_1_ = None 2025-03-04T20:52:05.9917347Z topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0] 2025-03-04T20:52:05.9917625Z topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1]; topk_1 = None 2025-03-04T20:52:05.9917851Z 2025-03-04T20:52:05.9918398Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:52:05.9919056Z getitem_86: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T20:52:05.9919475Z topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_86, topk_idx_1)]; proposals_i_6 = getitem_86 = topk_idx_1 = None 2025-03-04T20:52:05.9919821Z 2025-03-04T20:52:05.9920370Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:52:05.9921053Z full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:52:05.9921329Z 2025-03-04T20:52:05.9921713Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:52:05.9922194Z to_7: "i64[1000][1]cpu" = full_1.to(device(type='cpu')); full_1 = None 2025-03-04T20:52:05.9922431Z 2025-03-04T20:52:05.9922951Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:52:05.9923627Z topk_2 = l_pred_objectness_logits_2_.topk(1000, dim = 1); l_pred_objectness_logits_2_ = None 2025-03-04T20:52:05.9923961Z topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0] 2025-03-04T20:52:05.9924236Z topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1]; topk_2 = None 2025-03-04T20:52:05.9924469Z 2025-03-04T20:52:05.9925012Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:52:05.9925651Z getitem_90: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T20:52:05.9926065Z topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_90, topk_idx_2)]; proposals_i_7 = getitem_90 = topk_idx_2 = None 2025-03-04T20:52:05.9926417Z 2025-03-04T20:52:05.9926943Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:52:05.9927616Z full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:52:05.9927889Z 2025-03-04T20:52:05.9928259Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:52:05.9928718Z to_8: "i64[1000][1]cpu" = full_2.to(device(type='cpu')); full_2 = None 2025-03-04T20:52:05.9928967Z 2025-03-04T20:52:05.9929469Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:52:05.9930103Z topk_3 = l_pred_objectness_logits_3_.topk(1000, dim = 1); l_pred_objectness_logits_3_ = None 2025-03-04T20:52:05.9930422Z topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0] 2025-03-04T20:52:05.9930708Z topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1]; topk_3 = None 2025-03-04T20:52:05.9930945Z 2025-03-04T20:52:05.9931480Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:52:05.9932108Z getitem_94: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T20:52:05.9932518Z topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_94, topk_idx_3)]; proposals_i_8 = getitem_94 = topk_idx_3 = None 2025-03-04T20:52:05.9932860Z 2025-03-04T20:52:05.9933389Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:52:05.9934054Z full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:52:05.9934332Z 2025-03-04T20:52:05.9934708Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:52:05.9935169Z to_9: "i64[1000][1]cpu" = full_3.to(device(type='cpu')); full_3 = None 2025-03-04T20:52:05.9935404Z 2025-03-04T20:52:05.9935912Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:52:05.9936558Z topk_4 = l_pred_objectness_logits_4_.topk(1000, dim = 1); l_pred_objectness_logits_4_ = None 2025-03-04T20:52:05.9936902Z topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0] 2025-03-04T20:52:05.9937171Z topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1]; topk_4 = None 2025-03-04T20:52:05.9937405Z 2025-03-04T20:52:05.9937950Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:52:05.9938601Z getitem_98: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T20:52:05.9939033Z topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_98, topk_idx_4)]; proposals_i_9 = getitem_98 = topk_idx_4 = None 2025-03-04T20:52:05.9939374Z 2025-03-04T20:52:05.9939897Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:52:05.9940547Z full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:52:05.9940837Z 2025-03-04T20:52:05.9941202Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:52:05.9941662Z to_10: "i64[1000][1]cpu" = full_4.to(device(type='cpu')); full_4 = None 2025-03-04T20:52:05.9941896Z 2025-03-04T20:52:05.9942261Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:52:05.9942963Z topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1); topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None 2025-03-04T20:52:05.9943452Z 2025-03-04T20:52:05.9943815Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:52:05.9944637Z topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1); topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None 2025-03-04T20:52:05.9945197Z 2025-03-04T20:52:05.9945555Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:52:05.9946074Z level_ids: "i64[5000][1]cpu" = torch.cat([to_6, to_7, to_8, to_9, to_10], 0); to_6 = to_7 = to_8 = to_9 = to_10 = level_ids = None 2025-03-04T20:52:05.9946371Z 2025-03-04T20:52:05.9946844Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T20:52:05.9947425Z getitem_100: "f32[5000, 4][4, 1]cpu" = topk_proposals[0]; topk_proposals = None 2025-03-04T20:52:05.9947688Z 2025-03-04T20:52:05.9948071Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:52:05.9948564Z tensor: "f32[5000, 4][4, 1]cpu" = getitem_100.to(torch.float32); getitem_100 = None 2025-03-04T20:52:05.9948824Z 2025-03-04T20:52:05.9949282Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T20:52:05.9949842Z scores_per_img: "f32[5000][1]cpu" = topk_scores[0]; topk_scores = None 2025-03-04T20:52:05.9950108Z 2025-03-04T20:52:05.9950673Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T20:52:05.9951347Z isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor); tensor = None 2025-03-04T20:52:05.9951654Z all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:52:05.9951984Z isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T20:52:05.9952334Z valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T20:52:05.9952590Z 2025-03-04T20:52:05.9953054Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T20:52:05.9953699Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T20:52:05.9953944Z 2025-03-04T20:52:31.5008721Z 2025-03-04T20:52:31.5009216Z class GraphModule(torch.nn.Module): 2025-03-04T20:52:31.5011876Z def forward(self, L_stack0_: "f32[4000, 256, 7, 7][12544, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 12544][12544, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"): 2025-03-04T20:52:31.5019563Z l_stack0_ = L_stack0_ 2025-03-04T20:52:31.5023677Z l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_ 2025-03-04T20:52:31.5029244Z l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_ 2025-03-04T20:52:31.5029947Z l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_ 2025-03-04T20:52:31.5030465Z l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_ 2025-03-04T20:52:31.5030986Z l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_ 2025-03-04T20:52:31.5031565Z l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_ 2025-03-04T20:52:31.5032144Z l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ 2025-03-04T20:52:31.5032708Z l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ 2025-03-04T20:52:31.5033189Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T20:52:31.5033594Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T20:52:31.5033985Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T20:52:31.5034581Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T20:52:31.5034876Z 2025-03-04T20:52:31.5035274Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x) 2025-03-04T20:52:31.5037015Z x: "f32[4000, 12544][12544, 1]cpu" = l_stack0_.flatten(1, -1); l_stack0_ = None 2025-03-04T20:52:31.5037869Z x_1: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_); x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None 2025-03-04T20:52:31.5038755Z x_2: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False); x_1 = None 2025-03-04T20:52:31.5039593Z x_3: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_); x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None 2025-03-04T20:52:31.5041308Z x_4: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False); x_3 = None 2025-03-04T20:52:31.5041631Z 2025-03-04T20:52:31.5042143Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x) 2025-03-04T20:52:31.5043388Z scores: "f32[4000, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_); l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None 2025-03-04T20:52:31.5044146Z 2025-03-04T20:52:31.5044638Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x) 2025-03-04T20:52:31.5045826Z proposal_deltas: "f32[4000, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_); x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None 2025-03-04T20:52:31.5046649Z 2025-03-04T20:52:31.5047077Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:52:31.5047660Z size = l_proposals_0_fields_proposal_boxes_tensor.size() 2025-03-04T20:52:31.5047935Z getitem: "Sym(s0)" = size[0] 2025-03-04T20:52:31.5048169Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T20:52:31.5048452Z size_1 = l_proposals_1_fields_proposal_boxes_tensor.size() 2025-03-04T20:52:31.5048706Z getitem_2: "Sym(s1)" = size_1[0] 2025-03-04T20:52:31.5048948Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T20:52:31.5049232Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T20:52:31.5049469Z getitem_4: "Sym(s2)" = size_2[0] 2025-03-04T20:52:31.5049692Z getitem_5 = size_2[1]; size_2 = getitem_5 = None 2025-03-04T20:52:31.5049948Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T20:52:31.5050179Z getitem_6: "Sym(s3)" = size_3[0] 2025-03-04T20:52:31.5050395Z getitem_7 = size_3[1]; size_3 = getitem_7 = None 2025-03-04T20:52:31.5050603Z 2025-03-04T20:52:31.5050967Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:52:31.5051725Z proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0) 2025-03-04T20:52:31.5052288Z 2025-03-04T20:52:31.5052742Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:52:31.5053311Z deltas: "f32[4000, 320][320, 1]cpu" = proposal_deltas.float(); proposal_deltas = None 2025-03-04T20:52:31.5053578Z 2025-03-04T20:52:31.5053967Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:52:31.5054484Z boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T20:52:31.5054761Z 2025-03-04T20:52:31.5055154Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:52:31.5055651Z getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:52:31.5055989Z getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:52:31.5056316Z widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9; getitem_8 = getitem_9 = None 2025-03-04T20:52:31.5056574Z 2025-03-04T20:52:31.5056989Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:52:31.5057479Z getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:52:31.5057781Z getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:52:31.5058106Z heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T20:52:31.5058372Z 2025-03-04T20:52:31.5058783Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:52:31.5059267Z getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:52:31.5059538Z mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths 2025-03-04T20:52:31.5059803Z ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul; getitem_12 = mul = None 2025-03-04T20:52:31.5060046Z 2025-03-04T20:52:31.5060437Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:52:31.5060942Z getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:52:31.5061241Z mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights 2025-03-04T20:52:31.5061514Z ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1; getitem_13 = mul_1 = None 2025-03-04T20:52:31.5061757Z 2025-03-04T20:52:31.5062177Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:52:31.5062674Z getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:52:31.5062991Z dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0; getitem_14 = None 2025-03-04T20:52:31.5063218Z 2025-03-04T20:52:31.5063608Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:52:31.5064124Z getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:52:31.5064451Z dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0; getitem_15 = None 2025-03-04T20:52:31.5064674Z 2025-03-04T20:52:31.5065046Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:52:31.5065542Z getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:52:31.5065855Z dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0; getitem_16 = None 2025-03-04T20:52:31.5066080Z 2025-03-04T20:52:31.5066467Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:52:31.5067004Z getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:52:31.5067351Z dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0; getitem_17 = None 2025-03-04T20:52:31.5067588Z 2025-03-04T20:52:31.5068037Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:52:31.5068593Z dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:52:31.5068855Z 2025-03-04T20:52:31.5069289Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:52:31.5070061Z dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:52:31.5070335Z 2025-03-04T20:52:31.5070792Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:52:31.5071360Z getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:52:31.5071739Z mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18; dx = getitem_18 = None 2025-03-04T20:52:31.5072067Z getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:52:31.5072410Z pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19; mul_2 = getitem_19 = None 2025-03-04T20:52:31.5072660Z 2025-03-04T20:52:31.5073092Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:52:31.5073689Z getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:52:31.5074009Z mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20; dy = getitem_20 = None 2025-03-04T20:52:31.5074334Z getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:52:31.5074672Z pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21; mul_3 = getitem_21 = None 2025-03-04T20:52:31.5074926Z 2025-03-04T20:52:31.5075343Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:52:31.5075846Z exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:52:31.5076175Z getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:52:31.5076521Z pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22; exp = getitem_22 = None 2025-03-04T20:52:31.5076764Z 2025-03-04T20:52:31.5077184Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:52:31.5077753Z exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:52:31.5078086Z getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:52:31.5078472Z pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23; exp_1 = getitem_23 = None 2025-03-04T20:52:31.5078723Z 2025-03-04T20:52:31.5079123Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:52:31.5079587Z mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T20:52:31.5079845Z x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:52:31.5080075Z 2025-03-04T20:52:31.5080468Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:52:31.5080922Z mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T20:52:31.5081181Z y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:52:31.5081422Z 2025-03-04T20:52:31.5081812Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:52:31.5082279Z mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:52:31.5082580Z x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:52:31.5082814Z 2025-03-04T20:52:31.5083200Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:52:31.5083665Z mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:52:31.5083946Z y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:52:31.5084184Z 2025-03-04T20:52:31.5084637Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:52:31.5085219Z pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:52:31.5085496Z 2025-03-04T20:52:31.5085903Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:52:31.5086439Z predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320)); pred_boxes = None 2025-03-04T20:52:31.5086711Z 2025-03-04T20:52:31.5087141Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T20:52:31.5087809Z split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]); predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None 2025-03-04T20:52:31.5088222Z boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0] 2025-03-04T20:52:31.5088496Z getitem_25: "f32[s1, 320][320, 1]cpu" = split[1]; getitem_25 = None 2025-03-04T20:52:31.5088779Z getitem_26: "f32[s2, 320][320, 1]cpu" = split[2]; getitem_26 = None 2025-03-04T20:52:31.5089069Z getitem_27: "f32[s3, 320][320, 1]cpu" = split[3]; split = getitem_27 = None 2025-03-04T20:52:31.5089310Z 2025-03-04T20:52:31.5089677Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:52:31.5090216Z size_4 = l_proposals_0_fields_proposal_boxes_tensor.size(); l_proposals_0_fields_proposal_boxes_tensor = None 2025-03-04T20:52:31.5090571Z getitem_28: "Sym(s0)" = size_4[0] 2025-03-04T20:52:31.5090803Z getitem_29 = size_4[1]; size_4 = getitem_29 = None 2025-03-04T20:52:31.5091159Z size_5 = l_proposals_1_fields_proposal_boxes_tensor.size(); l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T20:52:31.5091493Z getitem_30: "Sym(s1)" = size_5[0] 2025-03-04T20:52:31.5091717Z getitem_31 = size_5[1]; size_5 = getitem_31 = None 2025-03-04T20:52:31.5092066Z size_6 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T20:52:31.5092392Z getitem_32: "Sym(s2)" = size_6[0] 2025-03-04T20:52:31.5092615Z getitem_33 = size_6[1]; size_6 = getitem_33 = None 2025-03-04T20:52:31.5092964Z size_7 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T20:52:31.5093291Z getitem_34: "Sym(s3)" = size_7[0] 2025-03-04T20:52:31.5093514Z getitem_35 = size_7[1]; size_7 = getitem_35 = None 2025-03-04T20:52:31.5093720Z 2025-03-04T20:52:31.5094146Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T20:52:31.5094685Z probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1); scores = None 2025-03-04T20:52:31.5094961Z 2025-03-04T20:52:31.5095415Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T20:52:31.5096079Z split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0); probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None 2025-03-04T20:52:31.5096488Z scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0] 2025-03-04T20:52:31.5096764Z getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1]; getitem_37 = None 2025-03-04T20:52:31.5097057Z getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2]; getitem_38 = None 2025-03-04T20:52:31.5097364Z getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_39 = None 2025-03-04T20:52:31.5097601Z 2025-03-04T20:52:31.5098151Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T20:52:31.5098848Z isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T20:52:31.5099170Z all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:52:31.5099480Z isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T20:52:31.5099802Z all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T20:52:31.5100075Z valid_mask: "b8[s0][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T20:52:31.5100300Z 2025-03-04T20:52:31.5100728Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T20:52:31.5101230Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T20:52:31.5101455Z 2025-03-04T20:52:31.5101595Z 2025-03-04T20:52:31.5101689Z class GraphModule(torch.nn.Module): 2025-03-04T20:52:31.5103759Z def forward(self, L_stack0_: "f32[4000, 256, 7, 7][12544, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 12544][12544, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"): 2025-03-04T20:52:31.5105839Z l_stack0_ = L_stack0_ 2025-03-04T20:52:31.5106185Z l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_ 2025-03-04T20:52:31.5106672Z l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_ 2025-03-04T20:52:31.5107151Z l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_ 2025-03-04T20:52:31.5107656Z l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_ 2025-03-04T20:52:31.5108187Z l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_ 2025-03-04T20:52:31.5108791Z l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_ 2025-03-04T20:52:31.5109366Z l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ 2025-03-04T20:52:31.5110101Z l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ 2025-03-04T20:52:31.5110598Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T20:52:31.5111051Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T20:52:31.5111464Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T20:52:31.5111884Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T20:52:31.5112189Z 2025-03-04T20:52:31.5112570Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x) 2025-03-04T20:52:31.5113054Z x: "f32[4000, 12544][12544, 1]cpu" = l_stack0_.flatten(1, -1); l_stack0_ = None 2025-03-04T20:52:31.5113786Z x_1: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_); x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None 2025-03-04T20:52:31.5114538Z x_2: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False); x_1 = None 2025-03-04T20:52:31.5115290Z x_3: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_); x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None 2025-03-04T20:52:31.5116031Z x_4: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False); x_3 = None 2025-03-04T20:52:31.5116321Z 2025-03-04T20:52:31.5116737Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x) 2025-03-04T20:52:31.5117729Z scores: "f32[4000, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_); l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None 2025-03-04T20:52:31.5118423Z 2025-03-04T20:52:31.5118824Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x) 2025-03-04T20:52:31.5119799Z proposal_deltas: "f32[4000, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_); x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None 2025-03-04T20:52:31.5120517Z 2025-03-04T20:52:31.5120876Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:52:31.5121318Z size = l_proposals_0_fields_proposal_boxes_tensor.size() 2025-03-04T20:52:31.5121578Z getitem: "Sym(s0)" = size[0] 2025-03-04T20:52:31.5121800Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T20:52:31.5122061Z size_1 = l_proposals_1_fields_proposal_boxes_tensor.size() 2025-03-04T20:52:31.5122299Z getitem_2: "Sym(s1)" = size_1[0] 2025-03-04T20:52:31.5122523Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T20:52:31.5122816Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T20:52:31.5123053Z getitem_4: "Sym(s2)" = size_2[0] 2025-03-04T20:52:31.5123270Z getitem_5 = size_2[1]; size_2 = getitem_5 = None 2025-03-04T20:52:31.5123528Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T20:52:31.5123765Z getitem_6: "Sym(s3)" = size_3[0] 2025-03-04T20:52:31.5123987Z getitem_7 = size_3[1]; size_3 = getitem_7 = None 2025-03-04T20:52:31.5124212Z 2025-03-04T20:52:31.5124576Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:52:31.5125332Z proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0) 2025-03-04T20:52:31.5125869Z 2025-03-04T20:52:31.5126321Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:52:31.5126886Z deltas: "f32[4000, 320][320, 1]cpu" = proposal_deltas.float(); proposal_deltas = None 2025-03-04T20:52:31.5127153Z 2025-03-04T20:52:31.5127540Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:52:31.5128057Z boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T20:52:31.5128330Z 2025-03-04T20:52:31.5128719Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:52:31.5129211Z getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:52:31.5129512Z getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:52:31.5129828Z widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9; getitem_8 = getitem_9 = None 2025-03-04T20:52:31.5130098Z 2025-03-04T20:52:31.5130487Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:52:31.5130974Z getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:52:31.5131275Z getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:52:31.5131602Z heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T20:52:31.5131865Z 2025-03-04T20:52:31.5132252Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:52:31.5132729Z getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:52:31.5133000Z mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths 2025-03-04T20:52:31.5133265Z ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul; getitem_12 = mul = None 2025-03-04T20:52:31.5133504Z 2025-03-04T20:52:31.5133912Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:52:31.5134414Z getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:52:31.5134705Z mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights 2025-03-04T20:52:31.5134975Z ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1; getitem_13 = mul_1 = None 2025-03-04T20:52:31.5135224Z 2025-03-04T20:52:31.5135623Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:52:31.5136118Z getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:52:31.5136430Z dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0; getitem_14 = None 2025-03-04T20:52:31.5136660Z 2025-03-04T20:52:31.5137047Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:52:31.5137536Z getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:52:31.5137843Z dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0; getitem_15 = None 2025-03-04T20:52:31.5138066Z 2025-03-04T20:52:31.5138442Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:52:31.5138931Z getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:52:31.5139238Z dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0; getitem_16 = None 2025-03-04T20:52:31.5139464Z 2025-03-04T20:52:31.5139843Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:52:31.5140370Z getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:52:31.5140696Z dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0; getitem_17 = None 2025-03-04T20:52:31.5140919Z 2025-03-04T20:52:31.5141329Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:52:31.5141851Z dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:52:31.5142088Z 2025-03-04T20:52:31.5142497Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:52:31.5143437Z dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:52:31.5143686Z 2025-03-04T20:52:31.5144110Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:52:31.5144638Z getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:52:31.5144953Z mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18; dx = getitem_18 = None 2025-03-04T20:52:31.5145280Z getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:52:31.5145622Z pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19; mul_2 = getitem_19 = None 2025-03-04T20:52:31.5145871Z 2025-03-04T20:52:31.5146300Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:52:31.5146844Z getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:52:31.5147206Z mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20; dy = getitem_20 = None 2025-03-04T20:52:31.5147541Z getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:52:31.5147899Z pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21; mul_3 = getitem_21 = None 2025-03-04T20:52:31.5148158Z 2025-03-04T20:52:31.5148620Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:52:31.5149127Z exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:52:31.5149470Z getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:52:31.5149907Z pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22; exp = getitem_22 = None 2025-03-04T20:52:31.5150157Z 2025-03-04T20:52:31.5150583Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:52:31.5151091Z exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:52:31.5151434Z getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:52:31.5151785Z pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23; exp_1 = getitem_23 = None 2025-03-04T20:52:31.5152036Z 2025-03-04T20:52:31.5152443Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:52:31.5152912Z mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T20:52:31.5153170Z x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:52:31.5153404Z 2025-03-04T20:52:31.5153796Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:52:31.5154259Z mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T20:52:31.5154514Z y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:52:31.5154739Z 2025-03-04T20:52:31.5164475Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:52:31.5164990Z mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:52:31.5165379Z x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:52:31.5165631Z 2025-03-04T20:52:31.5166039Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:52:31.5166511Z mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:52:31.5166796Z y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:52:31.5167033Z 2025-03-04T20:52:31.5167470Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:52:31.5168046Z pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:52:31.5168335Z 2025-03-04T20:52:31.5168752Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:52:31.5169331Z predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320)); pred_boxes = None 2025-03-04T20:52:31.5169615Z 2025-03-04T20:52:31.5170053Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T20:52:31.5170741Z split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]); predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None 2025-03-04T20:52:31.5171153Z boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0] 2025-03-04T20:52:31.5171428Z getitem_25: "f32[s1, 320][320, 1]cpu" = split[1]; getitem_25 = None 2025-03-04T20:52:31.5171717Z getitem_26: "f32[s2, 320][320, 1]cpu" = split[2]; getitem_26 = None 2025-03-04T20:52:31.5172014Z getitem_27: "f32[s3, 320][320, 1]cpu" = split[3]; split = getitem_27 = None 2025-03-04T20:52:31.5172278Z 2025-03-04T20:52:31.5172648Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:52:31.5173193Z size_4 = l_proposals_0_fields_proposal_boxes_tensor.size(); l_proposals_0_fields_proposal_boxes_tensor = None 2025-03-04T20:52:31.5173528Z getitem_28: "Sym(s0)" = size_4[0] 2025-03-04T20:52:31.5173762Z getitem_29 = size_4[1]; size_4 = getitem_29 = None 2025-03-04T20:52:31.5174117Z size_5 = l_proposals_1_fields_proposal_boxes_tensor.size(); l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T20:52:31.5174450Z getitem_30: "Sym(s1)" = size_5[0] 2025-03-04T20:52:31.5174669Z getitem_31 = size_5[1]; size_5 = getitem_31 = None 2025-03-04T20:52:31.5175021Z size_6 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T20:52:31.5175349Z getitem_32: "Sym(s2)" = size_6[0] 2025-03-04T20:52:31.5175575Z getitem_33 = size_6[1]; size_6 = getitem_33 = None 2025-03-04T20:52:31.5175925Z size_7 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T20:52:31.5176251Z getitem_34: "Sym(s3)" = size_7[0] 2025-03-04T20:52:31.5176471Z getitem_35 = size_7[1]; size_7 = getitem_35 = None 2025-03-04T20:52:31.5176679Z 2025-03-04T20:52:31.5177088Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T20:52:31.5177630Z probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1); scores = None 2025-03-04T20:52:31.5177906Z 2025-03-04T20:52:31.5178388Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T20:52:31.5179051Z split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0); probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None 2025-03-04T20:52:31.5179455Z scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0] 2025-03-04T20:52:31.5179733Z getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1]; getitem_37 = None 2025-03-04T20:52:31.5181301Z getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2]; getitem_38 = None 2025-03-04T20:52:31.5181631Z getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_39 = None 2025-03-04T20:52:31.5181879Z 2025-03-04T20:52:31.5182486Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T20:52:31.5183189Z isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T20:52:31.5183557Z all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:52:31.5183890Z isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T20:52:31.5184225Z all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T20:52:31.5185132Z valid_mask: "b8[s0][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T20:52:31.5185380Z 2025-03-04T20:52:31.5185861Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T20:52:31.5186398Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T20:52:31.5186638Z 2025-03-04T20:52:31.5186758Z 2025-03-04T20:52:31.5186846Z class GraphModule(torch.nn.Module): 2025-03-04T20:52:31.5189873Z def forward(self, L_stack0_: "f32[4000, 256, 7, 7][12544, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 12544][12544, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"): 2025-03-04T20:52:31.5191978Z l_stack0_ = L_stack0_ 2025-03-04T20:52:31.5192334Z l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_ 2025-03-04T20:52:31.5192819Z l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_ 2025-03-04T20:52:31.5193300Z l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_ 2025-03-04T20:52:31.5193776Z l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_ 2025-03-04T20:52:31.5194301Z l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_ 2025-03-04T20:52:31.5194949Z l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_ 2025-03-04T20:52:31.5195523Z l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ 2025-03-04T20:52:31.5196089Z l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ 2025-03-04T20:52:31.5196574Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T20:52:31.5196970Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T20:52:31.5197356Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T20:52:31.5197739Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T20:52:31.5198026Z 2025-03-04T20:52:31.5198393Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x) 2025-03-04T20:52:31.5198872Z x: "f32[4000, 12544][12544, 1]cpu" = l_stack0_.flatten(1, -1); l_stack0_ = None 2025-03-04T20:52:31.5199561Z x_1: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_); x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None 2025-03-04T20:52:31.5200279Z x_2: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False); x_1 = None 2025-03-04T20:52:31.5200986Z x_3: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_); x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None 2025-03-04T20:52:31.5201680Z x_4: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False); x_3 = None 2025-03-04T20:52:31.5202127Z 2025-03-04T20:52:31.5202530Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x) 2025-03-04T20:52:31.5203487Z scores: "f32[4000, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_); l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None 2025-03-04T20:52:31.5204190Z 2025-03-04T20:52:31.5204595Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x) 2025-03-04T20:52:31.5205585Z proposal_deltas: "f32[4000, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_); x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None 2025-03-04T20:52:31.5206315Z 2025-03-04T20:52:31.5206685Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:52:31.5207140Z size = l_proposals_0_fields_proposal_boxes_tensor.size() 2025-03-04T20:52:31.5207388Z getitem: "Sym(s0)" = size[0] 2025-03-04T20:52:31.5207616Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T20:52:31.5207884Z size_1 = l_proposals_1_fields_proposal_boxes_tensor.size() 2025-03-04T20:52:31.5208129Z getitem_2: "Sym(s1)" = size_1[0] 2025-03-04T20:52:31.5208420Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T20:52:31.5208681Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T20:52:31.5208920Z getitem_4: "Sym(s2)" = size_2[0] 2025-03-04T20:52:31.5209140Z getitem_5 = size_2[1]; size_2 = getitem_5 = None 2025-03-04T20:52:31.5209387Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T20:52:31.5209627Z getitem_6: "Sym(s3)" = size_3[0] 2025-03-04T20:52:31.5209853Z getitem_7 = size_3[1]; size_3 = getitem_7 = None 2025-03-04T20:52:31.5210062Z 2025-03-04T20:52:31.5210425Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:52:31.5211181Z proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0) 2025-03-04T20:52:31.5211717Z 2025-03-04T20:52:31.5212188Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:52:31.5212765Z deltas: "f32[4000, 320][320, 1]cpu" = proposal_deltas.float(); proposal_deltas = None 2025-03-04T20:52:31.5213031Z 2025-03-04T20:52:31.5213409Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:52:31.5213956Z boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T20:52:31.5214226Z 2025-03-04T20:52:31.5214616Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:52:31.5215108Z getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:52:31.5215457Z getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:52:31.5215802Z widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9; getitem_8 = getitem_9 = None 2025-03-04T20:52:31.5216076Z 2025-03-04T20:52:31.5216509Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:52:31.5217046Z getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:52:31.5217376Z getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:52:31.5217710Z heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T20:52:31.5217981Z 2025-03-04T20:52:31.5218377Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:52:31.5218897Z getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:52:31.5219186Z mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths 2025-03-04T20:52:31.5219468Z ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul; getitem_12 = mul = None 2025-03-04T20:52:31.5219726Z 2025-03-04T20:52:31.5220145Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:52:31.5220688Z getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:52:31.5221036Z mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights 2025-03-04T20:52:31.5221347Z ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1; getitem_13 = mul_1 = None 2025-03-04T20:52:31.5221608Z 2025-03-04T20:52:31.5222046Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:52:31.5222592Z getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:52:31.5222947Z dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0; getitem_14 = None 2025-03-04T20:52:31.5223200Z 2025-03-04T20:52:31.5223656Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:52:31.5224248Z getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:52:31.5224595Z dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0; getitem_15 = None 2025-03-04T20:52:31.5224840Z 2025-03-04T20:52:31.5225256Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:52:31.5225841Z getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:52:31.5226191Z dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0; getitem_16 = None 2025-03-04T20:52:31.5226443Z 2025-03-04T20:52:31.5226876Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:52:31.5227501Z getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:52:31.5227863Z dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0; getitem_17 = None 2025-03-04T20:52:31.5228101Z 2025-03-04T20:52:31.5228549Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:52:31.5229134Z dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:52:31.5229398Z 2025-03-04T20:52:31.5229902Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:52:31.5230451Z dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:52:31.5230702Z 2025-03-04T20:52:31.5231137Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:52:31.5231678Z getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:52:31.5232000Z mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18; dx = getitem_18 = None 2025-03-04T20:52:31.5232333Z getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:52:31.5232685Z pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19; mul_2 = getitem_19 = None 2025-03-04T20:52:31.5232943Z 2025-03-04T20:52:31.5233378Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:52:31.5233920Z getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:52:31.5234234Z mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20; dy = getitem_20 = None 2025-03-04T20:52:31.5234560Z getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:52:31.5234917Z pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21; mul_3 = getitem_21 = None 2025-03-04T20:52:31.5235168Z 2025-03-04T20:52:31.5235592Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:52:31.5236097Z exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:52:31.5236436Z getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:52:31.5236778Z pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22; exp = getitem_22 = None 2025-03-04T20:52:31.5237025Z 2025-03-04T20:52:31.5237448Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:52:31.5237954Z exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:52:31.5238293Z getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:52:31.5238661Z pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23; exp_1 = getitem_23 = None 2025-03-04T20:52:31.5238908Z 2025-03-04T20:52:31.5239301Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:52:31.5239754Z mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T20:52:31.5240028Z x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:52:31.5240260Z 2025-03-04T20:52:31.5240646Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:52:31.5241096Z mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T20:52:31.5241341Z y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:52:31.5241578Z 2025-03-04T20:52:31.5241971Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:52:31.5242428Z mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:52:31.5242708Z x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:52:31.5242943Z 2025-03-04T20:52:31.5243324Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:52:31.5243780Z mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:52:31.5244056Z y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:52:31.5244288Z 2025-03-04T20:52:31.5244705Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:52:31.5245268Z pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:52:31.5245545Z 2025-03-04T20:52:31.5245949Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:52:31.5246490Z predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320)); pred_boxes = None 2025-03-04T20:52:31.5246768Z 2025-03-04T20:52:31.5247196Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T20:52:31.5247867Z split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]); predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None 2025-03-04T20:52:31.5248276Z boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0] 2025-03-04T20:52:31.5248548Z getitem_25: "f32[s1, 320][320, 1]cpu" = split[1]; getitem_25 = None 2025-03-04T20:52:31.5248830Z getitem_26: "f32[s2, 320][320, 1]cpu" = split[2]; getitem_26 = None 2025-03-04T20:52:31.5249120Z getitem_27: "f32[s3, 320][320, 1]cpu" = split[3]; split = getitem_27 = None 2025-03-04T20:52:31.5249358Z 2025-03-04T20:52:31.5249722Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:52:31.5250259Z size_4 = l_proposals_0_fields_proposal_boxes_tensor.size(); l_proposals_0_fields_proposal_boxes_tensor = None 2025-03-04T20:52:31.5250593Z getitem_28: "Sym(s0)" = size_4[0] 2025-03-04T20:52:31.5250824Z getitem_29 = size_4[1]; size_4 = getitem_29 = None 2025-03-04T20:52:31.5251167Z size_5 = l_proposals_1_fields_proposal_boxes_tensor.size(); l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T20:52:31.5251517Z getitem_30: "Sym(s1)" = size_5[0] 2025-03-04T20:52:31.5251742Z getitem_31 = size_5[1]; size_5 = getitem_31 = None 2025-03-04T20:52:31.5252083Z size_6 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T20:52:31.5252406Z getitem_32: "Sym(s2)" = size_6[0] 2025-03-04T20:52:31.5252645Z getitem_33 = size_6[1]; size_6 = getitem_33 = None 2025-03-04T20:52:31.5252993Z size_7 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T20:52:31.5253313Z getitem_34: "Sym(s3)" = size_7[0] 2025-03-04T20:52:31.5253533Z getitem_35 = size_7[1]; size_7 = getitem_35 = None 2025-03-04T20:52:31.5253742Z 2025-03-04T20:52:31.5254144Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T20:52:31.5254707Z probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1); scores = None 2025-03-04T20:52:31.5254983Z 2025-03-04T20:52:31.5255406Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T20:52:31.5256054Z split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0); probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None 2025-03-04T20:52:31.5256462Z scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0] 2025-03-04T20:52:31.5256741Z getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1]; getitem_37 = None 2025-03-04T20:52:31.5257037Z getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2]; getitem_38 = None 2025-03-04T20:52:31.5257323Z getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_39 = None 2025-03-04T20:52:31.5257561Z 2025-03-04T20:52:31.5258097Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T20:52:31.5258762Z isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T20:52:31.5259091Z all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:52:31.5259408Z isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T20:52:31.5259729Z all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T20:52:31.5260035Z valid_mask: "b8[s0][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T20:52:31.5260257Z 2025-03-04T20:52:31.5260691Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T20:52:31.5261209Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T20:52:31.5261439Z 2025-03-04T20:52:31.5269143Z 2025-03-04T20:52:31.5274611Z class GraphModule(torch.nn.Module): 2025-03-04T20:52:31.5280791Z def forward(self, L_stack0_: "f32[4000, 256, 7, 7][12544, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 12544][12544, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"): 2025-03-04T20:52:31.5283097Z l_stack0_ = L_stack0_ 2025-03-04T20:52:31.5283595Z l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_ 2025-03-04T20:52:31.5284193Z l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_ 2025-03-04T20:52:31.5284688Z l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_ 2025-03-04T20:52:31.5285168Z l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_ 2025-03-04T20:52:31.5285749Z l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_ 2025-03-04T20:52:31.5286369Z l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_ 2025-03-04T20:52:31.5286946Z l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ 2025-03-04T20:52:31.5287523Z l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ 2025-03-04T20:52:31.5288016Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T20:52:31.5288431Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T20:52:31.5288837Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T20:52:31.5289247Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T20:52:31.5289551Z 2025-03-04T20:52:31.5289958Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x) 2025-03-04T20:52:31.5290451Z x: "f32[4000, 12544][12544, 1]cpu" = l_stack0_.flatten(1, -1); l_stack0_ = None 2025-03-04T20:52:31.5291175Z x_1: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_); x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None 2025-03-04T20:52:31.5291963Z x_2: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False); x_1 = None 2025-03-04T20:52:31.5292698Z x_3: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_); x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None 2025-03-04T20:52:31.5293392Z x_4: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False); x_3 = None 2025-03-04T20:52:31.5293665Z 2025-03-04T20:52:31.5294061Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x) 2025-03-04T20:52:31.5294998Z scores: "f32[4000, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_); l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None 2025-03-04T20:52:31.5295693Z 2025-03-04T20:52:31.5296116Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x) 2025-03-04T20:52:31.5297138Z proposal_deltas: "f32[4000, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_); x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None 2025-03-04T20:52:31.5297849Z 2025-03-04T20:52:31.5298213Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:52:31.5298667Z size = l_proposals_0_fields_proposal_boxes_tensor.size() 2025-03-04T20:52:31.5298913Z getitem: "Sym(s0)" = size[0] 2025-03-04T20:52:31.5299153Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T20:52:31.5299416Z size_1 = l_proposals_1_fields_proposal_boxes_tensor.size() 2025-03-04T20:52:31.5299655Z getitem_2: "Sym(s1)" = size_1[0] 2025-03-04T20:52:31.5299878Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T20:52:31.5300134Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T20:52:31.5300369Z getitem_4: "Sym(s2)" = size_2[0] 2025-03-04T20:52:31.5300587Z getitem_5 = size_2[1]; size_2 = getitem_5 = None 2025-03-04T20:52:31.5300835Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T20:52:31.5301063Z getitem_6: "Sym(s3)" = size_3[0] 2025-03-04T20:52:31.5301276Z getitem_7 = size_3[1]; size_3 = getitem_7 = None 2025-03-04T20:52:31.5301483Z 2025-03-04T20:52:31.5301842Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:52:31.5302812Z proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0) 2025-03-04T20:52:31.5303359Z 2025-03-04T20:52:31.5303818Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:52:31.5304394Z deltas: "f32[4000, 320][320, 1]cpu" = proposal_deltas.float(); proposal_deltas = None 2025-03-04T20:52:31.5304664Z 2025-03-04T20:52:31.5305057Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:52:31.5305658Z boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T20:52:31.5305940Z 2025-03-04T20:52:31.5306342Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:52:31.5306851Z getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:52:31.5307170Z getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:52:31.5307498Z widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9; getitem_8 = getitem_9 = None 2025-03-04T20:52:31.5307763Z 2025-03-04T20:52:31.5308171Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:52:31.5308680Z getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:52:31.5308992Z getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:52:31.5309368Z heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T20:52:31.5309726Z 2025-03-04T20:52:31.5310118Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:52:31.5310646Z getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:52:31.5310920Z mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths 2025-03-04T20:52:31.5311192Z ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul; getitem_12 = mul = None 2025-03-04T20:52:31.5311436Z 2025-03-04T20:52:31.5311832Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:52:31.5312370Z getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:52:31.5312674Z mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights 2025-03-04T20:52:31.5312955Z ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1; getitem_13 = mul_1 = None 2025-03-04T20:52:31.5313202Z 2025-03-04T20:52:31.5313606Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:52:31.5314117Z getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:52:31.5314437Z dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0; getitem_14 = None 2025-03-04T20:52:31.5314669Z 2025-03-04T20:52:31.5315053Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:52:31.5315553Z getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:52:31.5315864Z dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0; getitem_15 = None 2025-03-04T20:52:31.5316089Z 2025-03-04T20:52:31.5316468Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:52:31.5316965Z getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:52:31.5317278Z dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0; getitem_16 = None 2025-03-04T20:52:31.5317501Z 2025-03-04T20:52:31.5317883Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:52:31.5318436Z getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:52:31.5318779Z dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0; getitem_17 = None 2025-03-04T20:52:31.5319002Z 2025-03-04T20:52:31.5319424Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:52:31.5319951Z dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:52:31.5320202Z 2025-03-04T20:52:31.5320613Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:52:31.5321137Z dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:52:31.5321398Z 2025-03-04T20:52:31.5321839Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:52:31.5322370Z getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:52:31.5322698Z mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18; dx = getitem_18 = None 2025-03-04T20:52:31.5323052Z getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:52:31.5323389Z pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19; mul_2 = getitem_19 = None 2025-03-04T20:52:31.5323635Z 2025-03-04T20:52:31.5324055Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:52:31.5324585Z getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:52:31.5324913Z mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20; dy = getitem_20 = None 2025-03-04T20:52:31.5325232Z getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:52:31.5325564Z pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21; mul_3 = getitem_21 = None 2025-03-04T20:52:31.5325813Z 2025-03-04T20:52:31.5326224Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:52:31.5326717Z exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:52:31.5327038Z getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:52:31.5327375Z pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22; exp = getitem_22 = None 2025-03-04T20:52:31.5327619Z 2025-03-04T20:52:31.5328032Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:52:31.5328525Z exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:52:31.5328848Z getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:52:31.5329188Z pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23; exp_1 = getitem_23 = None 2025-03-04T20:52:31.5329428Z 2025-03-04T20:52:31.5329816Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:52:31.5330284Z mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T20:52:31.5330530Z x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:52:31.5330756Z 2025-03-04T20:52:31.5331139Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:52:31.5331587Z mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T20:52:31.5331837Z y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:52:31.5332061Z 2025-03-04T20:52:31.5332444Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:52:31.5332908Z mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:52:31.5333193Z x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:52:31.5333430Z 2025-03-04T20:52:31.5333810Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:52:31.5334289Z mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:52:31.5334566Z y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:52:31.5334802Z 2025-03-04T20:52:31.5335234Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:52:31.5335819Z pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:52:31.5336103Z 2025-03-04T20:52:31.5336511Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:52:31.5337057Z predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320)); pred_boxes = None 2025-03-04T20:52:31.5337350Z 2025-03-04T20:52:31.5337802Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T20:52:31.5338467Z split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]); predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None 2025-03-04T20:52:31.5338886Z boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0] 2025-03-04T20:52:31.5339169Z getitem_25: "f32[s1, 320][320, 1]cpu" = split[1]; getitem_25 = None 2025-03-04T20:52:31.5339461Z getitem_26: "f32[s2, 320][320, 1]cpu" = split[2]; getitem_26 = None 2025-03-04T20:52:31.5339761Z getitem_27: "f32[s3, 320][320, 1]cpu" = split[3]; split = getitem_27 = None 2025-03-04T20:52:31.5340011Z 2025-03-04T20:52:31.5340385Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:52:31.5340934Z size_4 = l_proposals_0_fields_proposal_boxes_tensor.size(); l_proposals_0_fields_proposal_boxes_tensor = None 2025-03-04T20:52:31.5341281Z getitem_28: "Sym(s0)" = size_4[0] 2025-03-04T20:52:31.5341520Z getitem_29 = size_4[1]; size_4 = getitem_29 = None 2025-03-04T20:52:31.5341882Z size_5 = l_proposals_1_fields_proposal_boxes_tensor.size(); l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T20:52:31.5342223Z getitem_30: "Sym(s1)" = size_5[0] 2025-03-04T20:52:31.5342454Z getitem_31 = size_5[1]; size_5 = getitem_31 = None 2025-03-04T20:52:31.5342809Z size_6 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T20:52:31.5343161Z getitem_32: "Sym(s2)" = size_6[0] 2025-03-04T20:52:31.5343382Z getitem_33 = size_6[1]; size_6 = getitem_33 = None 2025-03-04T20:52:31.5343749Z size_7 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T20:52:31.5344084Z getitem_34: "Sym(s3)" = size_7[0] 2025-03-04T20:52:31.5344318Z getitem_35 = size_7[1]; size_7 = getitem_35 = None 2025-03-04T20:52:31.5344523Z 2025-03-04T20:52:31.5344975Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T20:52:31.5345534Z probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1); scores = None 2025-03-04T20:52:31.5345821Z 2025-03-04T20:52:31.5346261Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T20:52:31.5346927Z split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0); probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None 2025-03-04T20:52:31.5347342Z scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0] 2025-03-04T20:52:31.5347616Z getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1]; getitem_37 = None 2025-03-04T20:52:31.5347893Z getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2]; getitem_38 = None 2025-03-04T20:52:31.5348181Z getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_39 = None 2025-03-04T20:52:31.5348422Z 2025-03-04T20:52:31.5348998Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T20:52:31.5349763Z isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T20:52:31.5350106Z all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:52:31.5350459Z isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T20:52:31.5350796Z all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T20:52:31.5351071Z valid_mask: "b8[s0][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T20:52:31.5351291Z 2025-03-04T20:52:31.5351717Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T20:52:31.5352223Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T20:52:31.5352449Z 2025-03-04T20:52:33.2479553Z 2025-03-04T20:52:33.2480175Z class GraphModule(torch.nn.Module): 2025-03-04T20:52:33.2481126Z def forward(self, L_predictions_0_: "f32[4000, 81][81, 1]cpu", L_predictions_1_: "f32[4000, 320][320, 1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"): 2025-03-04T20:52:33.2481959Z l_predictions_0_ = L_predictions_0_ 2025-03-04T20:52:33.2482178Z l_predictions_1_ = L_predictions_1_ 2025-03-04T20:52:33.2482503Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T20:52:33.2482916Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T20:52:33.2483314Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T20:52:33.2483712Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T20:52:33.2485694Z 2025-03-04T20:52:33.2486127Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:52:33.2486623Z size = l_proposals_0_fields_proposal_boxes_tensor.size() 2025-03-04T20:52:33.2486889Z getitem: "Sym(s0)" = size[0] 2025-03-04T20:52:33.2487112Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T20:52:33.2487379Z size_1 = l_proposals_1_fields_proposal_boxes_tensor.size() 2025-03-04T20:52:33.2487622Z getitem_2: "Sym(s1)" = size_1[0] 2025-03-04T20:52:33.2487848Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T20:52:33.2488113Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T20:52:33.2488354Z getitem_4: "Sym(s2)" = size_2[0] 2025-03-04T20:52:33.2488578Z getitem_5 = size_2[1]; size_2 = getitem_5 = None 2025-03-04T20:52:33.2488839Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T20:52:33.2489077Z getitem_6: "Sym(s3)" = size_3[0] 2025-03-04T20:52:33.2489299Z getitem_7 = size_3[1]; size_3 = getitem_7 = None 2025-03-04T20:52:33.2489506Z 2025-03-04T20:52:33.2490940Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:52:33.2491760Z proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0) 2025-03-04T20:52:33.2492306Z 2025-03-04T20:52:33.2492769Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:52:33.2493346Z deltas: "f32[4000, 320][320, 1]cpu" = l_predictions_1_.float(); l_predictions_1_ = None 2025-03-04T20:52:33.2493615Z 2025-03-04T20:52:33.2494067Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:52:33.2494658Z boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T20:52:33.2494943Z 2025-03-04T20:52:33.2495352Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:52:33.2495863Z getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:52:33.2496172Z getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:52:33.2496496Z widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9; getitem_8 = getitem_9 = None 2025-03-04T20:52:33.2496751Z 2025-03-04T20:52:33.2497150Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:52:33.2497644Z getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:52:33.2497948Z getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:52:33.2498267Z heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T20:52:33.2498533Z 2025-03-04T20:52:33.2498920Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:52:33.2499398Z getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:52:33.2499696Z mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths 2025-03-04T20:52:33.2499963Z ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul; getitem_12 = mul = None 2025-03-04T20:52:33.2500203Z 2025-03-04T20:52:33.2500598Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:52:33.2501103Z getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:52:33.2501400Z mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights 2025-03-04T20:52:33.2501678Z ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1; getitem_13 = mul_1 = None 2025-03-04T20:52:33.2502517Z 2025-03-04T20:52:33.2503009Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:52:33.2503532Z getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:52:33.2503858Z dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0; getitem_14 = None 2025-03-04T20:52:33.2504094Z 2025-03-04T20:52:33.2504524Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:52:33.2505022Z getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:52:33.2505335Z dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0; getitem_15 = None 2025-03-04T20:52:33.2505621Z 2025-03-04T20:52:33.2506012Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:52:33.2506533Z getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:52:33.2506858Z dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0; getitem_16 = None 2025-03-04T20:52:33.2507084Z 2025-03-04T20:52:33.2507493Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:52:33.2508027Z getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:52:33.2508368Z dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0; getitem_17 = None 2025-03-04T20:52:33.2508596Z 2025-03-04T20:52:33.2509020Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:52:33.2509713Z dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:52:33.2510336Z 2025-03-04T20:52:33.2510861Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:52:33.2511411Z dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:52:33.2511668Z 2025-03-04T20:52:33.2512623Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:52:33.2513489Z getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:52:33.2513817Z mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18; dx = getitem_18 = None 2025-03-04T20:52:33.2514147Z getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:52:33.2514502Z pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19; mul_2 = getitem_19 = None 2025-03-04T20:52:33.2514810Z 2025-03-04T20:52:33.2515266Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:52:33.2515826Z getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:52:33.2516150Z mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20; dy = getitem_20 = None 2025-03-04T20:52:33.2517290Z getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:52:33.2517655Z pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21; mul_3 = getitem_21 = None 2025-03-04T20:52:33.2517910Z 2025-03-04T20:52:33.2518346Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:52:33.2518863Z exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:52:33.2519189Z getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:52:33.2519618Z pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22; exp = getitem_22 = None 2025-03-04T20:52:33.2519866Z 2025-03-04T20:52:33.2520295Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:52:33.2520825Z exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:52:33.2521161Z getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:52:33.2521509Z pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23; exp_1 = getitem_23 = None 2025-03-04T20:52:33.2521757Z 2025-03-04T20:52:33.2522161Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:52:33.2522646Z mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T20:52:33.2522909Z x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:52:33.2523142Z 2025-03-04T20:52:33.2523537Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:52:33.2523999Z mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T20:52:33.2524254Z y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:52:33.2524483Z 2025-03-04T20:52:33.2524874Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:52:33.2525347Z mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:52:33.2525632Z x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:52:33.2525876Z 2025-03-04T20:52:33.2526273Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:52:33.2526731Z mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:52:33.2527006Z y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:52:33.2527241Z 2025-03-04T20:52:33.2527660Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:52:33.2528223Z pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:52:33.2528522Z 2025-03-04T20:52:33.2528931Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:52:33.2529473Z predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320)); pred_boxes = None 2025-03-04T20:52:33.2529748Z 2025-03-04T20:52:33.2530176Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T20:52:33.2530842Z split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]); predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None 2025-03-04T20:52:33.2531248Z boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0] 2025-03-04T20:52:33.2531515Z getitem_25: "f32[s1, 320][320, 1]cpu" = split[1]; getitem_25 = None 2025-03-04T20:52:33.2531799Z getitem_26: "f32[s2, 320][320, 1]cpu" = split[2]; getitem_26 = None 2025-03-04T20:52:33.2532087Z getitem_27: "f32[s3, 320][320, 1]cpu" = split[3]; split = getitem_27 = None 2025-03-04T20:52:33.2532343Z 2025-03-04T20:52:33.2532712Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:52:33.2533254Z size_4 = l_proposals_0_fields_proposal_boxes_tensor.size(); l_proposals_0_fields_proposal_boxes_tensor = None 2025-03-04T20:52:33.2533592Z getitem_28: "Sym(s0)" = size_4[0] 2025-03-04T20:52:33.2533859Z getitem_29 = size_4[1]; size_4 = getitem_29 = None 2025-03-04T20:52:33.2534213Z size_5 = l_proposals_1_fields_proposal_boxes_tensor.size(); l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T20:52:33.2534541Z getitem_30: "Sym(s1)" = size_5[0] 2025-03-04T20:52:33.2534765Z getitem_31 = size_5[1]; size_5 = getitem_31 = None 2025-03-04T20:52:33.2535111Z size_6 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T20:52:33.2535458Z getitem_32: "Sym(s2)" = size_6[0] 2025-03-04T20:52:33.2535680Z getitem_33 = size_6[1]; size_6 = getitem_33 = None 2025-03-04T20:52:33.2536026Z size_7 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T20:52:33.2536353Z getitem_34: "Sym(s3)" = size_7[0] 2025-03-04T20:52:33.2536575Z getitem_35 = size_7[1]; size_7 = getitem_35 = None 2025-03-04T20:52:33.2536785Z 2025-03-04T20:52:33.2537193Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T20:52:33.2537774Z probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1); l_predictions_0_ = None 2025-03-04T20:52:33.2538087Z 2025-03-04T20:52:33.2538519Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T20:52:33.2539162Z split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0); probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None 2025-03-04T20:52:33.2539557Z scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0] 2025-03-04T20:52:33.2539828Z getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1]; getitem_37 = None 2025-03-04T20:52:33.2540111Z getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2]; getitem_38 = None 2025-03-04T20:52:33.2540399Z getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_39 = None 2025-03-04T20:52:33.2540636Z 2025-03-04T20:52:33.2541168Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T20:52:33.2541864Z isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T20:52:33.2542186Z all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:52:33.2542501Z isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T20:52:33.2542821Z all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T20:52:33.2543097Z valid_mask: "b8[s0][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T20:52:33.2543323Z 2025-03-04T20:52:33.2543748Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T20:52:33.2544251Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T20:52:33.2544472Z 2025-03-04T20:52:33.2544554Z 2025-03-04T20:52:33.2544649Z class GraphModule(torch.nn.Module): 2025-03-04T20:52:33.2545444Z def forward(self, L_predictions_0_: "f32[4000, 81][81, 1]cpu", L_predictions_1_: "f32[4000, 320][320, 1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"): 2025-03-04T20:52:33.2546215Z l_predictions_0_ = L_predictions_0_ 2025-03-04T20:52:33.2546432Z l_predictions_1_ = L_predictions_1_ 2025-03-04T20:52:33.2546733Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T20:52:33.2547119Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T20:52:33.2547501Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T20:52:33.2547897Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T20:52:33.2548178Z 2025-03-04T20:52:33.2548541Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:52:33.2548993Z size = l_proposals_0_fields_proposal_boxes_tensor.size() 2025-03-04T20:52:33.2549328Z getitem: "Sym(s0)" = size[0] 2025-03-04T20:52:33.2549579Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T20:52:33.2549850Z size_1 = l_proposals_1_fields_proposal_boxes_tensor.size() 2025-03-04T20:52:33.2550095Z getitem_2: "Sym(s1)" = size_1[0] 2025-03-04T20:52:33.2550326Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T20:52:33.2550601Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T20:52:33.2550835Z getitem_4: "Sym(s2)" = size_2[0] 2025-03-04T20:52:33.2551059Z getitem_5 = size_2[1]; size_2 = getitem_5 = None 2025-03-04T20:52:33.2551319Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T20:52:33.2551555Z getitem_6: "Sym(s3)" = size_3[0] 2025-03-04T20:52:33.2551773Z getitem_7 = size_3[1]; size_3 = getitem_7 = None 2025-03-04T20:52:33.2551981Z 2025-03-04T20:52:33.2552341Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:52:33.2553093Z proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0) 2025-03-04T20:52:33.2553646Z 2025-03-04T20:52:33.2554090Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:52:33.2554655Z deltas: "f32[4000, 320][320, 1]cpu" = l_predictions_1_.float(); l_predictions_1_ = None 2025-03-04T20:52:33.2554915Z 2025-03-04T20:52:33.2555299Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:52:33.2555814Z boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T20:52:33.2556083Z 2025-03-04T20:52:33.2556470Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:52:33.2556961Z getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:52:33.2557266Z getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:52:33.2557599Z widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9; getitem_8 = getitem_9 = None 2025-03-04T20:52:33.2557856Z 2025-03-04T20:52:33.2558250Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:52:33.2558741Z getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:52:33.2559058Z getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:52:33.2559380Z heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T20:52:33.2559638Z 2025-03-04T20:52:33.2560019Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:52:33.2560497Z getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:52:33.2560785Z mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths 2025-03-04T20:52:33.2561048Z ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul; getitem_12 = mul = None 2025-03-04T20:52:33.2561276Z 2025-03-04T20:52:33.2561660Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:52:33.2562160Z getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:52:33.2562455Z mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights 2025-03-04T20:52:33.2562728Z ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1; getitem_13 = mul_1 = None 2025-03-04T20:52:33.2562969Z 2025-03-04T20:52:33.2563367Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:52:33.2563861Z getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:52:33.2564178Z dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0; getitem_14 = None 2025-03-04T20:52:33.2564402Z 2025-03-04T20:52:33.2564780Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:52:33.2565269Z getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:52:33.2565579Z dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0; getitem_15 = None 2025-03-04T20:52:33.2565802Z 2025-03-04T20:52:33.2566205Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:52:33.2566711Z getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:52:33.2567025Z dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0; getitem_16 = None 2025-03-04T20:52:33.2567250Z 2025-03-04T20:52:33.2567637Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:52:33.2568173Z getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:52:33.2568510Z dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0; getitem_17 = None 2025-03-04T20:52:33.2568736Z 2025-03-04T20:52:33.2569158Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:52:33.2569687Z dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:52:33.2569938Z 2025-03-04T20:52:33.2570373Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:52:33.2570888Z dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:52:33.2571132Z 2025-03-04T20:52:33.2571566Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:52:33.2572094Z getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:52:33.2572405Z mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18; dx = getitem_18 = None 2025-03-04T20:52:33.2572730Z getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:52:33.2573088Z pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19; mul_2 = getitem_19 = None 2025-03-04T20:52:33.2573341Z 2025-03-04T20:52:33.2573772Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:52:33.2574306Z getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:52:33.2574622Z mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20; dy = getitem_20 = None 2025-03-04T20:52:33.2574947Z getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:52:33.2575285Z pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21; mul_3 = getitem_21 = None 2025-03-04T20:52:33.2575537Z 2025-03-04T20:52:33.2575952Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:52:33.2576450Z exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:52:33.2576775Z getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:52:33.2577114Z pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22; exp = getitem_22 = None 2025-03-04T20:52:33.2577359Z 2025-03-04T20:52:33.2577774Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:52:33.2578268Z exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:52:33.2578609Z getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:52:33.2578945Z pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23; exp_1 = getitem_23 = None 2025-03-04T20:52:33.2579187Z 2025-03-04T20:52:33.2579579Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:52:33.2580032Z mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T20:52:33.2580282Z x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:52:33.2580502Z 2025-03-04T20:52:33.2580888Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:52:33.2581334Z mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T20:52:33.2581583Z y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:52:33.2581809Z 2025-03-04T20:52:33.2582189Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:52:33.2582685Z mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:52:33.2582967Z x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:52:33.2583205Z 2025-03-04T20:52:33.2583605Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:52:33.2584059Z mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:52:33.2584334Z y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:52:33.2584565Z 2025-03-04T20:52:33.2584988Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:52:33.2585590Z pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:52:33.2585890Z 2025-03-04T20:52:33.2586325Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:52:33.2586869Z predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320)); pred_boxes = None 2025-03-04T20:52:33.2587146Z 2025-03-04T20:52:33.2587580Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T20:52:33.2588250Z split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]); predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None 2025-03-04T20:52:33.2588820Z boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0] 2025-03-04T20:52:33.2590406Z getitem_25: "f32[s1, 320][320, 1]cpu" = split[1]; getitem_25 = None 2025-03-04T20:52:33.2590794Z getitem_26: "f32[s2, 320][320, 1]cpu" = split[2]; getitem_26 = None 2025-03-04T20:52:33.2591101Z getitem_27: "f32[s3, 320][320, 1]cpu" = split[3]; split = getitem_27 = None 2025-03-04T20:52:33.2591357Z 2025-03-04T20:52:33.2591757Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:52:33.2592307Z size_4 = l_proposals_0_fields_proposal_boxes_tensor.size(); l_proposals_0_fields_proposal_boxes_tensor = None 2025-03-04T20:52:33.2592981Z getitem_28: "Sym(s0)" = size_4[0] 2025-03-04T20:52:33.2593309Z getitem_29 = size_4[1]; size_4 = getitem_29 = None 2025-03-04T20:52:33.2593743Z size_5 = l_proposals_1_fields_proposal_boxes_tensor.size(); l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T20:52:33.2594085Z getitem_30: "Sym(s1)" = size_5[0] 2025-03-04T20:52:33.2594317Z getitem_31 = size_5[1]; size_5 = getitem_31 = None 2025-03-04T20:52:33.2594676Z size_6 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T20:52:33.2595013Z getitem_32: "Sym(s2)" = size_6[0] 2025-03-04T20:52:33.2595244Z getitem_33 = size_6[1]; size_6 = getitem_33 = None 2025-03-04T20:52:33.2595636Z size_7 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T20:52:33.2596343Z getitem_34: "Sym(s3)" = size_7[0] 2025-03-04T20:52:33.2596581Z getitem_35 = size_7[1]; size_7 = getitem_35 = None 2025-03-04T20:52:33.2597047Z 2025-03-04T20:52:33.2598008Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T20:52:33.2598659Z probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1); l_predictions_0_ = None 2025-03-04T20:52:33.2598987Z 2025-03-04T20:52:33.2599441Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T20:52:33.2600138Z split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0); probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None 2025-03-04T20:52:33.2600552Z scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0] 2025-03-04T20:52:33.2600834Z getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1]; getitem_37 = None 2025-03-04T20:52:33.2601738Z getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2]; getitem_38 = None 2025-03-04T20:52:33.2602403Z getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_39 = None 2025-03-04T20:52:33.2602717Z 2025-03-04T20:52:33.2603272Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T20:52:33.2603971Z isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T20:52:33.2604312Z all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:52:33.2604647Z isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T20:52:33.2604983Z all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T20:52:33.2605267Z valid_mask: "b8[s0][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T20:52:33.2605500Z 2025-03-04T20:52:33.2605936Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T20:52:33.2606455Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T20:52:33.2606679Z 2025-03-04T20:52:33.2606802Z 2025-03-04T20:52:33.2606896Z class GraphModule(torch.nn.Module): 2025-03-04T20:52:33.2607680Z def forward(self, L_predictions_0_: "f32[4000, 81][81, 1]cpu", L_predictions_1_: "f32[4000, 320][320, 1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"): 2025-03-04T20:52:33.2608439Z l_predictions_0_ = L_predictions_0_ 2025-03-04T20:52:33.2608685Z l_predictions_1_ = L_predictions_1_ 2025-03-04T20:52:33.2608985Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T20:52:33.2609376Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T20:52:33.2609757Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T20:52:33.2610134Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T20:52:33.2610413Z 2025-03-04T20:52:33.2610784Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:52:33.2611226Z size = l_proposals_0_fields_proposal_boxes_tensor.size() 2025-03-04T20:52:33.2611458Z getitem: "Sym(s0)" = size[0] 2025-03-04T20:52:33.2611676Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T20:52:33.2611935Z size_1 = l_proposals_1_fields_proposal_boxes_tensor.size() 2025-03-04T20:52:33.2612170Z getitem_2: "Sym(s1)" = size_1[0] 2025-03-04T20:52:33.2612392Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T20:52:33.2612673Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T20:52:33.2612907Z getitem_4: "Sym(s2)" = size_2[0] 2025-03-04T20:52:33.2613125Z getitem_5 = size_2[1]; size_2 = getitem_5 = None 2025-03-04T20:52:33.2613381Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T20:52:33.2613615Z getitem_6: "Sym(s3)" = size_3[0] 2025-03-04T20:52:33.2613858Z getitem_7 = size_3[1]; size_3 = getitem_7 = None 2025-03-04T20:52:33.2614065Z 2025-03-04T20:52:33.2614426Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:52:33.2615177Z proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0) 2025-03-04T20:52:33.2615724Z 2025-03-04T20:52:33.2616172Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:52:33.2616730Z deltas: "f32[4000, 320][320, 1]cpu" = l_predictions_1_.float(); l_predictions_1_ = None 2025-03-04T20:52:33.2616997Z 2025-03-04T20:52:33.2617393Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:52:33.2617909Z boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T20:52:33.2618190Z 2025-03-04T20:52:33.2618587Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:52:33.2619082Z getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:52:33.2619393Z getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:52:33.2619717Z widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9; getitem_8 = getitem_9 = None 2025-03-04T20:52:33.2619978Z 2025-03-04T20:52:33.2620379Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:52:33.2620869Z getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:52:33.2621170Z getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:52:33.2621511Z heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T20:52:33.2621769Z 2025-03-04T20:52:33.2622152Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:52:33.2622630Z getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:52:33.2622897Z mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths 2025-03-04T20:52:33.2623159Z ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul; getitem_12 = mul = None 2025-03-04T20:52:33.2623394Z 2025-03-04T20:52:33.2623770Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:52:33.2624278Z getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:52:33.2624564Z mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights 2025-03-04T20:52:33.2624839Z ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1; getitem_13 = mul_1 = None 2025-03-04T20:52:33.2625092Z 2025-03-04T20:52:33.2625486Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:52:33.2625983Z getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:52:33.2626317Z dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0; getitem_14 = None 2025-03-04T20:52:33.2626546Z 2025-03-04T20:52:33.2626924Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:52:33.2627420Z getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:52:33.2627729Z dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0; getitem_15 = None 2025-03-04T20:52:33.2627971Z 2025-03-04T20:52:33.2628347Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:52:33.2628837Z getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:52:33.2629144Z dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0; getitem_16 = None 2025-03-04T20:52:33.2629469Z 2025-03-04T20:52:33.2629862Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:52:33.2630399Z getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:52:33.2630744Z dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0; getitem_17 = None 2025-03-04T20:52:33.2630972Z 2025-03-04T20:52:33.2631399Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:52:33.2631929Z dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:52:33.2632181Z 2025-03-04T20:52:33.2632601Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:52:33.2633122Z dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:52:33.2633377Z 2025-03-04T20:52:33.2633813Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:52:33.2634398Z getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:52:33.2634722Z mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18; dx = getitem_18 = None 2025-03-04T20:52:33.2635059Z getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:52:33.2635401Z pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19; mul_2 = getitem_19 = None 2025-03-04T20:52:33.2635656Z 2025-03-04T20:52:33.2636091Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:52:33.2636637Z getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:52:33.2636962Z mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20; dy = getitem_20 = None 2025-03-04T20:52:33.2637289Z getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:52:33.2637647Z pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21; mul_3 = getitem_21 = None 2025-03-04T20:52:33.2637900Z 2025-03-04T20:52:33.2638322Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:52:33.2638831Z exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:52:33.2639173Z getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:52:33.2639515Z pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22; exp = getitem_22 = None 2025-03-04T20:52:33.2639762Z 2025-03-04T20:52:33.2640185Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:52:33.2640686Z exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:52:33.2641037Z getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:52:33.2641384Z pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23; exp_1 = getitem_23 = None 2025-03-04T20:52:33.2641630Z 2025-03-04T20:52:33.2642028Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:52:33.2642496Z mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T20:52:33.2642751Z x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:52:33.2642982Z 2025-03-04T20:52:33.2643375Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:52:33.2643833Z mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T20:52:33.2644083Z y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:52:33.2644308Z 2025-03-04T20:52:33.2644691Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:52:33.2645169Z mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:52:33.2645458Z x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:52:33.2645696Z 2025-03-04T20:52:33.2646091Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:52:33.2646568Z mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:52:33.2646846Z y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:52:33.2647077Z 2025-03-04T20:52:33.2647499Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:52:33.2648060Z pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:52:33.2648336Z 2025-03-04T20:52:33.2648744Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:52:33.2649280Z predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320)); pred_boxes = None 2025-03-04T20:52:33.2649555Z 2025-03-04T20:52:33.2649988Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T20:52:33.2650664Z split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]); predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None 2025-03-04T20:52:33.2651076Z boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0] 2025-03-04T20:52:33.2651356Z getitem_25: "f32[s1, 320][320, 1]cpu" = split[1]; getitem_25 = None 2025-03-04T20:52:33.2651646Z getitem_26: "f32[s2, 320][320, 1]cpu" = split[2]; getitem_26 = None 2025-03-04T20:52:33.2651961Z getitem_27: "f32[s3, 320][320, 1]cpu" = split[3]; split = getitem_27 = None 2025-03-04T20:52:33.2652197Z 2025-03-04T20:52:33.2652564Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:52:33.2653111Z size_4 = l_proposals_0_fields_proposal_boxes_tensor.size(); l_proposals_0_fields_proposal_boxes_tensor = None 2025-03-04T20:52:33.2653446Z getitem_28: "Sym(s0)" = size_4[0] 2025-03-04T20:52:33.2653694Z getitem_29 = size_4[1]; size_4 = getitem_29 = None 2025-03-04T20:52:33.2654045Z size_5 = l_proposals_1_fields_proposal_boxes_tensor.size(); l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T20:52:33.2654374Z getitem_30: "Sym(s1)" = size_5[0] 2025-03-04T20:52:33.2654596Z getitem_31 = size_5[1]; size_5 = getitem_31 = None 2025-03-04T20:52:33.2654939Z size_6 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T20:52:33.2655263Z getitem_32: "Sym(s2)" = size_6[0] 2025-03-04T20:52:33.2655484Z getitem_33 = size_6[1]; size_6 = getitem_33 = None 2025-03-04T20:52:33.2655829Z size_7 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T20:52:33.2656570Z getitem_34: "Sym(s3)" = size_7[0] 2025-03-04T20:52:33.2656804Z getitem_35 = size_7[1]; size_7 = getitem_35 = None 2025-03-04T20:52:33.2657017Z 2025-03-04T20:52:33.2657642Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T20:52:33.2658235Z probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1); l_predictions_0_ = None 2025-03-04T20:52:33.2658549Z 2025-03-04T20:52:33.2658983Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T20:52:33.2659633Z split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0); probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None 2025-03-04T20:52:33.2660065Z scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0] 2025-03-04T20:52:33.2660341Z getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1]; getitem_37 = None 2025-03-04T20:52:33.2660624Z getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2]; getitem_38 = None 2025-03-04T20:52:33.2660918Z getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_39 = None 2025-03-04T20:52:33.2661156Z 2025-03-04T20:52:33.2661693Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T20:52:33.2662370Z isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T20:52:33.2662698Z all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:52:33.2663023Z isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T20:52:33.2663358Z all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T20:52:33.2663646Z valid_mask: "b8[s0][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T20:52:33.2663898Z 2025-03-04T20:52:33.2664355Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T20:52:33.2664963Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T20:52:33.2665205Z 2025-03-04T20:52:36.6201190Z 2025-03-04T20:52:36.6204327Z class GraphModule(torch.nn.Module): 2025-03-04T20:52:36.6204967Z def forward(self, s0: "Sym(s0)", L_scores_0_: "f32[s0, 81][81, 1]cpu", s1: "Sym(s0)", L_boxes_0_: "f32[s0, 320][320, 1]cpu"): 2025-03-04T20:52:36.6210227Z l_scores_0_ = L_scores_0_ 2025-03-04T20:52:36.6212200Z l_boxes_0_ = L_boxes_0_ 2025-03-04T20:52:36.6212544Z 2025-03-04T20:52:36.6217845Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T20:52:36.6219025Z isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(l_boxes_0_); l_boxes_0_ = None 2025-03-04T20:52:36.6219351Z all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:52:36.6219653Z isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(l_scores_0_); l_scores_0_ = None 2025-03-04T20:52:36.6219962Z all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T20:52:36.6220241Z valid_mask: "b8[s0][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T20:52:36.6220476Z 2025-03-04T20:52:36.6220918Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T20:52:36.6221429Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T20:52:36.6221660Z 2025-03-04T20:52:36.6221749Z 2025-03-04T20:52:36.6221843Z class GraphModule(torch.nn.Module): 2025-03-04T20:52:36.6222165Z def forward(self, s0: "Sym(s0)", L_scores_0_: "f32[s0, 81][81, 1]cpu", s1: "Sym(s0)", L_boxes_0_: "f32[s0, 320][320, 1]cpu"): 2025-03-04T20:52:36.6222474Z l_scores_0_ = L_scores_0_ 2025-03-04T20:52:36.6222670Z l_boxes_0_ = L_boxes_0_ 2025-03-04T20:52:36.6222841Z 2025-03-04T20:52:36.6223384Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T20:52:36.6224030Z isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(l_boxes_0_); l_boxes_0_ = None 2025-03-04T20:52:36.6224408Z all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:52:36.6224720Z isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(l_scores_0_); l_scores_0_ = None 2025-03-04T20:52:36.6225035Z all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T20:52:36.6225323Z valid_mask: "b8[s0][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T20:52:36.6225560Z 2025-03-04T20:52:36.6226006Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T20:52:36.6226531Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T20:52:36.6226764Z 2025-03-04T20:52:55.0025134Z Compilation time (from dynamo_timed): 81.38683263 2025-03-04T20:52:55.0025440Z pass 2025-03-04T20:52:55.0028952Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T20:52:55.0030362Z TIMING: entire_frame_compile:81.38683 gc:0.0464 _recursive_pre_grad_passes:0.03923 async_compile.wait:30.8441 backend_compile:60.04969 _recursive_joint_graph_passes:0.30486 _recursive_post_grad_passes:0.22963 code_gen:39.68606 inductor_compile:44.60543 total_wall_time:81.38683 2025-03-04T20:52:55.0031807Z STATS: call_* op count: 1160 | FakeTensorMode.__torch_dispatch__:42548 | FakeTensor.__torch_dispatch__:4400 | ProxyTorchDispatchMode.__torch_dispatch__:13986 | attempt fast:202 | slow no contiguity match:72 | fast is_contiguous:130 2025-03-04T20:52:55.0032594Z Dynamo produced 61 graphs covering 1160 ops with 46 graph breaks (6 unique) 2025-03-04T20:53:01.5577067Z 2025-03-04T20:53:13.1817572Z loading model: 0it [00:00, ?it/s] 2025-03-04T20:53:13.1819930Z loading model: 0it [00:11, ?it/s] 2025-03-04T20:53:13.1824880Z cpu eval detectron2_fasterrcnn_r_50_c4 2025-03-04T20:53:20.4042919Z WARNING:common:fp64 golden ref were not generated for detectron2_fasterrcnn_r_50_c4. Setting accuracy check to cosine 2025-03-04T20:53:20.4153513Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T20:53:39.3398685Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T20:53:58.1778922Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T20:54:06.7926862Z 2025-03-04T20:54:06.7927353Z class GraphModule(torch.nn.Module): 2025-03-04T20:54:06.7981705Z def forward(self, L_stack0_tensor: "f32[4, 3, 1156, 1199][4158132, 1386044, 1199, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_: "f32[512, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[512][1]cpu", 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L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", 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L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_: "f32[1024, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_: 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1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[1024, 1024, 3, 3][9216, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[1024][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"): 2025-03-04T20:54:06.8024166Z l_stack0_tensor = L_stack0_tensor 2025-03-04T20:54:06.8024711Z l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ 2025-03-04T20:54:06.8025470Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8026171Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8026848Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8027500Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8028144Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:54:06.8028820Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8029571Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8030358Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8031066Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8031738Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:54:06.8032434Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8033188Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8034008Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8034718Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8035415Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:54:06.8036103Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8036872Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8037614Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8038300Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8038966Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:54:06.8039677Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8040439Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8041187Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8041977Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8042744Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:54:06.8043496Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8044277Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8045015Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8045747Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8046452Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:54:06.8047210Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8048001Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8048795Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8049551Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8050276Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:54:06.8051014Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8051809Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8052611Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8053370Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8054050Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:54:06.8054751Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8055508Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8056242Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8056950Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8057626Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:54:06.8058325Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8060340Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8061074Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8061777Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8062441Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:54:06.8063133Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8063904Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8064632Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8065335Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8065995Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:54:06.8066684Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8067440Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8068163Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8068865Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8069568Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:54:06.8070344Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8071181Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8071984Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8072759Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8073494Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:54:06.8074363Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8075202Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8076007Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8076781Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8077530Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:54:06.8078373Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8079166Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8079910Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8080614Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8081269Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:54:06.8081941Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8082685Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8083387Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8084064Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8084708Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:54:06.8085383Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8086102Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8086802Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8087479Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8088135Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:54:06.8088810Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8089535Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8090239Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8090932Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8091594Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:54:06.8092289Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8093020Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8093738Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8094414Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8095054Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:54:06.8095745Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8096476Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8097202Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8097878Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8098523Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:54:06.8099196Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8099927Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8100656Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8101433Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8102364Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ 2025-03-04T20:54:06.8103072Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8103844Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8104658Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8105433Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8106232Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ 2025-03-04T20:54:06.8107004Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8107866Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8108664Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8109440Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8110212Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ 2025-03-04T20:54:06.8110957Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8111711Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8112432Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8113131Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8113841Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:54:06.8114527Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8115278Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8115986Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8116693Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8117339Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:54:06.8118012Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8118737Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8119440Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8120121Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8120779Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:54:06.8121454Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8122192Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8122903Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8123582Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8124260Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:54:06.8124965Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8125712Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8126439Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8127168Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8127821Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:54:06.8128499Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8129227Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8129927Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8130639Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8131297Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:54:06.8131971Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8132683Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8133383Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8134074Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8134711Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:54:06.8135396Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8136116Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8136822Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8137506Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8138144Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:54:06.8138818Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8139539Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8140238Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8140911Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8141548Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:54:06.8142216Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8142948Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8143662Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8144335Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8144973Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:54:06.8145638Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8146361Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8147076Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8147751Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8148401Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ 2025-03-04T20:54:06.8149067Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8149789Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8150495Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8151214Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8151894Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ 2025-03-04T20:54:06.8152612Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8153358Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8154144Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8154862Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8155544Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ 2025-03-04T20:54:06.8156296Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8157158Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8157927Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8158626Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8159293Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ 2025-03-04T20:54:06.8159980Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8160724Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8161472Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8162180Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8162894Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ 2025-03-04T20:54:06.8163616Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8164372Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8165102Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8165791Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8166458Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ 2025-03-04T20:54:06.8167138Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8167875Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8168591Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8169279Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8169930Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ 2025-03-04T20:54:06.8170610Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8171363Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8172077Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8172760Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8173415Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ 2025-03-04T20:54:06.8174095Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8174847Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8175566Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8176279Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8176943Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ 2025-03-04T20:54:06.8177607Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8178326Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8179075Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8179750Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8180449Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T20:54:06.8181164Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T20:54:06.8181852Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T20:54:06.8182596Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T20:54:06.8183391Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T20:54:06.8184147Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T20:54:06.8184900Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T20:54:06.8185362Z 2025-03-04T20:54:06.8185758Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8186556Z x: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1); l_stack0_tensor = l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = None 2025-03-04T20:54:06.8187162Z 2025-03-04T20:54:06.8187541Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8189366Z x_1: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8190955Z 2025-03-04T20:54:06.8191333Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x) 2025-03-04T20:54:06.8191812Z x_2: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.relu_(x_1); x_1 = None 2025-03-04T20:54:06.8192072Z 2025-03-04T20:54:06.8192548Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1) 2025-03-04T20:54:06.8193209Z x_3: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1); x_2 = None 2025-03-04T20:54:06.8193631Z 2025-03-04T20:54:06.8193978Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8194716Z x_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:54:06.8195252Z 2025-03-04T20:54:06.8195604Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8197581Z x_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_4 = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8199283Z 2025-03-04T20:54:06.8199665Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8200151Z out: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_5); x_5 = None 2025-03-04T20:54:06.8200407Z 2025-03-04T20:54:06.8200748Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8201480Z x_6: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out = l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:54:06.8202114Z 2025-03-04T20:54:06.8202482Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8204416Z x_7: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_6 = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8206013Z 2025-03-04T20:54:06.8206374Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8206867Z out_1: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_7); x_7 = None 2025-03-04T20:54:06.8207122Z 2025-03-04T20:54:06.8207453Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8208181Z x_8: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_1 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:54:06.8208722Z 2025-03-04T20:54:06.8209066Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8210900Z x_9: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_8 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8212543Z 2025-03-04T20:54:06.8212910Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8213662Z x_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); x_3 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:54:06.8214214Z 2025-03-04T20:54:06.8214559Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8216461Z x_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_10 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8218160Z 2025-03-04T20:54:06.8218549Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:54:06.8219032Z x_9 += x_11; out_2: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_9; x_9 = x_11 = None 2025-03-04T20:54:06.8219301Z 2025-03-04T20:54:06.8219659Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8220152Z out_3: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_2); out_2 = None 2025-03-04T20:54:06.8220433Z 2025-03-04T20:54:06.8220768Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8221507Z x_12: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:54:06.8222045Z 2025-03-04T20:54:06.8222397Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8224328Z x_13: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_12 = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8226013Z 2025-03-04T20:54:06.8226382Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8226883Z out_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_13); x_13 = None 2025-03-04T20:54:06.8227144Z 2025-03-04T20:54:06.8227471Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8228215Z x_14: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_4 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:54:06.8228772Z 2025-03-04T20:54:06.8229130Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8231024Z x_15: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_14 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8232676Z 2025-03-04T20:54:06.8233045Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8233576Z out_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_15); x_15 = None 2025-03-04T20:54:06.8233852Z 2025-03-04T20:54:06.8234187Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8234966Z x_16: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_5 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:54:06.8235523Z 2025-03-04T20:54:06.8235880Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8237755Z x_17: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_16 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8239385Z 2025-03-04T20:54:06.8239746Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:54:06.8240224Z x_17 += out_3; out_6: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_17; x_17 = out_3 = None 2025-03-04T20:54:06.8240505Z 2025-03-04T20:54:06.8240864Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8241347Z out_7: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_6); out_6 = None 2025-03-04T20:54:06.8241604Z 2025-03-04T20:54:06.8241935Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8242653Z x_18: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:54:06.8243179Z 2025-03-04T20:54:06.8243522Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8245359Z x_19: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_18 = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8246974Z 2025-03-04T20:54:06.8247333Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8247802Z out_8: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_19); x_19 = None 2025-03-04T20:54:06.8248066Z 2025-03-04T20:54:06.8248396Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8249133Z x_20: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_8 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:54:06.8249666Z 2025-03-04T20:54:06.8250009Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8251840Z x_21: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_20 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8253466Z 2025-03-04T20:54:06.8253834Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8254321Z out_9: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_21); x_21 = None 2025-03-04T20:54:06.8254576Z 2025-03-04T20:54:06.8254901Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8255630Z x_22: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_9 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:54:06.8256174Z 2025-03-04T20:54:06.8256514Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8258366Z x_23: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_22 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8260009Z 2025-03-04T20:54:06.8260369Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:54:06.8260847Z x_23 += out_7; out_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_23; x_23 = out_7 = None 2025-03-04T20:54:06.8261109Z 2025-03-04T20:54:06.8261491Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8261980Z out_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_10); out_10 = None 2025-03-04T20:54:06.8262241Z 2025-03-04T20:54:06.8262572Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8263290Z x_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:54:06.8263816Z 2025-03-04T20:54:06.8264159Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8265975Z x_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_24 = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8267677Z 2025-03-04T20:54:06.8268068Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8268584Z out_12: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_25); x_25 = None 2025-03-04T20:54:06.8268855Z 2025-03-04T20:54:06.8269209Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8269988Z x_26: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_12 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:54:06.8270561Z 2025-03-04T20:54:06.8270939Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8272964Z x_27: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_26 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8274768Z 2025-03-04T20:54:06.8275167Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8275746Z out_13: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_27); x_27 = None 2025-03-04T20:54:06.8276026Z 2025-03-04T20:54:06.8276390Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8277211Z x_28: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_13 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:54:06.8277805Z 2025-03-04T20:54:06.8278167Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8280020Z x_29: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_28 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8281674Z 2025-03-04T20:54:06.8282007Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8282760Z x_30: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); out_11 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:54:06.8283315Z 2025-03-04T20:54:06.8283656Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8285601Z x_31: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_30 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8287314Z 2025-03-04T20:54:06.8287703Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:54:06.8288182Z x_29 += x_31; out_14: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_29; x_29 = x_31 = None 2025-03-04T20:54:06.8288444Z 2025-03-04T20:54:06.8288805Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8289295Z out_15: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_14); out_14 = None 2025-03-04T20:54:06.8289565Z 2025-03-04T20:54:06.8289894Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8290624Z x_32: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:54:06.8291167Z 2025-03-04T20:54:06.8291502Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8293353Z x_33: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_32 = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8294972Z 2025-03-04T20:54:06.8295335Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8295832Z out_16: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_33); x_33 = None 2025-03-04T20:54:06.8296086Z 2025-03-04T20:54:06.8296414Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8297141Z x_34: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_16 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:54:06.8297672Z 2025-03-04T20:54:06.8298015Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8299874Z x_35: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_34 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8301494Z 2025-03-04T20:54:06.8301853Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8302462Z out_17: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_35); x_35 = None 2025-03-04T20:54:06.8302724Z 2025-03-04T20:54:06.8303057Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8303830Z x_36: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_17 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:54:06.8304370Z 2025-03-04T20:54:06.8304712Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8306532Z x_37: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_36 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8308169Z 2025-03-04T20:54:06.8308533Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:54:06.8309039Z x_37 += out_15; out_18: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_37; x_37 = out_15 = None 2025-03-04T20:54:06.8309362Z 2025-03-04T20:54:06.8309740Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8310272Z out_19: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_18); out_18 = None 2025-03-04T20:54:06.8310550Z 2025-03-04T20:54:06.8310894Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8311667Z x_38: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:54:06.8312229Z 2025-03-04T20:54:06.8312590Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8314564Z x_39: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_38 = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8316245Z 2025-03-04T20:54:06.8316628Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8317113Z out_20: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_39); x_39 = None 2025-03-04T20:54:06.8317390Z 2025-03-04T20:54:06.8317742Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8318497Z x_40: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_20 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:54:06.8319057Z 2025-03-04T20:54:06.8319422Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8321313Z x_41: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_40 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8323000Z 2025-03-04T20:54:06.8323378Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8323874Z out_21: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_41); x_41 = None 2025-03-04T20:54:06.8324141Z 2025-03-04T20:54:06.8324482Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8325230Z x_42: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_21 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:54:06.8325800Z 2025-03-04T20:54:06.8326147Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8327989Z x_43: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_42 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8329595Z 2025-03-04T20:54:06.8329970Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:54:06.8330443Z x_43 += out_19; out_22: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_43; x_43 = out_19 = None 2025-03-04T20:54:06.8330708Z 2025-03-04T20:54:06.8331063Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8331554Z out_23: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_22); out_22 = None 2025-03-04T20:54:06.8331813Z 2025-03-04T20:54:06.8332140Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8332851Z x_44: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T20:54:06.8333373Z 2025-03-04T20:54:06.8333717Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8335531Z x_45: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_44 = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8337151Z 2025-03-04T20:54:06.8337509Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8337975Z out_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_45); x_45 = None 2025-03-04T20:54:06.8338227Z 2025-03-04T20:54:06.8338548Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8339261Z x_46: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_24 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T20:54:06.8339796Z 2025-03-04T20:54:06.8340134Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8341969Z x_47: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_46 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8343572Z 2025-03-04T20:54:06.8343931Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8344421Z out_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_47); x_47 = None 2025-03-04T20:54:06.8344676Z 2025-03-04T20:54:06.8345004Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8345733Z x_48: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_25 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T20:54:06.8346275Z 2025-03-04T20:54:06.8346618Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8348445Z x_49: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_48 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8350073Z 2025-03-04T20:54:06.8350432Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:54:06.8350920Z x_49 += out_23; out_26: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_49; x_49 = out_23 = None 2025-03-04T20:54:06.8351185Z 2025-03-04T20:54:06.8351535Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8352010Z out_27: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_26); out_26 = None 2025-03-04T20:54:06.8352274Z 2025-03-04T20:54:06.8352608Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8353321Z x_50: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:54:06.8353905Z 2025-03-04T20:54:06.8354257Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8356195Z x_51: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_50 = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8357850Z 2025-03-04T20:54:06.8358266Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8358743Z out_28: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_51); x_51 = None 2025-03-04T20:54:06.8358999Z 2025-03-04T20:54:06.8359354Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8360107Z x_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_28 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:54:06.8360659Z 2025-03-04T20:54:06.8361010Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8362905Z x_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_52 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8364591Z 2025-03-04T20:54:06.8364958Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8365434Z out_29: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_53); x_53 = None 2025-03-04T20:54:06.8365686Z 2025-03-04T20:54:06.8366018Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8366799Z x_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_29 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:54:06.8367371Z 2025-03-04T20:54:06.8367739Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8369745Z x_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_54 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8371399Z 2025-03-04T20:54:06.8371725Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8372472Z x_56: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); out_27 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:54:06.8373031Z 2025-03-04T20:54:06.8373379Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8375285Z x_57: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_56 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8377028Z 2025-03-04T20:54:06.8377391Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:54:06.8377867Z x_55 += x_57; out_30: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_55; x_55 = x_57 = None 2025-03-04T20:54:06.8378122Z 2025-03-04T20:54:06.8378487Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8378983Z out_31: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_30); out_30 = None 2025-03-04T20:54:06.8379234Z 2025-03-04T20:54:06.8379560Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8380266Z x_58: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:54:06.8380787Z 2025-03-04T20:54:06.8381130Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8383005Z x_59: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_58 = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8384654Z 2025-03-04T20:54:06.8385017Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8385484Z out_32: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_59); x_59 = None 2025-03-04T20:54:06.8385753Z 2025-03-04T20:54:06.8386075Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8386788Z x_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_32 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:54:06.8387323Z 2025-03-04T20:54:06.8387675Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8389479Z x_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_60 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8391073Z 2025-03-04T20:54:06.8391430Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8391893Z out_33: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_61); x_61 = None 2025-03-04T20:54:06.8392153Z 2025-03-04T20:54:06.8392474Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8393195Z x_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_33 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:54:06.8393774Z 2025-03-04T20:54:06.8394143Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8396042Z x_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_62 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8397644Z 2025-03-04T20:54:06.8398049Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:54:06.8398520Z x_63 += out_31; out_34: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_63; x_63 = out_31 = None 2025-03-04T20:54:06.8398778Z 2025-03-04T20:54:06.8399137Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8399624Z out_35: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_34); out_34 = None 2025-03-04T20:54:06.8399872Z 2025-03-04T20:54:06.8400194Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8400906Z x_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:54:06.8401424Z 2025-03-04T20:54:06.8401765Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8403732Z x_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_64 = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8405334Z 2025-03-04T20:54:06.8405694Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8406201Z out_36: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_65); x_65 = None 2025-03-04T20:54:06.8406451Z 2025-03-04T20:54:06.8406784Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8407508Z x_66: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_36 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:54:06.8408035Z 2025-03-04T20:54:06.8408381Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8410249Z x_67: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_66 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8411857Z 2025-03-04T20:54:06.8412212Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8412679Z out_37: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_67); x_67 = None 2025-03-04T20:54:06.8412950Z 2025-03-04T20:54:06.8413282Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8414005Z x_68: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_37 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:54:06.8414541Z 2025-03-04T20:54:06.8414876Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8416687Z x_69: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_68 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8418289Z 2025-03-04T20:54:06.8418646Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:54:06.8419116Z x_69 += out_35; out_38: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_69; x_69 = out_35 = None 2025-03-04T20:54:06.8419390Z 2025-03-04T20:54:06.8419759Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8420240Z out_39: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_38); out_38 = None 2025-03-04T20:54:06.8420498Z 2025-03-04T20:54:06.8420829Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8421549Z x_70: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T20:54:06.8422072Z 2025-03-04T20:54:06.8422421Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8424260Z x_71: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_70 = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8425863Z 2025-03-04T20:54:06.8426225Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8426704Z out_40: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_71); x_71 = None 2025-03-04T20:54:06.8426951Z 2025-03-04T20:54:06.8427276Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8427988Z x_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_40 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T20:54:06.8428513Z 2025-03-04T20:54:06.8428850Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8430668Z x_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_72 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8432287Z 2025-03-04T20:54:06.8432644Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8433127Z out_41: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_73); x_73 = None 2025-03-04T20:54:06.8433371Z 2025-03-04T20:54:06.8433751Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8434494Z x_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_41 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T20:54:06.8435040Z 2025-03-04T20:54:06.8435381Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8437241Z x_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_74 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8438875Z 2025-03-04T20:54:06.8439234Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:54:06.8439703Z x_75 += out_39; out_42: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_75; x_75 = out_39 = None 2025-03-04T20:54:06.8439976Z 2025-03-04T20:54:06.8440337Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8440832Z out_43: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_42); out_42 = None 2025-03-04T20:54:06.8441090Z 2025-03-04T20:54:06.8441438Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8442186Z x_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = None 2025-03-04T20:54:06.8442733Z 2025-03-04T20:54:06.8443085Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8444975Z x_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_76 = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8446639Z 2025-03-04T20:54:06.8447014Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8447493Z out_44: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_77); x_77 = None 2025-03-04T20:54:06.8447745Z 2025-03-04T20:54:06.8448083Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8448819Z x_78: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_44 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = None 2025-03-04T20:54:06.8449360Z 2025-03-04T20:54:06.8449712Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8451617Z x_79: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_78 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8453275Z 2025-03-04T20:54:06.8453644Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8454131Z out_45: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_79); x_79 = None 2025-03-04T20:54:06.8454382Z 2025-03-04T20:54:06.8454714Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8455433Z x_80: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_45 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = None 2025-03-04T20:54:06.8455974Z 2025-03-04T20:54:06.8456326Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8458197Z x_81: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_80 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8459849Z 2025-03-04T20:54:06.8460212Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:54:06.8460709Z x_81 += out_43; out_46: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_81; x_81 = out_43 = None 2025-03-04T20:54:06.8460962Z 2025-03-04T20:54:06.8461327Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8461806Z out_47: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_46); out_46 = None 2025-03-04T20:54:06.8462062Z 2025-03-04T20:54:06.8462412Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8463136Z x_82: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = None 2025-03-04T20:54:06.8463671Z 2025-03-04T20:54:06.8464036Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8465942Z x_83: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_82 = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8467592Z 2025-03-04T20:54:06.8467959Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8468439Z out_48: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_83); x_83 = None 2025-03-04T20:54:06.8468694Z 2025-03-04T20:54:06.8469024Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8469763Z x_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_48 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = None 2025-03-04T20:54:06.8470314Z 2025-03-04T20:54:06.8470678Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8472541Z x_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_84 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8474390Z 2025-03-04T20:54:06.8474782Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8475281Z out_49: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_85); x_85 = None 2025-03-04T20:54:06.8475543Z 2025-03-04T20:54:06.8475886Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8476630Z x_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_49 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = None 2025-03-04T20:54:06.8477176Z 2025-03-04T20:54:06.8477529Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8479412Z x_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_86 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8481075Z 2025-03-04T20:54:06.8481447Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:54:06.8481957Z x_87 += out_47; out_50: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_87; x_87 = out_47 = None 2025-03-04T20:54:06.8482225Z 2025-03-04T20:54:06.8482604Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8483101Z out_51: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_50); out_50 = None 2025-03-04T20:54:06.8483364Z 2025-03-04T20:54:06.8483900Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:54:06.8484554Z arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32) 2025-03-04T20:54:06.8484827Z 2025-03-04T20:54:06.8485219Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:54:06.8485722Z shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T20:54:06.8485977Z 2025-03-04T20:54:06.8486519Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:54:06.8487176Z arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32) 2025-03-04T20:54:06.8487447Z 2025-03-04T20:54:06.8487825Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:54:06.8488327Z shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T20:54:06.8488581Z 2025-03-04T20:54:06.8489044Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:54:06.8489649Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T20:54:06.8489980Z shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0] 2025-03-04T20:54:06.8490244Z shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T20:54:06.8490478Z 2025-03-04T20:54:06.8490897Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:54:06.8491410Z shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T20:54:06.8491655Z 2025-03-04T20:54:06.8492058Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:54:06.8492575Z shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T20:54:06.8492811Z 2025-03-04T20:54:06.8493278Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:54:06.8493946Z shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T20:54:06.8494270Z 2025-03-04T20:54:06.8494785Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:54:06.8495384Z view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T20:54:06.8496042Z view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T20:54:06.8496666Z add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T20:54:06.8496969Z x_88: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4); add = None 2025-03-04T20:54:06.8497212Z 2025-03-04T20:54:06.8497619Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:54:06.8498096Z tensor: "f32[82125, 4][4, 1]cpu" = x_88.to(torch.float32); x_88 = None 2025-03-04T20:54:06.8498337Z 2025-03-04T20:54:06.8498676Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8499756Z x_89: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); out_51 = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T20:54:06.8500639Z 2025-03-04T20:54:06.8500991Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:54:06.8501503Z x_90: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_89, inplace = False); x_89 = None 2025-03-04T20:54:06.8501816Z 2025-03-04T20:54:06.8502420Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:54:06.8503722Z score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_90, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T20:54:06.8504692Z 2025-03-04T20:54:06.8505149Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:54:06.8506394Z x_91: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_90, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_90 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T20:54:06.8507332Z 2025-03-04T20:54:06.8507784Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T20:54:06.8508335Z permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T20:54:06.8508684Z logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T20:54:06.8508948Z 2025-03-04T20:54:06.8509460Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T20:54:06.8510108Z view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_91.view(4, -1, 4, 73, 75); x_91 = None 2025-03-04T20:54:06.8510492Z permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2); view_2 = None 2025-03-04T20:54:06.8510892Z pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2); permute_1 = None 2025-03-04T20:54:06.8511184Z 2025-03-04T20:54:06.8511672Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:54:06.8512340Z pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T20:54:06.8512666Z 2025-03-04T20:54:06.8513190Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:54:06.8513892Z unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T20:54:06.8514262Z expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T20:54:06.8514623Z anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T20:54:06.8514869Z 2025-03-04T20:54:06.8515314Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:54:06.8515943Z deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T20:54:06.8516222Z 2025-03-04T20:54:06.8516630Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:54:06.8517166Z boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T20:54:06.8517420Z 2025-03-04T20:54:06.8517837Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:54:06.8518396Z getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:54:06.8518736Z getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:54:06.8519097Z widths: "f32[328500][1]cpu" = getitem_2 - getitem_3; getitem_2 = getitem_3 = None 2025-03-04T20:54:06.8519392Z 2025-03-04T20:54:06.8519872Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:54:06.8520357Z getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:54:06.8520647Z getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:54:06.8520983Z heights: "f32[328500][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T20:54:06.8521273Z 2025-03-04T20:54:06.8521752Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:54:06.8522312Z getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:54:06.8522603Z mul: "f32[328500][1]cpu" = 0.5 * widths 2025-03-04T20:54:06.8522895Z ctr_x: "f32[328500][1]cpu" = getitem_6 + mul; getitem_6 = mul = None 2025-03-04T20:54:06.8523162Z 2025-03-04T20:54:06.8523637Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:54:06.8524224Z getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:54:06.8524549Z mul_1: "f32[328500][1]cpu" = 0.5 * heights 2025-03-04T20:54:06.8524848Z ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1; getitem_7 = mul_1 = None 2025-03-04T20:54:06.8525121Z 2025-03-04T20:54:06.8525625Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:54:06.8526223Z getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:54:06.8526593Z dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0; getitem_8 = None 2025-03-04T20:54:06.8526846Z 2025-03-04T20:54:06.8527234Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:54:06.8527728Z getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:54:06.8528037Z dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0; getitem_9 = None 2025-03-04T20:54:06.8528261Z 2025-03-04T20:54:06.8528640Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:54:06.8529137Z getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:54:06.8529452Z dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0; getitem_10 = None 2025-03-04T20:54:06.8529693Z 2025-03-04T20:54:06.8530074Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:54:06.8530602Z getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:54:06.8530937Z dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0; getitem_11 = None 2025-03-04T20:54:06.8531159Z 2025-03-04T20:54:06.8531575Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:54:06.8532092Z dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:54:06.8532343Z 2025-03-04T20:54:06.8532750Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:54:06.8533259Z dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:54:06.8533504Z 2025-03-04T20:54:06.8533942Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:54:06.8534475Z getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:54:06.8534785Z mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12; dx = getitem_12 = None 2025-03-04T20:54:06.8535123Z getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:54:06.8535463Z pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13; mul_2 = getitem_13 = None 2025-03-04T20:54:06.8535714Z 2025-03-04T20:54:06.8536136Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:54:06.8536684Z getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:54:06.8536999Z mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14; dy = getitem_14 = None 2025-03-04T20:54:06.8537322Z getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:54:06.8537662Z pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15; mul_3 = getitem_15 = None 2025-03-04T20:54:06.8537913Z 2025-03-04T20:54:06.8538342Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:54:06.8538856Z exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:54:06.8539181Z getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:54:06.8539522Z pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16; exp = getitem_16 = None 2025-03-04T20:54:06.8539774Z 2025-03-04T20:54:06.8540197Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:54:06.8540698Z exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:54:06.8541034Z getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:54:06.8541388Z pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17; exp_1 = getitem_17 = None 2025-03-04T20:54:06.8541640Z 2025-03-04T20:54:06.8542040Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:54:06.8542527Z mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T20:54:06.8542784Z x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:54:06.8543011Z 2025-03-04T20:54:06.8543391Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:54:06.8543832Z mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T20:54:06.8544081Z y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:54:06.8544304Z 2025-03-04T20:54:06.8544684Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:54:06.8545147Z mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:54:06.8545430Z x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:54:06.8545673Z 2025-03-04T20:54:06.8546052Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:54:06.8546528Z mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:54:06.8546808Z y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:54:06.8547041Z 2025-03-04T20:54:06.8547479Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:54:06.8548046Z pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:54:06.8548333Z 2025-03-04T20:54:06.8548739Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:54:06.8549275Z proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4)); pred_boxes = None 2025-03-04T20:54:06.8549558Z 2025-03-04T20:54:06.8550026Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:54:06.8550631Z proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T20:54:06.8550938Z 2025-03-04T20:54:06.8551577Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T20:54:06.8552345Z arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T20:54:06.8552623Z 2025-03-04T20:54:06.8553050Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:54:06.8553683Z batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T20:54:06.8553977Z 2025-03-04T20:54:06.8554568Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:54:06.8555247Z topk = logits_i.topk(6000, dim = 1); logits_i = None 2025-03-04T20:54:06.8555547Z topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0] 2025-03-04T20:54:06.8555846Z topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1]; topk = None 2025-03-04T20:54:06.8556093Z 2025-03-04T20:54:06.8556729Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:54:06.8557491Z getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T20:54:06.8558003Z topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)]; proposals_i_1 = getitem_20 = topk_idx = None 2025-03-04T20:54:06.8558396Z 2025-03-04T20:54:06.8559010Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:54:06.8559742Z full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:54:06.8560028Z 2025-03-04T20:54:06.8560413Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:54:06.8560943Z level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu')); full = level_ids = None 2025-03-04T20:54:06.8561210Z 2025-03-04T20:54:06.8561686Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T20:54:06.8562277Z getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0]; topk_proposals_i = None 2025-03-04T20:54:06.8562557Z 2025-03-04T20:54:06.8562940Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:54:06.8563446Z tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32); getitem_22 = None 2025-03-04T20:54:06.8563709Z 2025-03-04T20:54:06.8564184Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T20:54:06.8564789Z scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0]; topk_scores_i = None 2025-03-04T20:54:06.8565041Z 2025-03-04T20:54:06.8565620Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T20:54:06.8566301Z isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1); tensor_1 = None 2025-03-04T20:54:06.8566608Z all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:54:06.8566933Z isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T20:54:06.8567271Z valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T20:54:06.8567518Z 2025-03-04T20:54:06.8567972Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T20:54:06.8568505Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T20:54:06.8568736Z 2025-03-04T20:54:06.8569127Z 2025-03-04T20:54:06.8569218Z class GraphModule(torch.nn.Module): 2025-03-04T20:54:06.8611939Z def forward(self, L_stack0_tensor: "f32[4, 3, 1156, 1199][4158132, 1386044, 1199, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_: "f32[512, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_: "f32[1024, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[1024, 1024, 3, 3][9216, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[1024][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"): 2025-03-04T20:54:06.8653256Z l_stack0_tensor = L_stack0_tensor 2025-03-04T20:54:06.8653670Z l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ 2025-03-04T20:54:06.8654320Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8655020Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8655725Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8656378Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8657028Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:54:06.8657724Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8658482Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8659208Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8659904Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8660588Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:54:06.8661333Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8662064Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8662800Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8663544Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8664244Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:54:06.8664975Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8665778Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8666555Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8667348Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8668124Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:54:06.8668944Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8669784Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8670586Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8671430Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8672240Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:54:06.8672979Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8673843Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8674697Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8675456Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8676138Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:54:06.8676826Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8677581Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8678328Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8679068Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8679750Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:54:06.8680481Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8681268Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8682030Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8682768Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8683465Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:54:06.8684204Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8685008Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8685759Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8686472Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8687150Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:54:06.8687852Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8688603Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8689310Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8689997Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8690649Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:54:06.8691339Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8692097Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8692816Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8693513Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8694173Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:54:06.8694873Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8695616Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8696332Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8697031Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8697695Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:54:06.8698416Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8699160Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8699880Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8700574Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8701237Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:54:06.8702040Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8702802Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8703556Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8704271Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8704943Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:54:06.8705677Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8706469Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8707242Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8707989Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8708685Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:54:06.8709400Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8710165Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8710908Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8711620Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8712317Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:54:06.8713026Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8713833Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8714589Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8715302Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8715973Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:54:06.8716689Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8717447Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8718206Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8718902Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8719588Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:54:06.8720275Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8721002Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8721724Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8722400Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8723059Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:54:06.8723736Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8724467Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8725171Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8725849Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8726513Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:54:06.8727187Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8727915Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8728618Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8729293Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8729948Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ 2025-03-04T20:54:06.8730627Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8731380Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8732084Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8732763Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8733424Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ 2025-03-04T20:54:06.8734089Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8734813Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8735515Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8736188Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8736834Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ 2025-03-04T20:54:06.8737507Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8738225Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8738922Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8739605Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8740247Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:54:06.8740919Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8741634Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8742332Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8743008Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8743675Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:54:06.8744350Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8745088Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8745788Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8746473Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8747155Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:54:06.8747828Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8748550Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8749269Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8749974Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8750651Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:54:06.8751369Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8752139Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8752892Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8753687Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8754403Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:54:06.8755124Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8755866Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8756591Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8757307Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8757970Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:54:06.8758676Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8759419Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8760145Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8760859Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8761514Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:54:06.8762210Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8762956Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8763676Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8764368Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8765034Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:54:06.8765700Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8766416Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8767130Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8767807Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8768444Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:54:06.8769113Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8769831Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8770547Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8771228Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8771884Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:54:06.8772579Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8773303Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8774007Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8774707Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8775351Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ 2025-03-04T20:54:06.8776021Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8776749Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8777479Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8778168Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8778827Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ 2025-03-04T20:54:06.8779494Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8780235Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8780553Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8780870Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8781153Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ 2025-03-04T20:54:06.8781500Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8781867Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8782213Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8782523Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8782814Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ 2025-03-04T20:54:06.8783155Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8783485Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8783825Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8784132Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8784421Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ 2025-03-04T20:54:06.8784755Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8785094Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8785417Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8785725Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8786017Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ 2025-03-04T20:54:06.8786357Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8786717Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8787039Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8787356Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8787644Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ 2025-03-04T20:54:06.8788009Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8788390Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8788713Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8789046Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8789331Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ 2025-03-04T20:54:06.8789681Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8790040Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8790388Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8790724Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8791026Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ 2025-03-04T20:54:06.8791409Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.8791756Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.8792089Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:54:06.8792408Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:54:06.8792779Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T20:54:06.8793124Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T20:54:06.8793453Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T20:54:06.8793897Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T20:54:06.8794279Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T20:54:06.8794663Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T20:54:06.8795037Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T20:54:06.8795111Z 2025-03-04T20:54:06.8795406Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8795915Z x: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1); l_stack0_tensor = l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = None 2025-03-04T20:54:06.8795982Z 2025-03-04T20:54:06.8796274Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8797835Z x_1: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8797918Z 2025-03-04T20:54:06.8798230Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x) 2025-03-04T20:54:06.8798374Z x_2: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.relu_(x_1); x_1 = None 2025-03-04T20:54:06.8798446Z 2025-03-04T20:54:06.8798817Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1) 2025-03-04T20:54:06.8799065Z x_3: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1); x_2 = None 2025-03-04T20:54:06.8799128Z 2025-03-04T20:54:06.8799398Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8799833Z x_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:54:06.8799921Z 2025-03-04T20:54:06.8800194Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8801806Z x_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_4 = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8801967Z 2025-03-04T20:54:06.8802325Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8802472Z out: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_5); x_5 = None 2025-03-04T20:54:06.8802534Z 2025-03-04T20:54:06.8802798Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8803257Z x_6: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out = l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:54:06.8803331Z 2025-03-04T20:54:06.8803602Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8805171Z x_7: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_6 = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8805245Z 2025-03-04T20:54:06.8805534Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8805684Z out_1: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_7); x_7 = None 2025-03-04T20:54:06.8805746Z 2025-03-04T20:54:06.8806008Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8806450Z x_8: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_1 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:54:06.8806521Z 2025-03-04T20:54:06.8806788Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8808388Z x_9: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_8 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8808461Z 2025-03-04T20:54:06.8808718Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8809180Z x_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); x_3 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:54:06.8809243Z 2025-03-04T20:54:06.8809531Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8811157Z x_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_10 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8811236Z 2025-03-04T20:54:06.8811529Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:54:06.8811673Z x_9 += x_11; out_2: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_9; x_9 = x_11 = None 2025-03-04T20:54:06.8811742Z 2025-03-04T20:54:06.8812028Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8812184Z out_3: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_2); out_2 = None 2025-03-04T20:54:06.8812247Z 2025-03-04T20:54:06.8812509Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8812935Z x_12: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:54:06.8813005Z 2025-03-04T20:54:06.8813282Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8814819Z x_13: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_12 = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8814889Z 2025-03-04T20:54:06.8815168Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8815313Z out_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_13); x_13 = None 2025-03-04T20:54:06.8815373Z 2025-03-04T20:54:06.8815637Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8816076Z x_14: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_4 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:54:06.8816137Z 2025-03-04T20:54:06.8816414Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8818130Z x_15: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_14 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8818226Z 2025-03-04T20:54:06.8818546Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8818711Z out_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_15); x_15 = None 2025-03-04T20:54:06.8818781Z 2025-03-04T20:54:06.8819075Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8819572Z x_16: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_5 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:54:06.8819643Z 2025-03-04T20:54:06.8819942Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8821546Z x_17: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_16 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8821635Z 2025-03-04T20:54:06.8821928Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:54:06.8822097Z x_17 += out_3; out_6: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_17; x_17 = out_3 = None 2025-03-04T20:54:06.8822169Z 2025-03-04T20:54:06.8822469Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8822647Z out_7: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_6); out_6 = None 2025-03-04T20:54:06.8822714Z 2025-03-04T20:54:06.8822981Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8823435Z x_18: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:54:06.8823502Z 2025-03-04T20:54:06.8823757Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8825272Z x_19: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_18 = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8825355Z 2025-03-04T20:54:06.8825639Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8825782Z out_8: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_19); x_19 = None 2025-03-04T20:54:06.8825841Z 2025-03-04T20:54:06.8826092Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8826534Z x_20: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_8 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:54:06.8826605Z 2025-03-04T20:54:06.8826893Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8828441Z x_21: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_20 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8828514Z 2025-03-04T20:54:06.8828801Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8828941Z out_9: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_21); x_21 = None 2025-03-04T20:54:06.8829003Z 2025-03-04T20:54:06.8829278Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8829723Z x_22: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_9 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:54:06.8829792Z 2025-03-04T20:54:06.8830057Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8831603Z x_23: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_22 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8831687Z 2025-03-04T20:54:06.8831979Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:54:06.8832148Z x_23 += out_7; out_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_23; x_23 = out_7 = None 2025-03-04T20:54:06.8832215Z 2025-03-04T20:54:06.8832520Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8832681Z out_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_10); out_10 = None 2025-03-04T20:54:06.8832754Z 2025-03-04T20:54:06.8833020Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8833478Z x_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:54:06.8833619Z 2025-03-04T20:54:06.8833896Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8835497Z x_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_24 = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8835568Z 2025-03-04T20:54:06.8835858Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8836017Z out_12: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_25); x_25 = None 2025-03-04T20:54:06.8836088Z 2025-03-04T20:54:06.8836339Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8836787Z x_26: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_12 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:54:06.8836849Z 2025-03-04T20:54:06.8837124Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8838700Z x_27: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_26 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8838783Z 2025-03-04T20:54:06.8839076Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8839222Z out_13: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_27); x_27 = None 2025-03-04T20:54:06.8839291Z 2025-03-04T20:54:06.8839540Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8839984Z x_28: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_13 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:54:06.8840044Z 2025-03-04T20:54:06.8840316Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8841894Z x_29: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_28 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8841957Z 2025-03-04T20:54:06.8842217Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8842672Z x_30: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); out_11 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:54:06.8842744Z 2025-03-04T20:54:06.8843007Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8844641Z x_31: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_30 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8844725Z 2025-03-04T20:54:06.8845012Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:54:06.8845165Z x_29 += x_31; out_14: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_29; x_29 = x_31 = None 2025-03-04T20:54:06.8845227Z 2025-03-04T20:54:06.8845518Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8845673Z out_15: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_14); out_14 = None 2025-03-04T20:54:06.8845743Z 2025-03-04T20:54:06.8846002Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8846439Z x_32: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:54:06.8846502Z 2025-03-04T20:54:06.8846784Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8848314Z x_33: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_32 = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8848393Z 2025-03-04T20:54:06.8848675Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8848812Z out_16: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_33); x_33 = None 2025-03-04T20:54:06.8848883Z 2025-03-04T20:54:06.8849141Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8849565Z x_34: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_16 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:54:06.8849638Z 2025-03-04T20:54:06.8849904Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8851417Z x_35: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_34 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8851494Z 2025-03-04T20:54:06.8851777Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8851912Z out_17: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_35); x_35 = None 2025-03-04T20:54:06.8851981Z 2025-03-04T20:54:06.8852224Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8852652Z x_36: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_17 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:54:06.8852711Z 2025-03-04T20:54:06.8852977Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8854481Z x_37: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_36 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8854564Z 2025-03-04T20:54:06.8854845Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:54:06.8854994Z x_37 += out_15; out_18: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_37; x_37 = out_15 = None 2025-03-04T20:54:06.8855062Z 2025-03-04T20:54:06.8855336Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8855502Z out_19: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_18); out_18 = None 2025-03-04T20:54:06.8872484Z 2025-03-04T20:54:06.8872833Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8873406Z x_38: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:54:06.8873486Z 2025-03-04T20:54:06.8873836Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8875453Z x_39: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_38 = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8875552Z 2025-03-04T20:54:06.8875866Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8876030Z out_20: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_39); x_39 = None 2025-03-04T20:54:06.8876096Z 2025-03-04T20:54:06.8876366Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8876816Z x_40: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_20 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:54:06.8876889Z 2025-03-04T20:54:06.8877163Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8878740Z x_41: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_40 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8878811Z 2025-03-04T20:54:06.8879105Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8879258Z out_21: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_41); x_41 = None 2025-03-04T20:54:06.8879321Z 2025-03-04T20:54:06.8879610Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8880066Z x_42: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_21 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:54:06.8880136Z 2025-03-04T20:54:06.8880411Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8881954Z x_43: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_42 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8882040Z 2025-03-04T20:54:06.8882319Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:54:06.8882486Z x_43 += out_19; out_22: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_43; x_43 = out_19 = None 2025-03-04T20:54:06.8882547Z 2025-03-04T20:54:06.8882839Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8882990Z out_23: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_22); out_22 = None 2025-03-04T20:54:06.8883057Z 2025-03-04T20:54:06.8883304Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8883729Z x_44: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T20:54:06.8883804Z 2025-03-04T20:54:06.8884069Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8885577Z x_45: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_44 = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8885646Z 2025-03-04T20:54:06.8885933Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8886088Z out_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_45); x_45 = None 2025-03-04T20:54:06.8886158Z 2025-03-04T20:54:06.8886406Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8886856Z x_46: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_24 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T20:54:06.8886917Z 2025-03-04T20:54:06.8887185Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8888707Z x_47: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_46 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8888792Z 2025-03-04T20:54:06.8889083Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8889223Z out_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_47); x_47 = None 2025-03-04T20:54:06.8889290Z 2025-03-04T20:54:06.8889535Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8889967Z x_48: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_25 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T20:54:06.8890026Z 2025-03-04T20:54:06.8890292Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8891810Z x_49: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_48 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8891872Z 2025-03-04T20:54:06.8892154Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:54:06.8892305Z x_49 += out_23; out_26: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_49; x_49 = out_23 = None 2025-03-04T20:54:06.8892386Z 2025-03-04T20:54:06.8892662Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8892817Z out_27: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_26); out_26 = None 2025-03-04T20:54:06.8892876Z 2025-03-04T20:54:06.8893160Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8893571Z x_50: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:54:06.8893640Z 2025-03-04T20:54:06.8893899Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8895414Z x_51: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_50 = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8895485Z 2025-03-04T20:54:06.8895764Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8895905Z out_28: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_51); x_51 = None 2025-03-04T20:54:06.8895965Z 2025-03-04T20:54:06.8896219Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8896630Z x_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_28 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:54:06.8896716Z 2025-03-04T20:54:06.8896973Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8898493Z x_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_52 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8898562Z 2025-03-04T20:54:06.8898856Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8898997Z out_29: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_53); x_53 = None 2025-03-04T20:54:06.8899056Z 2025-03-04T20:54:06.8899308Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8899744Z x_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_29 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:54:06.8899812Z 2025-03-04T20:54:06.8900069Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8901580Z x_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_54 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8901666Z 2025-03-04T20:54:06.8902062Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8902510Z x_56: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); out_27 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:54:06.8902569Z 2025-03-04T20:54:06.8902841Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8904407Z x_57: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_56 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8904526Z 2025-03-04T20:54:06.8904815Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:54:06.8904950Z x_55 += x_57; out_30: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_55; x_55 = x_57 = None 2025-03-04T20:54:06.8905019Z 2025-03-04T20:54:06.8905300Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8905471Z out_31: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_30); out_30 = None 2025-03-04T20:54:06.8905530Z 2025-03-04T20:54:06.8905782Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8906214Z x_58: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:54:06.8906284Z 2025-03-04T20:54:06.8906539Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8908058Z x_59: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_58 = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8908153Z 2025-03-04T20:54:06.8908434Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8908573Z out_32: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_59); x_59 = None 2025-03-04T20:54:06.8908634Z 2025-03-04T20:54:06.8908890Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8909312Z x_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_32 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:54:06.8909381Z 2025-03-04T20:54:06.8909656Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8911200Z x_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_60 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8911289Z 2025-03-04T20:54:06.8911603Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8911750Z out_33: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_61); x_61 = None 2025-03-04T20:54:06.8911815Z 2025-03-04T20:54:06.8912103Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8912532Z x_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_33 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:54:06.8912605Z 2025-03-04T20:54:06.8912895Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8914545Z x_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_62 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8914632Z 2025-03-04T20:54:06.8914940Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:54:06.8915098Z x_63 += out_31; out_34: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_63; x_63 = out_31 = None 2025-03-04T20:54:06.8915164Z 2025-03-04T20:54:06.8915463Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8915608Z out_35: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_34); out_34 = None 2025-03-04T20:54:06.8915678Z 2025-03-04T20:54:06.8915939Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8916379Z x_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:54:06.8916441Z 2025-03-04T20:54:06.8916723Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8918324Z x_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_64 = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8918396Z 2025-03-04T20:54:06.8918699Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8918834Z out_36: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_65); x_65 = None 2025-03-04T20:54:06.8918928Z 2025-03-04T20:54:06.8919186Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8919636Z x_66: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_36 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:54:06.8919699Z 2025-03-04T20:54:06.8919979Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8921575Z x_67: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_66 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8921661Z 2025-03-04T20:54:06.8921959Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8922093Z out_37: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_67); x_67 = None 2025-03-04T20:54:06.8922164Z 2025-03-04T20:54:06.8922421Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8922858Z x_68: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_37 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:54:06.8922923Z 2025-03-04T20:54:06.8923199Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8924721Z x_69: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_68 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8924801Z 2025-03-04T20:54:06.8925083Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:54:06.8925228Z x_69 += out_35; out_38: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_69; x_69 = out_35 = None 2025-03-04T20:54:06.8925295Z 2025-03-04T20:54:06.8925588Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8925732Z out_39: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_38); out_38 = None 2025-03-04T20:54:06.8925791Z 2025-03-04T20:54:06.8926042Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8926495Z x_70: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T20:54:06.8926564Z 2025-03-04T20:54:06.8926824Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8928333Z x_71: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_70 = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8928402Z 2025-03-04T20:54:06.8928687Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8928820Z out_40: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_71); x_71 = None 2025-03-04T20:54:06.8928880Z 2025-03-04T20:54:06.8929132Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8929542Z x_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_40 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T20:54:06.8929610Z 2025-03-04T20:54:06.8929872Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8931376Z x_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_72 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8931443Z 2025-03-04T20:54:06.8931723Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8931857Z out_41: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_73); x_73 = None 2025-03-04T20:54:06.8931929Z 2025-03-04T20:54:06.8932179Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8932597Z x_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_41 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T20:54:06.8932665Z 2025-03-04T20:54:06.8932925Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8934433Z x_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_74 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8934513Z 2025-03-04T20:54:06.8934787Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:54:06.8934937Z x_75 += out_39; out_42: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_75; x_75 = out_39 = None 2025-03-04T20:54:06.8934997Z 2025-03-04T20:54:06.8935279Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8935415Z out_43: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_42); out_42 = None 2025-03-04T20:54:06.8935481Z 2025-03-04T20:54:06.8935726Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8936137Z x_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = None 2025-03-04T20:54:06.8936211Z 2025-03-04T20:54:06.8936474Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8937988Z x_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_76 = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8938048Z 2025-03-04T20:54:06.8938353Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8938481Z out_44: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_77); x_77 = None 2025-03-04T20:54:06.8938547Z 2025-03-04T20:54:06.8938790Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8939214Z x_78: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_44 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = None 2025-03-04T20:54:06.8939273Z 2025-03-04T20:54:06.8939539Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8941055Z x_79: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_78 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8941115Z 2025-03-04T20:54:06.8941398Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8941526Z out_45: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_79); x_79 = None 2025-03-04T20:54:06.8941592Z 2025-03-04T20:54:06.8941834Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8942253Z x_80: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_45 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = None 2025-03-04T20:54:06.8942312Z 2025-03-04T20:54:06.8942578Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8944089Z x_81: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_80 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8944151Z 2025-03-04T20:54:06.8944432Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:54:06.8944570Z x_81 += out_43; out_46: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_81; x_81 = out_43 = None 2025-03-04T20:54:06.8944653Z 2025-03-04T20:54:06.8944927Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8945070Z out_47: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_46); out_46 = None 2025-03-04T20:54:06.8945131Z 2025-03-04T20:54:06.8945392Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8945802Z x_82: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = None 2025-03-04T20:54:06.8945872Z 2025-03-04T20:54:06.8946147Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8947648Z x_83: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_82 = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8947715Z 2025-03-04T20:54:06.8947992Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8948125Z out_48: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_83); x_83 = None 2025-03-04T20:54:06.8948185Z 2025-03-04T20:54:06.8948435Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8948839Z x_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_48 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = None 2025-03-04T20:54:06.8948921Z 2025-03-04T20:54:06.8949183Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8950689Z x_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_84 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8950756Z 2025-03-04T20:54:06.8951047Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8951178Z out_49: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_85); x_85 = None 2025-03-04T20:54:06.8951236Z 2025-03-04T20:54:06.8951487Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8951928Z x_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_49 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = None 2025-03-04T20:54:06.8951988Z 2025-03-04T20:54:06.8952253Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.8953803Z x_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_86 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.8953879Z 2025-03-04T20:54:06.8954160Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:54:06.8954300Z x_87 += out_47; out_50: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_87; x_87 = out_47 = None 2025-03-04T20:54:06.8954367Z 2025-03-04T20:54:06.8954645Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.8954785Z out_51: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_50); out_50 = None 2025-03-04T20:54:06.8954845Z 2025-03-04T20:54:06.8955293Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:54:06.8955458Z arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32) 2025-03-04T20:54:06.8955524Z 2025-03-04T20:54:06.8955818Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:54:06.8955959Z shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T20:54:06.8956020Z 2025-03-04T20:54:06.8956459Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:54:06.8956605Z arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32) 2025-03-04T20:54:06.8956673Z 2025-03-04T20:54:06.8956961Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:54:06.8957101Z shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T20:54:06.8957162Z 2025-03-04T20:54:06.8957555Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:54:06.8957730Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T20:54:06.8957829Z shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0] 2025-03-04T20:54:06.8957957Z shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T20:54:06.8958024Z 2025-03-04T20:54:06.8958353Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:54:06.8958483Z shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T20:54:06.8958541Z 2025-03-04T20:54:06.8958872Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:54:06.8959002Z shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T20:54:06.8959068Z 2025-03-04T20:54:06.8959443Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:54:06.8959661Z shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T20:54:06.8959719Z 2025-03-04T20:54:06.8960141Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:54:06.8960263Z view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T20:54:06.8960689Z view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T20:54:06.8960811Z add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T20:54:06.8960919Z x_88: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4); add = None 2025-03-04T20:54:06.8960984Z 2025-03-04T20:54:06.8961277Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:54:06.8961400Z tensor: "f32[82125, 4][4, 1]cpu" = x_88.to(torch.float32); x_88 = None 2025-03-04T20:54:06.8961475Z 2025-03-04T20:54:06.8961729Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.8962500Z x_89: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); out_51 = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T20:54:06.8962566Z 2025-03-04T20:54:06.8962837Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:54:06.8963021Z x_90: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_89, inplace = False); x_89 = None 2025-03-04T20:54:06.8963083Z 2025-03-04T20:54:06.8963483Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:54:06.8964686Z score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_90, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T20:54:06.8964762Z 2025-03-04T20:54:06.8965129Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:54:06.8965926Z x_91: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_90, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_90 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T20:54:06.8966015Z 2025-03-04T20:54:06.8966349Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T20:54:06.8966513Z permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T20:54:06.8966659Z logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T20:54:06.8966730Z 2025-03-04T20:54:06.8967171Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T20:54:06.8967343Z view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_91.view(4, -1, 4, 73, 75); x_91 = None 2025-03-04T20:54:06.8967523Z permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2); view_2 = None 2025-03-04T20:54:06.8967718Z pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2); permute_1 = None 2025-03-04T20:54:06.8967780Z 2025-03-04T20:54:06.8968202Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:54:06.8968419Z pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T20:54:06.8968489Z 2025-03-04T20:54:06.8968926Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:54:06.8969071Z unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T20:54:06.8969223Z expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T20:54:06.8969359Z anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T20:54:06.8969428Z 2025-03-04T20:54:06.8969805Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:54:06.8969982Z deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T20:54:06.8970045Z 2025-03-04T20:54:06.8970377Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:54:06.8970516Z boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T20:54:06.8970584Z 2025-03-04T20:54:06.8970911Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:54:06.8971048Z getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:54:06.8971172Z getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:54:06.8971323Z widths: "f32[328500][1]cpu" = getitem_2 - getitem_3; getitem_2 = getitem_3 = None 2025-03-04T20:54:06.8971384Z 2025-03-04T20:54:06.8971730Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:54:06.8971850Z getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:54:06.8971972Z getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:54:06.8972114Z heights: "f32[328500][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T20:54:06.8972182Z 2025-03-04T20:54:06.8972490Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:54:06.8972614Z getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:54:06.8972700Z mul: "f32[328500][1]cpu" = 0.5 * widths 2025-03-04T20:54:06.8972824Z ctr_x: "f32[328500][1]cpu" = getitem_6 + mul; getitem_6 = mul = None 2025-03-04T20:54:06.8972886Z 2025-03-04T20:54:06.8973204Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:54:06.8973342Z getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:54:06.8973435Z mul_1: "f32[328500][1]cpu" = 0.5 * heights 2025-03-04T20:54:06.8973562Z ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1; getitem_7 = mul_1 = None 2025-03-04T20:54:06.8973629Z 2025-03-04T20:54:06.8973974Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:54:06.8974131Z getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:54:06.8974264Z dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0; getitem_8 = None 2025-03-04T20:54:06.8974335Z 2025-03-04T20:54:06.8974645Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:54:06.8974803Z getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:54:06.8974915Z dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0; getitem_9 = None 2025-03-04T20:54:06.8974985Z 2025-03-04T20:54:06.8975294Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:54:06.8975455Z getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:54:06.8975567Z dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0; getitem_10 = None 2025-03-04T20:54:06.8975637Z 2025-03-04T20:54:06.8975950Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:54:06.8976154Z getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:54:06.8976270Z dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0; getitem_11 = None 2025-03-04T20:54:06.8976331Z 2025-03-04T20:54:06.8976701Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:54:06.8976844Z dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:54:06.8976912Z 2025-03-04T20:54:06.8977269Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:54:06.8977411Z dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:54:06.8977498Z 2025-03-04T20:54:06.8977858Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:54:06.8977996Z getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:54:06.8978125Z mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12; dx = getitem_12 = None 2025-03-04T20:54:06.8978279Z getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:54:06.8978423Z pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13; mul_2 = getitem_13 = None 2025-03-04T20:54:06.8978485Z 2025-03-04T20:54:06.8978848Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:54:06.8978991Z getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:54:06.8979118Z mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14; dy = getitem_14 = None 2025-03-04T20:54:06.8979271Z getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:54:06.8979413Z pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15; mul_3 = getitem_15 = None 2025-03-04T20:54:06.8979477Z 2025-03-04T20:54:06.8979826Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:54:06.8979941Z exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:54:06.8980122Z getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:54:06.8980248Z pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16; exp = getitem_16 = None 2025-03-04T20:54:06.8980315Z 2025-03-04T20:54:06.8980640Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:54:06.8980755Z exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:54:06.8980915Z getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:54:06.8981049Z pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17; exp_1 = getitem_17 = None 2025-03-04T20:54:06.8981107Z 2025-03-04T20:54:06.8981416Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:54:06.8981508Z mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T20:54:06.8981628Z x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:54:06.8981686Z 2025-03-04T20:54:06.8982009Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:54:06.8982097Z mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T20:54:06.8982215Z y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:54:06.8982289Z 2025-03-04T20:54:06.8982604Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:54:06.8982715Z mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:54:06.8982849Z x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:54:06.8982909Z 2025-03-04T20:54:06.8983219Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:54:06.8983343Z mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:54:06.8983472Z y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:54:06.8983533Z 2025-03-04T20:54:06.8983886Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:54:06.8984068Z pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:54:06.8984129Z 2025-03-04T20:54:06.8984474Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:54:06.8984633Z proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4)); pred_boxes = None 2025-03-04T20:54:06.8984703Z 2025-03-04T20:54:06.8985085Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:54:06.8985261Z proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T20:54:06.8985325Z 2025-03-04T20:54:06.8985812Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T20:54:06.8985958Z arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T20:54:06.8986025Z 2025-03-04T20:54:06.8986325Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:54:06.8986468Z batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T20:54:06.8986527Z 2025-03-04T20:54:06.8986956Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:54:06.8987064Z topk = logits_i.topk(6000, dim = 1); logits_i = None 2025-03-04T20:54:06.8987167Z topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0] 2025-03-04T20:54:06.8987277Z topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1]; topk = None 2025-03-04T20:54:06.8987347Z 2025-03-04T20:54:06.8987795Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:54:06.8987978Z getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T20:54:06.8988210Z topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)]; proposals_i_1 = getitem_20 = topk_idx = None 2025-03-04T20:54:06.8988276Z 2025-03-04T20:54:06.8988753Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:54:06.8988921Z full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:54:06.8988980Z 2025-03-04T20:54:06.8989272Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:54:06.8989433Z level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu')); full = level_ids = None 2025-03-04T20:54:06.8989500Z 2025-03-04T20:54:06.8989874Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T20:54:06.8990021Z getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0]; topk_proposals_i = None 2025-03-04T20:54:06.8990082Z 2025-03-04T20:54:06.8990378Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:54:06.8990515Z tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32); getitem_22 = None 2025-03-04T20:54:06.8990582Z 2025-03-04T20:54:06.8990956Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T20:54:06.8991090Z scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0]; topk_scores_i = None 2025-03-04T20:54:06.8991155Z 2025-03-04T20:54:06.8991637Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T20:54:06.8991778Z isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1); tensor_1 = None 2025-03-04T20:54:06.8991896Z all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:54:06.8992053Z isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T20:54:06.8992195Z valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T20:54:06.8992266Z 2025-03-04T20:54:06.8992631Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T20:54:06.8992751Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T20:54:06.8992810Z 2025-03-04T20:54:06.8992821Z 2025-03-04T20:54:06.8992915Z class GraphModule(torch.nn.Module): 2025-03-04T20:54:06.9035368Z def forward(self, L_stack0_tensor: "f32[4, 3, 1156, 1199][4158132, 1386044, 1199, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", 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L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[1024, 1024, 3, 3][9216, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[1024][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"): 2025-03-04T20:54:06.9038241Z l_stack0_tensor = L_stack0_tensor 2025-03-04T20:54:06.9038560Z l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ 2025-03-04T20:54:06.9038971Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.9039367Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.9039757Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:54:06.9040130Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:54:06.9040538Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:54:06.9040962Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.9041369Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.9041797Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:54:06.9042188Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:54:06.9042547Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:54:06.9042951Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.9043358Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.9043759Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:54:06.9044146Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:54:06.9044499Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:54:06.9044863Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.9045213Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.9045544Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:54:06.9045858Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:54:06.9046223Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:54:06.9046575Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.9046930Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.9047284Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:54:06.9047631Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:54:06.9047915Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:54:06.9048263Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.9048610Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.9048939Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:54:06.9049253Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:54:06.9049532Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:54:06.9049871Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.9050202Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.9050519Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:54:06.9050824Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:54:06.9051108Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:54:06.9051445Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.9051786Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.9052145Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:54:06.9052512Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:54:06.9052834Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:54:06.9053207Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.9053614Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.9053930Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:54:06.9054255Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:54:06.9054543Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:54:06.9054880Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.9055231Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.9055550Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:54:06.9055865Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:54:06.9056141Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:54:06.9056480Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.9056811Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.9057131Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:54:06.9057443Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:54:06.9057723Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:54:06.9058063Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.9058398Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.9058717Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:54:06.9059040Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:54:06.9059325Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:54:06.9059672Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.9060007Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.9060341Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:54:06.9060651Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:54:06.9060935Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:54:06.9061266Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.9061622Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.9061941Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:54:06.9062252Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:54:06.9062545Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:54:06.9062902Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.9063249Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.9063582Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:54:06.9063906Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:54:06.9064185Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:54:06.9064527Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.9064855Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.9065196Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:54:06.9065507Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:54:06.9065788Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:54:06.9066143Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.9066471Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.9066814Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:54:06.9067118Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:54:06.9067404Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:54:06.9067755Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.9068090Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.9068415Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:54:06.9068717Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:54:06.9069000Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:54:06.9069333Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.9069706Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.9070055Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:54:06.9070405Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:54:06.9070722Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:54:06.9071109Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.9071513Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.9071861Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:54:06.9072206Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:54:06.9072534Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:54:06.9072922Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.9073306Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.9073745Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:54:06.9074101Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:54:06.9074432Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ 2025-03-04T20:54:06.9074821Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.9075184Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.9075519Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:54:06.9075857Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:54:06.9076163Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ 2025-03-04T20:54:06.9076539Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.9076900Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.9077239Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:54:06.9077570Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:54:06.9077875Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ 2025-03-04T20:54:06.9078239Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.9078618Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.9078948Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:54:06.9079290Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:54:06.9079616Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:54:06.9079972Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.9080344Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.9080678Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:54:06.9081007Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:54:06.9081318Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:54:06.9081681Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.9082029Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.9082349Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:54:06.9082662Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:54:06.9082940Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:54:06.9083286Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.9083619Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.9083937Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:54:06.9084246Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:54:06.9084549Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:54:06.9084912Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.9085260Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.9085599Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:54:06.9085931Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:54:06.9086221Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:54:06.9086572Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.9086908Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.9087221Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:54:06.9087552Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:54:06.9087832Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:54:06.9088173Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.9088510Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.9088823Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:54:06.9089139Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:54:06.9089417Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:54:06.9089759Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.9090089Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.9090412Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:54:06.9090718Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:54:06.9091006Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:54:06.9091363Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.9091690Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.9092030Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:54:06.9092336Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:54:06.9092647Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:54:06.9092980Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.9093318Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.9093650Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:54:06.9093966Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:54:06.9094264Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:54:06.9094608Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.9094956Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.9095298Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:54:06.9095645Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:54:06.9095937Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ 2025-03-04T20:54:06.9096289Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.9096630Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.9096966Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:54:06.9097289Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:54:06.9097587Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ 2025-03-04T20:54:06.9097935Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.9098272Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.9098618Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:54:06.9098933Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:54:06.9099242Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ 2025-03-04T20:54:06.9099588Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.9099934Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.9100281Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:54:06.9100600Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:54:06.9100900Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ 2025-03-04T20:54:06.9101247Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.9101602Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.9102028Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:54:06.9102363Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:54:06.9102657Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ 2025-03-04T20:54:06.9103019Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.9103391Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.9103724Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:54:06.9104092Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:54:06.9104378Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ 2025-03-04T20:54:06.9104738Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.9105106Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.9105438Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:54:06.9105777Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:54:06.9106073Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ 2025-03-04T20:54:06.9106436Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.9106814Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.9107151Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:54:06.9107470Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:54:06.9107764Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ 2025-03-04T20:54:06.9108117Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.9108463Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.9108785Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:54:06.9109106Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:54:06.9109401Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ 2025-03-04T20:54:06.9109753Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:54:06.9110101Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:54:06.9110421Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:54:06.9110756Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:54:06.9111111Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T20:54:06.9111431Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T20:54:06.9111750Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T20:54:06.9112124Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T20:54:06.9112503Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T20:54:06.9112850Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T20:54:06.9113196Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T20:54:06.9113277Z 2025-03-04T20:54:06.9113611Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.9114083Z x: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1); l_stack0_tensor = l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = None 2025-03-04T20:54:06.9114155Z 2025-03-04T20:54:06.9114428Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.9115879Z x_1: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.9115949Z 2025-03-04T20:54:06.9116229Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x) 2025-03-04T20:54:06.9116369Z x_2: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.relu_(x_1); x_1 = None 2025-03-04T20:54:06.9116431Z 2025-03-04T20:54:06.9116794Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1) 2025-03-04T20:54:06.9117023Z x_3: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1); x_2 = None 2025-03-04T20:54:06.9117113Z 2025-03-04T20:54:06.9117375Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.9117825Z x_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:54:06.9117890Z 2025-03-04T20:54:06.9118203Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.9119866Z x_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_4 = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.9119949Z 2025-03-04T20:54:06.9120273Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.9120421Z out: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_5); x_5 = None 2025-03-04T20:54:06.9120496Z 2025-03-04T20:54:06.9120768Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.9121195Z x_6: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out = l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:54:06.9121256Z 2025-03-04T20:54:06.9121523Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.9123027Z x_7: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_6 = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.9123088Z 2025-03-04T20:54:06.9123381Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.9123514Z out_1: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_7); x_7 = None 2025-03-04T20:54:06.9123584Z 2025-03-04T20:54:06.9123830Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.9124279Z x_8: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_1 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:54:06.9124340Z 2025-03-04T20:54:06.9124605Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.9126143Z x_9: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_8 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.9126207Z 2025-03-04T20:54:06.9126460Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.9126899Z x_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); x_3 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:54:06.9126967Z 2025-03-04T20:54:06.9127226Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.9128799Z x_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_10 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.9128870Z 2025-03-04T20:54:06.9129144Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:54:06.9129291Z x_9 += x_11; out_2: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_9; x_9 = x_11 = None 2025-03-04T20:54:06.9129351Z 2025-03-04T20:54:06.9129639Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.9129786Z out_3: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_2); out_2 = None 2025-03-04T20:54:06.9129853Z 2025-03-04T20:54:06.9130095Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.9130516Z x_12: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:54:06.9130597Z 2025-03-04T20:54:06.9130867Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.9132447Z x_13: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_12 = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.9132512Z 2025-03-04T20:54:06.9132798Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.9132935Z out_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_13); x_13 = None 2025-03-04T20:54:06.9133045Z 2025-03-04T20:54:06.9133288Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.9133710Z x_14: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_4 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:54:06.9133777Z 2025-03-04T20:54:06.9134035Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.9135545Z x_15: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_14 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.9135608Z 2025-03-04T20:54:06.9135890Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.9136031Z out_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_15); x_15 = None 2025-03-04T20:54:06.9136093Z 2025-03-04T20:54:06.9136338Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.9136766Z x_16: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_5 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:54:06.9136854Z 2025-03-04T20:54:06.9137113Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.9138642Z x_17: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_16 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.9138714Z 2025-03-04T20:54:06.9138997Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:54:06.9139152Z x_17 += out_3; out_6: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_17; x_17 = out_3 = None 2025-03-04T20:54:06.9139212Z 2025-03-04T20:54:06.9139492Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.9139654Z out_7: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_6); out_6 = None 2025-03-04T20:54:06.9139721Z 2025-03-04T20:54:06.9139967Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.9140387Z x_18: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:54:06.9140447Z 2025-03-04T20:54:06.9140714Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.9142211Z x_19: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_18 = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.9142280Z 2025-03-04T20:54:06.9142566Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.9142699Z out_8: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_19); x_19 = None 2025-03-04T20:54:06.9142766Z 2025-03-04T20:54:06.9143012Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.9143462Z x_20: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_8 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:54:06.9143522Z 2025-03-04T20:54:06.9143788Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.9145335Z x_21: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_20 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.9145406Z 2025-03-04T20:54:06.9145687Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.9145817Z out_9: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_21); x_21 = None 2025-03-04T20:54:06.9145897Z 2025-03-04T20:54:06.9146140Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.9146567Z x_22: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_9 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:54:06.9146628Z 2025-03-04T20:54:06.9146890Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.9148387Z x_23: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_22 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.9148454Z 2025-03-04T20:54:06.9148733Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:54:06.9148883Z x_23 += out_7; out_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_23; x_23 = out_7 = None 2025-03-04T20:54:06.9148950Z 2025-03-04T20:54:06.9149225Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.9149385Z out_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_10); out_10 = None 2025-03-04T20:54:06.9149482Z 2025-03-04T20:54:06.9149771Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.9150265Z x_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:54:06.9150336Z 2025-03-04T20:54:06.9150632Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.9152328Z x_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_24 = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.9152398Z 2025-03-04T20:54:06.9152694Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.9152842Z out_12: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_25); x_25 = None 2025-03-04T20:54:06.9152902Z 2025-03-04T20:54:06.9153160Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.9153711Z x_26: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_12 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:54:06.9153793Z 2025-03-04T20:54:06.9154102Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.9155801Z x_27: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_26 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.9155878Z 2025-03-04T20:54:06.9156202Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.9156369Z out_13: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_27); x_27 = None 2025-03-04T20:54:06.9156439Z 2025-03-04T20:54:06.9156735Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.9157250Z x_28: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_13 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:54:06.9157327Z 2025-03-04T20:54:06.9157636Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.9159394Z x_29: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_28 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.9159475Z 2025-03-04T20:54:06.9159768Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.9160298Z x_30: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); out_11 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:54:06.9160366Z 2025-03-04T20:54:06.9160685Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.9162364Z x_31: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_30 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.9162427Z 2025-03-04T20:54:06.9162708Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:54:06.9162851Z x_29 += x_31; out_14: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_29; x_29 = x_31 = None 2025-03-04T20:54:06.9162918Z 2025-03-04T20:54:06.9163196Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.9163350Z out_15: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_14); out_14 = None 2025-03-04T20:54:06.9163412Z 2025-03-04T20:54:06.9163665Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.9164081Z x_32: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:54:06.9164170Z 2025-03-04T20:54:06.9164432Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.9165979Z x_33: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_32 = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.9166051Z 2025-03-04T20:54:06.9166330Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.9166472Z out_16: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_33); x_33 = None 2025-03-04T20:54:06.9166546Z 2025-03-04T20:54:06.9166798Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.9167219Z x_34: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_16 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:54:06.9167288Z 2025-03-04T20:54:06.9167546Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.9169059Z x_35: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_34 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.9169130Z 2025-03-04T20:54:06.9169406Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.9169549Z out_17: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_35); x_35 = None 2025-03-04T20:54:06.9169608Z 2025-03-04T20:54:06.9169862Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.9170281Z x_36: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_17 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:54:06.9170364Z 2025-03-04T20:54:06.9170625Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.9172162Z x_37: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_36 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.9172234Z 2025-03-04T20:54:06.9172504Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:54:06.9172660Z x_37 += out_15; out_18: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_37; x_37 = out_15 = None 2025-03-04T20:54:06.9172719Z 2025-03-04T20:54:06.9172998Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.9173158Z out_19: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_18); out_18 = None 2025-03-04T20:54:06.9173225Z 2025-03-04T20:54:06.9173468Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.9173888Z x_38: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:54:06.9173953Z 2025-03-04T20:54:06.9174215Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.9175723Z x_39: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_38 = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.9175784Z 2025-03-04T20:54:06.9176066Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.9176202Z out_20: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_39); x_39 = None 2025-03-04T20:54:06.9176266Z 2025-03-04T20:54:06.9176508Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.9176933Z x_40: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_20 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:54:06.9177018Z 2025-03-04T20:54:06.9177277Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.9178835Z x_41: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_40 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.9178896Z 2025-03-04T20:54:06.9179178Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.9179318Z out_21: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_41); x_41 = None 2025-03-04T20:54:06.9179392Z 2025-03-04T20:54:06.9179645Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.9180063Z x_42: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_21 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:54:06.9180130Z 2025-03-04T20:54:06.9180388Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.9181898Z x_43: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_42 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.9181967Z 2025-03-04T20:54:06.9182239Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:54:06.9182393Z x_43 += out_19; out_22: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_43; x_43 = out_19 = None 2025-03-04T20:54:06.9182454Z 2025-03-04T20:54:06.9182735Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.9182875Z out_23: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_22); out_22 = None 2025-03-04T20:54:06.9182941Z 2025-03-04T20:54:06.9183204Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.9183629Z x_44: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T20:54:06.9183689Z 2025-03-04T20:54:06.9183953Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.9185494Z x_45: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_44 = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.9185562Z 2025-03-04T20:54:06.9185853Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.9186001Z out_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_45); x_45 = None 2025-03-04T20:54:06.9186069Z 2025-03-04T20:54:06.9186312Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.9186744Z x_46: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_24 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T20:54:06.9186803Z 2025-03-04T20:54:06.9187075Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.9188597Z x_47: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_46 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.9188665Z 2025-03-04T20:54:06.9188951Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.9189086Z out_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_47); x_47 = None 2025-03-04T20:54:06.9189151Z 2025-03-04T20:54:06.9189394Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.9189834Z x_48: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_25 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T20:54:06.9189893Z 2025-03-04T20:54:06.9190158Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.9191716Z x_49: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_48 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.9191787Z 2025-03-04T20:54:06.9192067Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:54:06.9192232Z x_49 += out_23; out_26: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_49; x_49 = out_23 = None 2025-03-04T20:54:06.9192298Z 2025-03-04T20:54:06.9192574Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.9192725Z out_27: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_26); out_26 = None 2025-03-04T20:54:06.9192787Z 2025-03-04T20:54:06.9193038Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.9193452Z x_50: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:54:06.9193558Z 2025-03-04T20:54:06.9193829Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.9195338Z x_51: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_50 = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.9195408Z 2025-03-04T20:54:06.9195685Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.9195821Z out_28: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_51); x_51 = None 2025-03-04T20:54:06.9195900Z 2025-03-04T20:54:06.9196159Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.9196567Z x_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_28 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:54:06.9196631Z 2025-03-04T20:54:06.9196888Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.9198416Z x_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_52 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.9198482Z 2025-03-04T20:54:06.9198776Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.9198913Z out_29: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_53); x_53 = None 2025-03-04T20:54:06.9198972Z 2025-03-04T20:54:06.9199224Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.9199639Z x_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_29 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:54:06.9199707Z 2025-03-04T20:54:06.9199964Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.9201474Z x_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_54 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.9201541Z 2025-03-04T20:54:06.9201786Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.9202383Z x_56: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); out_27 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:54:06.9202484Z 2025-03-04T20:54:06.9202755Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.9204338Z x_57: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_56 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.9204421Z 2025-03-04T20:54:06.9204701Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:54:06.9204836Z x_55 += x_57; out_30: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_55; x_55 = x_57 = None 2025-03-04T20:54:06.9204904Z 2025-03-04T20:54:06.9205181Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.9205348Z out_31: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_30); out_30 = None 2025-03-04T20:54:06.9205407Z 2025-03-04T20:54:06.9205658Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.9206063Z x_58: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:54:06.9206133Z 2025-03-04T20:54:06.9206391Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.9207906Z x_59: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_58 = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.9207976Z 2025-03-04T20:54:06.9208259Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.9208398Z out_32: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_59); x_59 = None 2025-03-04T20:54:06.9208460Z 2025-03-04T20:54:06.9208717Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.9209138Z x_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_32 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:54:06.9209220Z 2025-03-04T20:54:06.9209484Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.9211073Z x_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_60 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.9211147Z 2025-03-04T20:54:06.9211434Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.9211573Z out_33: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_61); x_61 = None 2025-03-04T20:54:06.9211651Z 2025-03-04T20:54:06.9211914Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.9212344Z x_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_33 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:54:06.9212415Z 2025-03-04T20:54:06.9212683Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.9214246Z x_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_62 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.9214317Z 2025-03-04T20:54:06.9214604Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:54:06.9214758Z x_63 += out_31; out_34: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_63; x_63 = out_31 = None 2025-03-04T20:54:06.9214820Z 2025-03-04T20:54:06.9215116Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.9215260Z out_35: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_34); out_34 = None 2025-03-04T20:54:06.9215329Z 2025-03-04T20:54:06.9215583Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.9216031Z x_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:54:06.9216100Z 2025-03-04T20:54:06.9216365Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.9217945Z x_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_64 = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.9218009Z 2025-03-04T20:54:06.9218298Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.9218447Z out_36: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_65); x_65 = None 2025-03-04T20:54:06.9218513Z 2025-03-04T20:54:06.9218768Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.9219195Z x_66: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_36 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:54:06.9219265Z 2025-03-04T20:54:06.9219536Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.9221080Z x_67: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_66 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.9221144Z 2025-03-04T20:54:06.9221437Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.9221565Z out_37: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_67); x_67 = None 2025-03-04T20:54:06.9221633Z 2025-03-04T20:54:06.9221881Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.9222311Z x_68: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_37 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:54:06.9222568Z 2025-03-04T20:54:06.9222834Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.9224396Z x_69: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_68 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.9224458Z 2025-03-04T20:54:06.9224741Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:54:06.9224890Z x_69 += out_35; out_38: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_69; x_69 = out_35 = None 2025-03-04T20:54:06.9224966Z 2025-03-04T20:54:06.9225252Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.9225388Z out_39: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_38); out_38 = None 2025-03-04T20:54:06.9225456Z 2025-03-04T20:54:06.9225703Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.9226114Z x_70: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T20:54:06.9226173Z 2025-03-04T20:54:06.9226442Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.9227957Z x_71: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_70 = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.9228026Z 2025-03-04T20:54:06.9228311Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.9228440Z out_40: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_71); x_71 = None 2025-03-04T20:54:06.9228508Z 2025-03-04T20:54:06.9228752Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.9229186Z x_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_40 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T20:54:06.9229246Z 2025-03-04T20:54:06.9229511Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.9231036Z x_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_72 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.9231106Z 2025-03-04T20:54:06.9231386Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.9231529Z out_41: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_73); x_73 = None 2025-03-04T20:54:06.9231595Z 2025-03-04T20:54:06.9231838Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.9232259Z x_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_41 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T20:54:06.9232319Z 2025-03-04T20:54:06.9232584Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.9234203Z x_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_74 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.9234277Z 2025-03-04T20:54:06.9234571Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:54:06.9234709Z x_75 += out_39; out_42: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_75; x_75 = out_39 = None 2025-03-04T20:54:06.9234776Z 2025-03-04T20:54:06.9235055Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.9235200Z out_43: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_42); out_42 = None 2025-03-04T20:54:06.9235276Z 2025-03-04T20:54:06.9235537Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.9235944Z x_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = None 2025-03-04T20:54:06.9236012Z 2025-03-04T20:54:06.9236276Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.9237876Z x_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_76 = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.9237966Z 2025-03-04T20:54:06.9238245Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.9238379Z out_44: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_77); x_77 = None 2025-03-04T20:54:06.9238440Z 2025-03-04T20:54:06.9238695Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.9239110Z x_78: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_44 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = None 2025-03-04T20:54:06.9239176Z 2025-03-04T20:54:06.9239439Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.9240955Z x_79: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_78 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.9241023Z 2025-03-04T20:54:06.9241303Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.9241439Z out_45: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_79); x_79 = None 2025-03-04T20:54:06.9241499Z 2025-03-04T20:54:06.9241751Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.9242180Z x_80: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_45 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = None 2025-03-04T20:54:06.9242250Z 2025-03-04T20:54:06.9242515Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.9244064Z x_81: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_80 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.9244140Z 2025-03-04T20:54:06.9244415Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:54:06.9244579Z x_81 += out_43; out_46: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_81; x_81 = out_43 = None 2025-03-04T20:54:06.9244637Z 2025-03-04T20:54:06.9244919Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.9245054Z out_47: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_46); out_46 = None 2025-03-04T20:54:06.9245121Z 2025-03-04T20:54:06.9245367Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.9245778Z x_82: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = None 2025-03-04T20:54:06.9245840Z 2025-03-04T20:54:06.9246106Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.9247624Z x_83: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_82 = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.9247686Z 2025-03-04T20:54:06.9247972Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.9248100Z out_48: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_83); x_83 = None 2025-03-04T20:54:06.9248180Z 2025-03-04T20:54:06.9248424Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.9248839Z x_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_48 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = None 2025-03-04T20:54:06.9248899Z 2025-03-04T20:54:06.9249164Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.9250696Z x_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_84 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.9250771Z 2025-03-04T20:54:06.9251059Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.9251184Z out_49: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_85); x_85 = None 2025-03-04T20:54:06.9251252Z 2025-03-04T20:54:06.9251495Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.9251919Z x_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_49 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = None 2025-03-04T20:54:06.9251980Z 2025-03-04T20:54:06.9252246Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:54:06.9253765Z x_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_86 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:54:06.9253825Z 2025-03-04T20:54:06.9254104Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:54:06.9254243Z x_87 += out_47; out_50: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_87; x_87 = out_47 = None 2025-03-04T20:54:06.9254308Z 2025-03-04T20:54:06.9254584Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:54:06.9254748Z out_51: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_50); out_50 = None 2025-03-04T20:54:06.9254806Z 2025-03-04T20:54:06.9255241Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:54:06.9255386Z arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32) 2025-03-04T20:54:06.9255453Z 2025-03-04T20:54:06.9255755Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:54:06.9255894Z shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T20:54:06.9255953Z 2025-03-04T20:54:06.9256402Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:54:06.9256555Z arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32) 2025-03-04T20:54:06.9256615Z 2025-03-04T20:54:06.9256908Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:54:06.9257043Z shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T20:54:06.9257122Z 2025-03-04T20:54:06.9257501Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:54:06.9257685Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T20:54:06.9257780Z shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0] 2025-03-04T20:54:06.9257903Z shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T20:54:06.9257963Z 2025-03-04T20:54:06.9258300Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:54:06.9258420Z shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T20:54:06.9258486Z 2025-03-04T20:54:06.9258821Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:54:06.9258943Z shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T20:54:06.9259002Z 2025-03-04T20:54:06.9259394Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:54:06.9259606Z shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T20:54:06.9259672Z 2025-03-04T20:54:06.9260094Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:54:06.9260224Z view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T20:54:06.9260659Z view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T20:54:06.9260802Z add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T20:54:06.9260911Z x_88: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4); add = None 2025-03-04T20:54:06.9260980Z 2025-03-04T20:54:06.9261275Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:54:06.9261406Z tensor: "f32[82125, 4][4, 1]cpu" = x_88.to(torch.float32); x_88 = None 2025-03-04T20:54:06.9261466Z 2025-03-04T20:54:06.9261723Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:06.9262519Z x_89: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); out_51 = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T20:54:06.9262589Z 2025-03-04T20:54:06.9262868Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:54:06.9263053Z x_90: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_89, inplace = False); x_89 = None 2025-03-04T20:54:06.9263123Z 2025-03-04T20:54:06.9263520Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:54:06.9264388Z score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_90, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T20:54:06.9264452Z 2025-03-04T20:54:06.9264828Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:54:06.9265709Z x_91: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_90, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_90 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T20:54:06.9265781Z 2025-03-04T20:54:06.9266138Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T20:54:06.9266286Z permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T20:54:06.9266428Z logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T20:54:06.9266489Z 2025-03-04T20:54:06.9266947Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T20:54:06.9267108Z view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_91.view(4, -1, 4, 73, 75); x_91 = None 2025-03-04T20:54:06.9267315Z permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2); view_2 = None 2025-03-04T20:54:06.9267504Z pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2); permute_1 = None 2025-03-04T20:54:06.9267572Z 2025-03-04T20:54:06.9267977Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:54:06.9268186Z pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T20:54:06.9268252Z 2025-03-04T20:54:06.9268776Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:54:06.9268939Z unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T20:54:06.9269128Z expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T20:54:06.9269281Z anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T20:54:06.9269359Z 2025-03-04T20:54:06.9269821Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:54:06.9270018Z deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T20:54:06.9270104Z 2025-03-04T20:54:06.9270485Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:54:06.9270638Z boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T20:54:06.9270714Z 2025-03-04T20:54:06.9271105Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:54:06.9271256Z getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:54:06.9271394Z getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:54:06.9271559Z widths: "f32[328500][1]cpu" = getitem_2 - getitem_3; getitem_2 = getitem_3 = None 2025-03-04T20:54:06.9271628Z 2025-03-04T20:54:06.9272049Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:54:06.9272189Z getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:54:06.9272320Z getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:54:06.9272489Z heights: "f32[328500][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T20:54:06.9272556Z 2025-03-04T20:54:06.9272911Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:54:06.9273042Z getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:54:06.9273146Z mul: "f32[328500][1]cpu" = 0.5 * widths 2025-03-04T20:54:06.9273280Z ctr_x: "f32[328500][1]cpu" = getitem_6 + mul; getitem_6 = mul = None 2025-03-04T20:54:06.9273356Z 2025-03-04T20:54:06.9273784Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:54:06.9273950Z getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:54:06.9274079Z mul_1: "f32[328500][1]cpu" = 0.5 * heights 2025-03-04T20:54:06.9274229Z ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1; getitem_7 = mul_1 = None 2025-03-04T20:54:06.9274297Z 2025-03-04T20:54:06.9274682Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:54:06.9274842Z getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:54:06.9274966Z dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0; getitem_8 = None 2025-03-04T20:54:06.9275030Z 2025-03-04T20:54:06.9275384Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:54:06.9275542Z getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:54:06.9275664Z dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0; getitem_9 = None 2025-03-04T20:54:06.9275751Z 2025-03-04T20:54:06.9276073Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:54:06.9276223Z getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:54:06.9276337Z dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0; getitem_10 = None 2025-03-04T20:54:06.9276399Z 2025-03-04T20:54:06.9276709Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:54:06.9276905Z getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:54:06.9277022Z dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0; getitem_11 = None 2025-03-04T20:54:06.9277084Z 2025-03-04T20:54:06.9277438Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:54:06.9277576Z dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:54:06.9277645Z 2025-03-04T20:54:06.9277981Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:54:06.9278122Z dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:54:06.9278196Z 2025-03-04T20:54:06.9278544Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:54:06.9278683Z getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:54:06.9278809Z mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12; dx = getitem_12 = None 2025-03-04T20:54:06.9278955Z getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:54:06.9279096Z pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13; mul_2 = getitem_13 = None 2025-03-04T20:54:06.9279166Z 2025-03-04T20:54:06.9279504Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:54:06.9279645Z getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:54:06.9279763Z mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14; dy = getitem_14 = None 2025-03-04T20:54:06.9279912Z getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:54:06.9280065Z pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15; mul_3 = getitem_15 = None 2025-03-04T20:54:06.9280133Z 2025-03-04T20:54:06.9280456Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:54:06.9280575Z exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:54:06.9280730Z getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:54:06.9280887Z pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16; exp = getitem_16 = None 2025-03-04T20:54:06.9280948Z 2025-03-04T20:54:06.9281278Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:54:06.9281390Z exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:54:06.9281578Z getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:54:06.9281707Z pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17; exp_1 = getitem_17 = None 2025-03-04T20:54:06.9281774Z 2025-03-04T20:54:06.9282084Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:54:06.9282184Z mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T20:54:06.9282318Z x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:54:06.9282385Z 2025-03-04T20:54:06.9282696Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:54:06.9282794Z mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T20:54:06.9282909Z y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:54:06.9282977Z 2025-03-04T20:54:06.9283286Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:54:06.9283404Z mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:54:06.9283528Z x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:54:06.9283599Z 2025-03-04T20:54:06.9283915Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:54:06.9284027Z mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:54:06.9284146Z y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:54:06.9284213Z 2025-03-04T20:54:06.9284554Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:54:06.9284739Z pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:54:06.9284798Z 2025-03-04T20:54:06.9285137Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:54:06.9285295Z proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4)); pred_boxes = None 2025-03-04T20:54:06.9285362Z 2025-03-04T20:54:06.9285737Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:54:06.9285931Z proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T20:54:06.9285990Z 2025-03-04T20:54:06.9286485Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T20:54:06.9286618Z arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T20:54:06.9286689Z 2025-03-04T20:54:06.9287016Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:54:06.9287164Z batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T20:54:06.9287225Z 2025-03-04T20:54:06.9287700Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:54:06.9287820Z topk = logits_i.topk(6000, dim = 1); logits_i = None 2025-03-04T20:54:06.9287924Z topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0] 2025-03-04T20:54:06.9288045Z topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1]; topk = None 2025-03-04T20:54:06.9288107Z 2025-03-04T20:54:06.9288593Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:54:06.9288775Z getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T20:54:06.9289023Z topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)]; proposals_i_1 = getitem_20 = topk_idx = None 2025-03-04T20:54:06.9289087Z 2025-03-04T20:54:06.9289566Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:54:06.9289735Z full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:54:06.9289803Z 2025-03-04T20:54:06.9290109Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:54:06.9290268Z level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu')); full = level_ids = None 2025-03-04T20:54:06.9290330Z 2025-03-04T20:54:06.9290731Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T20:54:06.9290883Z getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0]; topk_proposals_i = None 2025-03-04T20:54:06.9290952Z 2025-03-04T20:54:06.9291258Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:54:06.9291412Z tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32); getitem_22 = None 2025-03-04T20:54:06.9291484Z 2025-03-04T20:54:06.9291870Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T20:54:06.9292007Z scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0]; topk_scores_i = None 2025-03-04T20:54:06.9292076Z 2025-03-04T20:54:06.9292571Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T20:54:06.9292730Z isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1); tensor_1 = None 2025-03-04T20:54:06.9292847Z all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:54:06.9293007Z isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T20:54:06.9293135Z valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T20:54:06.9293206Z 2025-03-04T20:54:06.9293585Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T20:54:06.9293708Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T20:54:06.9293771Z 2025-03-04T20:54:14.3515547Z 2025-03-04T20:54:14.3518226Z class GraphModule(torch.nn.Module): 2025-03-04T20:54:14.3519826Z def forward(self, L_features_res4_: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_weight_: "f32[1024, 1024, 3, 3][9216, 9, 3, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_bias_: "f32[1024][1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"): 2025-03-04T20:54:14.3521376Z l_features_res4_ = L_features_res4_ 2025-03-04T20:54:14.3521855Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T20:54:14.3522470Z l_self_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T20:54:14.3527808Z l_self_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T20:54:14.3530587Z l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T20:54:14.3531390Z l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T20:54:14.3531980Z l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T20:54:14.3537915Z l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T20:54:14.3539214Z 2025-03-04T20:54:14.3542651Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:54:14.3547020Z arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32) 2025-03-04T20:54:14.3548962Z 2025-03-04T20:54:14.3549613Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:54:14.3550275Z shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T20:54:14.3550668Z 2025-03-04T20:54:14.3551236Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:54:14.3552146Z arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32) 2025-03-04T20:54:14.3552424Z 2025-03-04T20:54:14.3552835Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:54:14.3553478Z shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T20:54:14.3553764Z 2025-03-04T20:54:14.3554355Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:54:14.3554977Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T20:54:14.3555300Z shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0] 2025-03-04T20:54:14.3555565Z shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T20:54:14.3555820Z 2025-03-04T20:54:14.3556245Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:54:14.3556752Z shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T20:54:14.3556987Z 2025-03-04T20:54:14.3557397Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:54:14.3557913Z shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T20:54:14.3558138Z 2025-03-04T20:54:14.3558590Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:54:14.3559224Z shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T20:54:14.3559534Z 2025-03-04T20:54:14.3560026Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:54:14.3560597Z view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T20:54:14.3561081Z view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T20:54:14.3561561Z add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T20:54:14.3561829Z x: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4); add = None 2025-03-04T20:54:14.3562050Z 2025-03-04T20:54:14.3562426Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:54:14.3562888Z tensor: "f32[82125, 4][4, 1]cpu" = x.to(torch.float32); x = None 2025-03-04T20:54:14.3563119Z 2025-03-04T20:54:14.3563454Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:14.3564347Z x_1: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(l_features_res4_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_res4_ = l_self_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T20:54:14.3565031Z 2025-03-04T20:54:14.3565383Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:54:14.3565906Z x_2: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False); x_1 = None 2025-03-04T20:54:14.3566194Z 2025-03-04T20:54:14.3566652Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:54:14.3568177Z score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T20:54:14.3568966Z 2025-03-04T20:54:14.3569449Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:54:14.3570481Z x_3: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_2 = l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T20:54:14.3571254Z 2025-03-04T20:54:14.3571700Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T20:54:14.3572261Z permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T20:54:14.3572605Z logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T20:54:14.3572862Z 2025-03-04T20:54:14.3573366Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T20:54:14.3573982Z view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_3.view(4, -1, 4, 73, 75); x_3 = None 2025-03-04T20:54:14.3574350Z permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2); view_2 = None 2025-03-04T20:54:14.3574743Z pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2); permute_1 = None 2025-03-04T20:54:14.3575035Z 2025-03-04T20:54:14.3575523Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:54:14.3576183Z pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T20:54:14.3576497Z 2025-03-04T20:54:14.3577013Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:54:14.3577645Z unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T20:54:14.3577991Z expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T20:54:14.3578324Z anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T20:54:14.3578578Z 2025-03-04T20:54:14.3579030Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:54:14.3579651Z deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T20:54:14.3579933Z 2025-03-04T20:54:14.3580337Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:54:14.3580847Z boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T20:54:14.3581105Z 2025-03-04T20:54:14.3581536Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:54:14.3582040Z getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:54:14.3582346Z getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:54:14.3582672Z widths: "f32[328500][1]cpu" = getitem_2 - getitem_3; getitem_2 = getitem_3 = None 2025-03-04T20:54:14.3582951Z 2025-03-04T20:54:14.3583357Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:54:14.3583858Z getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:54:14.3584156Z getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:54:14.3584478Z heights: "f32[328500][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T20:54:14.3584759Z 2025-03-04T20:54:14.3585159Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:54:14.3585644Z getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:54:14.3585906Z mul: "f32[328500][1]cpu" = 0.5 * widths 2025-03-04T20:54:14.3586174Z ctr_x: "f32[328500][1]cpu" = getitem_6 + mul; getitem_6 = mul = None 2025-03-04T20:54:14.3586426Z 2025-03-04T20:54:14.3586849Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:54:14.3587396Z getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:54:14.3587683Z mul_1: "f32[328500][1]cpu" = 0.5 * heights 2025-03-04T20:54:14.3587955Z ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1; getitem_7 = mul_1 = None 2025-03-04T20:54:14.3588220Z 2025-03-04T20:54:14.3588669Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:54:14.3589223Z getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:54:14.3589565Z dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0; getitem_8 = None 2025-03-04T20:54:14.3589807Z 2025-03-04T20:54:14.3590227Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:54:14.3590767Z getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:54:14.3591100Z dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0; getitem_9 = None 2025-03-04T20:54:14.3591343Z 2025-03-04T20:54:14.3591754Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:54:14.3592288Z getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:54:14.3592622Z dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0; getitem_10 = None 2025-03-04T20:54:14.3592904Z 2025-03-04T20:54:14.3593395Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:54:14.3594015Z getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:54:14.3594381Z dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0; getitem_11 = None 2025-03-04T20:54:14.3594623Z 2025-03-04T20:54:14.3595116Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:54:14.3595689Z dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:54:14.3595956Z 2025-03-04T20:54:14.3596418Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:54:14.3596995Z dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:54:14.3597261Z 2025-03-04T20:54:14.3597707Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:54:14.3598292Z getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:54:14.3598636Z mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12; dx = getitem_12 = None 2025-03-04T20:54:14.3598986Z getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:54:14.3599334Z pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13; mul_2 = getitem_13 = None 2025-03-04T20:54:14.3599593Z 2025-03-04T20:54:14.3600037Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:54:14.3600584Z getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:54:14.3600904Z mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14; dy = getitem_14 = None 2025-03-04T20:54:14.3601234Z getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:54:14.3601581Z pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15; mul_3 = getitem_15 = None 2025-03-04T20:54:14.3601842Z 2025-03-04T20:54:14.3602445Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:54:14.3602948Z exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:54:14.3603279Z getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:54:14.3603626Z pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16; exp = getitem_16 = None 2025-03-04T20:54:14.3603876Z 2025-03-04T20:54:14.3604295Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:54:14.3604793Z exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:54:14.3605132Z getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:54:14.3605482Z pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17; exp_1 = getitem_17 = None 2025-03-04T20:54:14.3605733Z 2025-03-04T20:54:14.3606133Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:54:14.3606652Z mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T20:54:14.3606913Z x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:54:14.3607144Z 2025-03-04T20:54:14.3607542Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:54:14.3607999Z mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T20:54:14.3608260Z y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:54:14.3608494Z 2025-03-04T20:54:14.3608916Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:54:14.3609393Z mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:54:14.3609681Z x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:54:14.3609925Z 2025-03-04T20:54:14.3610339Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:54:14.3610807Z mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:54:14.3611150Z y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:54:14.3611388Z 2025-03-04T20:54:14.3611820Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:54:14.3612424Z pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:54:14.3612713Z 2025-03-04T20:54:14.3613136Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:54:14.3613682Z proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4)); pred_boxes = None 2025-03-04T20:54:14.3613957Z 2025-03-04T20:54:14.3614429Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:54:14.3615015Z proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T20:54:14.3615295Z 2025-03-04T20:54:14.3615841Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T20:54:14.3616501Z arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T20:54:14.3616739Z 2025-03-04T20:54:14.3617112Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:54:14.3617586Z batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T20:54:14.3617834Z 2025-03-04T20:54:14.3618347Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:54:14.3618933Z topk = logits_i.topk(6000, dim = 1); logits_i = None 2025-03-04T20:54:14.3619194Z topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0] 2025-03-04T20:54:14.3619453Z topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1]; topk = None 2025-03-04T20:54:14.3619672Z 2025-03-04T20:54:14.3620228Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:54:14.3620889Z getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T20:54:14.3621328Z topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)]; proposals_i_1 = getitem_20 = topk_idx = None 2025-03-04T20:54:14.3621671Z 2025-03-04T20:54:14.3622218Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:54:14.3622879Z full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:54:14.3623154Z 2025-03-04T20:54:14.3623540Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:54:14.3624042Z level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu')); full = level_ids = None 2025-03-04T20:54:14.3624295Z 2025-03-04T20:54:14.3624747Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T20:54:14.3625321Z getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0]; topk_proposals_i = None 2025-03-04T20:54:14.3625577Z 2025-03-04T20:54:14.3625947Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:54:14.3626421Z tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32); getitem_22 = None 2025-03-04T20:54:14.3626675Z 2025-03-04T20:54:14.3627122Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T20:54:14.3627672Z scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0]; topk_scores_i = None 2025-03-04T20:54:14.3627920Z 2025-03-04T20:54:14.3628465Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T20:54:14.3629118Z isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1); tensor_1 = None 2025-03-04T20:54:14.3629426Z all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:54:14.3629744Z isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T20:54:14.3630083Z valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T20:54:14.3630332Z 2025-03-04T20:54:14.3630782Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T20:54:14.3631312Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T20:54:14.3631541Z 2025-03-04T20:54:14.3631623Z 2025-03-04T20:54:14.3631720Z class GraphModule(torch.nn.Module): 2025-03-04T20:54:14.3633045Z def forward(self, L_features_res4_: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_weight_: "f32[1024, 1024, 3, 3][9216, 9, 3, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_bias_: "f32[1024][1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"): 2025-03-04T20:54:14.3634476Z l_features_res4_ = L_features_res4_ 2025-03-04T20:54:14.3634869Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T20:54:14.3635423Z l_self_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T20:54:14.3635917Z l_self_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T20:54:14.3636462Z l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T20:54:14.3637084Z l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T20:54:14.3637645Z l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T20:54:14.3638192Z l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T20:54:14.3638559Z 2025-03-04T20:54:14.3639125Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:54:14.3639760Z arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32) 2025-03-04T20:54:14.3640022Z 2025-03-04T20:54:14.3640585Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:54:14.3641073Z shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T20:54:14.3641322Z 2025-03-04T20:54:14.3641847Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:54:14.3642483Z arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32) 2025-03-04T20:54:14.3642749Z 2025-03-04T20:54:14.3643130Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:54:14.3643616Z shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T20:54:14.3643871Z 2025-03-04T20:54:14.3644336Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:54:14.3644948Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T20:54:14.3645275Z shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0] 2025-03-04T20:54:14.3645535Z shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T20:54:14.3645765Z 2025-03-04T20:54:14.3646183Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:54:14.3646688Z shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T20:54:14.3646922Z 2025-03-04T20:54:14.3647356Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:54:14.3647868Z shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T20:54:14.3648097Z 2025-03-04T20:54:14.3648553Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:54:14.3649181Z shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T20:54:14.3650317Z 2025-03-04T20:54:14.3650812Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:54:14.3651392Z view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T20:54:14.3651895Z view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T20:54:14.3652371Z add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T20:54:14.3652647Z x: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4); add = None 2025-03-04T20:54:14.3652866Z 2025-03-04T20:54:14.3653243Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:54:14.3653714Z tensor: "f32[82125, 4][4, 1]cpu" = x.to(torch.float32); x = None 2025-03-04T20:54:14.3653942Z 2025-03-04T20:54:14.3654269Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:54:14.3655161Z x_1: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(l_features_res4_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_res4_ = l_self_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T20:54:14.3655848Z 2025-03-04T20:54:14.3656198Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:54:14.3656695Z x_2: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False); x_1 = None 2025-03-04T20:54:14.3656984Z 2025-03-04T20:54:14.3657437Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:54:14.3658477Z score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T20:54:14.3659211Z 2025-03-04T20:54:14.3659643Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:54:14.3660628Z x_3: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_2 = l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T20:54:14.3661346Z 2025-03-04T20:54:14.3661761Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T20:54:14.3662295Z permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T20:54:14.3662631Z logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T20:54:14.3662879Z 2025-03-04T20:54:14.3663391Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T20:54:14.3663998Z view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_3.view(4, -1, 4, 73, 75); x_3 = None 2025-03-04T20:54:14.3664384Z permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2); view_2 = None 2025-03-04T20:54:14.3664770Z pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2); permute_1 = None 2025-03-04T20:54:14.3665056Z 2025-03-04T20:54:14.3665527Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:54:14.3666171Z pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T20:54:14.3666501Z 2025-03-04T20:54:14.3667007Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:54:14.3667623Z unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T20:54:14.3667960Z expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T20:54:14.3668292Z anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T20:54:14.3668543Z 2025-03-04T20:54:14.3668987Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:54:14.3669567Z deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T20:54:14.3669848Z 2025-03-04T20:54:14.3670230Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:54:14.3670734Z boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T20:54:14.3670987Z 2025-03-04T20:54:14.3671385Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:54:14.3671883Z getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:54:14.3672185Z getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:54:14.3672504Z widths: "f32[328500][1]cpu" = getitem_2 - getitem_3; getitem_2 = getitem_3 = None 2025-03-04T20:54:14.3672772Z 2025-03-04T20:54:14.3673254Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:54:14.3673759Z getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:54:14.3674052Z getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:54:14.3674405Z heights: "f32[328500][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T20:54:14.3674667Z 2025-03-04T20:54:14.3675063Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:54:14.3675551Z getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:54:14.3675813Z mul: "f32[328500][1]cpu" = 0.5 * widths 2025-03-04T20:54:14.3676071Z ctr_x: "f32[328500][1]cpu" = getitem_6 + mul; getitem_6 = mul = None 2025-03-04T20:54:14.3676313Z 2025-03-04T20:54:14.3676727Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:54:14.3677239Z getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:54:14.3677525Z mul_1: "f32[328500][1]cpu" = 0.5 * heights 2025-03-04T20:54:14.3677809Z ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1; getitem_7 = mul_1 = None 2025-03-04T20:54:14.3678056Z 2025-03-04T20:54:14.3678465Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:54:14.3678980Z getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:54:14.3679307Z dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0; getitem_8 = None 2025-03-04T20:54:14.3679558Z 2025-03-04T20:54:14.3679952Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:54:14.3680466Z getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:54:14.3680792Z dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0; getitem_9 = None 2025-03-04T20:54:14.3681026Z 2025-03-04T20:54:14.3681418Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:54:14.3681924Z getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:54:14.3682247Z dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0; getitem_10 = None 2025-03-04T20:54:14.3682481Z 2025-03-04T20:54:14.3682884Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:54:14.3683427Z getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:54:14.3683776Z dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0; getitem_11 = None 2025-03-04T20:54:14.3684012Z 2025-03-04T20:54:14.3684441Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:54:14.3684981Z dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:54:14.3685241Z 2025-03-04T20:54:14.3685664Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:54:14.3686196Z dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:54:14.3686456Z 2025-03-04T20:54:14.3686893Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:54:14.3687454Z getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:54:14.3687772Z mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12; dx = getitem_12 = None 2025-03-04T20:54:14.3688099Z getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:54:14.3688438Z pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13; mul_2 = getitem_13 = None 2025-03-04T20:54:14.3688704Z 2025-03-04T20:54:14.3689121Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:54:14.3689662Z getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:54:14.3689968Z mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14; dy = getitem_14 = None 2025-03-04T20:54:14.3690285Z getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:54:14.3690652Z pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15; mul_3 = getitem_15 = None 2025-03-04T20:54:14.3690900Z 2025-03-04T20:54:14.3691308Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:54:14.3691794Z exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:54:14.3692109Z getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:54:14.3692462Z pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16; exp = getitem_16 = None 2025-03-04T20:54:14.3692697Z 2025-03-04T20:54:14.3693106Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:54:14.3693600Z exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:54:14.3693926Z getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:54:14.3694268Z pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17; exp_1 = getitem_17 = None 2025-03-04T20:54:14.3694516Z 2025-03-04T20:54:14.3694906Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:54:14.3695357Z mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T20:54:14.3695615Z x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:54:14.3695841Z 2025-03-04T20:54:14.3696224Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:54:14.3696667Z mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T20:54:14.3696924Z y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:54:14.3697147Z 2025-03-04T20:54:14.3697527Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:54:14.3697988Z mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:54:14.3698271Z x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:54:14.3698511Z 2025-03-04T20:54:14.3698891Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:54:14.3699346Z mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:54:14.3699625Z y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:54:14.3699882Z 2025-03-04T20:54:14.3700465Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:54:14.3701034Z pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:54:14.3701319Z 2025-03-04T20:54:14.3701734Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:54:14.3702403Z proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4)); pred_boxes = None 2025-03-04T20:54:14.3702677Z 2025-03-04T20:54:14.3703139Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:54:14.3703755Z proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T20:54:14.3704037Z 2025-03-04T20:54:14.3704589Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T20:54:14.3705241Z arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T20:54:14.3705479Z 2025-03-04T20:54:14.3705880Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:54:14.3706363Z batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T20:54:14.3706612Z 2025-03-04T20:54:14.3707124Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:54:14.3707723Z topk = logits_i.topk(6000, dim = 1); logits_i = None 2025-03-04T20:54:14.3707979Z topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0] 2025-03-04T20:54:14.3708240Z topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1]; topk = None 2025-03-04T20:54:14.3708461Z 2025-03-04T20:54:14.3708994Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:54:14.3709669Z getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T20:54:14.3710109Z topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)]; proposals_i_1 = getitem_20 = topk_idx = None 2025-03-04T20:54:14.3710452Z 2025-03-04T20:54:14.3710999Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:54:14.3711673Z full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:54:14.3711954Z 2025-03-04T20:54:14.3712329Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:54:14.3712839Z level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu')); full = level_ids = None 2025-03-04T20:54:14.3713101Z 2025-03-04T20:54:14.3713615Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T20:54:14.3714244Z getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0]; topk_proposals_i = None 2025-03-04T20:54:14.3714519Z 2025-03-04T20:54:14.3714893Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:54:14.3715377Z tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32); getitem_22 = None 2025-03-04T20:54:14.3715633Z 2025-03-04T20:54:14.3716107Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T20:54:14.3716669Z scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0]; topk_scores_i = None 2025-03-04T20:54:14.3716917Z 2025-03-04T20:54:14.3717485Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T20:54:14.3718139Z isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1); tensor_1 = None 2025-03-04T20:54:14.3718438Z all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:54:14.3718753Z isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T20:54:14.3719083Z valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T20:54:14.3719350Z 2025-03-04T20:54:14.3719798Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T20:54:14.3720325Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T20:54:14.3720558Z 2025-03-04T20:54:14.7758907Z 2025-03-04T20:54:14.7763967Z class GraphModule(torch.nn.Module): 2025-03-04T20:54:14.7769312Z def forward(self, L_pred_anchor_deltas_0_: "f32[4, 82125, 4][328500, 4, 1]cpu", L_anchors_0_tensor: "f32[82125, 4][4, 1]cpu", L_pred_objectness_logits_0_: "f32[4, 82125][82125, 1]cpu"): 2025-03-04T20:54:14.7772562Z l_pred_anchor_deltas_0_ = L_pred_anchor_deltas_0_ 2025-03-04T20:54:14.7774672Z l_anchors_0_tensor = L_anchors_0_tensor 2025-03-04T20:54:14.7775089Z l_pred_objectness_logits_0_ = L_pred_objectness_logits_0_ 2025-03-04T20:54:14.7779803Z 2025-03-04T20:54:14.7782478Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:54:14.7783358Z pred_anchor_deltas_i: "f32[328500, 4][4, 1]cpu" = l_pred_anchor_deltas_0_.reshape(-1, 4); l_pred_anchor_deltas_0_ = None 2025-03-04T20:54:14.7785461Z 2025-03-04T20:54:14.7786280Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:54:14.7787119Z unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = l_anchors_0_tensor.unsqueeze(0); l_anchors_0_tensor = None 2025-03-04T20:54:14.7787662Z expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T20:54:14.7788563Z anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T20:54:14.7788930Z 2025-03-04T20:54:14.7789447Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:54:14.7790050Z deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.float(); pred_anchor_deltas_i = None 2025-03-04T20:54:14.7790668Z 2025-03-04T20:54:14.7791088Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:54:14.7791609Z boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T20:54:14.7791880Z 2025-03-04T20:54:14.7792298Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:54:14.7792872Z getitem: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:54:14.7793294Z getitem_1: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:54:14.7793639Z widths: "f32[328500][1]cpu" = getitem - getitem_1; getitem = getitem_1 = None 2025-03-04T20:54:14.7793900Z 2025-03-04T20:54:14.7794369Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:54:14.7794876Z getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:54:14.7795167Z getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:54:14.7795484Z heights: "f32[328500][1]cpu" = getitem_2 - getitem_3; getitem_2 = getitem_3 = None 2025-03-04T20:54:14.7795742Z 2025-03-04T20:54:14.7796134Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:54:14.7796667Z getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:54:14.7796925Z mul: "f32[328500][1]cpu" = 0.5 * widths 2025-03-04T20:54:14.7797176Z ctr_x: "f32[328500][1]cpu" = getitem_4 + mul; getitem_4 = mul = None 2025-03-04T20:54:14.7797412Z 2025-03-04T20:54:14.7797800Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:54:14.7798299Z getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:54:14.7798581Z mul_1: "f32[328500][1]cpu" = 0.5 * heights 2025-03-04T20:54:14.7798845Z ctr_y: "f32[328500][1]cpu" = getitem_5 + mul_1; getitem_5 = mul_1 = None 2025-03-04T20:54:14.7799080Z 2025-03-04T20:54:14.7799507Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:54:14.7800002Z getitem_6: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:54:14.7800319Z dx: "f32[328500, 1][1, 1]cpu" = getitem_6 / 1.0; getitem_6 = None 2025-03-04T20:54:14.7800546Z 2025-03-04T20:54:14.7800926Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:54:14.7801414Z getitem_7: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:54:14.7801722Z dy: "f32[328500, 1][1, 1]cpu" = getitem_7 / 1.0; getitem_7 = None 2025-03-04T20:54:14.7802126Z 2025-03-04T20:54:14.7802513Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:54:14.7803010Z getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:54:14.7803321Z dw: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0; getitem_8 = None 2025-03-04T20:54:14.7803542Z 2025-03-04T20:54:14.7803920Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:54:14.7804482Z getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:54:14.7804813Z dh: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0; getitem_9 = None 2025-03-04T20:54:14.7805033Z 2025-03-04T20:54:14.7805445Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:54:14.7805962Z dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:54:14.7806274Z 2025-03-04T20:54:14.7806686Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:54:14.7807197Z dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:54:14.7807447Z 2025-03-04T20:54:14.7807893Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:54:14.7808432Z getitem_10: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:54:14.7808740Z mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_10; dx = getitem_10 = None 2025-03-04T20:54:14.7809096Z getitem_11: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:54:14.7809476Z pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_11; mul_2 = getitem_11 = None 2025-03-04T20:54:14.7809725Z 2025-03-04T20:54:14.7810148Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:54:14.7810676Z getitem_12: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:54:14.7810982Z mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_12; dy = getitem_12 = None 2025-03-04T20:54:14.7811300Z getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:54:14.7811641Z pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_13; mul_3 = getitem_13 = None 2025-03-04T20:54:14.7811891Z 2025-03-04T20:54:14.7812298Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:54:14.7812787Z exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:54:14.7813103Z getitem_14: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:54:14.7813436Z pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_14; exp = getitem_14 = None 2025-03-04T20:54:14.7813672Z 2025-03-04T20:54:14.7814079Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:54:14.7814566Z exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:54:14.7814886Z getitem_15: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:54:14.7815225Z pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_15; exp_1 = getitem_15 = None 2025-03-04T20:54:14.7815467Z 2025-03-04T20:54:14.7815859Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:54:14.7816306Z mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T20:54:14.7816584Z x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:54:14.7816808Z 2025-03-04T20:54:14.7817190Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:54:14.7817632Z mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T20:54:14.7817879Z y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:54:14.7818101Z 2025-03-04T20:54:14.7818477Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:54:14.7818956Z mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:54:14.7819244Z x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:54:14.7819486Z 2025-03-04T20:54:14.7819900Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:54:14.7820362Z mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:54:14.7820640Z y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:54:14.7820874Z 2025-03-04T20:54:14.7821302Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:54:14.7821924Z pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:54:14.7822213Z 2025-03-04T20:54:14.7822625Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:54:14.7823171Z proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4)); pred_boxes = None 2025-03-04T20:54:14.7823449Z 2025-03-04T20:54:14.7823918Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:54:14.7824518Z proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T20:54:14.7824809Z 2025-03-04T20:54:14.7825378Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T20:54:14.7826062Z arange: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T20:54:14.7826340Z 2025-03-04T20:54:14.7826714Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:54:14.7827193Z batch_idx: "i64[4][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T20:54:14.7827441Z 2025-03-04T20:54:14.7827955Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:54:14.7828608Z topk = l_pred_objectness_logits_0_.topk(6000, dim = 1); l_pred_objectness_logits_0_ = None 2025-03-04T20:54:14.7828941Z topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0] 2025-03-04T20:54:14.7829207Z topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1]; topk = None 2025-03-04T20:54:14.7829434Z 2025-03-04T20:54:14.7829969Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:54:14.7830643Z getitem_18: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T20:54:14.7831081Z topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_18, topk_idx)]; proposals_i_1 = getitem_18 = topk_idx = None 2025-03-04T20:54:14.7831419Z 2025-03-04T20:54:14.7831964Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:54:14.7832640Z full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:54:14.7832922Z 2025-03-04T20:54:14.7833378Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:54:14.7833912Z level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu')); full = level_ids = None 2025-03-04T20:54:14.7834173Z 2025-03-04T20:54:14.7834639Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T20:54:14.7835220Z getitem_20: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0]; topk_proposals_i = None 2025-03-04T20:54:14.7835482Z 2025-03-04T20:54:14.7835883Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:54:14.7836376Z tensor: "f32[6000, 4][4, 1]cpu" = getitem_20.to(torch.float32); getitem_20 = None 2025-03-04T20:54:14.7836632Z 2025-03-04T20:54:14.7837096Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T20:54:14.7837665Z scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0]; topk_scores_i = None 2025-03-04T20:54:14.7837921Z 2025-03-04T20:54:14.7838507Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T20:54:14.7839169Z isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor); tensor = None 2025-03-04T20:54:14.7839478Z all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:54:14.7839806Z isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T20:54:14.7840150Z valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T20:54:14.7840404Z 2025-03-04T20:54:14.7840873Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T20:54:14.7841411Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T20:54:14.7841645Z 2025-03-04T20:54:27.9367570Z 2025-03-04T20:54:27.9370283Z class GraphModule(torch.nn.Module): 2025-03-04T20:54:27.9372038Z def forward(self, L_stack0_: "f32[3231, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1231 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1231 - s0, 4][4, 1]cpu"): 2025-03-04T20:54:27.9374087Z l_stack0_ = L_stack0_ 2025-03-04T20:54:27.9374593Z l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_ 2025-03-04T20:54:27.9375210Z l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_ 2025-03-04T20:54:27.9376017Z l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ 2025-03-04T20:54:27.9376592Z l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ 2025-03-04T20:54:27.9377084Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T20:54:27.9377552Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T20:54:27.9377964Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T20:54:27.9378369Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T20:54:27.9378668Z 2025-03-04T20:54:27.9379240Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/roi_heads.py:480 in torch_dynamo_resume_in_forward_at_477, code: predictions = self.box_predictor(box_features.mean(dim=[2, 3])) 2025-03-04T20:54:27.9379971Z mean: "f32[3231, 2048][2048, 1]cpu" = l_stack0_.mean(dim = [2, 3]); l_stack0_ = None 2025-03-04T20:54:27.9380241Z 2025-03-04T20:54:27.9380657Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x) 2025-03-04T20:54:27.9381678Z scores: "f32[3231, 81][81, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_); l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None 2025-03-04T20:54:27.9382422Z 2025-03-04T20:54:27.9382857Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x) 2025-03-04T20:54:27.9383918Z proposal_deltas: "f32[3231, 320][320, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_); mean = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None 2025-03-04T20:54:27.9384693Z 2025-03-04T20:54:27.9385085Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:54:27.9385567Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T20:54:27.9385826Z getitem: "Sym(s0)" = size[0] 2025-03-04T20:54:27.9386059Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T20:54:27.9386346Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T20:54:27.9386600Z getitem_2: "Sym(1231 - s0)" = size_1[0] 2025-03-04T20:54:27.9386836Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T20:54:27.9387046Z 2025-03-04T20:54:27.9387409Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:54:27.9388357Z proposal_boxes: "f32[3231, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T20:54:27.9389055Z 2025-03-04T20:54:27.9389503Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:54:27.9390074Z deltas: "f32[3231, 320][320, 1]cpu" = proposal_deltas.float(); proposal_deltas = None 2025-03-04T20:54:27.9390353Z 2025-03-04T20:54:27.9390747Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:54:27.9391256Z boxes: "f32[3231, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T20:54:27.9391522Z 2025-03-04T20:54:27.9391935Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:54:27.9392419Z getitem_4: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:54:27.9392710Z getitem_5: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:54:27.9393020Z widths: "f32[3231][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T20:54:27.9393294Z 2025-03-04T20:54:27.9393696Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:54:27.9394186Z getitem_6: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:54:27.9394474Z getitem_7: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:54:27.9394785Z heights: "f32[3231][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T20:54:27.9395038Z 2025-03-04T20:54:27.9395586Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:54:27.9396106Z getitem_8: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:54:27.9396375Z mul: "f32[3231][1]cpu" = 0.5 * widths 2025-03-04T20:54:27.9396635Z ctr_x: "f32[3231][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T20:54:27.9396866Z 2025-03-04T20:54:27.9397250Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:54:27.9397741Z getitem_9: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:54:27.9398016Z mul_1: "f32[3231][1]cpu" = 0.5 * heights 2025-03-04T20:54:27.9398266Z ctr_y: "f32[3231][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T20:54:27.9398495Z 2025-03-04T20:54:27.9398914Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:54:27.9399407Z getitem_10: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:54:27.9399720Z dx: "f32[3231, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T20:54:27.9399942Z 2025-03-04T20:54:27.9400317Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:54:27.9400806Z getitem_11: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:54:27.9401138Z dy: "f32[3231, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T20:54:27.9401360Z 2025-03-04T20:54:27.9401732Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:54:27.9402420Z getitem_12: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:54:27.9402728Z dw: "f32[3231, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T20:54:27.9402955Z 2025-03-04T20:54:27.9403410Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:54:27.9403946Z getitem_13: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:54:27.9404284Z dh: "f32[3231, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T20:54:27.9404512Z 2025-03-04T20:54:27.9404964Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:54:27.9405493Z dw_1: "f32[3231, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:54:27.9405744Z 2025-03-04T20:54:27.9406158Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:54:27.9406712Z dh_1: "f32[3231, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:54:27.9406961Z 2025-03-04T20:54:27.9407393Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:54:27.9407934Z getitem_14: "f32[3231, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:54:27.9408248Z mul_2: "f32[3231, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T20:54:27.9408576Z getitem_15: "f32[3231, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:54:27.9408917Z pred_ctr_x: "f32[3231, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T20:54:27.9409168Z 2025-03-04T20:54:27.9409610Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:54:27.9410155Z getitem_16: "f32[3231, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:54:27.9410465Z mul_3: "f32[3231, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T20:54:27.9410787Z getitem_17: "f32[3231, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:54:27.9411130Z pred_ctr_y: "f32[3231, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T20:54:27.9411382Z 2025-03-04T20:54:27.9411799Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:54:27.9412302Z exp: "f32[3231, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:54:27.9412625Z getitem_18: "f32[3231, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:54:27.9412965Z pred_w: "f32[3231, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T20:54:27.9413202Z 2025-03-04T20:54:27.9413624Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:54:27.9414148Z exp_1: "f32[3231, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:54:27.9414473Z getitem_19: "f32[3231, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:54:27.9414815Z pred_h: "f32[3231, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T20:54:27.9415060Z 2025-03-04T20:54:27.9415460Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:54:27.9415923Z mul_6: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T20:54:27.9416198Z x1: "f32[3231, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:54:27.9416433Z 2025-03-04T20:54:27.9416824Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:54:27.9417310Z mul_7: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T20:54:27.9417598Z y1: "f32[3231, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:54:27.9417826Z 2025-03-04T20:54:27.9418214Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:54:27.9418670Z mul_8: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:54:27.9418949Z x2: "f32[3231, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:54:27.9419202Z 2025-03-04T20:54:27.9419595Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:54:27.9420574Z mul_9: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:54:27.9421068Z y2: "f32[3231, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:54:27.9422357Z 2025-03-04T20:54:27.9422806Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:54:27.9423380Z pred_boxes: "f32[3231, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:54:27.9423665Z 2025-03-04T20:54:27.9424085Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:54:27.9426327Z predict_boxes: "f32[3231, 320][320, 1]cpu" = pred_boxes.reshape((3231, 320)); pred_boxes = None 2025-03-04T20:54:27.9426607Z 2025-03-04T20:54:27.9427346Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T20:54:27.9428022Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T20:54:27.9428383Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T20:54:27.9428912Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T20:54:27.9429254Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T20:54:27.9429568Z getitem_23: "f32[1231 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T20:54:27.9429828Z 2025-03-04T20:54:27.9430213Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:54:27.9430777Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T20:54:27.9431129Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T20:54:27.9431457Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T20:54:27.9431825Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T20:54:27.9432180Z getitem_26: "Sym(1231 - s0)" = size_3[0] 2025-03-04T20:54:27.9432425Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T20:54:27.9432651Z 2025-03-04T20:54:27.9433080Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T20:54:27.9433676Z probs: "f32[3231, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1); scores = None 2025-03-04T20:54:27.9433984Z 2025-03-04T20:54:27.9434482Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T20:54:27.9435147Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T20:54:27.9435603Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T20:54:27.9435905Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T20:54:27.9436214Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T20:54:27.9436541Z getitem_31: "f32[1231 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T20:54:27.9436846Z 2025-03-04T20:54:27.9437425Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T20:54:27.9438152Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T20:54:27.9438503Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:54:27.9438919Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T20:54:27.9439316Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T20:54:27.9439643Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T20:54:27.9439926Z 2025-03-04T20:54:27.9440429Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T20:54:27.9441010Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T20:54:27.9441245Z 2025-03-04T20:54:27.9441386Z 2025-03-04T20:54:27.9441478Z class GraphModule(torch.nn.Module): 2025-03-04T20:54:27.9442941Z def forward(self, L_stack0_: "f32[3231, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1231 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1231 - s0, 4][4, 1]cpu"): 2025-03-04T20:54:27.9444340Z l_stack0_ = L_stack0_ 2025-03-04T20:54:27.9444750Z l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_ 2025-03-04T20:54:27.9445408Z l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_ 2025-03-04T20:54:27.9446037Z l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ 2025-03-04T20:54:27.9446642Z l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ 2025-03-04T20:54:27.9447173Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T20:54:27.9447592Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T20:54:27.9448024Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T20:54:27.9448444Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T20:54:27.9448745Z 2025-03-04T20:54:27.9449288Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/roi_heads.py:480 in torch_dynamo_resume_in_forward_at_477, code: predictions = self.box_predictor(box_features.mean(dim=[2, 3])) 2025-03-04T20:54:27.9449921Z mean: "f32[3231, 2048][2048, 1]cpu" = l_stack0_.mean(dim = [2, 3]); l_stack0_ = None 2025-03-04T20:54:27.9450177Z 2025-03-04T20:54:27.9450571Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x) 2025-03-04T20:54:27.9451555Z scores: "f32[3231, 81][81, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_); l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None 2025-03-04T20:54:27.9452293Z 2025-03-04T20:54:27.9452706Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x) 2025-03-04T20:54:27.9453730Z proposal_deltas: "f32[3231, 320][320, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_); mean = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None 2025-03-04T20:54:27.9454483Z 2025-03-04T20:54:27.9454856Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:54:27.9455321Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T20:54:27.9455572Z getitem: "Sym(s0)" = size[0] 2025-03-04T20:54:27.9455801Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T20:54:27.9456074Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T20:54:27.9456334Z getitem_2: "Sym(1231 - s0)" = size_1[0] 2025-03-04T20:54:27.9456578Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T20:54:27.9456794Z 2025-03-04T20:54:27.9457162Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:54:27.9458110Z proposal_boxes: "f32[3231, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T20:54:27.9458828Z 2025-03-04T20:54:27.9459288Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:54:27.9459882Z deltas: "f32[3231, 320][320, 1]cpu" = proposal_deltas.float(); proposal_deltas = None 2025-03-04T20:54:27.9460151Z 2025-03-04T20:54:27.9460543Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:54:27.9461061Z boxes: "f32[3231, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T20:54:27.9461334Z 2025-03-04T20:54:27.9461733Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:54:27.9462246Z getitem_4: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:54:27.9462553Z getitem_5: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:54:27.9462872Z widths: "f32[3231][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T20:54:27.9463131Z 2025-03-04T20:54:27.9463553Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:54:27.9464037Z getitem_6: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:54:27.9464318Z getitem_7: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:54:27.9464625Z heights: "f32[3231][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T20:54:27.9464893Z 2025-03-04T20:54:27.9465286Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:54:27.9465753Z getitem_8: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:54:27.9465997Z mul: "f32[3231][1]cpu" = 0.5 * widths 2025-03-04T20:54:27.9466240Z ctr_x: "f32[3231][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T20:54:27.9466465Z 2025-03-04T20:54:27.9466854Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:54:27.9467348Z getitem_9: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:54:27.9467614Z mul_1: "f32[3231][1]cpu" = 0.5 * heights 2025-03-04T20:54:27.9467867Z ctr_y: "f32[3231][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T20:54:27.9468099Z 2025-03-04T20:54:27.9468506Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:54:27.9469005Z getitem_10: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:54:27.9469319Z dx: "f32[3231, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T20:54:27.9469540Z 2025-03-04T20:54:27.9469914Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:54:27.9470409Z getitem_11: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:54:27.9470722Z dy: "f32[3231, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T20:54:27.9470949Z 2025-03-04T20:54:27.9471335Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:54:27.9476049Z getitem_12: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:54:27.9515715Z dw: "f32[3231, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T20:54:27.9516145Z 2025-03-04T20:54:27.9516660Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:54:27.9517280Z getitem_13: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:54:27.9517698Z dh: "f32[3231, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T20:54:27.9517981Z 2025-03-04T20:54:27.9518464Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:54:27.9519229Z dw_1: "f32[3231, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:54:27.9519496Z 2025-03-04T20:54:27.9519920Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:54:27.9520481Z dh_1: "f32[3231, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:54:27.9520736Z 2025-03-04T20:54:27.9521173Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:54:27.9521719Z getitem_14: "f32[3231, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:54:27.9522035Z mul_2: "f32[3231, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T20:54:27.9522417Z getitem_15: "f32[3231, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:54:27.9522754Z pred_ctr_x: "f32[3231, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T20:54:27.9523000Z 2025-03-04T20:54:27.9523424Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:54:27.9523951Z getitem_16: "f32[3231, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:54:27.9524254Z mul_3: "f32[3231, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T20:54:27.9524568Z getitem_17: "f32[3231, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:54:27.9524905Z pred_ctr_y: "f32[3231, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T20:54:27.9525151Z 2025-03-04T20:54:27.9525577Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:54:27.9526069Z exp: "f32[3231, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:54:27.9526380Z getitem_18: "f32[3231, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:54:27.9526846Z pred_w: "f32[3231, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T20:54:27.9527239Z 2025-03-04T20:54:27.9527664Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:54:27.9528165Z exp_1: "f32[3231, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:54:27.9528490Z getitem_19: "f32[3231, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:54:27.9528839Z pred_h: "f32[3231, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T20:54:27.9529084Z 2025-03-04T20:54:27.9529493Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:54:27.9530032Z mul_6: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T20:54:27.9530291Z x1: "f32[3231, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:54:27.9530522Z 2025-03-04T20:54:27.9530917Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:54:27.9531376Z mul_7: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T20:54:27.9531632Z y1: "f32[3231, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:54:27.9531859Z 2025-03-04T20:54:27.9532268Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:54:27.9532779Z mul_8: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:54:27.9533232Z x2: "f32[3231, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:54:27.9533485Z 2025-03-04T20:54:27.9533931Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:54:27.9534404Z mul_9: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:54:27.9534687Z y2: "f32[3231, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:54:27.9534924Z 2025-03-04T20:54:27.9535350Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:54:27.9535955Z pred_boxes: "f32[3231, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:54:27.9536246Z 2025-03-04T20:54:27.9536662Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:54:27.9537218Z predict_boxes: "f32[3231, 320][320, 1]cpu" = pred_boxes.reshape((3231, 320)); pred_boxes = None 2025-03-04T20:54:27.9537652Z 2025-03-04T20:54:27.9538101Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T20:54:27.9538719Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T20:54:27.9539081Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T20:54:27.9539369Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T20:54:27.9539668Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T20:54:27.9539980Z getitem_23: "f32[1231 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T20:54:27.9540239Z 2025-03-04T20:54:27.9540627Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:54:27.9541169Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T20:54:27.9541503Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T20:54:27.9541728Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T20:54:27.9542080Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T20:54:27.9542422Z getitem_26: "Sym(1231 - s0)" = size_3[0] 2025-03-04T20:54:27.9542657Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T20:54:27.9542867Z 2025-03-04T20:54:27.9543274Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T20:54:27.9543845Z probs: "f32[3231, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1); scores = None 2025-03-04T20:54:27.9544124Z 2025-03-04T20:54:27.9544557Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T20:54:27.9545152Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T20:54:27.9545509Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T20:54:27.9545815Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T20:54:27.9546104Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T20:54:27.9546413Z getitem_31: "f32[1231 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T20:54:27.9546669Z 2025-03-04T20:54:27.9547233Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T20:54:27.9547917Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T20:54:27.9548254Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:54:27.9548589Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T20:54:27.9548954Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T20:54:27.9549239Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T20:54:27.9549467Z 2025-03-04T20:54:27.9549893Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T20:54:27.9550397Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T20:54:27.9550623Z 2025-03-04T20:54:27.9550779Z 2025-03-04T20:54:27.9550872Z class GraphModule(torch.nn.Module): 2025-03-04T20:54:27.9552225Z def forward(self, L_stack0_: "f32[3231, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1231 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1231 - s0, 4][4, 1]cpu"): 2025-03-04T20:54:27.9553545Z l_stack0_ = L_stack0_ 2025-03-04T20:54:27.9553929Z l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_ 2025-03-04T20:54:27.9554493Z l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_ 2025-03-04T20:54:27.9555055Z l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ 2025-03-04T20:54:27.9555718Z l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ 2025-03-04T20:54:27.9556195Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T20:54:27.9556593Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T20:54:27.9557022Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T20:54:27.9557437Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T20:54:27.9557728Z 2025-03-04T20:54:27.9558261Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/roi_heads.py:480 in torch_dynamo_resume_in_forward_at_477, code: predictions = self.box_predictor(box_features.mean(dim=[2, 3])) 2025-03-04T20:54:27.9558897Z mean: "f32[3231, 2048][2048, 1]cpu" = l_stack0_.mean(dim = [2, 3]); l_stack0_ = None 2025-03-04T20:54:27.9559178Z 2025-03-04T20:54:27.9559573Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x) 2025-03-04T20:54:27.9560562Z scores: "f32[3231, 81][81, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_); l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None 2025-03-04T20:54:27.9561280Z 2025-03-04T20:54:27.9561692Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x) 2025-03-04T20:54:27.9562714Z proposal_deltas: "f32[3231, 320][320, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_); mean = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None 2025-03-04T20:54:27.9563496Z 2025-03-04T20:54:27.9563863Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:54:27.9564325Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T20:54:27.9564576Z getitem: "Sym(s0)" = size[0] 2025-03-04T20:54:27.9564805Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T20:54:27.9565073Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T20:54:27.9565333Z getitem_2: "Sym(1231 - s0)" = size_1[0] 2025-03-04T20:54:27.9565571Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T20:54:27.9565788Z 2025-03-04T20:54:27.9566162Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:54:27.9567127Z proposal_boxes: "f32[3231, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T20:54:27.9567844Z 2025-03-04T20:54:27.9568307Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:54:27.9568885Z deltas: "f32[3231, 320][320, 1]cpu" = proposal_deltas.float(); proposal_deltas = None 2025-03-04T20:54:27.9569152Z 2025-03-04T20:54:27.9569554Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:54:27.9570079Z boxes: "f32[3231, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T20:54:27.9570345Z 2025-03-04T20:54:27.9570736Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:54:27.9571256Z getitem_4: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:54:27.9571549Z getitem_5: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:54:27.9571855Z widths: "f32[3231][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T20:54:27.9572108Z 2025-03-04T20:54:27.9572509Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:54:27.9573020Z getitem_6: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:54:27.9573312Z getitem_7: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:54:27.9573631Z heights: "f32[3231][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T20:54:27.9573888Z 2025-03-04T20:54:27.9574290Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:54:27.9574766Z getitem_8: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:54:27.9575015Z mul: "f32[3231][1]cpu" = 0.5 * widths 2025-03-04T20:54:27.9575257Z ctr_x: "f32[3231][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T20:54:27.9575544Z 2025-03-04T20:54:27.9575938Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:54:27.9576453Z getitem_9: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:54:27.9576727Z mul_1: "f32[3231][1]cpu" = 0.5 * heights 2025-03-04T20:54:27.9576976Z ctr_y: "f32[3231][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T20:54:27.9577210Z 2025-03-04T20:54:27.9577613Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:54:27.9578106Z getitem_10: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:54:27.9578417Z dx: "f32[3231, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T20:54:27.9580361Z 2025-03-04T20:54:27.9580844Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:54:27.9581381Z getitem_11: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:54:27.9581690Z dy: "f32[3231, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T20:54:27.9581917Z 2025-03-04T20:54:27.9582296Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:54:27.9582790Z getitem_12: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:54:27.9583101Z dw: "f32[3231, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T20:54:27.9583326Z 2025-03-04T20:54:27.9583706Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:54:27.9584273Z getitem_13: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:54:27.9584613Z dh: "f32[3231, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T20:54:27.9584833Z 2025-03-04T20:54:27.9585250Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:54:27.9585819Z dw_1: "f32[3231, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:54:27.9586072Z 2025-03-04T20:54:27.9586485Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:54:27.9586999Z dh_1: "f32[3231, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:54:27.9587253Z 2025-03-04T20:54:27.9587704Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:54:27.9588690Z getitem_14: "f32[3231, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:54:27.9589009Z mul_2: "f32[3231, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T20:54:27.9589362Z getitem_15: "f32[3231, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:54:27.9589703Z pred_ctr_x: "f32[3231, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T20:54:27.9589955Z 2025-03-04T20:54:27.9590403Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:54:27.9590957Z getitem_16: "f32[3231, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:54:27.9591289Z mul_3: "f32[3231, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T20:54:27.9591609Z getitem_17: "f32[3231, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:54:27.9591945Z pred_ctr_y: "f32[3231, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T20:54:27.9592194Z 2025-03-04T20:54:27.9592615Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:54:27.9593119Z exp: "f32[3231, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:54:27.9593437Z getitem_18: "f32[3231, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:54:27.9593771Z pred_w: "f32[3231, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T20:54:27.9594018Z 2025-03-04T20:54:27.9594439Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:54:27.9594944Z exp_1: "f32[3231, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:54:27.9595337Z getitem_19: "f32[3231, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:54:27.9595696Z pred_h: "f32[3231, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T20:54:27.9595942Z 2025-03-04T20:54:27.9596366Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:54:27.9596851Z mul_6: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T20:54:27.9597111Z x1: "f32[3231, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:54:27.9597341Z 2025-03-04T20:54:27.9597737Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:54:27.9598194Z mul_7: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T20:54:27.9598447Z y1: "f32[3231, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:54:27.9598702Z 2025-03-04T20:54:27.9599085Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:54:27.9599557Z mul_8: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:54:27.9599841Z x2: "f32[3231, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:54:27.9600083Z 2025-03-04T20:54:27.9600470Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:54:27.9600954Z mul_9: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:54:27.9601235Z y2: "f32[3231, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:54:27.9601474Z 2025-03-04T20:54:27.9602067Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:54:27.9602725Z pred_boxes: "f32[3231, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:54:27.9603016Z 2025-03-04T20:54:27.9603437Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:54:27.9603992Z predict_boxes: "f32[3231, 320][320, 1]cpu" = pred_boxes.reshape((3231, 320)); pred_boxes = None 2025-03-04T20:54:27.9604328Z 2025-03-04T20:54:27.9604774Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T20:54:27.9605404Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T20:54:27.9605763Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T20:54:27.9606050Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T20:54:27.9606357Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T20:54:27.9606670Z getitem_23: "f32[1231 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T20:54:27.9606921Z 2025-03-04T20:54:27.9607290Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:54:27.9607833Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T20:54:27.9608174Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T20:54:27.9608403Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T20:54:27.9608754Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T20:54:27.9609097Z getitem_26: "Sym(1231 - s0)" = size_3[0] 2025-03-04T20:54:27.9609333Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T20:54:27.9609542Z 2025-03-04T20:54:27.9609950Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T20:54:27.9610491Z probs: "f32[3231, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1); scores = None 2025-03-04T20:54:27.9610767Z 2025-03-04T20:54:27.9611196Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T20:54:27.9611778Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T20:54:27.9612150Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T20:54:27.9612425Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T20:54:27.9612711Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T20:54:27.9613011Z getitem_31: "f32[1231 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T20:54:27.9613256Z 2025-03-04T20:54:27.9613786Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T20:54:27.9614484Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T20:54:27.9614818Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:54:27.9615146Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T20:54:27.9615488Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T20:54:27.9615777Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T20:54:27.9616003Z 2025-03-04T20:54:27.9616445Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T20:54:27.9616977Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T20:54:27.9617235Z 2025-03-04T20:54:27.9617326Z 2025-03-04T20:54:27.9617411Z class GraphModule(torch.nn.Module): 2025-03-04T20:54:27.9618757Z def forward(self, L_stack0_: "f32[3231, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1231 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1231 - s0, 4][4, 1]cpu"): 2025-03-04T20:54:27.9620065Z l_stack0_ = L_stack0_ 2025-03-04T20:54:27.9620443Z l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_ 2025-03-04T20:54:27.9620991Z l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_ 2025-03-04T20:54:27.9621535Z l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ 2025-03-04T20:54:27.9622083Z l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ 2025-03-04T20:54:27.9622547Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T20:54:27.9622935Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T20:54:27.9623315Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T20:54:27.9623701Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T20:54:27.9623982Z 2025-03-04T20:54:27.9624490Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/roi_heads.py:480 in torch_dynamo_resume_in_forward_at_477, code: predictions = self.box_predictor(box_features.mean(dim=[2, 3])) 2025-03-04T20:54:27.9625117Z mean: "f32[3231, 2048][2048, 1]cpu" = l_stack0_.mean(dim = [2, 3]); l_stack0_ = None 2025-03-04T20:54:27.9625369Z 2025-03-04T20:54:27.9625747Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x) 2025-03-04T20:54:27.9626702Z scores: "f32[3231, 81][81, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_); l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None 2025-03-04T20:54:27.9627407Z 2025-03-04T20:54:27.9627804Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x) 2025-03-04T20:54:27.9628820Z proposal_deltas: "f32[3231, 320][320, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_); mean = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None 2025-03-04T20:54:27.9629560Z 2025-03-04T20:54:27.9629924Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:54:27.9630375Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T20:54:27.9630643Z getitem: "Sym(s0)" = size[0] 2025-03-04T20:54:27.9630862Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T20:54:27.9631122Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T20:54:27.9631369Z getitem_2: "Sym(1231 - s0)" = size_1[0] 2025-03-04T20:54:27.9631599Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T20:54:27.9631808Z 2025-03-04T20:54:27.9632173Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:54:27.9633088Z proposal_boxes: "f32[3231, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T20:54:27.9633799Z 2025-03-04T20:54:27.9634581Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:54:27.9635313Z deltas: "f32[3231, 320][320, 1]cpu" = proposal_deltas.float(); proposal_deltas = None 2025-03-04T20:54:27.9635600Z 2025-03-04T20:54:27.9636003Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:54:27.9636532Z boxes: "f32[3231, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T20:54:27.9636808Z 2025-03-04T20:54:27.9637214Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:54:27.9637713Z getitem_4: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:54:27.9638009Z getitem_5: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:54:27.9638327Z widths: "f32[3231][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T20:54:27.9638590Z 2025-03-04T20:54:27.9639001Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:54:27.9639524Z getitem_6: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:54:27.9639818Z getitem_7: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:54:27.9640127Z heights: "f32[3231][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T20:54:27.9640386Z 2025-03-04T20:54:27.9640781Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:54:27.9641285Z getitem_8: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:54:27.9641542Z mul: "f32[3231][1]cpu" = 0.5 * widths 2025-03-04T20:54:27.9641798Z ctr_x: "f32[3231][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T20:54:27.9642029Z 2025-03-04T20:54:27.9642446Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:54:27.9642953Z getitem_9: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:54:27.9643236Z mul_1: "f32[3231][1]cpu" = 0.5 * heights 2025-03-04T20:54:27.9643503Z ctr_y: "f32[3231][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T20:54:27.9643769Z 2025-03-04T20:54:27.9644179Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:54:27.9644701Z getitem_10: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:54:27.9645025Z dx: "f32[3231, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T20:54:27.9645254Z 2025-03-04T20:54:27.9645636Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:54:27.9646138Z getitem_11: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:54:27.9646450Z dy: "f32[3231, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T20:54:27.9646680Z 2025-03-04T20:54:27.9647057Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:54:27.9647551Z getitem_12: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:54:27.9647865Z dw: "f32[3231, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T20:54:27.9648092Z 2025-03-04T20:54:27.9648483Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:54:27.9648999Z getitem_13: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:54:27.9649327Z dh: "f32[3231, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T20:54:27.9649547Z 2025-03-04T20:54:27.9649956Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:54:27.9650467Z dw_1: "f32[3231, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:54:27.9650712Z 2025-03-04T20:54:27.9651116Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:54:27.9651617Z dh_1: "f32[3231, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:54:27.9651877Z 2025-03-04T20:54:27.9652297Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:54:27.9652821Z getitem_14: "f32[3231, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:54:27.9653122Z mul_2: "f32[3231, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T20:54:27.9653440Z getitem_15: "f32[3231, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:54:27.9653775Z pred_ctr_x: "f32[3231, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T20:54:27.9654037Z 2025-03-04T20:54:27.9654458Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:54:27.9654980Z getitem_16: "f32[3231, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:54:27.9655301Z mul_3: "f32[3231, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T20:54:27.9655614Z getitem_17: "f32[3231, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:54:27.9655944Z pred_ctr_y: "f32[3231, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T20:54:27.9656191Z 2025-03-04T20:54:27.9656598Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:54:27.9657111Z exp: "f32[3231, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:54:27.9657427Z getitem_18: "f32[3231, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:54:27.9657754Z pred_w: "f32[3231, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T20:54:27.9657995Z 2025-03-04T20:54:27.9658410Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:54:27.9658899Z exp_1: "f32[3231, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:54:27.9659218Z getitem_19: "f32[3231, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:54:27.9659557Z pred_h: "f32[3231, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T20:54:27.9659800Z 2025-03-04T20:54:27.9660193Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:54:27.9660644Z mul_6: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T20:54:27.9660897Z x1: "f32[3231, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:54:27.9661124Z 2025-03-04T20:54:27.9661512Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:54:27.9661957Z mul_7: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T20:54:27.9662207Z y1: "f32[3231, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:54:27.9662426Z 2025-03-04T20:54:27.9662805Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:54:27.9663276Z mul_8: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:54:27.9663556Z x2: "f32[3231, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:54:27.9663792Z 2025-03-04T20:54:27.9664170Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:54:27.9664649Z mul_9: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:54:27.9664923Z y2: "f32[3231, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:54:27.9665156Z 2025-03-04T20:54:27.9665575Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:54:27.9666135Z pred_boxes: "f32[3231, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:54:27.9666421Z 2025-03-04T20:54:27.9666843Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:54:27.9667390Z predict_boxes: "f32[3231, 320][320, 1]cpu" = pred_boxes.reshape((3231, 320)); pred_boxes = None 2025-03-04T20:54:27.9667672Z 2025-03-04T20:54:27.9668137Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T20:54:27.9668741Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T20:54:27.9669094Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T20:54:27.9669376Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T20:54:27.9669688Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T20:54:27.9669987Z getitem_23: "f32[1231 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T20:54:27.9670238Z 2025-03-04T20:54:27.9670602Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:54:27.9671145Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T20:54:27.9671483Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T20:54:27.9671715Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T20:54:27.9672069Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T20:54:27.9672407Z getitem_26: "Sym(1231 - s0)" = size_3[0] 2025-03-04T20:54:27.9672656Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T20:54:27.9672865Z 2025-03-04T20:54:27.9673274Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T20:54:27.9673813Z probs: "f32[3231, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1); scores = None 2025-03-04T20:54:27.9674086Z 2025-03-04T20:54:27.9674515Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T20:54:27.9675108Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T20:54:27.9675544Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T20:54:27.9675832Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T20:54:27.9676139Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T20:54:27.9676459Z getitem_31: "f32[1231 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T20:54:27.9676723Z 2025-03-04T20:54:27.9677263Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T20:54:27.9677984Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T20:54:27.9678323Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:54:27.9678663Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T20:54:27.9679006Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T20:54:27.9679302Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T20:54:27.9679562Z 2025-03-04T20:54:27.9680002Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T20:54:27.9680531Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T20:54:27.9680768Z 2025-03-04T20:54:29.4750170Z 2025-03-04T20:54:29.4750938Z class GraphModule(torch.nn.Module): 2025-03-04T20:54:29.4752132Z def forward(self, L_predictions_0_: "f32[3231, 81][81, 1]cpu", L_predictions_1_: "f32[3231, 320][320, 1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1231 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1231 - s0, 4][4, 1]cpu"): 2025-03-04T20:54:29.4753173Z l_predictions_0_ = L_predictions_0_ 2025-03-04T20:54:29.4753418Z l_predictions_1_ = L_predictions_1_ 2025-03-04T20:54:29.4753745Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T20:54:29.4754169Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T20:54:29.4754604Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T20:54:29.4755019Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T20:54:29.4755451Z 2025-03-04T20:54:29.4755897Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:54:29.4756399Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T20:54:29.4756654Z getitem: "Sym(s0)" = size[0] 2025-03-04T20:54:29.4756886Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T20:54:29.4757175Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T20:54:29.4757446Z getitem_2: "Sym(1231 - s0)" = size_1[0] 2025-03-04T20:54:29.4757690Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T20:54:29.4757921Z 2025-03-04T20:54:29.4758309Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:54:29.4759315Z proposal_boxes: "f32[3231, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T20:54:29.4760050Z 2025-03-04T20:54:29.4760512Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:54:29.4761078Z deltas: "f32[3231, 320][320, 1]cpu" = l_predictions_1_.float(); l_predictions_1_ = None 2025-03-04T20:54:29.4761340Z 2025-03-04T20:54:29.4761732Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:54:29.4762319Z boxes: "f32[3231, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T20:54:29.4762743Z 2025-03-04T20:54:29.4763148Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:54:29.4763631Z getitem_4: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:54:29.4763930Z getitem_5: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:54:29.4764314Z widths: "f32[3231][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T20:54:29.4764572Z 2025-03-04T20:54:29.4764969Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:54:29.4765483Z getitem_6: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:54:29.4765775Z getitem_7: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:54:29.4766089Z heights: "f32[3231][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T20:54:29.4766355Z 2025-03-04T20:54:29.4766760Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:54:29.4767253Z getitem_8: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:54:29.4767507Z mul: "f32[3231][1]cpu" = 0.5 * widths 2025-03-04T20:54:29.4767754Z ctr_x: "f32[3231][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T20:54:29.4767982Z 2025-03-04T20:54:29.4768366Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:54:29.4768862Z getitem_9: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:54:29.4769134Z mul_1: "f32[3231][1]cpu" = 0.5 * heights 2025-03-04T20:54:29.4769386Z ctr_y: "f32[3231][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T20:54:29.4769618Z 2025-03-04T20:54:29.4770041Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:54:29.4770542Z getitem_10: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:54:29.4770854Z dx: "f32[3231, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T20:54:29.4771077Z 2025-03-04T20:54:29.4771451Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:54:29.4771945Z getitem_11: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:54:29.4772253Z dy: "f32[3231, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T20:54:29.4772473Z 2025-03-04T20:54:29.4772844Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:54:29.4773329Z getitem_12: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:54:29.4773636Z dw: "f32[3231, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T20:54:29.4773848Z 2025-03-04T20:54:29.4774226Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:54:29.4774780Z getitem_13: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:54:29.4775102Z dh: "f32[3231, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T20:54:29.4775320Z 2025-03-04T20:54:29.4775731Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:54:29.4776247Z dw_1: "f32[3231, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:54:29.4776496Z 2025-03-04T20:54:29.4776927Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:54:29.4777437Z dh_1: "f32[3231, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:54:29.4777678Z 2025-03-04T20:54:29.4778120Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:54:29.4778644Z getitem_14: "f32[3231, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:54:29.4778948Z mul_2: "f32[3231, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T20:54:29.4779270Z getitem_15: "f32[3231, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:54:29.4779606Z pred_ctr_x: "f32[3231, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T20:54:29.4779870Z 2025-03-04T20:54:29.4780301Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:54:29.4780828Z getitem_16: "f32[3231, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:54:29.4781131Z mul_3: "f32[3231, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T20:54:29.4781449Z getitem_17: "f32[3231, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:54:29.4781777Z pred_ctr_y: "f32[3231, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T20:54:29.4782021Z 2025-03-04T20:54:29.4782430Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:54:29.4782923Z exp: "f32[3231, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:54:29.4783233Z getitem_18: "f32[3231, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:54:29.4783563Z pred_w: "f32[3231, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T20:54:29.4783801Z 2025-03-04T20:54:29.4784214Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:54:29.4784705Z exp_1: "f32[3231, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:54:29.4785027Z getitem_19: "f32[3231, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:54:29.4785364Z pred_h: "f32[3231, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T20:54:29.4785607Z 2025-03-04T20:54:29.4786001Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:54:29.4786452Z mul_6: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T20:54:29.4786704Z x1: "f32[3231, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:54:29.4786927Z 2025-03-04T20:54:29.4787327Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:54:29.4787776Z mul_7: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T20:54:29.4788021Z y1: "f32[3231, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:54:29.4788243Z 2025-03-04T20:54:29.4788620Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:54:29.4789083Z mul_8: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:54:29.4789386Z x2: "f32[3231, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:54:29.4789629Z 2025-03-04T20:54:29.4790011Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:54:29.4790478Z mul_9: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:54:29.4790775Z y2: "f32[3231, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:54:29.4791016Z 2025-03-04T20:54:29.4791451Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:54:29.4792049Z pred_boxes: "f32[3231, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:54:29.4792413Z 2025-03-04T20:54:29.4792854Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:54:29.4793425Z predict_boxes: "f32[3231, 320][320, 1]cpu" = pred_boxes.reshape((3231, 320)); pred_boxes = None 2025-03-04T20:54:29.4793730Z 2025-03-04T20:54:29.4794185Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T20:54:29.4794814Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T20:54:29.4795185Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T20:54:29.4795564Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T20:54:29.4795870Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T20:54:29.4796186Z getitem_23: "f32[1231 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T20:54:29.4796449Z 2025-03-04T20:54:29.4796828Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:54:29.4797396Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T20:54:29.4797745Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T20:54:29.4797988Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T20:54:29.4798352Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T20:54:29.4798746Z getitem_26: "Sym(1231 - s0)" = size_3[0] 2025-03-04T20:54:29.4798990Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T20:54:29.4799211Z 2025-03-04T20:54:29.4799636Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T20:54:29.4800234Z probs: "f32[3231, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1); l_predictions_0_ = None 2025-03-04T20:54:29.4800597Z 2025-03-04T20:54:29.4801046Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T20:54:29.4801650Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T20:54:29.4802184Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T20:54:29.4802477Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T20:54:29.4802776Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T20:54:29.4803134Z getitem_31: "f32[1231 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T20:54:29.4803393Z 2025-03-04T20:54:29.4803954Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T20:54:29.4804683Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T20:54:29.4805024Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:54:29.4805369Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T20:54:29.4805714Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T20:54:29.4806011Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T20:54:29.4806274Z 2025-03-04T20:54:29.4806734Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T20:54:29.4807239Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T20:54:29.4807464Z 2025-03-04T20:54:29.4807544Z 2025-03-04T20:54:29.4807640Z class GraphModule(torch.nn.Module): 2025-03-04T20:54:29.4808430Z def forward(self, L_predictions_0_: "f32[3231, 81][81, 1]cpu", L_predictions_1_: "f32[3231, 320][320, 1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1231 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1231 - s0, 4][4, 1]cpu"): 2025-03-04T20:54:29.4809200Z l_predictions_0_ = L_predictions_0_ 2025-03-04T20:54:29.4809421Z l_predictions_1_ = L_predictions_1_ 2025-03-04T20:54:29.4809723Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T20:54:29.4810110Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T20:54:29.4810496Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T20:54:29.4810878Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T20:54:29.4811157Z 2025-03-04T20:54:29.4811529Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:54:29.4811981Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T20:54:29.4812226Z getitem: "Sym(s0)" = size[0] 2025-03-04T20:54:29.4812453Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T20:54:29.4812717Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T20:54:29.4812967Z getitem_2: "Sym(1231 - s0)" = size_1[0] 2025-03-04T20:54:29.4813203Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T20:54:29.4813409Z 2025-03-04T20:54:29.4813771Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:54:29.4814744Z proposal_boxes: "f32[3231, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T20:54:29.4815442Z 2025-03-04T20:54:29.4815909Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:54:29.4816472Z deltas: "f32[3231, 320][320, 1]cpu" = l_predictions_1_.float(); l_predictions_1_ = None 2025-03-04T20:54:29.4816735Z 2025-03-04T20:54:29.4817123Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:54:29.4817657Z boxes: "f32[3231, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T20:54:29.4817920Z 2025-03-04T20:54:29.4818307Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:54:29.4818789Z getitem_4: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:54:29.4819081Z getitem_5: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:54:29.4819406Z widths: "f32[3231][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T20:54:29.4819659Z 2025-03-04T20:54:29.4820059Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:54:29.4820547Z getitem_6: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:54:29.4820835Z getitem_7: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:54:29.4821145Z heights: "f32[3231][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T20:54:29.4821396Z 2025-03-04T20:54:29.4821785Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:54:29.4822257Z getitem_8: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:54:29.4822508Z mul: "f32[3231][1]cpu" = 0.5 * widths 2025-03-04T20:54:29.4822754Z ctr_x: "f32[3231][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T20:54:29.4822982Z 2025-03-04T20:54:29.4823364Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:54:29.4823859Z getitem_9: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:54:29.4824131Z mul_1: "f32[3231][1]cpu" = 0.5 * heights 2025-03-04T20:54:29.4824410Z ctr_y: "f32[3231][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T20:54:29.4824639Z 2025-03-04T20:54:29.4836506Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:54:29.4837071Z getitem_10: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:54:29.4837427Z dx: "f32[3231, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T20:54:29.4837675Z 2025-03-04T20:54:29.4838085Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:54:29.4838696Z getitem_11: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:54:29.4839028Z dy: "f32[3231, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T20:54:29.4839262Z 2025-03-04T20:54:29.4839661Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:54:29.4840180Z getitem_12: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:54:29.4840504Z dw: "f32[3231, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T20:54:29.4840771Z 2025-03-04T20:54:29.4841174Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:54:29.4841732Z getitem_13: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:54:29.4842121Z dh: "f32[3231, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T20:54:29.4842353Z 2025-03-04T20:54:29.4842784Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:54:29.4843331Z dw_1: "f32[3231, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:54:29.4843589Z 2025-03-04T20:54:29.4844012Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:54:29.4844564Z dh_1: "f32[3231, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:54:29.4844814Z 2025-03-04T20:54:29.4845255Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:54:29.4845795Z getitem_14: "f32[3231, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:54:29.4846110Z mul_2: "f32[3231, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T20:54:29.4846439Z getitem_15: "f32[3231, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:54:29.4846782Z pred_ctr_x: "f32[3231, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T20:54:29.4847037Z 2025-03-04T20:54:29.4847489Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:54:29.4848012Z getitem_16: "f32[3231, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:54:29.4848317Z mul_3: "f32[3231, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T20:54:29.4848637Z getitem_17: "f32[3231, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:54:29.4848974Z pred_ctr_y: "f32[3231, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T20:54:29.4849226Z 2025-03-04T20:54:29.4849644Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:54:29.4850156Z exp: "f32[3231, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:54:29.4850483Z getitem_18: "f32[3231, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:54:29.4850826Z pred_w: "f32[3231, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T20:54:29.4851073Z 2025-03-04T20:54:29.4851501Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:54:29.4852006Z exp_1: "f32[3231, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:54:29.4852326Z getitem_19: "f32[3231, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:54:29.4852662Z pred_h: "f32[3231, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T20:54:29.4852903Z 2025-03-04T20:54:29.4853295Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:54:29.4853762Z mul_6: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T20:54:29.4854016Z x1: "f32[3231, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:54:29.4854245Z 2025-03-04T20:54:29.4854627Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:54:29.4855098Z mul_7: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T20:54:29.4855352Z y1: "f32[3231, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:54:29.4855576Z 2025-03-04T20:54:29.4855958Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:54:29.4856420Z mul_8: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:54:29.4856738Z x2: "f32[3231, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:54:29.4856975Z 2025-03-04T20:54:29.4857354Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:54:29.4857811Z mul_9: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:54:29.4858086Z y2: "f32[3231, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:54:29.4858320Z 2025-03-04T20:54:29.4858747Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:54:29.4859313Z pred_boxes: "f32[3231, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:54:29.4859591Z 2025-03-04T20:54:29.4860005Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:54:29.4860555Z predict_boxes: "f32[3231, 320][320, 1]cpu" = pred_boxes.reshape((3231, 320)); pred_boxes = None 2025-03-04T20:54:29.4860834Z 2025-03-04T20:54:29.4861265Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T20:54:29.4861866Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T20:54:29.4862219Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T20:54:29.4862502Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T20:54:29.4862795Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T20:54:29.4863105Z getitem_23: "f32[1231 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T20:54:29.4863358Z 2025-03-04T20:54:29.4863729Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:54:29.4864272Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T20:54:29.4864635Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T20:54:29.4864872Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T20:54:29.4865228Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T20:54:29.4865572Z getitem_26: "Sym(1231 - s0)" = size_3[0] 2025-03-04T20:54:29.4865812Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T20:54:29.4866025Z 2025-03-04T20:54:29.4866461Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T20:54:29.4867053Z probs: "f32[3231, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1); l_predictions_0_ = None 2025-03-04T20:54:29.4867373Z 2025-03-04T20:54:29.4867827Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T20:54:29.4868419Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T20:54:29.4868773Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T20:54:29.4869057Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T20:54:29.4869347Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T20:54:29.4869672Z getitem_31: "f32[1231 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T20:54:29.4869924Z 2025-03-04T20:54:29.4870465Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T20:54:29.4871151Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T20:54:29.4871483Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:54:29.4871825Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T20:54:29.4872174Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T20:54:29.4872461Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T20:54:29.4872693Z 2025-03-04T20:54:29.4873125Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T20:54:29.4873635Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T20:54:29.4873860Z 2025-03-04T20:54:29.4873984Z 2025-03-04T20:54:29.4874077Z class GraphModule(torch.nn.Module): 2025-03-04T20:54:29.4874881Z def forward(self, L_predictions_0_: "f32[3231, 81][81, 1]cpu", L_predictions_1_: "f32[3231, 320][320, 1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1231 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1231 - s0, 4][4, 1]cpu"): 2025-03-04T20:54:29.4875746Z l_predictions_0_ = L_predictions_0_ 2025-03-04T20:54:29.4875977Z l_predictions_1_ = L_predictions_1_ 2025-03-04T20:54:29.4876291Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T20:54:29.4876692Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T20:54:29.4877086Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T20:54:29.4877497Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T20:54:29.4877785Z 2025-03-04T20:54:29.4878152Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:54:29.4878605Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T20:54:29.4878848Z getitem: "Sym(s0)" = size[0] 2025-03-04T20:54:29.4879070Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T20:54:29.4879337Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T20:54:29.4879603Z getitem_2: "Sym(1231 - s0)" = size_1[0] 2025-03-04T20:54:29.4879838Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T20:54:29.4880047Z 2025-03-04T20:54:29.4880407Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:54:29.4881344Z proposal_boxes: "f32[3231, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T20:54:29.4882042Z 2025-03-04T20:54:29.4882488Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:54:29.4883072Z deltas: "f32[3231, 320][320, 1]cpu" = l_predictions_1_.float(); l_predictions_1_ = None 2025-03-04T20:54:29.4883336Z 2025-03-04T20:54:29.4883731Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:54:29.4884245Z boxes: "f32[3231, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T20:54:29.4884516Z 2025-03-04T20:54:29.4884905Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:54:29.4885391Z getitem_4: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:54:29.4885685Z getitem_5: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:54:29.4885993Z widths: "f32[3231][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T20:54:29.4886247Z 2025-03-04T20:54:29.4886647Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:54:29.4887131Z getitem_6: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:54:29.4887416Z getitem_7: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:54:29.4887729Z heights: "f32[3231][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T20:54:29.4887984Z 2025-03-04T20:54:29.4888377Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:54:29.4888853Z getitem_8: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:54:29.4889106Z mul: "f32[3231][1]cpu" = 0.5 * widths 2025-03-04T20:54:29.4889354Z ctr_x: "f32[3231][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T20:54:29.4889583Z 2025-03-04T20:54:29.4889973Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:54:29.4890485Z getitem_9: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:54:29.4890760Z mul_1: "f32[3231][1]cpu" = 0.5 * heights 2025-03-04T20:54:29.4891010Z ctr_y: "f32[3231][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T20:54:29.4891242Z 2025-03-04T20:54:29.4891632Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:54:29.4892131Z getitem_10: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:54:29.4892464Z dx: "f32[3231, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T20:54:29.4892699Z 2025-03-04T20:54:29.4893075Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:54:29.4893571Z getitem_11: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:54:29.4893897Z dy: "f32[3231, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T20:54:29.4894123Z 2025-03-04T20:54:29.4894503Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:54:29.4894998Z getitem_12: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:54:29.4895306Z dw: "f32[3231, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T20:54:29.4895545Z 2025-03-04T20:54:29.4895923Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:54:29.4896446Z getitem_13: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:54:29.4896789Z dh: "f32[3231, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T20:54:29.4897008Z 2025-03-04T20:54:29.4897432Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:54:29.4897948Z dw_1: "f32[3231, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:54:29.4898197Z 2025-03-04T20:54:29.4898611Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:54:29.4899123Z dh_1: "f32[3231, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:54:29.4899367Z 2025-03-04T20:54:29.4899787Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:54:29.4900310Z getitem_14: "f32[3231, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:54:29.4900611Z mul_2: "f32[3231, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T20:54:29.4900925Z getitem_15: "f32[3231, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:54:29.4901255Z pred_ctr_x: "f32[3231, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T20:54:29.4901500Z 2025-03-04T20:54:29.4902109Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:54:29.4902639Z getitem_16: "f32[3231, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:54:29.4902946Z mul_3: "f32[3231, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T20:54:29.4903326Z getitem_17: "f32[3231, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:54:29.4903657Z pred_ctr_y: "f32[3231, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T20:54:29.4903902Z 2025-03-04T20:54:29.4904315Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:54:29.4904805Z exp: "f32[3231, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:54:29.4905144Z getitem_18: "f32[3231, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:54:29.4905475Z pred_w: "f32[3231, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T20:54:29.4905720Z 2025-03-04T20:54:29.4906136Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:54:29.4906670Z exp_1: "f32[3231, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:54:29.4906993Z getitem_19: "f32[3231, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:54:29.4907333Z pred_h: "f32[3231, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T20:54:29.4907577Z 2025-03-04T20:54:29.4907968Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:54:29.4908447Z mul_6: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T20:54:29.4908703Z x1: "f32[3231, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:54:29.4908925Z 2025-03-04T20:54:29.4909304Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:54:29.4909743Z mul_7: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T20:54:29.4909990Z y1: "f32[3231, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:54:29.4910211Z 2025-03-04T20:54:29.4910592Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:54:29.4911050Z mul_8: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:54:29.4911331Z x2: "f32[3231, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:54:29.4911568Z 2025-03-04T20:54:29.4911943Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:54:29.4912399Z mul_9: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:54:29.4912677Z y2: "f32[3231, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:54:29.4912912Z 2025-03-04T20:54:29.4913331Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:54:29.4913905Z pred_boxes: "f32[3231, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:54:29.4914191Z 2025-03-04T20:54:29.4914608Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:54:29.4915156Z predict_boxes: "f32[3231, 320][320, 1]cpu" = pred_boxes.reshape((3231, 320)); pred_boxes = None 2025-03-04T20:54:29.4915494Z 2025-03-04T20:54:29.4915938Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T20:54:29.4916596Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T20:54:29.4916974Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T20:54:29.4917270Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T20:54:29.4917588Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T20:54:29.4917906Z getitem_23: "f32[1231 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T20:54:29.4918170Z 2025-03-04T20:54:29.4918571Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:54:29.4919135Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T20:54:29.4919483Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T20:54:29.4919738Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T20:54:29.4920100Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T20:54:29.4920450Z getitem_26: "Sym(1231 - s0)" = size_3[0] 2025-03-04T20:54:29.4920689Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T20:54:29.4920902Z 2025-03-04T20:54:29.4921321Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T20:54:29.4921941Z probs: "f32[3231, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1); l_predictions_0_ = None 2025-03-04T20:54:29.4922269Z 2025-03-04T20:54:29.4922718Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T20:54:29.4923325Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T20:54:29.4923681Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T20:54:29.4923971Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T20:54:29.4924268Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T20:54:29.4924581Z getitem_31: "f32[1231 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T20:54:29.4924841Z 2025-03-04T20:54:29.4925391Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T20:54:29.4926083Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T20:54:29.4926422Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:54:29.4926761Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T20:54:29.4927098Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T20:54:29.4927391Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T20:54:29.4927627Z 2025-03-04T20:54:29.4928069Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T20:54:29.4928588Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T20:54:29.4928819Z 2025-03-04T20:54:31.6558275Z 2025-03-04T20:54:31.6565245Z class GraphModule(torch.nn.Module): 2025-03-04T20:54:31.6567096Z def forward(self, L_scores_0_: "f32[1000, 81][81, 1]cpu", L_boxes_0_: "f32[1000, 320][320, 1]cpu"): 2025-03-04T20:54:31.6568148Z l_scores_0_ = L_scores_0_ 2025-03-04T20:54:31.6568845Z l_boxes_0_ = L_boxes_0_ 2025-03-04T20:54:31.6569183Z 2025-03-04T20:54:31.6570507Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T20:54:31.6571648Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(l_boxes_0_); l_boxes_0_ = None 2025-03-04T20:54:31.6572241Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:54:31.6572650Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(l_scores_0_); l_scores_0_ = None 2025-03-04T20:54:31.6573013Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T20:54:31.6573402Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T20:54:31.6573651Z 2025-03-04T20:54:31.6574160Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T20:54:31.6574742Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T20:54:31.6574981Z 2025-03-04T20:54:31.6575080Z 2025-03-04T20:54:31.6575172Z class GraphModule(torch.nn.Module): 2025-03-04T20:54:31.6575555Z def forward(self, L_scores_0_: "f32[1000, 81][81, 1]cpu", L_boxes_0_: "f32[1000, 320][320, 1]cpu"): 2025-03-04T20:54:31.6575859Z l_scores_0_ = L_scores_0_ 2025-03-04T20:54:31.6576068Z l_boxes_0_ = L_boxes_0_ 2025-03-04T20:54:31.6576254Z 2025-03-04T20:54:31.6576853Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T20:54:31.6577595Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(l_boxes_0_); l_boxes_0_ = None 2025-03-04T20:54:31.6577935Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:54:31.6578271Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(l_scores_0_); l_scores_0_ = None 2025-03-04T20:54:31.6578594Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T20:54:31.6578892Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T20:54:31.6579132Z 2025-03-04T20:54:31.6579566Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T20:54:31.6580075Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T20:54:31.6580302Z 2025-03-04T20:54:48.3183058Z Compilation time (from dynamo_timed): 32.95288335 2025-03-04T20:54:48.3184249Z pass 2025-03-04T20:54:48.3184592Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T20:54:48.3185363Z TIMING: entire_frame_compile:32.95288 gc:0.03228 _recursive_pre_grad_passes:0.02832 async_compile.wait:8.48947 backend_compile:22.27601 _recursive_joint_graph_passes:0.16416 _recursive_post_grad_passes:0.07301 code_gen:11.4066 inductor_compile:13.11108 total_wall_time:32.95288 2025-03-04T20:54:48.3191705Z STATS: call_* op count: 607 | FakeTensorMode.__torch_dispatch__:17713 | FakeTensor.__torch_dispatch__:1776 | ProxyTorchDispatchMode.__torch_dispatch__:5612 | attempt fast:51 | slow no contiguity match:20 | fast is_contiguous:31 2025-03-04T20:54:48.3192625Z Dynamo produced 53 graphs covering 607 ops with 42 graph breaks (6 unique) 2025-03-04T20:54:53.5635066Z 2025-03-04T20:55:02.5020087Z loading model: 0it [00:00, ?it/s] 2025-03-04T20:55:02.5025594Z loading model: 0it [00:08, ?it/s] 2025-03-04T20:55:02.5027896Z cpu eval detectron2_fasterrcnn_r_50_dc5 2025-03-04T20:55:15.0461058Z WARNING:common:fp64 golden ref were not generated for detectron2_fasterrcnn_r_50_dc5. Setting accuracy check to cosine 2025-03-04T20:55:15.0798550Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T20:55:26.9671812Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T20:55:39.7957534Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T20:55:49.1819736Z 2025-03-04T20:55:49.1820313Z class GraphModule(torch.nn.Module): 2025-03-04T20:55:49.1885417Z def forward(self, L_stack0_tensor: "f32[4, 3, 1156, 1199][4158132, 1386044, 1199, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_: "f32[512, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[512][1]cpu", 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L_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[2048, 2048, 3, 3][18432, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[2048][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"): 2025-03-04T20:55:49.1938456Z l_stack0_tensor = L_stack0_tensor 2025-03-04T20:55:49.1938998Z l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ 2025-03-04T20:55:49.1939763Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.1940473Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.1941159Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:55:49.1941813Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:55:49.1942450Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:55:49.1943136Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.1943870Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.1944587Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:55:49.1945272Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:55:49.1945947Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:55:49.1946626Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.1947407Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.1948279Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:55:49.1949145Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:55:49.1949950Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:55:49.1950780Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.1951704Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.1952560Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:55:49.1953351Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:55:49.1957189Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:55:49.1958040Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.1958894Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.1959696Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:55:49.1960477Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:55:49.1961208Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:55:49.1961946Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.1962746Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.1963521Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:55:49.1964309Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:55:49.1965010Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:55:49.1965743Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.1966555Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.1967290Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:55:49.1967971Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:55:49.1968887Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:55:49.1969567Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.1970315Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.1971023Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:55:49.1971716Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:55:49.1972357Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:55:49.1973027Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.1973751Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.1974451Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:55:49.1975133Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:55:49.1975771Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:55:49.1976445Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.1977172Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.1977886Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:55:49.1978560Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:55:49.1979516Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:55:49.1980231Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.1980965Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.1981689Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:55:49.1982369Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:55:49.1983013Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:55:49.1983704Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.1984423Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.1985126Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:55:49.1985803Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:55:49.1986458Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:55:49.1987134Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.1987861Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.1988812Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:55:49.1989524Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:55:49.1990184Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:55:49.1990878Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.1991622Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.1992367Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:55:49.1993073Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:55:49.1993820Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:55:49.1994668Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.1995592Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.1996402Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:55:49.1997141Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:55:49.1997843Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:55:49.1999153Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.1999934Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2000663Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2001380Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2002200Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:55:49.2002891Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2003614Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2004317Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2004993Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2005640Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:55:49.2006317Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2007098Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2008734Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2009424Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2010113Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:55:49.2010805Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2011562Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2012263Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2012943Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2013614Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:55:49.2014286Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2015014Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2015714Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2016393Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2017035Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:55:49.2017711Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2018439Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2019480Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2020192Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2020854Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ 2025-03-04T20:55:49.2021544Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2022300Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2023008Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2023707Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2024357Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ 2025-03-04T20:55:49.2025053Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2025783Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2026488Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2027179Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2027821Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ 2025-03-04T20:55:49.2028494Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2029420Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2030185Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2030889Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2031548Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:55:49.2032269Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2033018Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2033815Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2034524Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2035181Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:55:49.2035895Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2036643Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2037652Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2038384Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2039055Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:55:49.2039767Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2040509Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2041233Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2041954Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2042632Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:55:49.2043355Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2044130Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2044882Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2045607Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2046283Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:55:49.2046949Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2047680Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2048383Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2049071Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2050041Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:55:49.2050713Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2051437Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2052175Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2052858Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2053524Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:55:49.2054208Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2054939Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2055675Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2056368Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2057015Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:55:49.2057686Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2058406Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2059342Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2060033Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2060686Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:55:49.2061366Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2062101Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2062828Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2063526Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2064207Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:55:49.2064899Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2065686Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2066415Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2067162Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2067840Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ 2025-03-04T20:55:49.2068836Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2069621Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2070391Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2071113Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2071801Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ 2025-03-04T20:55:49.2072524Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2073298Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2074105Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2074827Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2075487Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ 2025-03-04T20:55:49.2076172Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2076914Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2077630Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2078343Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2079575Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ 2025-03-04T20:55:49.2080316Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2081093Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2081826Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2082523Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2083167Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ 2025-03-04T20:55:49.2083834Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2084566Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2085272Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2085961Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2086615Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ 2025-03-04T20:55:49.2087296Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2088039Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2088755Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2089443Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2090094Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ 2025-03-04T20:55:49.2091651Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2092425Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2093153Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2093893Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2094568Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ 2025-03-04T20:55:49.2095343Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2096171Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2097190Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2098027Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2098737Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ 2025-03-04T20:55:49.2099521Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2100381Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2101191Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2102064Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2102820Z l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:55:49.2103600Z l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2104430Z l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2105237Z l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2106009Z l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2106713Z l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:55:49.2108013Z l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2108863Z l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2109751Z l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2110535Z l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2111270Z l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:55:49.2112072Z l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2112908Z l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2113805Z l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2114601Z l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2115399Z l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:55:49.2116186Z l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2117212Z l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2118031Z l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2118788Z l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2119480Z l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:55:49.2120191Z l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2120954Z l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2121700Z l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2122412Z l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2123099Z l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:55:49.2123807Z l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2124591Z l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2125332Z l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2126041Z l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2126736Z l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:55:49.2127715Z l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2128527Z l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2129276Z l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2129995Z l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2130691Z l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:55:49.2131412Z l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2132158Z l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2132897Z l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2133621Z l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2134311Z l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:55:49.2135015Z l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2135782Z l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2136521Z l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2137473Z l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2138173Z l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:55:49.2138898Z l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2139661Z l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2140383Z l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2141081Z l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2141838Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T20:55:49.2142594Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T20:55:49.2143289Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T20:55:49.2144049Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T20:55:49.2144848Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T20:55:49.2145639Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T20:55:49.2146402Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T20:55:49.2146874Z 2025-03-04T20:55:49.2147800Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2148630Z x: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1); l_stack0_tensor = l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = None 2025-03-04T20:55:49.2149258Z 2025-03-04T20:55:49.2149634Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2151468Z x_1: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2153063Z 2025-03-04T20:55:49.2153449Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x) 2025-03-04T20:55:49.2154069Z x_2: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.relu_(x_1); x_1 = None 2025-03-04T20:55:49.2154386Z 2025-03-04T20:55:49.2154900Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1) 2025-03-04T20:55:49.2155650Z x_3: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1); x_2 = None 2025-03-04T20:55:49.2156061Z 2025-03-04T20:55:49.2156422Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2157474Z x_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:55:49.2158081Z 2025-03-04T20:55:49.2158472Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2160498Z x_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_4 = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2162272Z 2025-03-04T20:55:49.2162642Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2163111Z out: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_5); x_5 = None 2025-03-04T20:55:49.2163363Z 2025-03-04T20:55:49.2163699Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2164428Z x_6: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out = l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:55:49.2164963Z 2025-03-04T20:55:49.2165311Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2167367Z x_7: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_6 = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2169078Z 2025-03-04T20:55:49.2169458Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2169937Z out_1: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_7); x_7 = None 2025-03-04T20:55:49.2170197Z 2025-03-04T20:55:49.2170533Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2171300Z x_8: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_1 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:55:49.2171846Z 2025-03-04T20:55:49.2172193Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2174053Z x_9: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_8 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2175699Z 2025-03-04T20:55:49.2176029Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2176769Z x_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); x_3 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:55:49.2177553Z 2025-03-04T20:55:49.2177926Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2179814Z x_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_10 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2181461Z 2025-03-04T20:55:49.2181822Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:55:49.2182295Z x_9 += x_11; out_2: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_9; x_9 = x_11 = None 2025-03-04T20:55:49.2182547Z 2025-03-04T20:55:49.2182908Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2183415Z out_3: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_2); out_2 = None 2025-03-04T20:55:49.2183681Z 2025-03-04T20:55:49.2184011Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2184737Z x_12: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:55:49.2185287Z 2025-03-04T20:55:49.2185642Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2187739Z x_13: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_12 = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2189401Z 2025-03-04T20:55:49.2189776Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2190260Z out_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_13); x_13 = None 2025-03-04T20:55:49.2190514Z 2025-03-04T20:55:49.2190844Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2191572Z x_14: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_4 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:55:49.2192125Z 2025-03-04T20:55:49.2192480Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2194495Z x_15: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_14 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2196222Z 2025-03-04T20:55:49.2196592Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2197615Z out_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_15); x_15 = None 2025-03-04T20:55:49.2197926Z 2025-03-04T20:55:49.2198281Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2199044Z x_16: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_5 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:55:49.2199593Z 2025-03-04T20:55:49.2199962Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2201877Z x_17: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_16 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2203735Z 2025-03-04T20:55:49.2204104Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:55:49.2204595Z x_17 += out_3; out_6: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_17; x_17 = out_3 = None 2025-03-04T20:55:49.2204870Z 2025-03-04T20:55:49.2205245Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2205739Z out_7: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_6); out_6 = None 2025-03-04T20:55:49.2206008Z 2025-03-04T20:55:49.2206347Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2207091Z x_18: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:55:49.2207887Z 2025-03-04T20:55:49.2208267Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2210137Z x_19: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_18 = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2211738Z 2025-03-04T20:55:49.2212098Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2212626Z out_8: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_19); x_19 = None 2025-03-04T20:55:49.2212881Z 2025-03-04T20:55:49.2213214Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2213936Z x_20: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_8 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:55:49.2214499Z 2025-03-04T20:55:49.2214852Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2216744Z x_21: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_20 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2218840Z 2025-03-04T20:55:49.2219231Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2219723Z out_9: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_21); x_21 = None 2025-03-04T20:55:49.2219982Z 2025-03-04T20:55:49.2220310Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2221067Z x_22: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_9 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:55:49.2221624Z 2025-03-04T20:55:49.2221987Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2223812Z x_23: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_22 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2225415Z 2025-03-04T20:55:49.2225771Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:55:49.2226245Z x_23 += out_7; out_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_23; x_23 = out_7 = None 2025-03-04T20:55:49.2226532Z 2025-03-04T20:55:49.2226894Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2227634Z out_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_10); out_10 = None 2025-03-04T20:55:49.2227906Z 2025-03-04T20:55:49.2228243Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2228990Z x_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:55:49.2229530Z 2025-03-04T20:55:49.2229876Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2231764Z x_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_24 = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2233436Z 2025-03-04T20:55:49.2233884Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2234382Z out_12: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_25); x_25 = None 2025-03-04T20:55:49.2234643Z 2025-03-04T20:55:49.2234978Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2235722Z x_26: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_12 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:55:49.2236272Z 2025-03-04T20:55:49.2236626Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2238517Z x_27: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_26 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2240718Z 2025-03-04T20:55:49.2241106Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2241624Z out_13: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_27); x_27 = None 2025-03-04T20:55:49.2241890Z 2025-03-04T20:55:49.2242232Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2243001Z x_28: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_13 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:55:49.2243566Z 2025-03-04T20:55:49.2243928Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2245809Z x_29: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_28 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2247685Z 2025-03-04T20:55:49.2248053Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2248828Z x_30: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); out_11 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:55:49.2249401Z 2025-03-04T20:55:49.2249758Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2251741Z x_31: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_30 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2253427Z 2025-03-04T20:55:49.2253784Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:55:49.2254259Z x_29 += x_31; out_14: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_29; x_29 = x_31 = None 2025-03-04T20:55:49.2254516Z 2025-03-04T20:55:49.2254876Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2255383Z out_15: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_14); out_14 = None 2025-03-04T20:55:49.2255646Z 2025-03-04T20:55:49.2255974Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2256702Z x_32: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:55:49.2257242Z 2025-03-04T20:55:49.2257607Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2259749Z x_33: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_32 = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2261438Z 2025-03-04T20:55:49.2261812Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2262288Z out_16: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_33); x_33 = None 2025-03-04T20:55:49.2262545Z 2025-03-04T20:55:49.2262877Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2263601Z x_34: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_16 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:55:49.2264141Z 2025-03-04T20:55:49.2264494Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2266323Z x_35: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_34 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2268470Z 2025-03-04T20:55:49.2268856Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2269348Z out_17: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_35); x_35 = None 2025-03-04T20:55:49.2269642Z 2025-03-04T20:55:49.2270003Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2270760Z x_36: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_17 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:55:49.2271319Z 2025-03-04T20:55:49.2271674Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2273603Z x_37: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_36 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2275475Z 2025-03-04T20:55:49.2275851Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:55:49.2276341Z x_37 += out_15; out_18: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_37; x_37 = out_15 = None 2025-03-04T20:55:49.2276616Z 2025-03-04T20:55:49.2276991Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2277483Z out_19: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_18); out_18 = None 2025-03-04T20:55:49.2277750Z 2025-03-04T20:55:49.2278318Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2279086Z x_38: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:55:49.2279641Z 2025-03-04T20:55:49.2279995Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2281876Z x_39: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_38 = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2283551Z 2025-03-04T20:55:49.2283920Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2284430Z out_20: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_39); x_39 = None 2025-03-04T20:55:49.2284689Z 2025-03-04T20:55:49.2285023Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2285767Z x_40: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_20 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:55:49.2286339Z 2025-03-04T20:55:49.2286694Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2288816Z x_41: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_40 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2290490Z 2025-03-04T20:55:49.2290862Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2291337Z out_21: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_41); x_41 = None 2025-03-04T20:55:49.2291592Z 2025-03-04T20:55:49.2291920Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2292648Z x_42: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_21 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:55:49.2293186Z 2025-03-04T20:55:49.2293531Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2295356Z x_43: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_42 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2297009Z 2025-03-04T20:55:49.2297366Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:55:49.2297846Z x_43 += out_19; out_22: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_43; x_43 = out_19 = None 2025-03-04T20:55:49.2298169Z 2025-03-04T20:55:49.2298542Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2299474Z out_23: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_22); out_22 = None 2025-03-04T20:55:49.2299746Z 2025-03-04T20:55:49.2300077Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2300839Z x_44: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T20:55:49.2301391Z 2025-03-04T20:55:49.2301744Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2303786Z x_45: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_44 = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2305439Z 2025-03-04T20:55:49.2305804Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2306299Z out_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_45); x_45 = None 2025-03-04T20:55:49.2306565Z 2025-03-04T20:55:49.2306926Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2308184Z x_46: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_24 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T20:55:49.2308776Z 2025-03-04T20:55:49.2309155Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2311138Z x_47: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_46 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2312869Z 2025-03-04T20:55:49.2313263Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2313875Z out_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_47); x_47 = None 2025-03-04T20:55:49.2314145Z 2025-03-04T20:55:49.2314500Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2315307Z x_48: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_25 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T20:55:49.2315890Z 2025-03-04T20:55:49.2316260Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2318528Z x_49: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_48 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2320351Z 2025-03-04T20:55:49.2320719Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:55:49.2321204Z x_49 += out_23; out_26: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_49; x_49 = out_23 = None 2025-03-04T20:55:49.2321473Z 2025-03-04T20:55:49.2321836Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2322320Z out_27: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_26); out_26 = None 2025-03-04T20:55:49.2322582Z 2025-03-04T20:55:49.2322916Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2323643Z x_50: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:55:49.2324169Z 2025-03-04T20:55:49.2324514Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2326313Z x_51: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_50 = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2327945Z 2025-03-04T20:55:49.2328309Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2329063Z out_28: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_51); x_51 = None 2025-03-04T20:55:49.2329328Z 2025-03-04T20:55:49.2329665Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2330418Z x_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_28 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:55:49.2330957Z 2025-03-04T20:55:49.2331323Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2333142Z x_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_52 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2334764Z 2025-03-04T20:55:49.2335124Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2335590Z out_29: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_53); x_53 = None 2025-03-04T20:55:49.2335845Z 2025-03-04T20:55:49.2336188Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2336906Z x_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_29 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:55:49.2337442Z 2025-03-04T20:55:49.2337783Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2340521Z x_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_54 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2342161Z 2025-03-04T20:55:49.2342500Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2343263Z x_56: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); out_27 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:55:49.2343809Z 2025-03-04T20:55:49.2344154Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2346056Z x_57: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_56 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2347734Z 2025-03-04T20:55:49.2348646Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:55:49.2349190Z x_55 += x_57; out_30: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_55; x_55 = x_57 = None 2025-03-04T20:55:49.2349455Z 2025-03-04T20:55:49.2349828Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2350329Z out_31: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_30); out_30 = None 2025-03-04T20:55:49.2350591Z 2025-03-04T20:55:49.2350935Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2351675Z x_58: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:55:49.2352221Z 2025-03-04T20:55:49.2352575Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2354609Z x_59: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_58 = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2356262Z 2025-03-04T20:55:49.2356628Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2357137Z out_32: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_59); x_59 = None 2025-03-04T20:55:49.2357916Z 2025-03-04T20:55:49.2358300Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2359090Z x_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_32 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:55:49.2359658Z 2025-03-04T20:55:49.2360041Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2361996Z x_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_60 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2363801Z 2025-03-04T20:55:49.2364183Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2364687Z out_33: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_61); x_61 = None 2025-03-04T20:55:49.2364960Z 2025-03-04T20:55:49.2365306Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2366069Z x_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_33 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:55:49.2366597Z 2025-03-04T20:55:49.2366944Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2368998Z x_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_62 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2370663Z 2025-03-04T20:55:49.2371039Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:55:49.2371513Z x_63 += out_31; out_34: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_63; x_63 = out_31 = None 2025-03-04T20:55:49.2371772Z 2025-03-04T20:55:49.2372162Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2372640Z out_35: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_34); out_34 = None 2025-03-04T20:55:49.2372895Z 2025-03-04T20:55:49.2373223Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2373951Z x_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:55:49.2374485Z 2025-03-04T20:55:49.2374833Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2376745Z x_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_64 = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2378406Z 2025-03-04T20:55:49.2379966Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2380469Z out_36: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_65); x_65 = None 2025-03-04T20:55:49.2380723Z 2025-03-04T20:55:49.2381052Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2381790Z x_66: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_36 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:55:49.2382321Z 2025-03-04T20:55:49.2382665Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2384475Z x_67: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_66 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2386105Z 2025-03-04T20:55:49.2386473Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2386979Z out_37: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_67); x_67 = None 2025-03-04T20:55:49.2387467Z 2025-03-04T20:55:49.2387811Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2388558Z x_68: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_37 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:55:49.2389113Z 2025-03-04T20:55:49.2389482Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2391409Z x_69: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_68 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2393162Z 2025-03-04T20:55:49.2393529Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:55:49.2394103Z x_69 += out_35; out_38: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_69; x_69 = out_35 = None 2025-03-04T20:55:49.2394374Z 2025-03-04T20:55:49.2394748Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2395231Z out_39: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_38); out_38 = None 2025-03-04T20:55:49.2395493Z 2025-03-04T20:55:49.2395826Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2396565Z x_70: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T20:55:49.2397102Z 2025-03-04T20:55:49.2397480Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2399437Z x_71: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_70 = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2401690Z 2025-03-04T20:55:49.2402297Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2402788Z out_40: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_71); x_71 = None 2025-03-04T20:55:49.2403044Z 2025-03-04T20:55:49.2403384Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2404172Z x_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_40 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T20:55:49.2404721Z 2025-03-04T20:55:49.2405073Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2406964Z x_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_72 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2408643Z 2025-03-04T20:55:49.2409255Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2409760Z out_41: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_73); x_73 = None 2025-03-04T20:55:49.2410016Z 2025-03-04T20:55:49.2410348Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2411080Z x_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_41 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T20:55:49.2411624Z 2025-03-04T20:55:49.2411971Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2413808Z x_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_74 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2415430Z 2025-03-04T20:55:49.2415802Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:55:49.2416319Z x_75 += out_39; out_42: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_75; x_75 = out_39 = None 2025-03-04T20:55:49.2416583Z 2025-03-04T20:55:49.2416951Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2417436Z out_43: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_42); out_42 = None 2025-03-04T20:55:49.2417689Z 2025-03-04T20:55:49.2418014Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2418739Z x_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = None 2025-03-04T20:55:49.2419588Z 2025-03-04T20:55:49.2419972Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2421813Z x_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_76 = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2423446Z 2025-03-04T20:55:49.2423815Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2424283Z out_44: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_77); x_77 = None 2025-03-04T20:55:49.2424531Z 2025-03-04T20:55:49.2424857Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2425625Z x_78: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_44 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = None 2025-03-04T20:55:49.2426173Z 2025-03-04T20:55:49.2426524Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2428626Z x_79: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_78 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2430306Z 2025-03-04T20:55:49.2430709Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2431186Z out_45: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_79); x_79 = None 2025-03-04T20:55:49.2431439Z 2025-03-04T20:55:49.2431777Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2432535Z x_80: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_45 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = None 2025-03-04T20:55:49.2433092Z 2025-03-04T20:55:49.2433464Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2435628Z x_81: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_80 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2437297Z 2025-03-04T20:55:49.2437666Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:55:49.2438146Z x_81 += out_43; out_46: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_81; x_81 = out_43 = None 2025-03-04T20:55:49.2438408Z 2025-03-04T20:55:49.2438779Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2439265Z out_47: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_46); out_46 = None 2025-03-04T20:55:49.2439526Z 2025-03-04T20:55:49.2439864Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2441248Z x_82: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = None 2025-03-04T20:55:49.2441801Z 2025-03-04T20:55:49.2442161Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2444063Z x_83: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_82 = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2445804Z 2025-03-04T20:55:49.2446175Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2446642Z out_48: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_83); x_83 = None 2025-03-04T20:55:49.2446886Z 2025-03-04T20:55:49.2447212Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2447947Z x_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_48 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = None 2025-03-04T20:55:49.2448857Z 2025-03-04T20:55:49.2449905Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2451995Z x_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_84 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2453654Z 2025-03-04T20:55:49.2454031Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2454508Z out_49: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_85); x_85 = None 2025-03-04T20:55:49.2454759Z 2025-03-04T20:55:49.2455094Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2456042Z x_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_49 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = None 2025-03-04T20:55:49.2456606Z 2025-03-04T20:55:49.2456956Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2458799Z x_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_86 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2460445Z 2025-03-04T20:55:49.2460825Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:55:49.2461302Z x_87 += out_47; out_50: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_87; x_87 = out_47 = None 2025-03-04T20:55:49.2461560Z 2025-03-04T20:55:49.2462210Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2462716Z out_51: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_50); out_50 = None 2025-03-04T20:55:49.2462976Z 2025-03-04T20:55:49.2463327Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2464065Z x_88: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:55:49.2464596Z 2025-03-04T20:55:49.2464949Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2467082Z x_89: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_88 = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2468733Z 2025-03-04T20:55:49.2469106Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2469580Z out_52: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_89); x_89 = None 2025-03-04T20:55:49.2469837Z 2025-03-04T20:55:49.2470174Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2470917Z x_90: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1); out_52 = l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:55:49.2471646Z 2025-03-04T20:55:49.2472060Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2474049Z x_91: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_90 = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2475889Z 2025-03-04T20:55:49.2476264Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2476743Z out_53: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_91); x_91 = None 2025-03-04T20:55:49.2476997Z 2025-03-04T20:55:49.2477336Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2478099Z x_92: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_53 = l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:55:49.2478924Z 2025-03-04T20:55:49.2479324Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2481211Z x_93: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_92 = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2483122Z 2025-03-04T20:55:49.2483476Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2484231Z x_94: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_51 = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:55:49.2484793Z 2025-03-04T20:55:49.2485148Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2487286Z x_95: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_94 = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2488966Z 2025-03-04T20:55:49.2489334Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:55:49.2489799Z x_93 += x_95; out_54: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_93; x_93 = x_95 = None 2025-03-04T20:55:49.2490075Z 2025-03-04T20:55:49.2490444Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2490923Z out_55: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_54); out_54 = None 2025-03-04T20:55:49.2491183Z 2025-03-04T20:55:49.2491718Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2492511Z x_96: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:55:49.2493038Z 2025-03-04T20:55:49.2493378Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2495440Z x_97: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_96 = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2497181Z 2025-03-04T20:55:49.2497549Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2498017Z out_56: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_97); x_97 = None 2025-03-04T20:55:49.2498262Z 2025-03-04T20:55:49.2498592Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2499314Z x_98: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1); out_56 = l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:55:49.2499848Z 2025-03-04T20:55:49.2500193Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2502396Z x_99: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_98 = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2504069Z 2025-03-04T20:55:49.2504445Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2504959Z out_57: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_99); x_99 = None 2025-03-04T20:55:49.2505370Z 2025-03-04T20:55:49.2505754Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2506501Z x_100: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_57 = l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:55:49.2507047Z 2025-03-04T20:55:49.2507424Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2509331Z x_101: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_100 = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2510995Z 2025-03-04T20:55:49.2511686Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:55:49.2512265Z x_101 += out_55; out_58: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_101; x_101 = out_55 = None 2025-03-04T20:55:49.2512551Z 2025-03-04T20:55:49.2512952Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2513461Z out_59: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_58); out_58 = None 2025-03-04T20:55:49.2513793Z 2025-03-04T20:55:49.2514146Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2514924Z x_102: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:55:49.2515478Z 2025-03-04T20:55:49.2515851Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2518298Z x_103: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_102 = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2520080Z 2025-03-04T20:55:49.2520666Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2521208Z out_60: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_103); x_103 = None 2025-03-04T20:55:49.2521459Z 2025-03-04T20:55:49.2521792Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2522533Z x_104: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1); out_60 = l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:55:49.2523068Z 2025-03-04T20:55:49.2523409Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2525246Z x_105: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_104 = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2527179Z 2025-03-04T20:55:49.2527557Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2528033Z out_61: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_105); x_105 = None 2025-03-04T20:55:49.2528284Z 2025-03-04T20:55:49.2528613Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2529341Z x_106: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_61 = l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:55:49.2529878Z 2025-03-04T20:55:49.2530222Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2532325Z x_107: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_106 = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2534002Z 2025-03-04T20:55:49.2534366Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:55:49.2534865Z x_107 += out_59; out_62: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_107; x_107 = out_59 = None 2025-03-04T20:55:49.2535133Z 2025-03-04T20:55:49.2535513Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2536010Z out_63: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_62); out_62 = None 2025-03-04T20:55:49.2536493Z 2025-03-04T20:55:49.2537049Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:55:49.2537688Z arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32) 2025-03-04T20:55:49.2537949Z 2025-03-04T20:55:49.2538351Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:55:49.2538830Z shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T20:55:49.2539075Z 2025-03-04T20:55:49.2539587Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:55:49.2540695Z arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32) 2025-03-04T20:55:49.2541008Z 2025-03-04T20:55:49.2541396Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:55:49.2541880Z shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T20:55:49.2542132Z 2025-03-04T20:55:49.2542592Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:55:49.2543192Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T20:55:49.2543519Z shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0] 2025-03-04T20:55:49.2543782Z shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T20:55:49.2544002Z 2025-03-04T20:55:49.2544419Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:55:49.2544927Z shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T20:55:49.2545158Z 2025-03-04T20:55:49.2545567Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:55:49.2546304Z shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T20:55:49.2546590Z 2025-03-04T20:55:49.2547064Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:55:49.2547711Z shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T20:55:49.2548032Z 2025-03-04T20:55:49.2548536Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:55:49.2549174Z view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T20:55:49.2549873Z view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T20:55:49.2550892Z add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T20:55:49.2551206Z x_108: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4); add = None 2025-03-04T20:55:49.2551460Z 2025-03-04T20:55:49.2551905Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:55:49.2552416Z tensor: "f32[82125, 4][4, 1]cpu" = x_108.to(torch.float32); x_108 = None 2025-03-04T20:55:49.2552677Z 2025-03-04T20:55:49.2553041Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2554335Z x_109: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_63, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); out_63 = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T20:55:49.2555352Z 2025-03-04T20:55:49.2555746Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:55:49.2556321Z x_110: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_109, inplace = False); x_109 = None 2025-03-04T20:55:49.2556645Z 2025-03-04T20:55:49.2557144Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:55:49.2558813Z score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_110, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T20:55:49.2560306Z 2025-03-04T20:55:49.2560850Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:55:49.2562181Z x_111: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_110, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_110 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T20:55:49.2563162Z 2025-03-04T20:55:49.2563599Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T20:55:49.2564141Z permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T20:55:49.2564483Z logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T20:55:49.2564743Z 2025-03-04T20:55:49.2565244Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T20:55:49.2565893Z view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_111.view(4, -1, 4, 73, 75); x_111 = None 2025-03-04T20:55:49.2566283Z permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2); view_2 = None 2025-03-04T20:55:49.2566677Z pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2); permute_1 = None 2025-03-04T20:55:49.2567163Z 2025-03-04T20:55:49.2567668Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:55:49.2568319Z pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T20:55:49.2568632Z 2025-03-04T20:55:49.2569156Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:55:49.2569780Z unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T20:55:49.2570119Z expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T20:55:49.2570448Z anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T20:55:49.2570718Z 2025-03-04T20:55:49.2571452Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:55:49.2572073Z deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T20:55:49.2572360Z 2025-03-04T20:55:49.2572755Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:55:49.2573255Z boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T20:55:49.2573508Z 2025-03-04T20:55:49.2573901Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:55:49.2574396Z getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:55:49.2574696Z getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:55:49.2575006Z widths: "f32[328500][1]cpu" = getitem_2 - getitem_3; getitem_2 = getitem_3 = None 2025-03-04T20:55:49.2575260Z 2025-03-04T20:55:49.2575661Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:55:49.2576376Z getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:55:49.2576687Z getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:55:49.2577005Z heights: "f32[328500][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T20:55:49.2577265Z 2025-03-04T20:55:49.2577671Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:55:49.2578157Z getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:55:49.2578414Z mul: "f32[328500][1]cpu" = 0.5 * widths 2025-03-04T20:55:49.2578665Z ctr_x: "f32[328500][1]cpu" = getitem_6 + mul; getitem_6 = mul = None 2025-03-04T20:55:49.2578923Z 2025-03-04T20:55:49.2579318Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:55:49.2579825Z getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:55:49.2580110Z mul_1: "f32[328500][1]cpu" = 0.5 * heights 2025-03-04T20:55:49.2580372Z ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1; getitem_7 = mul_1 = None 2025-03-04T20:55:49.2580612Z 2025-03-04T20:55:49.2581080Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:55:49.2581971Z getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:55:49.2582331Z dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0; getitem_8 = None 2025-03-04T20:55:49.2582571Z 2025-03-04T20:55:49.2582988Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:55:49.2583485Z getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:55:49.2583796Z dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0; getitem_9 = None 2025-03-04T20:55:49.2584023Z 2025-03-04T20:55:49.2584402Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:55:49.2585092Z getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:55:49.2585445Z dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0; getitem_10 = None 2025-03-04T20:55:49.2585674Z 2025-03-04T20:55:49.2586062Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:55:49.2586598Z getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:55:49.2586936Z dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0; getitem_11 = None 2025-03-04T20:55:49.2587160Z 2025-03-04T20:55:49.2587588Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:55:49.2588129Z dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:55:49.2588385Z 2025-03-04T20:55:49.2588805Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:55:49.2589330Z dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:55:49.2589585Z 2025-03-04T20:55:49.2590012Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:55:49.2590550Z getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:55:49.2590863Z mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12; dx = getitem_12 = None 2025-03-04T20:55:49.2591193Z getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:55:49.2591542Z pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13; mul_2 = getitem_13 = None 2025-03-04T20:55:49.2592741Z 2025-03-04T20:55:49.2593218Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:55:49.2593887Z getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:55:49.2594218Z mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14; dy = getitem_14 = None 2025-03-04T20:55:49.2594564Z getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:55:49.2594924Z pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15; mul_3 = getitem_15 = None 2025-03-04T20:55:49.2595194Z 2025-03-04T20:55:49.2595647Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:55:49.2596142Z exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:55:49.2596469Z getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:55:49.2596816Z pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16; exp = getitem_16 = None 2025-03-04T20:55:49.2597085Z 2025-03-04T20:55:49.2597511Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:55:49.2598198Z exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:55:49.2598562Z getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:55:49.2598934Z pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17; exp_1 = getitem_17 = None 2025-03-04T20:55:49.2599224Z 2025-03-04T20:55:49.2599634Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:55:49.2600100Z mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T20:55:49.2600362Z x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:55:49.2600599Z 2025-03-04T20:55:49.2600998Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:55:49.2601461Z mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T20:55:49.2601721Z y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:55:49.2602117Z 2025-03-04T20:55:49.2602518Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:55:49.2602998Z mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:55:49.2603290Z x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:55:49.2603540Z 2025-03-04T20:55:49.2603932Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:55:49.2604407Z mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:55:49.2604693Z y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:55:49.2604940Z 2025-03-04T20:55:49.2605374Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:55:49.2605956Z pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:55:49.2606239Z 2025-03-04T20:55:49.2606660Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:55:49.2607271Z proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4)); pred_boxes = None 2025-03-04T20:55:49.2607550Z 2025-03-04T20:55:49.2608017Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:55:49.2608623Z proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T20:55:49.2608911Z 2025-03-04T20:55:49.2609508Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T20:55:49.2610201Z arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T20:55:49.2610447Z 2025-03-04T20:55:49.2610849Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:55:49.2611877Z batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T20:55:49.2612143Z 2025-03-04T20:55:49.2612688Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:55:49.2613288Z topk = logits_i.topk(6000, dim = 1); logits_i = None 2025-03-04T20:55:49.2613576Z topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0] 2025-03-04T20:55:49.2613839Z topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1]; topk = None 2025-03-04T20:55:49.2614061Z 2025-03-04T20:55:49.2614599Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:55:49.2615262Z getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T20:55:49.2615702Z topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)]; proposals_i_1 = getitem_20 = topk_idx = None 2025-03-04T20:55:49.2616041Z 2025-03-04T20:55:49.2616574Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:55:49.2617237Z full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:55:49.2617507Z 2025-03-04T20:55:49.2617883Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:55:49.2618368Z level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu')); full = level_ids = None 2025-03-04T20:55:49.2618629Z 2025-03-04T20:55:49.2619086Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T20:55:49.2619928Z getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0]; topk_proposals_i = None 2025-03-04T20:55:49.2620198Z 2025-03-04T20:55:49.2620585Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:55:49.2621077Z tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32); getitem_22 = None 2025-03-04T20:55:49.2621332Z 2025-03-04T20:55:49.2621792Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T20:55:49.2622379Z scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0]; topk_scores_i = None 2025-03-04T20:55:49.2622629Z 2025-03-04T20:55:49.2623188Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T20:55:49.2623850Z isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1); tensor_1 = None 2025-03-04T20:55:49.2624168Z all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:55:49.2624485Z isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T20:55:49.2624814Z valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T20:55:49.2625059Z 2025-03-04T20:55:49.2625518Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T20:55:49.2626047Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T20:55:49.2626277Z 2025-03-04T20:55:49.2626673Z 2025-03-04T20:55:49.2626764Z class GraphModule(torch.nn.Module): 2025-03-04T20:55:49.2681474Z def forward(self, L_stack0_tensor: "f32[4, 3, 1156, 1199][4158132, 1386044, 1199, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_: "f32[512, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_: 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"f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", 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L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_: "f32[1024, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_: 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L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_: "f32[512, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_: "f32[2048, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[2048, 2048, 3, 3][18432, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[2048][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"): 2025-03-04T20:55:49.2736170Z l_stack0_tensor = L_stack0_tensor 2025-03-04T20:55:49.2736695Z l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ 2025-03-04T20:55:49.2737486Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2738303Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2739113Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2740429Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2741208Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:55:49.2742032Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2742955Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2743800Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2744654Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2745428Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:55:49.2746269Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2747113Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2747903Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2748756Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2750024Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:55:49.2750856Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2751735Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2752523Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2753296Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2754205Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:55:49.2755154Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2756021Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2756837Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2757632Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2758383Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:55:49.2759196Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2760279Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2761111Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2761884Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2762652Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:55:49.2763416Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2763771Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2764086Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2764410Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2764692Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:55:49.2765065Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2765439Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2765797Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2766140Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2766454Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:55:49.2766830Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2767203Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2767558Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2767897Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2768206Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:55:49.2768536Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2768874Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2769218Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2769525Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2769824Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:55:49.2770157Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2770855Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2771215Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2771535Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2771821Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:55:49.2772162Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2772501Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2772818Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2773129Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2773410Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:55:49.2773749Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2774079Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2774401Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2774704Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2775011Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:55:49.2775352Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2775697Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2776031Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2776394Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2776725Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:55:49.2777073Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2777441Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2777773Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2778101Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2778389Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:55:49.2778721Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2779286Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2779627Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2779966Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2780249Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:55:49.2780598Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2780934Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2781258Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2781597Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2781876Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:55:49.2782215Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2782563Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2782889Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2783209Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2783500Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:55:49.2783839Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2784194Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2784516Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2784819Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2785104Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:55:49.2785455Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2785830Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2786183Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2786530Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2786827Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:55:49.2787177Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2787550Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2787918Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2788529Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2788842Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ 2025-03-04T20:55:49.2789226Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2789581Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2789943Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2790270Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2790574Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ 2025-03-04T20:55:49.2790959Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2791304Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2791645Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2791966Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2792270Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ 2025-03-04T20:55:49.2792625Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2792983Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2793317Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2793705Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2794039Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:55:49.2794409Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2794780Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2795145Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2795485Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2795790Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:55:49.2796177Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2796552Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2796892Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2797232Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2797537Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:55:49.2797920Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2798279Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2798618Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2798947Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2799263Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:55:49.2799625Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2800005Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2800365Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2800921Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2801226Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:55:49.2801558Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2802000Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2802322Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2802640Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2802953Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:55:49.2803411Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2803764Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2804102Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2804417Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2804723Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:55:49.2805067Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2805402Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2805730Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2806038Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2806331Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:55:49.2806663Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2807001Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2807321Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2807628Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2807919Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:55:49.2808253Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2808617Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2808927Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2809256Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2809536Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:55:49.2810115Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2810495Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2810813Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2811148Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2811431Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ 2025-03-04T20:55:49.2811779Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2812112Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2812432Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2812739Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2813025Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ 2025-03-04T20:55:49.2813366Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2813692Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2814015Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2814324Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2814614Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ 2025-03-04T20:55:49.2814964Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2815305Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2815620Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2815946Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2816232Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ 2025-03-04T20:55:49.2816587Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2816926Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2817242Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2817573Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2817853Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ 2025-03-04T20:55:49.2818196Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2818525Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2818847Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2819158Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2819439Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ 2025-03-04T20:55:49.2819782Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2820111Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2820439Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2820742Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2821045Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ 2025-03-04T20:55:49.2821604Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2821964Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2822307Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2822614Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2822920Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ 2025-03-04T20:55:49.2823256Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2823601Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2823932Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2824247Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2824535Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ 2025-03-04T20:55:49.2824886Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2825240Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2825573Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2825898Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2826194Z l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:55:49.2826554Z l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2826897Z l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2827232Z l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2827557Z l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2827862Z l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:55:49.2828213Z l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2828552Z l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2828898Z l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2829215Z l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2829527Z l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:55:49.2830260Z l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2830662Z l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2831019Z l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2831334Z l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2831653Z l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:55:49.2832013Z l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2832378Z l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2832722Z l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2833064Z l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2833353Z l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:55:49.2833771Z l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2834130Z l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2834452Z l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2834795Z l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2835086Z l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:55:49.2835431Z l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2835787Z l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2836119Z l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2836447Z l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2836747Z l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:55:49.2837100Z l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2837454Z l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2837781Z l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2838095Z l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2838401Z l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:55:49.2838748Z l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2839096Z l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2839418Z l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2839739Z l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2840032Z l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:55:49.2840757Z l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2841128Z l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2841459Z l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2841790Z l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2842071Z l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:55:49.2842430Z l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.2842761Z l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.2843100Z l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:55:49.2843418Z l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:55:49.2843762Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T20:55:49.2844101Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T20:55:49.2844407Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T20:55:49.2844786Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T20:55:49.2845141Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T20:55:49.2845493Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T20:55:49.2845830Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T20:55:49.2845902Z 2025-03-04T20:55:49.2846180Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2846659Z x: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1); l_stack0_tensor = l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = None 2025-03-04T20:55:49.2846728Z 2025-03-04T20:55:49.2847007Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2848550Z x_1: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2848633Z 2025-03-04T20:55:49.2848923Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x) 2025-03-04T20:55:49.2849077Z x_2: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.relu_(x_1); x_1 = None 2025-03-04T20:55:49.2849147Z 2025-03-04T20:55:49.2849504Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1) 2025-03-04T20:55:49.2849764Z x_3: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1); x_2 = None 2025-03-04T20:55:49.2849834Z 2025-03-04T20:55:49.2850520Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2850985Z x_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:55:49.2851074Z 2025-03-04T20:55:49.2851360Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2852932Z x_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_4 = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2853017Z 2025-03-04T20:55:49.2853311Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2853447Z out: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_5); x_5 = None 2025-03-04T20:55:49.2853518Z 2025-03-04T20:55:49.2853769Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2854201Z x_6: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out = l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:55:49.2854261Z 2025-03-04T20:55:49.2854533Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2856071Z x_7: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_6 = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2856172Z 2025-03-04T20:55:49.2856481Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2856616Z out_1: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_7); x_7 = None 2025-03-04T20:55:49.2856685Z 2025-03-04T20:55:49.2856948Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2857394Z x_8: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_1 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:55:49.2857456Z 2025-03-04T20:55:49.2857725Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2859487Z x_9: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_8 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2859571Z 2025-03-04T20:55:49.2859864Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2860302Z x_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); x_3 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:55:49.2860371Z 2025-03-04T20:55:49.2860638Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2862203Z x_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_10 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2862295Z 2025-03-04T20:55:49.2862577Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:55:49.2862727Z x_9 += x_11; out_2: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_9; x_9 = x_11 = None 2025-03-04T20:55:49.2862788Z 2025-03-04T20:55:49.2863075Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2863236Z out_3: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_2); out_2 = None 2025-03-04T20:55:49.2863305Z 2025-03-04T20:55:49.2863548Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2863986Z x_12: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:55:49.2864049Z 2025-03-04T20:55:49.2864317Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2865815Z x_13: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_12 = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2865900Z 2025-03-04T20:55:49.2866189Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2866331Z out_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_13); x_13 = None 2025-03-04T20:55:49.2866400Z 2025-03-04T20:55:49.2866655Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2867085Z x_14: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_4 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:55:49.2867148Z 2025-03-04T20:55:49.2867415Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2869156Z x_15: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_14 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2869259Z 2025-03-04T20:55:49.2869564Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2869703Z out_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_15); x_15 = None 2025-03-04T20:55:49.2869773Z 2025-03-04T20:55:49.2870062Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2870518Z x_16: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_5 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:55:49.2870582Z 2025-03-04T20:55:49.2870853Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2872406Z x_17: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_16 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2872485Z 2025-03-04T20:55:49.2872773Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:55:49.2872924Z x_17 += out_3; out_6: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_17; x_17 = out_3 = None 2025-03-04T20:55:49.2872993Z 2025-03-04T20:55:49.2873274Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2873425Z out_7: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_6); out_6 = None 2025-03-04T20:55:49.2873486Z 2025-03-04T20:55:49.2873812Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2874237Z x_18: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:55:49.2874306Z 2025-03-04T20:55:49.2874570Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2876118Z x_19: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_18 = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2876207Z 2025-03-04T20:55:49.2876512Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2876655Z out_8: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_19); x_19 = None 2025-03-04T20:55:49.2876720Z 2025-03-04T20:55:49.2876981Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2877434Z x_20: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_8 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:55:49.2877505Z 2025-03-04T20:55:49.2877777Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2879327Z x_21: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_20 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2879413Z 2025-03-04T20:55:49.2879701Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2879845Z out_9: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_21); x_21 = None 2025-03-04T20:55:49.2879906Z 2025-03-04T20:55:49.2880166Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2880826Z x_22: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_9 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:55:49.2880906Z 2025-03-04T20:55:49.2881203Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2882787Z x_23: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_22 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2882879Z 2025-03-04T20:55:49.2883164Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:55:49.2883329Z x_23 += out_7; out_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_23; x_23 = out_7 = None 2025-03-04T20:55:49.2883413Z 2025-03-04T20:55:49.2883708Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2883862Z out_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_10); out_10 = None 2025-03-04T20:55:49.2883933Z 2025-03-04T20:55:49.2884199Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2884637Z x_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:55:49.2884698Z 2025-03-04T20:55:49.2884990Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2886540Z x_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_24 = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2886603Z 2025-03-04T20:55:49.2886892Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2887032Z out_12: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_25); x_25 = None 2025-03-04T20:55:49.2887105Z 2025-03-04T20:55:49.2887353Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2887793Z x_26: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_12 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:55:49.2887860Z 2025-03-04T20:55:49.2888117Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2890147Z x_27: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_26 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2890249Z 2025-03-04T20:55:49.2890587Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2890732Z out_13: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_27); x_27 = None 2025-03-04T20:55:49.2890803Z 2025-03-04T20:55:49.2891054Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2891506Z x_28: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_13 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:55:49.2891577Z 2025-03-04T20:55:49.2891843Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2893435Z x_29: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_28 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2893500Z 2025-03-04T20:55:49.2893759Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2894215Z x_30: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); out_11 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:55:49.2894278Z 2025-03-04T20:55:49.2894553Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2896147Z x_31: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_30 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2896237Z 2025-03-04T20:55:49.2896512Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:55:49.2896663Z x_29 += x_31; out_14: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_29; x_29 = x_31 = None 2025-03-04T20:55:49.2896728Z 2025-03-04T20:55:49.2897004Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2897176Z out_15: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_14); out_14 = None 2025-03-04T20:55:49.2897236Z 2025-03-04T20:55:49.2897485Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2897913Z x_32: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:55:49.2897981Z 2025-03-04T20:55:49.2898241Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2899752Z x_33: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_32 = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2899838Z 2025-03-04T20:55:49.2900125Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2900269Z out_16: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_33); x_33 = None 2025-03-04T20:55:49.2900331Z 2025-03-04T20:55:49.2900585Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2901226Z x_34: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_16 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:55:49.2901305Z 2025-03-04T20:55:49.2901580Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2903254Z x_35: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_34 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2903372Z 2025-03-04T20:55:49.2903654Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2903799Z out_17: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_35); x_35 = None 2025-03-04T20:55:49.2903862Z 2025-03-04T20:55:49.2904141Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2904608Z x_36: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_17 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:55:49.2904681Z 2025-03-04T20:55:49.2904955Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2906559Z x_37: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_36 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2906658Z 2025-03-04T20:55:49.2906949Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:55:49.2907114Z x_37 += out_15; out_18: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_37; x_37 = out_15 = None 2025-03-04T20:55:49.2907178Z 2025-03-04T20:55:49.2907480Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2907633Z out_19: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_18); out_18 = None 2025-03-04T20:55:49.2907701Z 2025-03-04T20:55:49.2908137Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2908652Z x_38: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:55:49.2908722Z 2025-03-04T20:55:49.2909015Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2910635Z x_39: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_38 = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2910726Z 2025-03-04T20:55:49.2911027Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2911188Z out_20: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_39); x_39 = None 2025-03-04T20:55:49.2911259Z 2025-03-04T20:55:49.2911515Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2911987Z x_40: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_20 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:55:49.2912051Z 2025-03-04T20:55:49.2912329Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2914016Z x_41: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_40 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2914110Z 2025-03-04T20:55:49.2914420Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2914568Z out_21: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_41); x_41 = None 2025-03-04T20:55:49.2914642Z 2025-03-04T20:55:49.2914916Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2915372Z x_42: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_21 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:55:49.2915435Z 2025-03-04T20:55:49.2915715Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2917321Z x_43: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_42 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2917401Z 2025-03-04T20:55:49.2917689Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:55:49.2917846Z x_43 += out_19; out_22: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_43; x_43 = out_19 = None 2025-03-04T20:55:49.2917929Z 2025-03-04T20:55:49.2918214Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2918382Z out_23: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_22); out_22 = None 2025-03-04T20:55:49.2918444Z 2025-03-04T20:55:49.2918725Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2919150Z x_44: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T20:55:49.2919685Z 2025-03-04T20:55:49.2920015Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2921587Z x_45: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_44 = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2921666Z 2025-03-04T20:55:49.2921957Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2922105Z out_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_45); x_45 = None 2025-03-04T20:55:49.2922168Z 2025-03-04T20:55:49.2922432Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2922864Z x_46: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_24 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T20:55:49.2922935Z 2025-03-04T20:55:49.2923201Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2924757Z x_47: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_46 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2924846Z 2025-03-04T20:55:49.2925153Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2925301Z out_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_47); x_47 = None 2025-03-04T20:55:49.2925363Z 2025-03-04T20:55:49.2925624Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2926079Z x_48: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_25 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T20:55:49.2926149Z 2025-03-04T20:55:49.2926413Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2927974Z x_49: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_48 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2928046Z 2025-03-04T20:55:49.2928325Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:55:49.2928486Z x_49 += out_23; out_26: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_49; x_49 = out_23 = None 2025-03-04T20:55:49.2928547Z 2025-03-04T20:55:49.2928839Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2928988Z out_27: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_26); out_26 = None 2025-03-04T20:55:49.2929069Z 2025-03-04T20:55:49.2929312Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2929728Z x_50: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:55:49.2929797Z 2025-03-04T20:55:49.2930060Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2931860Z x_51: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_50 = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2931948Z 2025-03-04T20:55:49.2932254Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2932388Z out_28: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_51); x_51 = None 2025-03-04T20:55:49.2932458Z 2025-03-04T20:55:49.2932721Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2933147Z x_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_28 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:55:49.2933231Z 2025-03-04T20:55:49.2933493Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2935001Z x_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_52 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2935063Z 2025-03-04T20:55:49.2935347Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2935476Z out_29: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_53); x_53 = None 2025-03-04T20:55:49.2935542Z 2025-03-04T20:55:49.2935785Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2936205Z x_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_29 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:55:49.2936272Z 2025-03-04T20:55:49.2936527Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2938031Z x_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_54 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2938105Z 2025-03-04T20:55:49.2938374Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2938802Z x_56: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); out_27 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:55:49.2938862Z 2025-03-04T20:55:49.2939142Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2940921Z x_57: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_56 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2941024Z 2025-03-04T20:55:49.2941318Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:55:49.2941466Z x_55 += x_57; out_30: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_55; x_55 = x_57 = None 2025-03-04T20:55:49.2941534Z 2025-03-04T20:55:49.2941818Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2941964Z out_31: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_30); out_30 = None 2025-03-04T20:55:49.2942025Z 2025-03-04T20:55:49.2942277Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2942692Z x_58: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:55:49.2942757Z 2025-03-04T20:55:49.2943017Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2944548Z x_59: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_58 = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2944635Z 2025-03-04T20:55:49.2944915Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2945075Z out_32: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_59); x_59 = None 2025-03-04T20:55:49.2945135Z 2025-03-04T20:55:49.2945389Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2945823Z x_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_32 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:55:49.2945894Z 2025-03-04T20:55:49.2946153Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2947682Z x_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_60 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2947767Z 2025-03-04T20:55:49.2948452Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2948619Z out_33: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_61); x_61 = None 2025-03-04T20:55:49.2948688Z 2025-03-04T20:55:49.2948966Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2949400Z x_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_33 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:55:49.2949474Z 2025-03-04T20:55:49.2949748Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2951328Z x_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_62 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2951425Z 2025-03-04T20:55:49.2951708Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:55:49.2951866Z x_63 += out_31; out_34: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_63; x_63 = out_31 = None 2025-03-04T20:55:49.2951931Z 2025-03-04T20:55:49.2952238Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2952380Z out_35: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_34); out_34 = None 2025-03-04T20:55:49.2952448Z 2025-03-04T20:55:49.2952716Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2953150Z x_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:55:49.2953212Z 2025-03-04T20:55:49.2953490Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2955176Z x_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_64 = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2955251Z 2025-03-04T20:55:49.2955546Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2955677Z out_36: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_65); x_65 = None 2025-03-04T20:55:49.2955745Z 2025-03-04T20:55:49.2955995Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2956422Z x_66: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_36 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:55:49.2956485Z 2025-03-04T20:55:49.2956759Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2958285Z x_67: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_66 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2958377Z 2025-03-04T20:55:49.2958670Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2958816Z out_37: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_67); x_67 = None 2025-03-04T20:55:49.2958888Z 2025-03-04T20:55:49.2959134Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2959581Z x_68: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_37 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:55:49.2959645Z 2025-03-04T20:55:49.2959921Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2961830Z x_69: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_68 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2961925Z 2025-03-04T20:55:49.2962239Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:55:49.2962388Z x_69 += out_35; out_38: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_69; x_69 = out_35 = None 2025-03-04T20:55:49.2962459Z 2025-03-04T20:55:49.2962741Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2962889Z out_39: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_38); out_38 = None 2025-03-04T20:55:49.2962952Z 2025-03-04T20:55:49.2963213Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2963640Z x_70: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T20:55:49.2963710Z 2025-03-04T20:55:49.2963982Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2965560Z x_71: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_70 = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2965647Z 2025-03-04T20:55:49.2965942Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2966075Z out_40: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_71); x_71 = None 2025-03-04T20:55:49.2966134Z 2025-03-04T20:55:49.2966399Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2966805Z x_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_40 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T20:55:49.2966872Z 2025-03-04T20:55:49.2967127Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2968651Z x_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_72 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2968721Z 2025-03-04T20:55:49.2968999Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2969135Z out_41: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_73); x_73 = None 2025-03-04T20:55:49.2969194Z 2025-03-04T20:55:49.2969445Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2969853Z x_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_41 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T20:55:49.2969922Z 2025-03-04T20:55:49.2970180Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2972225Z x_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_74 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2972340Z 2025-03-04T20:55:49.2972627Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:55:49.2972794Z x_75 += out_39; out_42: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_75; x_75 = out_39 = None 2025-03-04T20:55:49.2972856Z 2025-03-04T20:55:49.2973143Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2973281Z out_43: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_42); out_42 = None 2025-03-04T20:55:49.2973367Z 2025-03-04T20:55:49.2973615Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2974035Z x_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = None 2025-03-04T20:55:49.2974114Z 2025-03-04T20:55:49.2974385Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2975892Z x_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_76 = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2975956Z 2025-03-04T20:55:49.2976244Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2976371Z out_44: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_77); x_77 = None 2025-03-04T20:55:49.2976440Z 2025-03-04T20:55:49.2976682Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2977095Z x_78: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_44 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = None 2025-03-04T20:55:49.2977154Z 2025-03-04T20:55:49.2977420Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2978926Z x_79: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_78 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2979295Z 2025-03-04T20:55:49.2979667Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2979806Z out_45: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_79); x_79 = None 2025-03-04T20:55:49.2979880Z 2025-03-04T20:55:49.2980149Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2980582Z x_80: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_45 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = None 2025-03-04T20:55:49.2980652Z 2025-03-04T20:55:49.2980914Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2982442Z x_81: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_80 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2982503Z 2025-03-04T20:55:49.2982784Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:55:49.2982925Z x_81 += out_43; out_46: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_81; x_81 = out_43 = None 2025-03-04T20:55:49.2982993Z 2025-03-04T20:55:49.2983270Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2983414Z out_47: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_46); out_46 = None 2025-03-04T20:55:49.2983474Z 2025-03-04T20:55:49.2983727Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2984141Z x_82: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = None 2025-03-04T20:55:49.2984203Z 2025-03-04T20:55:49.2984472Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2985986Z x_83: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_82 = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2986074Z 2025-03-04T20:55:49.2986354Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2986490Z out_48: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_83); x_83 = None 2025-03-04T20:55:49.2986563Z 2025-03-04T20:55:49.2986817Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2987238Z x_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_48 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = None 2025-03-04T20:55:49.2987312Z 2025-03-04T20:55:49.2987587Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2989385Z x_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_84 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2989466Z 2025-03-04T20:55:49.2989771Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2989906Z out_49: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_85); x_85 = None 2025-03-04T20:55:49.2989978Z 2025-03-04T20:55:49.2990230Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2990667Z x_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_49 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = None 2025-03-04T20:55:49.2990728Z 2025-03-04T20:55:49.2990999Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2992541Z x_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_86 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2992630Z 2025-03-04T20:55:49.2992938Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:55:49.2993084Z x_87 += out_47; out_50: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_87; x_87 = out_47 = None 2025-03-04T20:55:49.2993153Z 2025-03-04T20:55:49.2993455Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2993602Z out_51: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_50); out_50 = None 2025-03-04T20:55:49.2993749Z 2025-03-04T20:55:49.2994046Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2994502Z x_88: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:55:49.2994619Z 2025-03-04T20:55:49.2994880Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2996413Z x_89: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_88 = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.2996482Z 2025-03-04T20:55:49.2996760Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.2996899Z out_52: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_89); x_89 = None 2025-03-04T20:55:49.2996959Z 2025-03-04T20:55:49.2997220Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.2997663Z x_90: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1); out_52 = l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:55:49.2997737Z 2025-03-04T20:55:49.2998012Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.2999993Z x_91: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_90 = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.3000095Z 2025-03-04T20:55:49.3000437Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.3000583Z out_53: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_91); x_91 = None 2025-03-04T20:55:49.3000660Z 2025-03-04T20:55:49.3000922Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.3001355Z x_92: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_53 = l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:55:49.3001439Z 2025-03-04T20:55:49.3001704Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.3003450Z x_93: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_92 = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.3003526Z 2025-03-04T20:55:49.3003783Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.3004229Z x_94: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_51 = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:55:49.3004293Z 2025-03-04T20:55:49.3004569Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.3006162Z x_95: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_94 = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.3006276Z 2025-03-04T20:55:49.3006569Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:55:49.3006709Z x_93 += x_95; out_54: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_93; x_93 = x_95 = None 2025-03-04T20:55:49.3006778Z 2025-03-04T20:55:49.3007092Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.3007242Z out_55: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_54); out_54 = None 2025-03-04T20:55:49.3007302Z 2025-03-04T20:55:49.3007582Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.3008943Z x_96: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:55:49.3009029Z 2025-03-04T20:55:49.3009327Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.3010942Z x_97: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_96 = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.3011011Z 2025-03-04T20:55:49.3011300Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.3011436Z out_56: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_97); x_97 = None 2025-03-04T20:55:49.3011496Z 2025-03-04T20:55:49.3011751Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.3012177Z x_98: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1); out_56 = l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:55:49.3012245Z 2025-03-04T20:55:49.3012508Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.3014041Z x_99: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_98 = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.3014127Z 2025-03-04T20:55:49.3014406Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.3014557Z out_57: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_99); x_99 = None 2025-03-04T20:55:49.3014616Z 2025-03-04T20:55:49.3014870Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.3015305Z x_100: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_57 = l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:55:49.3015376Z 2025-03-04T20:55:49.3015639Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.3017227Z x_101: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_100 = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.3017315Z 2025-03-04T20:55:49.3017589Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:55:49.3017746Z x_101 += out_55; out_58: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_101; x_101 = out_55 = None 2025-03-04T20:55:49.3017806Z 2025-03-04T20:55:49.3018093Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.3018230Z out_59: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_58); out_58 = None 2025-03-04T20:55:49.3018297Z 2025-03-04T20:55:49.3018546Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.3018966Z x_102: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:55:49.3019215Z 2025-03-04T20:55:49.3019558Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.3021083Z x_103: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_102 = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.3021174Z 2025-03-04T20:55:49.3021488Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.3021626Z out_60: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_103); x_103 = None 2025-03-04T20:55:49.3021699Z 2025-03-04T20:55:49.3021971Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.3022400Z x_104: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1); out_60 = l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:55:49.3022460Z 2025-03-04T20:55:49.3022730Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.3024257Z x_105: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_104 = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.3024319Z 2025-03-04T20:55:49.3024609Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.3024742Z out_61: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_105); x_105 = None 2025-03-04T20:55:49.3024809Z 2025-03-04T20:55:49.3025052Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.3025479Z x_106: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_61 = l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:55:49.3025539Z 2025-03-04T20:55:49.3025806Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.3027312Z x_107: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_106 = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.3027391Z 2025-03-04T20:55:49.3027677Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:55:49.3027841Z x_107 += out_59; out_62: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_107; x_107 = out_59 = None 2025-03-04T20:55:49.3027913Z 2025-03-04T20:55:49.3028989Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.3029188Z out_63: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_62); out_62 = None 2025-03-04T20:55:49.3029254Z 2025-03-04T20:55:49.3029738Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:55:49.3029896Z arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32) 2025-03-04T20:55:49.3029979Z 2025-03-04T20:55:49.3030301Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:55:49.3030437Z shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T20:55:49.3030508Z 2025-03-04T20:55:49.3030952Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:55:49.3031111Z arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32) 2025-03-04T20:55:49.3031173Z 2025-03-04T20:55:49.3031477Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:55:49.3031613Z shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T20:55:49.3031680Z 2025-03-04T20:55:49.3032057Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:55:49.3032245Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T20:55:49.3032342Z shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0] 2025-03-04T20:55:49.3032480Z shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T20:55:49.3032541Z 2025-03-04T20:55:49.3032884Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:55:49.3033009Z shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T20:55:49.3033075Z 2025-03-04T20:55:49.3033409Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:55:49.3033536Z shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T20:55:49.3033926Z 2025-03-04T20:55:49.3034473Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:55:49.3035223Z shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T20:55:49.3035555Z 2025-03-04T20:55:49.3036074Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:55:49.3036670Z view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T20:55:49.3037296Z view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T20:55:49.3037910Z add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T20:55:49.3038217Z x_108: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4); add = None 2025-03-04T20:55:49.3038454Z 2025-03-04T20:55:49.3038846Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:55:49.3039336Z tensor: "f32[82125, 4][4, 1]cpu" = x_108.to(torch.float32); x_108 = None 2025-03-04T20:55:49.3039817Z 2025-03-04T20:55:49.3040186Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.3041323Z x_109: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_63, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); out_63 = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T20:55:49.3042229Z 2025-03-04T20:55:49.3042686Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:55:49.3043215Z x_110: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_109, inplace = False); x_109 = None 2025-03-04T20:55:49.3043519Z 2025-03-04T20:55:49.3043989Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:55:49.3045288Z score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_110, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T20:55:49.3046277Z 2025-03-04T20:55:49.3046720Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:55:49.3048024Z x_111: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_110, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_110 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T20:55:49.3048958Z 2025-03-04T20:55:49.3049667Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T20:55:49.3050247Z permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T20:55:49.3050600Z logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T20:55:49.3050853Z 2025-03-04T20:55:49.3051377Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T20:55:49.3051991Z view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_111.view(4, -1, 4, 73, 75); x_111 = None 2025-03-04T20:55:49.3052390Z permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2); view_2 = None 2025-03-04T20:55:49.3052783Z pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2); permute_1 = None 2025-03-04T20:55:49.3053072Z 2025-03-04T20:55:49.3053558Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:55:49.3054216Z pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T20:55:49.3054548Z 2025-03-04T20:55:49.3055055Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:55:49.3055668Z unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T20:55:49.3056010Z expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T20:55:49.3056336Z anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T20:55:49.3056588Z 2025-03-04T20:55:49.3057044Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:55:49.3057626Z deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T20:55:49.3057907Z 2025-03-04T20:55:49.3058299Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:55:49.3058793Z boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T20:55:49.3059049Z 2025-03-04T20:55:49.3059443Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:55:49.3059934Z getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:55:49.3060452Z getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:55:49.3060785Z widths: "f32[328500][1]cpu" = getitem_2 - getitem_3; getitem_2 = getitem_3 = None 2025-03-04T20:55:49.3061048Z 2025-03-04T20:55:49.3061460Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:55:49.3061952Z getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:55:49.3062246Z getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:55:49.3062588Z heights: "f32[328500][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T20:55:49.3062846Z 2025-03-04T20:55:49.3063237Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:55:49.3063716Z getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:55:49.3063972Z mul: "f32[328500][1]cpu" = 0.5 * widths 2025-03-04T20:55:49.3064226Z ctr_x: "f32[328500][1]cpu" = getitem_6 + mul; getitem_6 = mul = None 2025-03-04T20:55:49.3064459Z 2025-03-04T20:55:49.3064874Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:55:49.3065372Z getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:55:49.3065655Z mul_1: "f32[328500][1]cpu" = 0.5 * heights 2025-03-04T20:55:49.3065932Z ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1; getitem_7 = mul_1 = None 2025-03-04T20:55:49.3066168Z 2025-03-04T20:55:49.3066594Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:55:49.3067103Z getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:55:49.3067428Z dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0; getitem_8 = None 2025-03-04T20:55:49.3067673Z 2025-03-04T20:55:49.3068269Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:55:49.3068829Z getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:55:49.3069152Z dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0; getitem_9 = None 2025-03-04T20:55:49.3069380Z 2025-03-04T20:55:49.3069770Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:55:49.3070288Z getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:55:49.3070615Z dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0; getitem_10 = None 2025-03-04T20:55:49.3070852Z 2025-03-04T20:55:49.3071262Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:55:49.3071815Z getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:55:49.3072169Z dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0; getitem_11 = None 2025-03-04T20:55:49.3072408Z 2025-03-04T20:55:49.3072852Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:55:49.3073399Z dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:55:49.3073722Z 2025-03-04T20:55:49.3074176Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:55:49.3074715Z dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:55:49.3074977Z 2025-03-04T20:55:49.3075420Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:55:49.3075981Z getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:55:49.3076298Z mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12; dx = getitem_12 = None 2025-03-04T20:55:49.3076624Z getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:55:49.3076961Z pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13; mul_2 = getitem_13 = None 2025-03-04T20:55:49.3077212Z 2025-03-04T20:55:49.3077639Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:55:49.3078192Z getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:55:49.3078503Z mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14; dy = getitem_14 = None 2025-03-04T20:55:49.3078822Z getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:55:49.3079175Z pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15; mul_3 = getitem_15 = None 2025-03-04T20:55:49.3079435Z 2025-03-04T20:55:49.3080063Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:55:49.3080568Z exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:55:49.3080892Z getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:55:49.3081258Z pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16; exp = getitem_16 = None 2025-03-04T20:55:49.3081508Z 2025-03-04T20:55:49.3081925Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:55:49.3082418Z exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:55:49.3082747Z getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:55:49.3083088Z pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17; exp_1 = getitem_17 = None 2025-03-04T20:55:49.3083331Z 2025-03-04T20:55:49.3083719Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:55:49.3084171Z mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T20:55:49.3084428Z x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:55:49.3084655Z 2025-03-04T20:55:49.3085039Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:55:49.3085486Z mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T20:55:49.3085739Z y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:55:49.3085963Z 2025-03-04T20:55:49.3086335Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:55:49.3086800Z mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:55:49.3087074Z x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:55:49.3087312Z 2025-03-04T20:55:49.3087697Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:55:49.3088158Z mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:55:49.3088663Z y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:55:49.3088952Z 2025-03-04T20:55:49.3089395Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:55:49.3089981Z pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:55:49.3090275Z 2025-03-04T20:55:49.3090699Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:55:49.3091282Z proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4)); pred_boxes = None 2025-03-04T20:55:49.3091559Z 2025-03-04T20:55:49.3092082Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:55:49.3092914Z proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T20:55:49.3093198Z 2025-03-04T20:55:49.3094038Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T20:55:49.3094715Z arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T20:55:49.3094975Z 2025-03-04T20:55:49.3095386Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:55:49.3095875Z batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T20:55:49.3096130Z 2025-03-04T20:55:49.3096662Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:55:49.3097253Z topk = logits_i.topk(6000, dim = 1); logits_i = None 2025-03-04T20:55:49.3097521Z topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0] 2025-03-04T20:55:49.3097784Z topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1]; topk = None 2025-03-04T20:55:49.3098010Z 2025-03-04T20:55:49.3098546Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:55:49.3099211Z getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T20:55:49.3100166Z topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)]; proposals_i_1 = getitem_20 = topk_idx = None 2025-03-04T20:55:49.3100541Z 2025-03-04T20:55:49.3101087Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:55:49.3101758Z full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:55:49.3102194Z 2025-03-04T20:55:49.3102575Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:55:49.3103073Z level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu')); full = level_ids = None 2025-03-04T20:55:49.3103337Z 2025-03-04T20:55:49.3103795Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T20:55:49.3104435Z getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0]; topk_proposals_i = None 2025-03-04T20:55:49.3104684Z 2025-03-04T20:55:49.3105060Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:55:49.3105558Z tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32); getitem_22 = None 2025-03-04T20:55:49.3105821Z 2025-03-04T20:55:49.3106317Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T20:55:49.3106881Z scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0]; topk_scores_i = None 2025-03-04T20:55:49.3107137Z 2025-03-04T20:55:49.3107729Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T20:55:49.3108404Z isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1); tensor_1 = None 2025-03-04T20:55:49.3108711Z all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:55:49.3109036Z isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T20:55:49.3109374Z valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T20:55:49.3109650Z 2025-03-04T20:55:49.3110113Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T20:55:49.3110650Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T20:55:49.3110884Z 2025-03-04T20:55:49.3110974Z 2025-03-04T20:55:49.3111064Z class GraphModule(torch.nn.Module): 2025-03-04T20:55:49.3163877Z def forward(self, L_stack0_tensor: "f32[4, 3, 1156, 1199][4158132, 1386044, 1199, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", 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L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_: "f32[2048, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[2048, 2048, 3, 3][18432, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[2048][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"): 2025-03-04T20:55:49.3214915Z l_stack0_tensor = L_stack0_tensor 2025-03-04T20:55:49.3215338Z l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ 2025-03-04T20:55:49.3216002Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.3216703Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.3217440Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:55:49.3218092Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:55:49.3218729Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:55:49.3219437Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.3220175Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.3220926Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:55:49.3221616Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:55:49.3222266Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:55:49.3222971Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.3223705Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.3224429Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:55:49.3225114Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:55:49.3225766Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:55:49.3226446Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.3227176Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.3227904Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:55:49.3228605Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:55:49.3229288Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:55:49.3230018Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.3230794Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.3231567Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:55:49.3232298Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:55:49.3232992Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:55:49.3233767Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.3234616Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.3235325Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:55:49.3236006Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:55:49.3236667Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:55:49.3237339Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.3238069Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.3238769Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:55:49.3239444Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:55:49.3240085Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:55:49.3240758Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.3241488Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.3242194Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:55:49.3242870Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:55:49.3243522Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:55:49.3244195Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.3244950Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.3245651Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:55:49.3246327Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:55:49.3246989Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:55:49.3247665Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.3248408Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.3249119Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:55:49.3249797Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:55:49.3250450Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:55:49.3251112Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.3251832Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.3252524Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:55:49.3253194Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:55:49.3253836Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:55:49.3254502Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.3255226Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.3255927Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:55:49.3256621Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:55:49.3257282Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:55:49.3257953Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.3258699Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.3259401Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:55:49.3260094Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:55:49.3260740Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:55:49.3261433Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.3262161Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.3262863Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:55:49.3263559Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:55:49.3264219Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:55:49.3264928Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.3265712Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.3266453Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:55:49.3267169Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:55:49.3267838Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:55:49.3268521Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.3269266Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.3269992Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:55:49.3270699Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:55:49.3271368Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:55:49.3272523Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.3273274Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.3274133Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:55:49.3274853Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:55:49.3275567Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:55:49.3276246Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.3276969Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.3277695Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:55:49.3278405Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:55:49.3279057Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:55:49.3279731Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.3280463Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.3281173Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:55:49.3281858Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:55:49.3282503Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:55:49.3283183Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.3283914Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.3284618Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:55:49.3285315Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:55:49.3285993Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:55:49.3286680Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.3287440Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.3288153Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:55:49.3288829Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:55:49.3289499Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ 2025-03-04T20:55:49.3290190Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.3290937Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.3291659Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:55:49.3292342Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:55:49.3292992Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ 2025-03-04T20:55:49.3293673Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.3294401Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.3295107Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:55:49.3295784Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:55:49.3296444Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ 2025-03-04T20:55:49.3297129Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.3297862Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.3298585Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:55:49.3299288Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:55:49.3299969Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:55:49.3300680Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.3301445Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.3304762Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:55:49.3305525Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:55:49.3306204Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:55:49.3306931Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.3307702Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.3308513Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:55:49.3309230Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:55:49.3309905Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:55:49.3310610Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.3311376Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.3312117Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:55:49.3312828Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:55:49.3313519Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:55:49.3314358Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.3315181Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.3315961Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:55:49.3316752Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:55:49.3317449Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:55:49.3318168Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.3318968Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.3319771Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:55:49.3320486Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:55:49.3321133Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:55:49.3321807Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.3322563Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.3323273Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:55:49.3323954Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:55:49.3324613Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:55:49.3325310Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.3326069Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.3326800Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:55:49.3327508Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:55:49.3328173Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:55:49.3328875Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.3329626Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.3330377Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:55:49.3348318Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:55:49.3349204Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:55:49.3350052Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.3350906Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.3351691Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:55:49.3352439Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:55:49.3353149Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:55:49.3354037Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.3354840Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.3355596Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:55:49.3356331Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:55:49.3357032Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ 2025-03-04T20:55:49.3357757Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.3358542Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.3359302Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:55:49.3360054Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:55:49.3360786Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ 2025-03-04T20:55:49.3361545Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.3362412Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.3363232Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:55:49.3363981Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:55:49.3364666Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ 2025-03-04T20:55:49.3365380Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.3366147Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.3366871Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:55:49.3367572Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:55:49.3368237Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ 2025-03-04T20:55:49.3368948Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.3369699Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.3370421Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:55:49.3371118Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:55:49.3371784Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ 2025-03-04T20:55:49.3372473Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.3373222Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.3373948Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:55:49.3374640Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:55:49.3375302Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ 2025-03-04T20:55:49.3375991Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.3376751Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.3377476Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:55:49.3378144Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:55:49.3378804Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ 2025-03-04T20:55:49.3379513Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.3380246Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.3380950Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:55:49.3381631Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:55:49.3382289Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ 2025-03-04T20:55:49.3382958Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.3383686Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.3384393Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:55:49.3385066Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:55:49.3385723Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ 2025-03-04T20:55:49.3386415Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.3387152Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.3387866Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:55:49.3388552Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:55:49.3389208Z l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:55:49.3389897Z l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.3390660Z l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.3391378Z l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:55:49.3392086Z l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:55:49.3392759Z l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:55:49.3393474Z l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.3394350Z l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.3395160Z l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:55:49.3395945Z l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:55:49.3396660Z l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:55:49.3397368Z l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.3398134Z l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.3398879Z l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:55:49.3399589Z l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:55:49.3400286Z l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:55:49.3401023Z l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.3401810Z l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.3402814Z l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:55:49.3403561Z l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:55:49.3404253Z l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:55:49.3405018Z l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.3405773Z l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.3406505Z l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:55:49.3407241Z l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:55:49.3407921Z l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:55:49.3408591Z l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.3409310Z l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.3410013Z l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:55:49.3410714Z l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:55:49.3411386Z l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:55:49.3412058Z l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.3412775Z l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.3413477Z l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:55:49.3414155Z l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:55:49.3414798Z l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:55:49.3415468Z l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.3416187Z l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.3416889Z l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:55:49.3417575Z l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:55:49.3418230Z l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:55:49.3418935Z l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.3419674Z l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.3420414Z l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:55:49.3421123Z l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:55:49.3421783Z l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:55:49.3422467Z l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:55:49.3423180Z l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:55:49.3423898Z l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:55:49.3424574Z l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:55:49.3425286Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T20:55:49.3425998Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T20:55:49.3426696Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T20:55:49.3427417Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T20:55:49.3428207Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T20:55:49.3428977Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T20:55:49.3429729Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T20:55:49.3430196Z 2025-03-04T20:55:49.3430589Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.3431399Z x: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1); l_stack0_tensor = l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = None 2025-03-04T20:55:49.3432035Z 2025-03-04T20:55:49.3432413Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.3434420Z x_1: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.3436080Z 2025-03-04T20:55:49.3436462Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x) 2025-03-04T20:55:49.3436958Z x_2: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.relu_(x_1); x_1 = None 2025-03-04T20:55:49.3437221Z 2025-03-04T20:55:49.3437685Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1) 2025-03-04T20:55:49.3438378Z x_3: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1); x_2 = None 2025-03-04T20:55:49.3438743Z 2025-03-04T20:55:49.3439101Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.3439867Z x_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:55:49.3440416Z 2025-03-04T20:55:49.3440784Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.3442718Z x_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_4 = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.3444497Z 2025-03-04T20:55:49.3444958Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.3445501Z out: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_5); x_5 = None 2025-03-04T20:55:49.3445787Z 2025-03-04T20:55:49.3446167Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.3447024Z x_6: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out = l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:55:49.3447669Z 2025-03-04T20:55:49.3448062Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.3449943Z x_7: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_6 = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.3451596Z 2025-03-04T20:55:49.3451975Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.3452454Z out_1: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_7); x_7 = None 2025-03-04T20:55:49.3452733Z 2025-03-04T20:55:49.3453071Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.3453828Z x_8: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_1 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:55:49.3454388Z 2025-03-04T20:55:49.3454749Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.3456594Z x_9: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_8 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.3458350Z 2025-03-04T20:55:49.3458689Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.3459442Z x_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); x_3 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:55:49.3459999Z 2025-03-04T20:55:49.3460338Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.3462251Z x_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_10 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.3463975Z 2025-03-04T20:55:49.3464361Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:55:49.3464850Z x_9 += x_11; out_2: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_9; x_9 = x_11 = None 2025-03-04T20:55:49.3465102Z 2025-03-04T20:55:49.3465465Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.3465949Z out_3: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_2); out_2 = None 2025-03-04T20:55:49.3466214Z 2025-03-04T20:55:49.3466543Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.3466987Z x_12: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:55:49.3467051Z 2025-03-04T20:55:49.3467326Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.3468864Z x_13: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_12 = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.3468936Z 2025-03-04T20:55:49.3469228Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.3469371Z out_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_13); x_13 = None 2025-03-04T20:55:49.3469439Z 2025-03-04T20:55:49.3469689Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.3470142Z x_14: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_4 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:55:49.3470208Z 2025-03-04T20:55:49.3470499Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.3472170Z x_15: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_14 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.3472245Z 2025-03-04T20:55:49.3472553Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.3472699Z out_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_15); x_15 = None 2025-03-04T20:55:49.3472770Z 2025-03-04T20:55:49.3473042Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.3473539Z x_16: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_5 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:55:49.3473627Z 2025-03-04T20:55:49.3474024Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.3475717Z x_17: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_16 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.3475781Z 2025-03-04T20:55:49.3476068Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:55:49.3476220Z x_17 += out_3; out_6: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_17; x_17 = out_3 = None 2025-03-04T20:55:49.3476291Z 2025-03-04T20:55:49.3476573Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.3476727Z out_7: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_6); out_6 = None 2025-03-04T20:55:49.3476791Z 2025-03-04T20:55:49.3477050Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.3477475Z x_18: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:55:49.3477571Z 2025-03-04T20:55:49.3477837Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.3479406Z x_19: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_18 = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.3479481Z 2025-03-04T20:55:49.3479766Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.3479911Z out_8: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_19); x_19 = None 2025-03-04T20:55:49.3479973Z 2025-03-04T20:55:49.3480230Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.3480676Z x_20: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_8 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:55:49.3480748Z 2025-03-04T20:55:49.3481014Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.3482576Z x_21: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_20 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.3482649Z 2025-03-04T20:55:49.3482936Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.3483079Z out_9: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_21); x_21 = None 2025-03-04T20:55:49.3483140Z 2025-03-04T20:55:49.3483397Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.3483836Z x_22: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_9 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:55:49.3483908Z 2025-03-04T20:55:49.3484187Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.3485755Z x_23: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_22 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.3485831Z 2025-03-04T20:55:49.3486102Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:55:49.3486261Z x_23 += out_7; out_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_23; x_23 = out_7 = None 2025-03-04T20:55:49.3486321Z 2025-03-04T20:55:49.3486606Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.3486754Z out_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_10); out_10 = None 2025-03-04T20:55:49.3486843Z 2025-03-04T20:55:49.3487090Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.3487525Z x_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:55:49.3487597Z 2025-03-04T20:55:49.3487860Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.3489371Z x_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_24 = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.3489433Z 2025-03-04T20:55:49.3489715Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.3489852Z out_12: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_25); x_25 = None 2025-03-04T20:55:49.3489920Z 2025-03-04T20:55:49.3490161Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.3490590Z x_26: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_12 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:55:49.3490672Z 2025-03-04T20:55:49.3490932Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.3492458Z x_27: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_26 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.3492522Z 2025-03-04T20:55:49.3492822Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.3492966Z out_13: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_27); x_27 = None 2025-03-04T20:55:49.3493036Z 2025-03-04T20:55:49.3493302Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.3493748Z x_28: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_13 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:55:49.3493819Z 2025-03-04T20:55:49.3494079Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.3495585Z x_29: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_28 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.3495647Z 2025-03-04T20:55:49.3495897Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.3496337Z x_30: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); out_11 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:55:49.3496398Z 2025-03-04T20:55:49.3496664Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.3498327Z x_31: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_30 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.3498416Z 2025-03-04T20:55:49.3498723Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:55:49.3498879Z x_29 += x_31; out_14: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_29; x_29 = x_31 = None 2025-03-04T20:55:49.3498947Z 2025-03-04T20:55:49.3499227Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.3499383Z out_15: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_14); out_14 = None 2025-03-04T20:55:49.3499443Z 2025-03-04T20:55:49.3499706Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.3500133Z x_32: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:55:49.3500202Z 2025-03-04T20:55:49.3500463Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.3502107Z x_33: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_32 = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.3502186Z 2025-03-04T20:55:49.3502473Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.3502621Z out_16: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_33); x_33 = None 2025-03-04T20:55:49.3502683Z 2025-03-04T20:55:49.3502940Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.3503374Z x_34: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_16 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:55:49.3503445Z 2025-03-04T20:55:49.3503712Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.3505327Z x_35: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_34 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.3505422Z 2025-03-04T20:55:49.3505709Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.3505856Z out_17: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_35); x_35 = None 2025-03-04T20:55:49.3505918Z 2025-03-04T20:55:49.3506174Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.3506606Z x_36: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_17 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:55:49.3506699Z 2025-03-04T20:55:49.3506969Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.3508526Z x_37: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_36 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.3508594Z 2025-03-04T20:55:49.3508879Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:55:49.3509043Z x_37 += out_15; out_18: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_37; x_37 = out_15 = None 2025-03-04T20:55:49.3509104Z 2025-03-04T20:55:49.3509397Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.3509546Z out_19: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_18); out_18 = None 2025-03-04T20:55:49.3509613Z 2025-03-04T20:55:49.3509869Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.3510310Z x_38: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:55:49.3510403Z 2025-03-04T20:55:49.3510694Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.3512376Z x_39: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_38 = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.3512452Z 2025-03-04T20:55:49.3512756Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.3512900Z out_20: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_39); x_39 = None 2025-03-04T20:55:49.3512970Z 2025-03-04T20:55:49.3513232Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.3513794Z x_40: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_20 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:55:49.3513873Z 2025-03-04T20:55:49.3514162Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.3515809Z x_41: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_40 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.3515874Z 2025-03-04T20:55:49.3516166Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.3516304Z out_21: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_41); x_41 = None 2025-03-04T20:55:49.3516375Z 2025-03-04T20:55:49.3516624Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.3517062Z x_42: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_21 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:55:49.3517124Z 2025-03-04T20:55:49.3517398Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.3518998Z x_43: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_42 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.3519063Z 2025-03-04T20:55:49.3519345Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:55:49.3519495Z x_43 += out_19; out_22: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_43; x_43 = out_19 = None 2025-03-04T20:55:49.3519562Z 2025-03-04T20:55:49.3519838Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.3519989Z out_23: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_22); out_22 = None 2025-03-04T20:55:49.3520064Z 2025-03-04T20:55:49.3520315Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.3520726Z x_44: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T20:55:49.3520794Z 2025-03-04T20:55:49.3521052Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.3522563Z x_45: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_44 = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.3522631Z 2025-03-04T20:55:49.3522907Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.3523051Z out_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_45); x_45 = None 2025-03-04T20:55:49.3523112Z 2025-03-04T20:55:49.3523361Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.3523778Z x_46: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_24 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T20:55:49.3523864Z 2025-03-04T20:55:49.3524118Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.3525673Z x_47: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_46 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.3525744Z 2025-03-04T20:55:49.3526020Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.3526165Z out_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_47); x_47 = None 2025-03-04T20:55:49.3526224Z 2025-03-04T20:55:49.3526474Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.3526922Z x_48: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_25 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T20:55:49.3526990Z 2025-03-04T20:55:49.3527256Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.3528823Z x_49: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_48 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.3528891Z 2025-03-04T20:55:49.3529164Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:55:49.3529319Z x_49 += out_23; out_26: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_49; x_49 = out_23 = None 2025-03-04T20:55:49.3529377Z 2025-03-04T20:55:49.3529661Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.3529808Z out_27: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_26); out_26 = None 2025-03-04T20:55:49.3529874Z 2025-03-04T20:55:49.3530118Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.3530549Z x_50: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:55:49.3530615Z 2025-03-04T20:55:49.3530874Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.3532471Z x_51: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_50 = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.3532534Z 2025-03-04T20:55:49.3532826Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.3532986Z out_28: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_51); x_51 = None 2025-03-04T20:55:49.3533055Z 2025-03-04T20:55:49.3533304Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.3533737Z x_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_28 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:55:49.3533805Z 2025-03-04T20:55:49.3534069Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.3535604Z x_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_52 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.3535666Z 2025-03-04T20:55:49.3535962Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.3536095Z out_29: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_53); x_53 = None 2025-03-04T20:55:49.3536162Z 2025-03-04T20:55:49.3536413Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.3536845Z x_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_29 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:55:49.3536931Z 2025-03-04T20:55:49.3537213Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.3538789Z x_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_54 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.3538854Z 2025-03-04T20:55:49.3539111Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.3539553Z x_56: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); out_27 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:55:49.3539631Z 2025-03-04T20:55:49.3539900Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.3541478Z x_57: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_56 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.3541551Z 2025-03-04T20:55:49.3541832Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:55:49.3541978Z x_55 += x_57; out_30: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_55; x_55 = x_57 = None 2025-03-04T20:55:49.3542046Z 2025-03-04T20:55:49.3542326Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.3542470Z out_31: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_30); out_30 = None 2025-03-04T20:55:49.3542531Z 2025-03-04T20:55:49.3542796Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.3543216Z x_58: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:55:49.3543305Z 2025-03-04T20:55:49.3543570Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.3545146Z x_59: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_58 = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.3545220Z 2025-03-04T20:55:49.3545505Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.3545642Z out_32: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_59); x_59 = None 2025-03-04T20:55:49.3545703Z 2025-03-04T20:55:49.3545961Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.3546398Z x_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_32 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:55:49.3546466Z 2025-03-04T20:55:49.3546732Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.3548324Z x_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_60 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.3548400Z 2025-03-04T20:55:49.3548701Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.3548846Z out_33: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_61); x_61 = None 2025-03-04T20:55:49.3548911Z 2025-03-04T20:55:49.3549181Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.3549628Z x_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_33 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:55:49.3549701Z 2025-03-04T20:55:49.3549983Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.3551636Z x_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_62 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.3551709Z 2025-03-04T20:55:49.3552021Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:55:49.3552184Z x_63 += out_31; out_34: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_63; x_63 = out_31 = None 2025-03-04T20:55:49.3552249Z 2025-03-04T20:55:49.3552555Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.3552703Z out_35: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_34); out_34 = None 2025-03-04T20:55:49.3552793Z 2025-03-04T20:55:49.3553060Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.3553508Z x_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:55:49.3553576Z 2025-03-04T20:55:49.3553967Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.3555673Z x_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_64 = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.3555749Z 2025-03-04T20:55:49.3556057Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.3556197Z out_36: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_65); x_65 = None 2025-03-04T20:55:49.3556270Z 2025-03-04T20:55:49.3556532Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.3556981Z x_66: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_36 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:55:49.3557069Z 2025-03-04T20:55:49.3557368Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.3559052Z x_67: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_66 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.3559130Z 2025-03-04T20:55:49.3559438Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.3559576Z out_37: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_67); x_67 = None 2025-03-04T20:55:49.3559649Z 2025-03-04T20:55:49.3559913Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.3560393Z x_68: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_37 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:55:49.3560457Z 2025-03-04T20:55:49.3560750Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.3562378Z x_69: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_68 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.3562445Z 2025-03-04T20:55:49.3562750Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:55:49.3562900Z x_69 += out_35; out_38: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_69; x_69 = out_35 = None 2025-03-04T20:55:49.3562973Z 2025-03-04T20:55:49.3563271Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.3563424Z out_39: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_38); out_38 = None 2025-03-04T20:55:49.3563490Z 2025-03-04T20:55:49.3563764Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.3564208Z x_70: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T20:55:49.3564295Z 2025-03-04T20:55:49.3564562Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.3566148Z x_71: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_70 = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.3566219Z 2025-03-04T20:55:49.3566507Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.3566644Z out_40: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_71); x_71 = None 2025-03-04T20:55:49.3566723Z 2025-03-04T20:55:49.3566978Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.3567393Z x_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_40 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T20:55:49.3567465Z 2025-03-04T20:55:49.3567728Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.3569250Z x_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_72 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.3569321Z 2025-03-04T20:55:49.3569595Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.3569729Z out_41: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_73); x_73 = None 2025-03-04T20:55:49.3569789Z 2025-03-04T20:55:49.3570036Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.3570447Z x_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_41 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T20:55:49.3570529Z 2025-03-04T20:55:49.3570788Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.3572328Z x_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_74 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.3572402Z 2025-03-04T20:55:49.3572684Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:55:49.3572834Z x_75 += out_39; out_42: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_75; x_75 = out_39 = None 2025-03-04T20:55:49.3572896Z 2025-03-04T20:55:49.3573187Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.3573342Z out_43: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_42); out_42 = None 2025-03-04T20:55:49.3573412Z 2025-03-04T20:55:49.3573662Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.3574094Z x_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = None 2025-03-04T20:55:49.3574155Z 2025-03-04T20:55:49.3574422Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.3575931Z x_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_76 = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.3575994Z 2025-03-04T20:55:49.3576284Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.3576416Z out_44: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_77); x_77 = None 2025-03-04T20:55:49.3576487Z 2025-03-04T20:55:49.3576739Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.3577167Z x_78: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_44 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = None 2025-03-04T20:55:49.3577251Z 2025-03-04T20:55:49.3577535Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.3579091Z x_79: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_78 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.3579157Z 2025-03-04T20:55:49.3579462Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.3579589Z out_45: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_79); x_79 = None 2025-03-04T20:55:49.3579676Z 2025-03-04T20:55:49.3579921Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.3580352Z x_80: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_45 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = None 2025-03-04T20:55:49.3580421Z 2025-03-04T20:55:49.3580693Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.3582227Z x_81: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_80 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.3582289Z 2025-03-04T20:55:49.3582577Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:55:49.3582720Z x_81 += out_43; out_46: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_81; x_81 = out_43 = None 2025-03-04T20:55:49.3582790Z 2025-03-04T20:55:49.3583071Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.3583219Z out_47: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_46); out_46 = None 2025-03-04T20:55:49.3583281Z 2025-03-04T20:55:49.3583540Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.3583980Z x_82: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = None 2025-03-04T20:55:49.3584041Z 2025-03-04T20:55:49.3584313Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.3585889Z x_83: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_82 = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.3585962Z 2025-03-04T20:55:49.3586246Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.3586401Z out_48: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_83); x_83 = None 2025-03-04T20:55:49.3586462Z 2025-03-04T20:55:49.3586718Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.3587146Z x_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_48 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = None 2025-03-04T20:55:49.3587209Z 2025-03-04T20:55:49.3587480Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.3589019Z x_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_84 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.3589088Z 2025-03-04T20:55:49.3589379Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.3589507Z out_49: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_85); x_85 = None 2025-03-04T20:55:49.3589576Z 2025-03-04T20:55:49.3589829Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.3590261Z x_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_49 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = None 2025-03-04T20:55:49.3590336Z 2025-03-04T20:55:49.3590609Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.3592168Z x_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_86 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.3592240Z 2025-03-04T20:55:49.3592536Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:55:49.3592686Z x_87 += out_47; out_50: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_87; x_87 = out_47 = None 2025-03-04T20:55:49.3592776Z 2025-03-04T20:55:49.3593076Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.3593229Z out_51: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_50); out_50 = None 2025-03-04T20:55:49.3593295Z 2025-03-04T20:55:49.3593567Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.3594115Z x_88: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:55:49.3594195Z 2025-03-04T20:55:49.3594499Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.3596240Z x_89: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_88 = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.3596329Z 2025-03-04T20:55:49.3596613Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.3596756Z out_52: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_89); x_89 = None 2025-03-04T20:55:49.3596818Z 2025-03-04T20:55:49.3597079Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.3597525Z x_90: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1); out_52 = l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:55:49.3597596Z 2025-03-04T20:55:49.3597863Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.3599541Z x_91: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_90 = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.3599622Z 2025-03-04T20:55:49.3599941Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.3600111Z out_53: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_91); x_91 = None 2025-03-04T20:55:49.3600179Z 2025-03-04T20:55:49.3600467Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.3600943Z x_92: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_53 = l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:55:49.3601021Z 2025-03-04T20:55:49.3601319Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.3603223Z x_93: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_92 = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.3603307Z 2025-03-04T20:55:49.3603590Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.3604090Z x_94: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_51 = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:55:49.3604162Z 2025-03-04T20:55:49.3604466Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.3606111Z x_95: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_94 = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.3606204Z 2025-03-04T20:55:49.3606485Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:55:49.3606621Z x_93 += x_95; out_54: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_93; x_93 = x_95 = None 2025-03-04T20:55:49.3606689Z 2025-03-04T20:55:49.3606961Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.3607106Z out_55: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_54); out_54 = None 2025-03-04T20:55:49.3607190Z 2025-03-04T20:55:49.3607441Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.3607846Z x_96: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:55:49.3607916Z 2025-03-04T20:55:49.3608174Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.3609684Z x_97: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_96 = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.3609755Z 2025-03-04T20:55:49.3610032Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.3610164Z out_56: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_97); x_97 = None 2025-03-04T20:55:49.3610223Z 2025-03-04T20:55:49.3610471Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.3610880Z x_98: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1); out_56 = l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:55:49.3610972Z 2025-03-04T20:55:49.3611231Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.3612755Z x_99: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_98 = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.3612825Z 2025-03-04T20:55:49.3613112Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.3613245Z out_57: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_99); x_99 = None 2025-03-04T20:55:49.3613304Z 2025-03-04T20:55:49.3613562Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.3613995Z x_100: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_57 = l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:55:49.3614064Z 2025-03-04T20:55:49.3614323Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.3615850Z x_101: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_100 = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.3615920Z 2025-03-04T20:55:49.3616190Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:55:49.3616344Z x_101 += out_55; out_58: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_101; x_101 = out_55 = None 2025-03-04T20:55:49.3616402Z 2025-03-04T20:55:49.3616684Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.3616822Z out_59: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_58); out_58 = None 2025-03-04T20:55:49.3616891Z 2025-03-04T20:55:49.3617137Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.3617555Z x_102: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:55:49.3617633Z 2025-03-04T20:55:49.3617902Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.3619440Z x_103: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_102 = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.3619505Z 2025-03-04T20:55:49.3619789Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.3619921Z out_60: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_103); x_103 = None 2025-03-04T20:55:49.3620002Z 2025-03-04T20:55:49.3620246Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.3620657Z x_104: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1); out_60 = l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:55:49.3620719Z 2025-03-04T20:55:49.3620985Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.3622484Z x_105: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_104 = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.3622546Z 2025-03-04T20:55:49.3622828Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.3622960Z out_61: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_105); x_105 = None 2025-03-04T20:55:49.3623027Z 2025-03-04T20:55:49.3623271Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.3623692Z x_106: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_61 = l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:55:49.3623769Z 2025-03-04T20:55:49.3624031Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:55:49.3625573Z x_107: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_106 = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:55:49.3625635Z 2025-03-04T20:55:49.3625915Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:55:49.3626062Z x_107 += out_59; out_62: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_107; x_107 = out_59 = None 2025-03-04T20:55:49.3626129Z 2025-03-04T20:55:49.3626401Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:55:49.3626563Z out_63: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_62); out_62 = None 2025-03-04T20:55:49.3626621Z 2025-03-04T20:55:49.3627056Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:55:49.3627211Z arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32) 2025-03-04T20:55:49.3627273Z 2025-03-04T20:55:49.3627566Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:55:49.3627698Z shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T20:55:49.3627764Z 2025-03-04T20:55:49.3628192Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:55:49.3628345Z arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32) 2025-03-04T20:55:49.3628407Z 2025-03-04T20:55:49.3628698Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:55:49.3628830Z shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T20:55:49.3628896Z 2025-03-04T20:55:49.3629262Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:55:49.3629440Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T20:55:49.3629534Z shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0] 2025-03-04T20:55:49.3629655Z shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T20:55:49.3629715Z 2025-03-04T20:55:49.3630047Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:55:49.3630184Z shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T20:55:49.3630250Z 2025-03-04T20:55:49.3630568Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:55:49.3630690Z shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T20:55:49.3630749Z 2025-03-04T20:55:49.3631145Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:55:49.3631354Z shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T20:55:49.3631436Z 2025-03-04T20:55:49.3631849Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:55:49.3631980Z view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T20:55:49.3632401Z view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T20:55:49.3632568Z add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T20:55:49.3632681Z x_108: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4); add = None 2025-03-04T20:55:49.3632751Z 2025-03-04T20:55:49.3633050Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:55:49.3633182Z tensor: "f32[82125, 4][4, 1]cpu" = x_108.to(torch.float32); x_108 = None 2025-03-04T20:55:49.3633244Z 2025-03-04T20:55:49.3633504Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:49.3634394Z x_109: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_63, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); out_63 = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T20:55:49.3634472Z 2025-03-04T20:55:49.3634775Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:55:49.3634976Z x_110: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_109, inplace = False); x_109 = None 2025-03-04T20:55:49.3635048Z 2025-03-04T20:55:49.3635455Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:55:49.3636330Z score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_110, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T20:55:49.3636452Z 2025-03-04T20:55:49.3636823Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:55:49.3637664Z x_111: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_110, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_110 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T20:55:49.3637744Z 2025-03-04T20:55:49.3638094Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T20:55:49.3638259Z permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T20:55:49.3638405Z logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T20:55:49.3638465Z 2025-03-04T20:55:49.3638887Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T20:55:49.3639044Z view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_111.view(4, -1, 4, 73, 75); x_111 = None 2025-03-04T20:55:49.3639467Z permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2); view_2 = None 2025-03-04T20:55:49.3639640Z pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2); permute_1 = None 2025-03-04T20:55:49.3639710Z 2025-03-04T20:55:49.3640106Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:55:49.3640315Z pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T20:55:49.3640376Z 2025-03-04T20:55:49.3640810Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:55:49.3640957Z unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T20:55:49.3641104Z expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T20:55:49.3641236Z anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T20:55:49.3641307Z 2025-03-04T20:55:49.3641678Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:55:49.3641854Z deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T20:55:49.3641915Z 2025-03-04T20:55:49.3642230Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:55:49.3642373Z boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T20:55:49.3642434Z 2025-03-04T20:55:49.3642752Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:55:49.3642879Z getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:55:49.3643025Z getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:55:49.3643164Z widths: "f32[328500][1]cpu" = getitem_2 - getitem_3; getitem_2 = getitem_3 = None 2025-03-04T20:55:49.3643229Z 2025-03-04T20:55:49.3643542Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:55:49.3643666Z getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:55:49.3643780Z getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:55:49.3643941Z heights: "f32[328500][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T20:55:49.3644001Z 2025-03-04T20:55:49.3644330Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:55:49.3644450Z getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:55:49.3644541Z mul: "f32[328500][1]cpu" = 0.5 * widths 2025-03-04T20:55:49.3644656Z ctr_x: "f32[328500][1]cpu" = getitem_6 + mul; getitem_6 = mul = None 2025-03-04T20:55:49.3644732Z 2025-03-04T20:55:49.3645039Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:55:49.3645185Z getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:55:49.3645288Z mul_1: "f32[328500][1]cpu" = 0.5 * heights 2025-03-04T20:55:49.3645417Z ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1; getitem_7 = mul_1 = None 2025-03-04T20:55:49.3645475Z 2025-03-04T20:55:49.3645817Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:55:49.3645964Z getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:55:49.3646078Z dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0; getitem_8 = None 2025-03-04T20:55:49.3646138Z 2025-03-04T20:55:49.3646440Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:55:49.3646586Z getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:55:49.3646704Z dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0; getitem_9 = None 2025-03-04T20:55:49.3646763Z 2025-03-04T20:55:49.3647063Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:55:49.3647212Z getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:55:49.3647326Z dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0; getitem_10 = None 2025-03-04T20:55:49.3647385Z 2025-03-04T20:55:49.3647685Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:55:49.3647860Z getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:55:49.3647972Z dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0; getitem_11 = None 2025-03-04T20:55:49.3648033Z 2025-03-04T20:55:49.3648374Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:55:49.3648504Z dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:55:49.3648589Z 2025-03-04T20:55:49.3648914Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:55:49.3649048Z dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:55:49.3649112Z 2025-03-04T20:55:49.3649449Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:55:49.3649606Z getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:55:49.3649727Z mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12; dx = getitem_12 = None 2025-03-04T20:55:49.3649895Z getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:55:49.3650030Z pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13; mul_2 = getitem_13 = None 2025-03-04T20:55:49.3650097Z 2025-03-04T20:55:49.3650436Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:55:49.3650572Z getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:55:49.3650689Z mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14; dy = getitem_14 = None 2025-03-04T20:55:49.3650838Z getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:55:49.3650985Z pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15; mul_3 = getitem_15 = None 2025-03-04T20:55:49.3651052Z 2025-03-04T20:55:49.3651371Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:55:49.3651490Z exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:55:49.3651642Z getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:55:49.3651772Z pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16; exp = getitem_16 = None 2025-03-04T20:55:49.3651830Z 2025-03-04T20:55:49.3652156Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:55:49.3652264Z exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:55:49.3652427Z getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:55:49.3652554Z pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17; exp_1 = getitem_17 = None 2025-03-04T20:55:49.3652621Z 2025-03-04T20:55:49.3652924Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:55:49.3653020Z mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T20:55:49.3653131Z x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:55:49.3653195Z 2025-03-04T20:55:49.3653494Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:55:49.3653591Z mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T20:55:49.3653698Z y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:55:49.3653765Z 2025-03-04T20:55:49.3654060Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:55:49.3654188Z mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:55:49.3654309Z x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:55:49.3654375Z 2025-03-04T20:55:49.3654673Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:55:49.3654785Z mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:55:49.3654903Z y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:55:49.3654972Z 2025-03-04T20:55:49.3655324Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:55:49.3655528Z pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:55:49.3655591Z 2025-03-04T20:55:49.3655927Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:55:49.3656080Z proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4)); pred_boxes = None 2025-03-04T20:55:49.3656147Z 2025-03-04T20:55:49.3656520Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:55:49.3656712Z proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T20:55:49.3656771Z 2025-03-04T20:55:49.3657256Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T20:55:49.3657394Z arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T20:55:49.3657453Z 2025-03-04T20:55:49.3657750Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:55:49.3657884Z batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T20:55:49.3657950Z 2025-03-04T20:55:49.3658376Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:55:49.3658492Z topk = logits_i.topk(6000, dim = 1); logits_i = None 2025-03-04T20:55:49.3658590Z topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0] 2025-03-04T20:55:49.3658706Z topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1]; topk = None 2025-03-04T20:55:49.3658765Z 2025-03-04T20:55:49.3659230Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:55:49.3659390Z getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T20:55:49.3659624Z topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)]; proposals_i_1 = getitem_20 = topk_idx = None 2025-03-04T20:55:49.3659686Z 2025-03-04T20:55:49.3660146Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:55:49.3660326Z full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:55:49.3660393Z 2025-03-04T20:55:49.3660680Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:55:49.3660835Z level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu')); full = level_ids = None 2025-03-04T20:55:49.3660894Z 2025-03-04T20:55:49.3661281Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T20:55:49.3661441Z getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0]; topk_proposals_i = None 2025-03-04T20:55:49.3661508Z 2025-03-04T20:55:49.3661811Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:55:49.3661959Z tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32); getitem_22 = None 2025-03-04T20:55:49.3662019Z 2025-03-04T20:55:49.3662399Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T20:55:49.3662531Z scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0]; topk_scores_i = None 2025-03-04T20:55:49.3662598Z 2025-03-04T20:55:49.3663085Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T20:55:49.3663223Z isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1); tensor_1 = None 2025-03-04T20:55:49.3663337Z all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:55:49.3663494Z isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T20:55:49.3663627Z valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T20:55:49.3663687Z 2025-03-04T20:55:49.3664052Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T20:55:49.3664161Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T20:55:49.3664230Z 2025-03-04T20:55:58.4755196Z 2025-03-04T20:55:58.4755790Z class GraphModule(torch.nn.Module): 2025-03-04T20:55:58.4757458Z def forward(self, L_features_res5_: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_weight_: "f32[2048, 2048, 3, 3][18432, 9, 3, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_bias_: "f32[2048][1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"): 2025-03-04T20:55:58.4759221Z l_features_res5_ = L_features_res5_ 2025-03-04T20:55:58.4759695Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T20:55:58.4760328Z l_self_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T20:55:58.4760926Z l_self_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T20:55:58.4762795Z l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T20:55:58.4763442Z l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T20:55:58.4764093Z l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T20:55:58.4764681Z l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T20:55:58.4765147Z 2025-03-04T20:55:58.4765893Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:55:58.4766557Z arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32) 2025-03-04T20:55:58.4766826Z 2025-03-04T20:55:58.4767218Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:55:58.4767707Z shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T20:55:58.4767953Z 2025-03-04T20:55:58.4768467Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:55:58.4769264Z arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32) 2025-03-04T20:55:58.4769522Z 2025-03-04T20:55:58.4769896Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:55:58.4770373Z shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T20:55:58.4770619Z 2025-03-04T20:55:58.4771065Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:55:58.4771655Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T20:55:58.4771976Z shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0] 2025-03-04T20:55:58.4772238Z shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T20:55:58.4772466Z 2025-03-04T20:55:58.4772874Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:55:58.4773373Z shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T20:55:58.4773611Z 2025-03-04T20:55:58.4774024Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:55:58.4774518Z shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T20:55:58.4774754Z 2025-03-04T20:55:58.4775218Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:55:58.4775861Z shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T20:55:58.4776191Z 2025-03-04T20:55:58.4776682Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:55:58.4777290Z view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T20:55:58.4777776Z view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T20:55:58.4778246Z add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T20:55:58.4778525Z x: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4); add = None 2025-03-04T20:55:58.4778763Z 2025-03-04T20:55:58.4779194Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:55:58.4779674Z tensor: "f32[82125, 4][4, 1]cpu" = x.to(torch.float32); x = None 2025-03-04T20:55:58.4779907Z 2025-03-04T20:55:58.4780238Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:58.4781133Z x_1: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(l_features_res5_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_res5_ = l_self_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T20:55:58.4781817Z 2025-03-04T20:55:58.4782196Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:55:58.4782714Z x_2: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False); x_1 = None 2025-03-04T20:55:58.4783015Z 2025-03-04T20:55:58.4783485Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:55:58.4784577Z score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T20:55:58.4785339Z 2025-03-04T20:55:58.4785789Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:55:58.4786818Z x_3: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_2 = l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T20:55:58.4787536Z 2025-03-04T20:55:58.4787971Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T20:55:58.4788525Z permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T20:55:58.4788874Z logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T20:55:58.4789134Z 2025-03-04T20:55:58.4789642Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T20:55:58.4790274Z view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_3.view(4, -1, 4, 73, 75); x_3 = None 2025-03-04T20:55:58.4790639Z permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2); view_2 = None 2025-03-04T20:55:58.4791023Z pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2); permute_1 = None 2025-03-04T20:55:58.4791306Z 2025-03-04T20:55:58.4791791Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:55:58.4792469Z pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T20:55:58.4792786Z 2025-03-04T20:55:58.4793451Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:55:58.4794109Z unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T20:55:58.4794481Z expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T20:55:58.4794815Z anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T20:55:58.4795064Z 2025-03-04T20:55:58.4795527Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:55:58.4796140Z deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T20:55:58.4796429Z 2025-03-04T20:55:58.4796830Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:55:58.4797343Z boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T20:55:58.4797597Z 2025-03-04T20:55:58.4798001Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:55:58.4798504Z getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:55:58.4798807Z getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:55:58.4799135Z widths: "f32[328500][1]cpu" = getitem_2 - getitem_3; getitem_2 = getitem_3 = None 2025-03-04T20:55:58.4799399Z 2025-03-04T20:55:58.4799811Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:55:58.4800307Z getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:55:58.4800602Z getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:55:58.4800922Z heights: "f32[328500][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T20:55:58.4801184Z 2025-03-04T20:55:58.4801581Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:55:58.4802288Z getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:55:58.4802558Z mul: "f32[328500][1]cpu" = 0.5 * widths 2025-03-04T20:55:58.4802821Z ctr_x: "f32[328500][1]cpu" = getitem_6 + mul; getitem_6 = mul = None 2025-03-04T20:55:58.4803059Z 2025-03-04T20:55:58.4803466Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:55:58.4804020Z getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:55:58.4804314Z mul_1: "f32[328500][1]cpu" = 0.5 * heights 2025-03-04T20:55:58.4804579Z ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1; getitem_7 = mul_1 = None 2025-03-04T20:55:58.4804812Z 2025-03-04T20:55:58.4805238Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:55:58.4805734Z getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:55:58.4806072Z dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0; getitem_8 = None 2025-03-04T20:55:58.4806298Z 2025-03-04T20:55:58.4806699Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:55:58.4807187Z getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:55:58.4807492Z dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0; getitem_9 = None 2025-03-04T20:55:58.4807714Z 2025-03-04T20:55:58.4808085Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:55:58.4808573Z getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:55:58.4808918Z dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0; getitem_10 = None 2025-03-04T20:55:58.4809145Z 2025-03-04T20:55:58.4809533Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:55:58.4810072Z getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:55:58.4810427Z dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0; getitem_11 = None 2025-03-04T20:55:58.4810651Z 2025-03-04T20:55:58.4811068Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:55:58.4811590Z dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:55:58.4811935Z 2025-03-04T20:55:58.4812436Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:55:58.4812977Z dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:55:58.4813224Z 2025-03-04T20:55:58.4813658Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:55:58.4814198Z getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:55:58.4814517Z mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12; dx = getitem_12 = None 2025-03-04T20:55:58.4814851Z getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:55:58.4815200Z pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13; mul_2 = getitem_13 = None 2025-03-04T20:55:58.4815457Z 2025-03-04T20:55:58.4815903Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:55:58.4816486Z getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:55:58.4816874Z mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14; dy = getitem_14 = None 2025-03-04T20:55:58.4817263Z getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:55:58.4817602Z pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15; mul_3 = getitem_15 = None 2025-03-04T20:55:58.4817857Z 2025-03-04T20:55:58.4818277Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:55:58.4818778Z exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:55:58.4819124Z getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:55:58.4819468Z pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16; exp = getitem_16 = None 2025-03-04T20:55:58.4819715Z 2025-03-04T20:55:58.4820154Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:55:58.4820662Z exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:55:58.4821000Z getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:55:58.4821445Z pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17; exp_1 = getitem_17 = None 2025-03-04T20:55:58.4821695Z 2025-03-04T20:55:58.4822092Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:55:58.4822571Z mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T20:55:58.4822833Z x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:55:58.4823063Z 2025-03-04T20:55:58.4823461Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:55:58.4823923Z mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T20:55:58.4824190Z y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:55:58.4824435Z 2025-03-04T20:55:58.4824854Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:55:58.4825447Z mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:55:58.4825740Z x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:55:58.4825986Z 2025-03-04T20:55:58.4826374Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:55:58.4826851Z mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:55:58.4827137Z y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:55:58.4827379Z 2025-03-04T20:55:58.4827810Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:55:58.4828386Z pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:55:58.4828672Z 2025-03-04T20:55:58.4829086Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:55:58.4829642Z proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4)); pred_boxes = None 2025-03-04T20:55:58.4829912Z 2025-03-04T20:55:58.4830384Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:55:58.4831020Z proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T20:55:58.4831432Z 2025-03-04T20:55:58.4832007Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T20:55:58.4832692Z arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T20:55:58.4832958Z 2025-03-04T20:55:58.4833408Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:55:58.4833939Z batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T20:55:58.4834208Z 2025-03-04T20:55:58.4834755Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:55:58.4835363Z topk = logits_i.topk(6000, dim = 1); logits_i = None 2025-03-04T20:55:58.4835633Z topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0] 2025-03-04T20:55:58.4835901Z topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1]; topk = None 2025-03-04T20:55:58.4836148Z 2025-03-04T20:55:58.4836690Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:55:58.4837367Z getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T20:55:58.4837816Z topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)]; proposals_i_1 = getitem_20 = topk_idx = None 2025-03-04T20:55:58.4838160Z 2025-03-04T20:55:58.4838700Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:55:58.4839372Z full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:55:58.4839651Z 2025-03-04T20:55:58.4840029Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:55:58.4840528Z level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu')); full = level_ids = None 2025-03-04T20:55:58.4840791Z 2025-03-04T20:55:58.4841250Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T20:55:58.4841824Z getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0]; topk_proposals_i = None 2025-03-04T20:55:58.4842080Z 2025-03-04T20:55:58.4842459Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:55:58.4842948Z tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32); getitem_22 = None 2025-03-04T20:55:58.4843210Z 2025-03-04T20:55:58.4843680Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T20:55:58.4844248Z scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0]; topk_scores_i = None 2025-03-04T20:55:58.4844522Z 2025-03-04T20:55:58.4845142Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T20:55:58.4845820Z isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1); tensor_1 = None 2025-03-04T20:55:58.4846136Z all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:55:58.4846455Z isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T20:55:58.4846815Z valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T20:55:58.4847069Z 2025-03-04T20:55:58.4847545Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T20:55:58.4848078Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T20:55:58.4848311Z 2025-03-04T20:55:58.4848402Z 2025-03-04T20:55:58.4848490Z class GraphModule(torch.nn.Module): 2025-03-04T20:55:58.4849823Z def forward(self, L_features_res5_: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_weight_: "f32[2048, 2048, 3, 3][18432, 9, 3, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_bias_: "f32[2048][1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"): 2025-03-04T20:55:58.4851108Z l_features_res5_ = L_features_res5_ 2025-03-04T20:55:58.4851495Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T20:55:58.4852015Z l_self_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T20:55:58.4852491Z l_self_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T20:55:58.4853017Z l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T20:55:58.4853593Z l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T20:55:58.4854149Z l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T20:55:58.4854687Z l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T20:55:58.4855043Z 2025-03-04T20:55:58.4855574Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:55:58.4856206Z arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32) 2025-03-04T20:55:58.4856471Z 2025-03-04T20:55:58.4856849Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:55:58.4857327Z shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T20:55:58.4857573Z 2025-03-04T20:55:58.4858110Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:55:58.4858739Z arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32) 2025-03-04T20:55:58.4858999Z 2025-03-04T20:55:58.4859368Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:55:58.4859847Z shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T20:55:58.4860112Z 2025-03-04T20:55:58.4860561Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:55:58.4861177Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T20:55:58.4861499Z shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0] 2025-03-04T20:55:58.4861760Z shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T20:55:58.4861988Z 2025-03-04T20:55:58.4862397Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:55:58.4862900Z shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T20:55:58.4863156Z 2025-03-04T20:55:58.4863564Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:55:58.4864058Z shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T20:55:58.4864291Z 2025-03-04T20:55:58.4864757Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:55:58.4865402Z shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T20:55:58.4865725Z 2025-03-04T20:55:58.4866223Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:55:58.4866815Z view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T20:55:58.4867311Z view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T20:55:58.4867797Z add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T20:55:58.4868080Z x: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4); add = None 2025-03-04T20:55:58.4868304Z 2025-03-04T20:55:58.4868690Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:55:58.4869163Z tensor: "f32[82125, 4][4, 1]cpu" = x.to(torch.float32); x = None 2025-03-04T20:55:58.4869396Z 2025-03-04T20:55:58.4869735Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:55:58.4870647Z x_1: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(l_features_res5_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_res5_ = l_self_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T20:55:58.4871376Z 2025-03-04T20:55:58.4871739Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:55:58.4872248Z x_2: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False); x_1 = None 2025-03-04T20:55:58.4872540Z 2025-03-04T20:55:58.4873006Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:55:58.4874313Z score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T20:55:58.4875077Z 2025-03-04T20:55:58.4875527Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:55:58.4876543Z x_3: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_2 = l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T20:55:58.4877288Z 2025-03-04T20:55:58.4877711Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T20:55:58.4878249Z permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T20:55:58.4878589Z logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T20:55:58.4878846Z 2025-03-04T20:55:58.4879347Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T20:55:58.4879964Z view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_3.view(4, -1, 4, 73, 75); x_3 = None 2025-03-04T20:55:58.4880339Z permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2); view_2 = None 2025-03-04T20:55:58.4880731Z pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2); permute_1 = None 2025-03-04T20:55:58.4881017Z 2025-03-04T20:55:58.4881502Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:55:58.4882160Z pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T20:55:58.4882473Z 2025-03-04T20:55:58.4883018Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:55:58.4883676Z unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T20:55:58.4884024Z expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T20:55:58.4884358Z anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T20:55:58.4884632Z 2025-03-04T20:55:58.4885096Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:55:58.4885695Z deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T20:55:58.4885982Z 2025-03-04T20:55:58.4886379Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:55:58.4886886Z boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T20:55:58.4887162Z 2025-03-04T20:55:58.4887563Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:55:58.4888071Z getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:55:58.4888381Z getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:55:58.4888704Z widths: "f32[328500][1]cpu" = getitem_2 - getitem_3; getitem_2 = getitem_3 = None 2025-03-04T20:55:58.4888964Z 2025-03-04T20:55:58.4889372Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:55:58.4889863Z getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:55:58.4890174Z getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:55:58.4890491Z heights: "f32[328500][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T20:55:58.4890753Z 2025-03-04T20:55:58.4891155Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:55:58.4891644Z getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:55:58.4891907Z mul: "f32[328500][1]cpu" = 0.5 * widths 2025-03-04T20:55:58.4892168Z ctr_x: "f32[328500][1]cpu" = getitem_6 + mul; getitem_6 = mul = None 2025-03-04T20:55:58.4892400Z 2025-03-04T20:55:58.4892794Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:55:58.4893301Z getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:55:58.4893586Z mul_1: "f32[328500][1]cpu" = 0.5 * heights 2025-03-04T20:55:58.4893854Z ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1; getitem_7 = mul_1 = None 2025-03-04T20:55:58.4894093Z 2025-03-04T20:55:58.4894504Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:55:58.4895012Z getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:55:58.4895331Z dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0; getitem_8 = None 2025-03-04T20:55:58.4895559Z 2025-03-04T20:55:58.4895942Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:55:58.4896441Z getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:55:58.4896762Z dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0; getitem_9 = None 2025-03-04T20:55:58.4896982Z 2025-03-04T20:55:58.4897368Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:55:58.4897896Z getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:55:58.4898203Z dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0; getitem_10 = None 2025-03-04T20:55:58.4898430Z 2025-03-04T20:55:58.4898814Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:55:58.4899353Z getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:55:58.4899683Z dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0; getitem_11 = None 2025-03-04T20:55:58.4899908Z 2025-03-04T20:55:58.4900333Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:55:58.4900862Z dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:55:58.4901114Z 2025-03-04T20:55:58.4901523Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:55:58.4902214Z dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:55:58.4902470Z 2025-03-04T20:55:58.4902903Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:55:58.4903493Z getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:55:58.4903808Z mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12; dx = getitem_12 = None 2025-03-04T20:55:58.4904134Z getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:55:58.4904485Z pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13; mul_2 = getitem_13 = None 2025-03-04T20:55:58.4904750Z 2025-03-04T20:55:58.4905169Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:55:58.4905693Z getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:55:58.4906003Z mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14; dy = getitem_14 = None 2025-03-04T20:55:58.4906326Z getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:55:58.4906664Z pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15; mul_3 = getitem_15 = None 2025-03-04T20:55:58.4906914Z 2025-03-04T20:55:58.4907320Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:55:58.4907810Z exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:55:58.4908126Z getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:55:58.4908459Z pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16; exp = getitem_16 = None 2025-03-04T20:55:58.4908703Z 2025-03-04T20:55:58.4909114Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:55:58.4909604Z exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:55:58.4909925Z getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:55:58.4910262Z pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17; exp_1 = getitem_17 = None 2025-03-04T20:55:58.4910539Z 2025-03-04T20:55:58.4910932Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:55:58.4911379Z mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T20:55:58.4911635Z x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:55:58.4911862Z 2025-03-04T20:55:58.4912246Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:55:58.4912691Z mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T20:55:58.4912972Z y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:55:58.4913207Z 2025-03-04T20:55:58.4913681Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:55:58.4914164Z mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:55:58.4914448Z x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:55:58.4914690Z 2025-03-04T20:55:58.4915076Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:55:58.4915553Z mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:55:58.4915840Z y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:55:58.4916105Z 2025-03-04T20:55:58.4916523Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:55:58.4917087Z pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:55:58.4917376Z 2025-03-04T20:55:58.4917788Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:55:58.4918329Z proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4)); pred_boxes = None 2025-03-04T20:55:58.4918604Z 2025-03-04T20:55:58.4919064Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:55:58.4919667Z proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T20:55:58.4919951Z 2025-03-04T20:55:58.4920514Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T20:55:58.4921210Z arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T20:55:58.4921463Z 2025-03-04T20:55:58.4921838Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:55:58.4922323Z batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T20:55:58.4922574Z 2025-03-04T20:55:58.4923094Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:55:58.4923694Z topk = logits_i.topk(6000, dim = 1); logits_i = None 2025-03-04T20:55:58.4923958Z topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0] 2025-03-04T20:55:58.4924247Z topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1]; topk = None 2025-03-04T20:55:58.4924480Z 2025-03-04T20:55:58.4925035Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:55:58.4925712Z getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T20:55:58.4926173Z topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)]; proposals_i_1 = getitem_20 = topk_idx = None 2025-03-04T20:55:58.4926538Z 2025-03-04T20:55:58.4927118Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:55:58.4927802Z full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:55:58.4928082Z 2025-03-04T20:55:58.4928464Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:55:58.4928966Z level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu')); full = level_ids = None 2025-03-04T20:55:58.4929225Z 2025-03-04T20:55:58.4929684Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T20:55:58.4930271Z getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0]; topk_proposals_i = None 2025-03-04T20:55:58.4930526Z 2025-03-04T20:55:58.4930903Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:55:58.4931387Z tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32); getitem_22 = None 2025-03-04T20:55:58.4931640Z 2025-03-04T20:55:58.4932095Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T20:55:58.4932650Z scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0]; topk_scores_i = None 2025-03-04T20:55:58.4932897Z 2025-03-04T20:55:58.4933456Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T20:55:58.4934115Z isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1); tensor_1 = None 2025-03-04T20:55:58.4934420Z all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:55:58.4934733Z isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T20:55:58.4935061Z valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T20:55:58.4935304Z 2025-03-04T20:55:58.4935752Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T20:55:58.4936278Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T20:55:58.4936508Z 2025-03-04T20:55:59.4825359Z 2025-03-04T20:55:59.4826277Z class GraphModule(torch.nn.Module): 2025-03-04T20:55:59.4827103Z def forward(self, L_pred_anchor_deltas_0_: "f32[4, 82125, 4][328500, 4, 1]cpu", L_anchors_0_tensor: "f32[82125, 4][4, 1]cpu", L_pred_objectness_logits_0_: "f32[4, 82125][82125, 1]cpu"): 2025-03-04T20:55:59.4828457Z l_pred_anchor_deltas_0_ = L_pred_anchor_deltas_0_ 2025-03-04T20:55:59.4828923Z l_anchors_0_tensor = L_anchors_0_tensor 2025-03-04T20:55:59.4829212Z l_pred_objectness_logits_0_ = L_pred_objectness_logits_0_ 2025-03-04T20:55:59.4829452Z 2025-03-04T20:55:59.4830115Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:55:59.4830860Z pred_anchor_deltas_i: "f32[328500, 4][4, 1]cpu" = l_pred_anchor_deltas_0_.reshape(-1, 4); l_pred_anchor_deltas_0_ = None 2025-03-04T20:55:59.4831229Z 2025-03-04T20:55:59.4832066Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:55:59.4832831Z unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = l_anchors_0_tensor.unsqueeze(0); l_anchors_0_tensor = None 2025-03-04T20:55:59.4833357Z expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T20:55:59.4833712Z anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T20:55:59.4833974Z 2025-03-04T20:55:59.4834454Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:55:59.4835140Z deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.float(); pred_anchor_deltas_i = None 2025-03-04T20:55:59.4835434Z 2025-03-04T20:55:59.4835821Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:55:59.4836313Z boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T20:55:59.4836564Z 2025-03-04T20:55:59.4836947Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:55:59.4837435Z getitem: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:55:59.4837735Z getitem_1: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:55:59.4838048Z widths: "f32[328500][1]cpu" = getitem - getitem_1; getitem = getitem_1 = None 2025-03-04T20:55:59.4838300Z 2025-03-04T20:55:59.4838695Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:55:59.4839179Z getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:55:59.4839472Z getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:55:59.4839791Z heights: "f32[328500][1]cpu" = getitem_2 - getitem_3; getitem_2 = getitem_3 = None 2025-03-04T20:55:59.4840046Z 2025-03-04T20:55:59.4840433Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:55:59.4840909Z getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:55:59.4841164Z mul: "f32[328500][1]cpu" = 0.5 * widths 2025-03-04T20:55:59.4841412Z ctr_x: "f32[328500][1]cpu" = getitem_4 + mul; getitem_4 = mul = None 2025-03-04T20:55:59.4841645Z 2025-03-04T20:55:59.4842035Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:55:59.4842535Z getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:55:59.4842845Z mul_1: "f32[328500][1]cpu" = 0.5 * heights 2025-03-04T20:55:59.4843104Z ctr_y: "f32[328500][1]cpu" = getitem_5 + mul_1; getitem_5 = mul_1 = None 2025-03-04T20:55:59.4843336Z 2025-03-04T20:55:59.4843759Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:55:59.4844259Z getitem_6: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:55:59.4844573Z dx: "f32[328500, 1][1, 1]cpu" = getitem_6 / 1.0; getitem_6 = None 2025-03-04T20:55:59.4844799Z 2025-03-04T20:55:59.4845205Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:55:59.4845744Z getitem_7: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:55:59.4846048Z dy: "f32[328500, 1][1, 1]cpu" = getitem_7 / 1.0; getitem_7 = None 2025-03-04T20:55:59.4846274Z 2025-03-04T20:55:59.4846644Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:55:59.4847129Z getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:55:59.4847433Z dw: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0; getitem_8 = None 2025-03-04T20:55:59.4847654Z 2025-03-04T20:55:59.4848067Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:55:59.4848588Z getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:55:59.4848925Z dh: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0; getitem_9 = None 2025-03-04T20:55:59.4849151Z 2025-03-04T20:55:59.4849563Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:55:59.4850078Z dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:55:59.4850326Z 2025-03-04T20:55:59.4850729Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:55:59.4851235Z dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:55:59.4851477Z 2025-03-04T20:55:59.4851892Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:55:59.4852415Z getitem_10: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:55:59.4852722Z mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_10; dx = getitem_10 = None 2025-03-04T20:55:59.4853044Z getitem_11: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:55:59.4853380Z pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_11; mul_2 = getitem_11 = None 2025-03-04T20:55:59.4853630Z 2025-03-04T20:55:59.4854054Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:55:59.4854580Z getitem_12: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:55:59.4854890Z mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_12; dy = getitem_12 = None 2025-03-04T20:55:59.4855209Z getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:55:59.4855561Z pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_13; mul_3 = getitem_13 = None 2025-03-04T20:55:59.4855809Z 2025-03-04T20:55:59.4856213Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:55:59.4856700Z exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:55:59.4857017Z getitem_14: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:55:59.4857363Z pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_14; exp = getitem_14 = None 2025-03-04T20:55:59.4857607Z 2025-03-04T20:55:59.4858037Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:55:59.4858530Z exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:55:59.4858853Z getitem_15: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:55:59.4859191Z pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_15; exp_1 = getitem_15 = None 2025-03-04T20:55:59.4859433Z 2025-03-04T20:55:59.4859818Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:55:59.4860261Z mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T20:55:59.4860533Z x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:55:59.4860758Z 2025-03-04T20:55:59.4861142Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:55:59.4861583Z mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T20:55:59.4861833Z y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:55:59.4862054Z 2025-03-04T20:55:59.4862428Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:55:59.4862887Z mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:55:59.4863170Z x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:55:59.4863411Z 2025-03-04T20:55:59.4863791Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:55:59.4864248Z mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:55:59.4864528Z y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:55:59.4864763Z 2025-03-04T20:55:59.4865182Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:55:59.4865744Z pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:55:59.4866026Z 2025-03-04T20:55:59.4866433Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:55:59.4866967Z proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4)); pred_boxes = None 2025-03-04T20:55:59.4867233Z 2025-03-04T20:55:59.4867686Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:55:59.4868311Z proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T20:55:59.4868603Z 2025-03-04T20:55:59.4869166Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T20:55:59.4869866Z arange: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T20:55:59.4870111Z 2025-03-04T20:55:59.4870554Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:55:59.4871039Z batch_idx: "i64[4][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T20:55:59.4871292Z 2025-03-04T20:55:59.4871830Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:55:59.4872494Z topk = l_pred_objectness_logits_0_.topk(6000, dim = 1); l_pred_objectness_logits_0_ = None 2025-03-04T20:55:59.4872825Z topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0] 2025-03-04T20:55:59.4873125Z topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1]; topk = None 2025-03-04T20:55:59.4873457Z 2025-03-04T20:55:59.4874021Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:55:59.4874750Z getitem_18: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T20:55:59.4875226Z topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_18, topk_idx)]; proposals_i_1 = getitem_18 = topk_idx = None 2025-03-04T20:55:59.4875579Z 2025-03-04T20:55:59.4876118Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:55:59.4876797Z full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:55:59.4877079Z 2025-03-04T20:55:59.4877462Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:55:59.4877966Z level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu')); full = level_ids = None 2025-03-04T20:55:59.4878239Z 2025-03-04T20:55:59.4878711Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T20:55:59.4879297Z getitem_20: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0]; topk_proposals_i = None 2025-03-04T20:55:59.4879560Z 2025-03-04T20:55:59.4879959Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:55:59.4880446Z tensor: "f32[6000, 4][4, 1]cpu" = getitem_20.to(torch.float32); getitem_20 = None 2025-03-04T20:55:59.4880702Z 2025-03-04T20:55:59.4881170Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T20:55:59.4881747Z scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0]; topk_scores_i = None 2025-03-04T20:55:59.4882004Z 2025-03-04T20:55:59.4882593Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T20:55:59.4883281Z isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor); tensor = None 2025-03-04T20:55:59.4883598Z all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:55:59.4883939Z isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T20:55:59.4884289Z valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T20:55:59.4884546Z 2025-03-04T20:55:59.4885040Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T20:55:59.4885606Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T20:55:59.4885841Z 2025-03-04T20:56:06.7597156Z 2025-03-04T20:56:06.7597974Z class GraphModule(torch.nn.Module): 2025-03-04T20:56:06.7600924Z def forward(self, L_stack0_: "f32[3230, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 100352][100352, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1230 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1230 - s0, 4][4, 1]cpu"): 2025-03-04T20:56:06.7604362Z l_stack0_ = L_stack0_ 2025-03-04T20:56:06.7604717Z l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_ 2025-03-04T20:56:06.7605200Z l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_ 2025-03-04T20:56:06.7605676Z l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_ 2025-03-04T20:56:06.7606139Z l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_ 2025-03-04T20:56:06.7606655Z l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_ 2025-03-04T20:56:06.7607216Z l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_ 2025-03-04T20:56:06.7607775Z l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ 2025-03-04T20:56:06.7608332Z l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ 2025-03-04T20:56:06.7608804Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T20:56:06.7609213Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T20:56:06.7609611Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T20:56:06.7609989Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T20:56:06.7610348Z 2025-03-04T20:56:06.7610735Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x) 2025-03-04T20:56:06.7611206Z x: "f32[3230, 100352][100352, 1]cpu" = l_stack0_.flatten(1, -1); l_stack0_ = None 2025-03-04T20:56:06.7611895Z x_1: "f32[3230, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_); x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None 2025-03-04T20:56:06.7612642Z x_2: "f32[3230, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False); x_1 = None 2025-03-04T20:56:06.7613410Z x_3: "f32[3230, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_); x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None 2025-03-04T20:56:06.7614111Z x_4: "f32[3230, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False); x_3 = None 2025-03-04T20:56:06.7614383Z 2025-03-04T20:56:06.7614793Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x) 2025-03-04T20:56:06.7615771Z scores: "f32[3230, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_); l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None 2025-03-04T20:56:06.7616510Z 2025-03-04T20:56:06.7616930Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x) 2025-03-04T20:56:06.7617953Z proposal_deltas: "f32[3230, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_); x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None 2025-03-04T20:56:06.7618703Z 2025-03-04T20:56:06.7619074Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:56:06.7619542Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T20:56:06.7619799Z getitem: "Sym(s0)" = size[0] 2025-03-04T20:56:06.7620029Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T20:56:06.7620300Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T20:56:06.7620560Z getitem_2: "Sym(1230 - s0)" = size_1[0] 2025-03-04T20:56:06.7620802Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T20:56:06.7621016Z 2025-03-04T20:56:06.7621390Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:56:06.7622340Z proposal_boxes: "f32[3230, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T20:56:06.7623066Z 2025-03-04T20:56:06.7623516Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:56:06.7624103Z deltas: "f32[3230, 320][320, 1]cpu" = proposal_deltas.float(); proposal_deltas = None 2025-03-04T20:56:06.7624366Z 2025-03-04T20:56:06.7624753Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:56:06.7625260Z boxes: "f32[3230, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T20:56:06.7625529Z 2025-03-04T20:56:06.7625914Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:56:06.7626425Z getitem_4: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:56:06.7626720Z getitem_5: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:56:06.7627044Z widths: "f32[3230][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T20:56:06.7627295Z 2025-03-04T20:56:06.7627694Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:56:06.7628175Z getitem_6: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:56:06.7628457Z getitem_7: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:56:06.7628760Z heights: "f32[3230][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T20:56:06.7629012Z 2025-03-04T20:56:06.7629405Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:56:06.7629896Z getitem_8: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:56:06.7630146Z mul: "f32[3230][1]cpu" = 0.5 * widths 2025-03-04T20:56:06.7630393Z ctr_x: "f32[3230][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T20:56:06.7630621Z 2025-03-04T20:56:06.7631007Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:56:06.7631502Z getitem_9: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:56:06.7631775Z mul_1: "f32[3230][1]cpu" = 0.5 * heights 2025-03-04T20:56:06.7632033Z ctr_y: "f32[3230][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T20:56:06.7632267Z 2025-03-04T20:56:06.7632705Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:56:06.7633215Z getitem_10: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:56:06.7633537Z dx: "f32[3230, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T20:56:06.7633776Z 2025-03-04T20:56:06.7634148Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:56:06.7634782Z getitem_11: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:56:06.7635101Z dy: "f32[3230, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T20:56:06.7635323Z 2025-03-04T20:56:06.7635720Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:56:06.7636215Z getitem_12: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:56:06.7636524Z dw: "f32[3230, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T20:56:06.7636748Z 2025-03-04T20:56:06.7637152Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:56:06.7637672Z getitem_13: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:56:06.7638002Z dh: "f32[3230, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T20:56:06.7638226Z 2025-03-04T20:56:06.7638638Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:56:06.7639172Z dw_1: "f32[3230, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:56:06.7639419Z 2025-03-04T20:56:06.7639842Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:56:06.7640350Z dh_1: "f32[3230, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:56:06.7640592Z 2025-03-04T20:56:06.7641007Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:56:06.7641527Z getitem_14: "f32[3230, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:56:06.7641835Z mul_2: "f32[3230, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T20:56:06.7642177Z getitem_15: "f32[3230, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:56:06.7642507Z pred_ctr_x: "f32[3230, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T20:56:06.7642752Z 2025-03-04T20:56:06.7643174Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:56:06.7643694Z getitem_16: "f32[3230, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:56:06.7643993Z mul_3: "f32[3230, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T20:56:06.7644301Z getitem_17: "f32[3230, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:56:06.7644627Z pred_ctr_y: "f32[3230, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T20:56:06.7644872Z 2025-03-04T20:56:06.7645284Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:56:06.7645771Z exp: "f32[3230, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:56:06.7646082Z getitem_18: "f32[3230, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:56:06.7646411Z pred_w: "f32[3230, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T20:56:06.7646654Z 2025-03-04T20:56:06.7647068Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:56:06.7647562Z exp_1: "f32[3230, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:56:06.7647882Z getitem_19: "f32[3230, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:56:06.7648222Z pred_h: "f32[3230, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T20:56:06.7648471Z 2025-03-04T20:56:06.7648864Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:56:06.7649314Z mul_6: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T20:56:06.7649587Z x1: "f32[3230, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:56:06.7649810Z 2025-03-04T20:56:06.7650210Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:56:06.7650650Z mul_7: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T20:56:06.7651994Z y1: "f32[3230, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:56:06.7652301Z 2025-03-04T20:56:06.7652778Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:56:06.7653588Z mul_8: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:56:06.7654060Z x2: "f32[3230, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:56:06.7654325Z 2025-03-04T20:56:06.7655200Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:56:06.7656581Z mul_9: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:56:06.7656869Z y2: "f32[3230, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:56:06.7657101Z 2025-03-04T20:56:06.7657550Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:56:06.7658161Z pred_boxes: "f32[3230, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:56:06.7658484Z 2025-03-04T20:56:06.7658896Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:56:06.7659436Z predict_boxes: "f32[3230, 320][320, 1]cpu" = pred_boxes.reshape((3230, 320)); pred_boxes = None 2025-03-04T20:56:06.7659712Z 2025-03-04T20:56:06.7660142Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T20:56:06.7660734Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T20:56:06.7661087Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T20:56:06.7661367Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T20:56:06.7661658Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T20:56:06.7661963Z getitem_23: "f32[1230 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T20:56:06.7662215Z 2025-03-04T20:56:06.7662590Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:56:06.7663153Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T20:56:06.7663498Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T20:56:06.7663735Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T20:56:06.7664096Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T20:56:06.7664453Z getitem_26: "Sym(1230 - s0)" = size_3[0] 2025-03-04T20:56:06.7664690Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T20:56:06.7664901Z 2025-03-04T20:56:06.7665309Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T20:56:06.7665869Z probs: "f32[3230, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1); scores = None 2025-03-04T20:56:06.7666143Z 2025-03-04T20:56:06.7666570Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T20:56:06.7667154Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T20:56:06.7667507Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T20:56:06.7667812Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T20:56:06.7668112Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T20:56:06.7668423Z getitem_31: "f32[1230 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T20:56:06.7668691Z 2025-03-04T20:56:06.7669270Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T20:56:06.7669954Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T20:56:06.7670281Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:56:06.7670611Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T20:56:06.7670975Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T20:56:06.7671271Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T20:56:06.7671509Z 2025-03-04T20:56:06.7671952Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T20:56:06.7672483Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T20:56:06.7672719Z 2025-03-04T20:56:06.7672852Z 2025-03-04T20:56:06.7672950Z class GraphModule(torch.nn.Module): 2025-03-04T20:56:06.7674992Z def forward(self, L_stack0_: "f32[3230, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 100352][100352, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1230 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1230 - s0, 4][4, 1]cpu"): 2025-03-04T20:56:06.7677060Z l_stack0_ = L_stack0_ 2025-03-04T20:56:06.7677409Z l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_ 2025-03-04T20:56:06.7677895Z l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_ 2025-03-04T20:56:06.7678374Z l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_ 2025-03-04T20:56:06.7678850Z l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_ 2025-03-04T20:56:06.7679400Z l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_ 2025-03-04T20:56:06.7679982Z l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_ 2025-03-04T20:56:06.7680563Z l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ 2025-03-04T20:56:06.7681138Z l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ 2025-03-04T20:56:06.7681647Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T20:56:06.7682058Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T20:56:06.7682479Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T20:56:06.7682883Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T20:56:06.7683178Z 2025-03-04T20:56:06.7683553Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x) 2025-03-04T20:56:06.7684036Z x: "f32[3230, 100352][100352, 1]cpu" = l_stack0_.flatten(1, -1); l_stack0_ = None 2025-03-04T20:56:06.7684756Z x_1: "f32[3230, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_); x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None 2025-03-04T20:56:06.7685522Z x_2: "f32[3230, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False); x_1 = None 2025-03-04T20:56:06.7686233Z x_3: "f32[3230, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_); x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None 2025-03-04T20:56:06.7686946Z x_4: "f32[3230, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False); x_3 = None 2025-03-04T20:56:06.7687220Z 2025-03-04T20:56:06.7687616Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x) 2025-03-04T20:56:06.7688576Z scores: "f32[3230, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_); l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None 2025-03-04T20:56:06.7689265Z 2025-03-04T20:56:06.7689661Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x) 2025-03-04T20:56:06.7690630Z proposal_deltas: "f32[3230, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_); x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None 2025-03-04T20:56:06.7691364Z 2025-03-04T20:56:06.7691731Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:56:06.7692187Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T20:56:06.7692436Z getitem: "Sym(s0)" = size[0] 2025-03-04T20:56:06.7692667Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T20:56:06.7692950Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T20:56:06.7693207Z getitem_2: "Sym(1230 - s0)" = size_1[0] 2025-03-04T20:56:06.7693446Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T20:56:06.7693662Z 2025-03-04T20:56:06.7694043Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:56:06.7694993Z proposal_boxes: "f32[3230, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T20:56:06.7695696Z 2025-03-04T20:56:06.7696211Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:56:06.7696791Z deltas: "f32[3230, 320][320, 1]cpu" = proposal_deltas.float(); proposal_deltas = None 2025-03-04T20:56:06.7697066Z 2025-03-04T20:56:06.7697451Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:56:06.7697959Z boxes: "f32[3230, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T20:56:06.7698242Z 2025-03-04T20:56:06.7698631Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:56:06.7699108Z getitem_4: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:56:06.7699403Z getitem_5: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:56:06.7699712Z widths: "f32[3230][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T20:56:06.7699962Z 2025-03-04T20:56:06.7700356Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:56:06.7700836Z getitem_6: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:56:06.7701126Z getitem_7: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:56:06.7701451Z heights: "f32[3230][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T20:56:06.7701722Z 2025-03-04T20:56:06.7702358Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:56:06.7702903Z getitem_8: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:56:06.7703173Z mul: "f32[3230][1]cpu" = 0.5 * widths 2025-03-04T20:56:06.7703440Z ctr_x: "f32[3230][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T20:56:06.7703693Z 2025-03-04T20:56:06.7704114Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:56:06.7704658Z getitem_9: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:56:06.7704941Z mul_1: "f32[3230][1]cpu" = 0.5 * heights 2025-03-04T20:56:06.7705226Z ctr_y: "f32[3230][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T20:56:06.7705494Z 2025-03-04T20:56:06.7705952Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:56:06.7706524Z getitem_10: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:56:06.7708046Z dx: "f32[3230, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T20:56:06.7708309Z 2025-03-04T20:56:06.7708745Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:56:06.7710519Z getitem_11: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:56:06.7711282Z dy: "f32[3230, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T20:56:06.7711567Z 2025-03-04T20:56:06.7712074Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:56:06.7712655Z getitem_12: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:56:06.7713084Z dw: "f32[3230, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T20:56:06.7713346Z 2025-03-04T20:56:06.7713776Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:56:06.7714421Z getitem_13: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:56:06.7714813Z dh: "f32[3230, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T20:56:06.7715053Z 2025-03-04T20:56:06.7715499Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:56:06.7716100Z dw_1: "f32[3230, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:56:06.7716363Z 2025-03-04T20:56:06.7716816Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:56:06.7717378Z dh_1: "f32[3230, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:56:06.7717648Z 2025-03-04T20:56:06.7718107Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:56:06.7718683Z getitem_14: "f32[3230, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:56:06.7719018Z mul_2: "f32[3230, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T20:56:06.7719373Z getitem_15: "f32[3230, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:56:06.7719742Z pred_ctr_x: "f32[3230, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T20:56:06.7720015Z 2025-03-04T20:56:06.7720479Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:56:06.7721051Z getitem_16: "f32[3230, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:56:06.7721384Z mul_3: "f32[3230, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T20:56:06.7721732Z getitem_17: "f32[3230, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:56:06.7722092Z pred_ctr_y: "f32[3230, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T20:56:06.7722367Z 2025-03-04T20:56:06.7722818Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:56:06.7723354Z exp: "f32[3230, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:56:06.7723708Z getitem_18: "f32[3230, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:56:06.7724060Z pred_w: "f32[3230, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T20:56:06.7724325Z 2025-03-04T20:56:06.7724740Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:56:06.7725240Z exp_1: "f32[3230, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:56:06.7725555Z getitem_19: "f32[3230, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:56:06.7725903Z pred_h: "f32[3230, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T20:56:06.7726144Z 2025-03-04T20:56:06.7726560Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:56:06.7727010Z mul_6: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T20:56:06.7727259Z x1: "f32[3230, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:56:06.7727483Z 2025-03-04T20:56:06.7727864Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:56:06.7728317Z mul_7: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T20:56:06.7728580Z y1: "f32[3230, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:56:06.7728821Z 2025-03-04T20:56:06.7729202Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:56:06.7729663Z mul_8: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:56:06.7729950Z x2: "f32[3230, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:56:06.7730192Z 2025-03-04T20:56:06.7730578Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:56:06.7731046Z mul_9: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:56:06.7731329Z y2: "f32[3230, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:56:06.7731560Z 2025-03-04T20:56:06.7731990Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:56:06.7732554Z pred_boxes: "f32[3230, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:56:06.7732851Z 2025-03-04T20:56:06.7733254Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:56:06.7733795Z predict_boxes: "f32[3230, 320][320, 1]cpu" = pred_boxes.reshape((3230, 320)); pred_boxes = None 2025-03-04T20:56:06.7734072Z 2025-03-04T20:56:06.7734513Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T20:56:06.7735137Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T20:56:06.7735491Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T20:56:06.7735768Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T20:56:06.7736060Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T20:56:06.7736383Z getitem_23: "f32[1230 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T20:56:06.7736630Z 2025-03-04T20:56:06.7736998Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:56:06.7737550Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T20:56:06.7737888Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T20:56:06.7738124Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T20:56:06.7738504Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T20:56:06.7738863Z getitem_26: "Sym(1230 - s0)" = size_3[0] 2025-03-04T20:56:06.7739099Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T20:56:06.7739308Z 2025-03-04T20:56:06.7739733Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T20:56:06.7740278Z probs: "f32[3230, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1); scores = None 2025-03-04T20:56:06.7740551Z 2025-03-04T20:56:06.7740985Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T20:56:06.7741572Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T20:56:06.7741943Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T20:56:06.7742229Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T20:56:06.7742526Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T20:56:06.7742833Z getitem_31: "f32[1230 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T20:56:06.7743090Z 2025-03-04T20:56:06.7743623Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T20:56:06.7744299Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T20:56:06.7744627Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:56:06.7744954Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T20:56:06.7745283Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T20:56:06.7745565Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T20:56:06.7745795Z 2025-03-04T20:56:06.7746223Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T20:56:06.7746724Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T20:56:06.7746949Z 2025-03-04T20:56:06.7747080Z 2025-03-04T20:56:06.7747167Z class GraphModule(torch.nn.Module): 2025-03-04T20:56:06.7749097Z def forward(self, L_stack0_: "f32[3230, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 100352][100352, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1230 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1230 - s0, 4][4, 1]cpu"): 2025-03-04T20:56:06.7751090Z l_stack0_ = L_stack0_ 2025-03-04T20:56:06.7751433Z l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_ 2025-03-04T20:56:06.7751924Z l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_ 2025-03-04T20:56:06.7752407Z l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_ 2025-03-04T20:56:06.7752870Z l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_ 2025-03-04T20:56:06.7753380Z l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_ 2025-03-04T20:56:06.7753933Z l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_ 2025-03-04T20:56:06.7754593Z l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ 2025-03-04T20:56:06.7755218Z l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ 2025-03-04T20:56:06.7755718Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T20:56:06.7756154Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T20:56:06.7756556Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T20:56:06.7756949Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T20:56:06.7757252Z 2025-03-04T20:56:06.7757617Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x) 2025-03-04T20:56:06.7758081Z x: "f32[3230, 100352][100352, 1]cpu" = l_stack0_.flatten(1, -1); l_stack0_ = None 2025-03-04T20:56:06.7758772Z x_1: "f32[3230, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_); x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None 2025-03-04T20:56:06.7759469Z x_2: "f32[3230, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False); x_1 = None 2025-03-04T20:56:06.7760171Z x_3: "f32[3230, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_); x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None 2025-03-04T20:56:06.7762141Z x_4: "f32[3230, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False); x_3 = None 2025-03-04T20:56:06.7762419Z 2025-03-04T20:56:06.7762836Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x) 2025-03-04T20:56:06.7764117Z scores: "f32[3230, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_); l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None 2025-03-04T20:56:06.7765072Z 2025-03-04T20:56:06.7765497Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x) 2025-03-04T20:56:06.7766487Z proposal_deltas: "f32[3230, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_); x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None 2025-03-04T20:56:06.7767248Z 2025-03-04T20:56:06.7767631Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:56:06.7768116Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T20:56:06.7768372Z getitem: "Sym(s0)" = size[0] 2025-03-04T20:56:06.7768602Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T20:56:06.7768876Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T20:56:06.7769134Z getitem_2: "Sym(1230 - s0)" = size_1[0] 2025-03-04T20:56:06.7769380Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T20:56:06.7769595Z 2025-03-04T20:56:06.7770244Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:56:06.7771231Z proposal_boxes: "f32[3230, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T20:56:06.7771929Z 2025-03-04T20:56:06.7772389Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:56:06.7772992Z deltas: "f32[3230, 320][320, 1]cpu" = proposal_deltas.float(); proposal_deltas = None 2025-03-04T20:56:06.7773257Z 2025-03-04T20:56:06.7773658Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:56:06.7774190Z boxes: "f32[3230, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T20:56:06.7774466Z 2025-03-04T20:56:06.7774874Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:56:06.7775356Z getitem_4: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:56:06.7775648Z getitem_5: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:56:06.7776012Z widths: "f32[3230][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T20:56:06.7776268Z 2025-03-04T20:56:06.7776675Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:56:06.7777168Z getitem_6: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:56:06.7777464Z getitem_7: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:56:06.7777780Z heights: "f32[3230][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T20:56:06.7778040Z 2025-03-04T20:56:06.7778444Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:56:06.7778954Z getitem_8: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:56:06.7779206Z mul: "f32[3230][1]cpu" = 0.5 * widths 2025-03-04T20:56:06.7779458Z ctr_x: "f32[3230][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T20:56:06.7779688Z 2025-03-04T20:56:06.7780085Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:56:06.7780595Z getitem_9: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:56:06.7780895Z mul_1: "f32[3230][1]cpu" = 0.5 * heights 2025-03-04T20:56:06.7781160Z ctr_y: "f32[3230][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T20:56:06.7781400Z 2025-03-04T20:56:06.7781829Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:56:06.7782348Z getitem_10: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:56:06.7782668Z dx: "f32[3230, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T20:56:06.7782895Z 2025-03-04T20:56:06.7783274Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:56:06.7783781Z getitem_11: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:56:06.7784118Z dy: "f32[3230, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T20:56:06.7784343Z 2025-03-04T20:56:06.7784725Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:56:06.7785223Z getitem_12: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:56:06.7785537Z dw: "f32[3230, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T20:56:06.7785766Z 2025-03-04T20:56:06.7786151Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:56:06.7786689Z getitem_13: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:56:06.7787025Z dh: "f32[3230, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T20:56:06.7787253Z 2025-03-04T20:56:06.7787684Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:56:06.7788198Z dw_1: "f32[3230, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:56:06.7788442Z 2025-03-04T20:56:06.7788847Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:56:06.7789348Z dh_1: "f32[3230, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:56:06.7789586Z 2025-03-04T20:56:06.7790004Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:56:06.7790522Z getitem_14: "f32[3230, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:56:06.7790825Z mul_2: "f32[3230, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T20:56:06.7791145Z getitem_15: "f32[3230, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:56:06.7791506Z pred_ctr_x: "f32[3230, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T20:56:06.7791756Z 2025-03-04T20:56:06.7792192Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:56:06.7792727Z getitem_16: "f32[3230, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:56:06.7793032Z mul_3: "f32[3230, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T20:56:06.7793353Z getitem_17: "f32[3230, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:56:06.7793710Z pred_ctr_y: "f32[3230, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T20:56:06.7793954Z 2025-03-04T20:56:06.7794463Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:56:06.7794977Z exp: "f32[3230, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:56:06.7795300Z getitem_18: "f32[3230, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:56:06.7795638Z pred_w: "f32[3230, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T20:56:06.7795890Z 2025-03-04T20:56:06.7796334Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:56:06.7796863Z exp_1: "f32[3230, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:56:06.7797196Z getitem_19: "f32[3230, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:56:06.7797540Z pred_h: "f32[3230, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T20:56:06.7797785Z 2025-03-04T20:56:06.7798178Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:56:06.7798633Z mul_6: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T20:56:06.7798889Z x1: "f32[3230, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:56:06.7799118Z 2025-03-04T20:56:06.7799505Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:56:06.7799958Z mul_7: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T20:56:06.7800224Z y1: "f32[3230, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:56:06.7800446Z 2025-03-04T20:56:06.7800825Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:56:06.7801282Z mul_8: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:56:06.7801564Z x2: "f32[3230, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:56:06.7801802Z 2025-03-04T20:56:06.7802341Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:56:06.7802813Z mul_9: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:56:06.7803100Z y2: "f32[3230, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:56:06.7803348Z 2025-03-04T20:56:06.7803787Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:56:06.7804371Z pred_boxes: "f32[3230, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:56:06.7804699Z 2025-03-04T20:56:06.7805121Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:56:06.7805660Z predict_boxes: "f32[3230, 320][320, 1]cpu" = pred_boxes.reshape((3230, 320)); pred_boxes = None 2025-03-04T20:56:06.7805946Z 2025-03-04T20:56:06.7806391Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T20:56:06.7807025Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T20:56:06.7807387Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T20:56:06.7807710Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T20:56:06.7808017Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T20:56:06.7808325Z getitem_23: "f32[1230 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T20:56:06.7808585Z 2025-03-04T20:56:06.7808953Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:56:06.7809510Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T20:56:06.7809879Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T20:56:06.7810116Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T20:56:06.7810478Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T20:56:06.7810827Z getitem_26: "Sym(1230 - s0)" = size_3[0] 2025-03-04T20:56:06.7811068Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T20:56:06.7811282Z 2025-03-04T20:56:06.7811692Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T20:56:06.7812240Z probs: "f32[3230, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1); scores = None 2025-03-04T20:56:06.7812518Z 2025-03-04T20:56:06.7812952Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T20:56:06.7813552Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T20:56:06.7813906Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T20:56:06.7814194Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T20:56:06.7814490Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T20:56:06.7814795Z getitem_31: "f32[1230 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T20:56:06.7815046Z 2025-03-04T20:56:06.7815586Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T20:56:06.7816281Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T20:56:06.7816617Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:56:06.7816954Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T20:56:06.7817292Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T20:56:06.7817604Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T20:56:06.7817839Z 2025-03-04T20:56:06.7818279Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T20:56:06.7818795Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T20:56:06.7819027Z 2025-03-04T20:56:06.7819160Z 2025-03-04T20:56:06.7819248Z class GraphModule(torch.nn.Module): 2025-03-04T20:56:06.7822111Z def forward(self, L_stack0_: "f32[3230, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 100352][100352, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1230 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1230 - s0, 4][4, 1]cpu"): 2025-03-04T20:56:06.7824124Z l_stack0_ = L_stack0_ 2025-03-04T20:56:06.7824466Z l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_ 2025-03-04T20:56:06.7824939Z l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_ 2025-03-04T20:56:06.7825461Z l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_ 2025-03-04T20:56:06.7825920Z l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_ 2025-03-04T20:56:06.7826431Z l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_ 2025-03-04T20:56:06.7826992Z l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_ 2025-03-04T20:56:06.7827554Z l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ 2025-03-04T20:56:06.7828112Z l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ 2025-03-04T20:56:06.7828591Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T20:56:06.7828993Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T20:56:06.7829385Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T20:56:06.7829778Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T20:56:06.7830068Z 2025-03-04T20:56:06.7830430Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x) 2025-03-04T20:56:06.7830901Z x: "f32[3230, 100352][100352, 1]cpu" = l_stack0_.flatten(1, -1); l_stack0_ = None 2025-03-04T20:56:06.7831594Z x_1: "f32[3230, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_); x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None 2025-03-04T20:56:06.7832344Z x_2: "f32[3230, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False); x_1 = None 2025-03-04T20:56:06.7833065Z x_3: "f32[3230, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_); x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None 2025-03-04T20:56:06.7833775Z x_4: "f32[3230, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False); x_3 = None 2025-03-04T20:56:06.7834054Z 2025-03-04T20:56:06.7834545Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x) 2025-03-04T20:56:06.7835530Z scores: "f32[3230, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_); l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None 2025-03-04T20:56:06.7836256Z 2025-03-04T20:56:06.7836669Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x) 2025-03-04T20:56:06.7837686Z proposal_deltas: "f32[3230, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_); x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None 2025-03-04T20:56:06.7838442Z 2025-03-04T20:56:06.7838819Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:56:06.7839278Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T20:56:06.7839530Z getitem: "Sym(s0)" = size[0] 2025-03-04T20:56:06.7839754Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T20:56:06.7840022Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T20:56:06.7840278Z getitem_2: "Sym(1230 - s0)" = size_1[0] 2025-03-04T20:56:06.7840513Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T20:56:06.7840726Z 2025-03-04T20:56:06.7841100Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:56:06.7842037Z proposal_boxes: "f32[3230, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T20:56:06.7842746Z 2025-03-04T20:56:06.7843202Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:56:06.7843776Z deltas: "f32[3230, 320][320, 1]cpu" = proposal_deltas.float(); proposal_deltas = None 2025-03-04T20:56:06.7844045Z 2025-03-04T20:56:06.7844436Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:56:06.7844962Z boxes: "f32[3230, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T20:56:06.7845233Z 2025-03-04T20:56:06.7845636Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:56:06.7846156Z getitem_4: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:56:06.7846457Z getitem_5: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:56:06.7846775Z widths: "f32[3230][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T20:56:06.7847038Z 2025-03-04T20:56:06.7847434Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:56:06.7847936Z getitem_6: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:56:06.7848223Z getitem_7: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:56:06.7848528Z heights: "f32[3230][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T20:56:06.7848791Z 2025-03-04T20:56:06.7849188Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:56:06.7849659Z getitem_8: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:56:06.7849908Z mul: "f32[3230][1]cpu" = 0.5 * widths 2025-03-04T20:56:06.7850153Z ctr_x: "f32[3230][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T20:56:06.7850373Z 2025-03-04T20:56:06.7850773Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:56:06.7851297Z getitem_9: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:56:06.7851575Z mul_1: "f32[3230][1]cpu" = 0.5 * heights 2025-03-04T20:56:06.7851838Z ctr_y: "f32[3230][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T20:56:06.7852085Z 2025-03-04T20:56:06.7852502Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:56:06.7853032Z getitem_10: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:56:06.7853370Z dx: "f32[3230, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T20:56:06.7853610Z 2025-03-04T20:56:06.7854008Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:56:06.7854515Z getitem_11: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:56:06.7854832Z dy: "f32[3230, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T20:56:06.7855058Z 2025-03-04T20:56:06.7855441Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:56:06.7855948Z getitem_12: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:56:06.7856254Z dw: "f32[3230, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T20:56:06.7856471Z 2025-03-04T20:56:06.7856848Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:56:06.7857363Z getitem_13: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:56:06.7857695Z dh: "f32[3230, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T20:56:06.7857914Z 2025-03-04T20:56:06.7858325Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:56:06.7858879Z dw_1: "f32[3230, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:56:06.7859134Z 2025-03-04T20:56:06.7859556Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:56:06.7860073Z dh_1: "f32[3230, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:56:06.7860319Z 2025-03-04T20:56:06.7860779Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:56:06.7861311Z getitem_14: "f32[3230, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:56:06.7861639Z mul_2: "f32[3230, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T20:56:06.7861965Z getitem_15: "f32[3230, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:56:06.7862306Z pred_ctr_x: "f32[3230, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T20:56:06.7862560Z 2025-03-04T20:56:06.7862996Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:56:06.7863529Z getitem_16: "f32[3230, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:56:06.7863853Z mul_3: "f32[3230, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T20:56:06.7864174Z getitem_17: "f32[3230, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:56:06.7864509Z pred_ctr_y: "f32[3230, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T20:56:06.7864760Z 2025-03-04T20:56:06.7865181Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:56:06.7865680Z exp: "f32[3230, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:56:06.7865996Z getitem_18: "f32[3230, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:56:06.7866327Z pred_w: "f32[3230, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T20:56:06.7866570Z 2025-03-04T20:56:06.7866986Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:56:06.7867482Z exp_1: "f32[3230, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:56:06.7867809Z getitem_19: "f32[3230, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:56:06.7868149Z pred_h: "f32[3230, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T20:56:06.7868395Z 2025-03-04T20:56:06.7868787Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:56:06.7869243Z mul_6: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T20:56:06.7869500Z x1: "f32[3230, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:56:06.7869728Z 2025-03-04T20:56:06.7870123Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:56:06.7870577Z mul_7: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T20:56:06.7870830Z y1: "f32[3230, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:56:06.7871062Z 2025-03-04T20:56:06.7871448Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:56:06.7871942Z mul_8: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:56:06.7872232Z x2: "f32[3230, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:56:06.7872473Z 2025-03-04T20:56:06.7872862Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:56:06.7873335Z mul_9: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:56:06.7873654Z y2: "f32[3230, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:56:06.7873919Z 2025-03-04T20:56:06.7874498Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:56:06.7875125Z pred_boxes: "f32[3230, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:56:06.7875431Z 2025-03-04T20:56:06.7875864Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:56:06.7876425Z predict_boxes: "f32[3230, 320][320, 1]cpu" = pred_boxes.reshape((3230, 320)); pred_boxes = None 2025-03-04T20:56:06.7876711Z 2025-03-04T20:56:06.7877188Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T20:56:06.7877796Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T20:56:06.7878154Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T20:56:06.7878441Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T20:56:06.7878745Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T20:56:06.7879061Z getitem_23: "f32[1230 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T20:56:06.7879318Z 2025-03-04T20:56:06.7879685Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:56:06.7880247Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T20:56:06.7880584Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T20:56:06.7880814Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T20:56:06.7881166Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T20:56:06.7881510Z getitem_26: "Sym(1230 - s0)" = size_3[0] 2025-03-04T20:56:06.7881749Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T20:56:06.7881963Z 2025-03-04T20:56:06.7882377Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T20:56:06.7882914Z probs: "f32[3230, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1); scores = None 2025-03-04T20:56:06.7883185Z 2025-03-04T20:56:06.7883716Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T20:56:06.7884322Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T20:56:06.7884680Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T20:56:06.7884987Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T20:56:06.7885283Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T20:56:06.7885606Z getitem_31: "f32[1230 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T20:56:06.7885855Z 2025-03-04T20:56:06.7886389Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T20:56:06.7887091Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T20:56:06.7887432Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:56:06.7887787Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T20:56:06.7888128Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T20:56:06.7888420Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T20:56:06.7888654Z 2025-03-04T20:56:06.7889096Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T20:56:06.7889618Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T20:56:06.7889849Z 2025-03-04T20:56:08.7046867Z 2025-03-04T20:56:08.7051805Z class GraphModule(torch.nn.Module): 2025-03-04T20:56:08.7055029Z def forward(self, L_predictions_0_: "f32[3230, 81][81, 1]cpu", L_predictions_1_: "f32[3230, 320][320, 1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1230 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1230 - s0, 4][4, 1]cpu"): 2025-03-04T20:56:08.7057716Z l_predictions_0_ = L_predictions_0_ 2025-03-04T20:56:08.7058074Z l_predictions_1_ = L_predictions_1_ 2025-03-04T20:56:08.7063974Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T20:56:08.7065940Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T20:56:08.7066477Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T20:56:08.7066895Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T20:56:08.7071176Z 2025-03-04T20:56:08.7071748Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:56:08.7072258Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T20:56:08.7072516Z getitem: "Sym(s0)" = size[0] 2025-03-04T20:56:08.7072759Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T20:56:08.7073051Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T20:56:08.7073326Z getitem_2: "Sym(1230 - s0)" = size_1[0] 2025-03-04T20:56:08.7073577Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T20:56:08.7073801Z 2025-03-04T20:56:08.7074328Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:56:08.7075373Z proposal_boxes: "f32[3230, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T20:56:08.7076548Z 2025-03-04T20:56:08.7077034Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:56:08.7077643Z deltas: "f32[3230, 320][320, 1]cpu" = l_predictions_1_.float(); l_predictions_1_ = None 2025-03-04T20:56:08.7077962Z 2025-03-04T20:56:08.7078378Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:56:08.7078991Z boxes: "f32[3230, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T20:56:08.7079277Z 2025-03-04T20:56:08.7079739Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:56:08.7080257Z getitem_4: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:56:08.7080564Z getitem_5: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:56:08.7080890Z widths: "f32[3230][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T20:56:08.7081155Z 2025-03-04T20:56:08.7081577Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:56:08.7082082Z getitem_6: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:56:08.7082441Z getitem_7: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:56:08.7082764Z heights: "f32[3230][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T20:56:08.7083032Z 2025-03-04T20:56:08.7083446Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:56:08.7083950Z getitem_8: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:56:08.7084215Z mul: "f32[3230][1]cpu" = 0.5 * widths 2025-03-04T20:56:08.7084467Z ctr_x: "f32[3230][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T20:56:08.7084699Z 2025-03-04T20:56:08.7085091Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:56:08.7085593Z getitem_9: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:56:08.7085868Z mul_1: "f32[3230][1]cpu" = 0.5 * heights 2025-03-04T20:56:08.7086121Z ctr_y: "f32[3230][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T20:56:08.7086352Z 2025-03-04T20:56:08.7086788Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:56:08.7087305Z getitem_10: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:56:08.7087628Z dx: "f32[3230, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T20:56:08.7087859Z 2025-03-04T20:56:08.7088257Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:56:08.7088780Z getitem_11: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:56:08.7089109Z dy: "f32[3230, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T20:56:08.7089343Z 2025-03-04T20:56:08.7089741Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:56:08.7090291Z getitem_12: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:56:08.7090602Z dw: "f32[3230, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T20:56:08.7090838Z 2025-03-04T20:56:08.7091216Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:56:08.7091734Z getitem_13: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:56:08.7092088Z dh: "f32[3230, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T20:56:08.7092315Z 2025-03-04T20:56:08.7092790Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:56:08.7093317Z dw_1: "f32[3230, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:56:08.7093566Z 2025-03-04T20:56:08.7093982Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:56:08.7094506Z dh_1: "f32[3230, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:56:08.7094751Z 2025-03-04T20:56:08.7095176Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:56:08.7095736Z getitem_14: "f32[3230, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:56:08.7096041Z mul_2: "f32[3230, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T20:56:08.7096362Z getitem_15: "f32[3230, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:56:08.7096698Z pred_ctr_x: "f32[3230, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T20:56:08.7096940Z 2025-03-04T20:56:08.7097375Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:56:08.7097915Z getitem_16: "f32[3230, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:56:08.7098225Z mul_3: "f32[3230, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T20:56:08.7098550Z getitem_17: "f32[3230, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:56:08.7098891Z pred_ctr_y: "f32[3230, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T20:56:08.7099146Z 2025-03-04T20:56:08.7099568Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:56:08.7100071Z exp: "f32[3230, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:56:08.7100389Z getitem_18: "f32[3230, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:56:08.7100727Z pred_w: "f32[3230, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T20:56:08.7100970Z 2025-03-04T20:56:08.7101384Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:56:08.7101880Z exp_1: "f32[3230, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:56:08.7102553Z getitem_19: "f32[3230, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:56:08.7102902Z pred_h: "f32[3230, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T20:56:08.7103194Z 2025-03-04T20:56:08.7103597Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:56:08.7104057Z mul_6: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T20:56:08.7104314Z x1: "f32[3230, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:56:08.7104547Z 2025-03-04T20:56:08.7104939Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:56:08.7105414Z mul_7: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T20:56:08.7105675Z y1: "f32[3230, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:56:08.7105913Z 2025-03-04T20:56:08.7106329Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:56:08.7106811Z mul_8: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:56:08.7107098Z x2: "f32[3230, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:56:08.7107341Z 2025-03-04T20:56:08.7107730Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:56:08.7108201Z mul_9: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:56:08.7108516Z y2: "f32[3230, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:56:08.7108753Z 2025-03-04T20:56:08.7109186Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:56:08.7109772Z pred_boxes: "f32[3230, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:56:08.7110062Z 2025-03-04T20:56:08.7110515Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:56:08.7111077Z predict_boxes: "f32[3230, 320][320, 1]cpu" = pred_boxes.reshape((3230, 320)); pred_boxes = None 2025-03-04T20:56:08.7111363Z 2025-03-04T20:56:08.7111808Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T20:56:08.7112424Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T20:56:08.7112788Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T20:56:08.7113076Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T20:56:08.7113374Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T20:56:08.7113690Z getitem_23: "f32[1230 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T20:56:08.7113947Z 2025-03-04T20:56:08.7114409Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:56:08.7114989Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T20:56:08.7115350Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T20:56:08.7115605Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T20:56:08.7115971Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T20:56:08.7116326Z getitem_26: "Sym(1230 - s0)" = size_3[0] 2025-03-04T20:56:08.7116598Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T20:56:08.7116817Z 2025-03-04T20:56:08.7117251Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T20:56:08.7117838Z probs: "f32[3230, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1); l_predictions_0_ = None 2025-03-04T20:56:08.7118156Z 2025-03-04T20:56:08.7118620Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T20:56:08.7119232Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T20:56:08.7119622Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T20:56:08.7119914Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T20:56:08.7120223Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T20:56:08.7120519Z getitem_31: "f32[1230 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T20:56:08.7120764Z 2025-03-04T20:56:08.7121299Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T20:56:08.7121990Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T20:56:08.7122326Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:56:08.7122651Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T20:56:08.7122982Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T20:56:08.7123265Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T20:56:08.7123490Z 2025-03-04T20:56:08.7123916Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T20:56:08.7124418Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T20:56:08.7124639Z 2025-03-04T20:56:08.7124767Z 2025-03-04T20:56:08.7124857Z class GraphModule(torch.nn.Module): 2025-03-04T20:56:08.7125655Z def forward(self, L_predictions_0_: "f32[3230, 81][81, 1]cpu", L_predictions_1_: "f32[3230, 320][320, 1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1230 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1230 - s0, 4][4, 1]cpu"): 2025-03-04T20:56:08.7126432Z l_predictions_0_ = L_predictions_0_ 2025-03-04T20:56:08.7126654Z l_predictions_1_ = L_predictions_1_ 2025-03-04T20:56:08.7126960Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T20:56:08.7127353Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T20:56:08.7127738Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T20:56:08.7128133Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T20:56:08.7128418Z 2025-03-04T20:56:08.7128788Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:56:08.7129243Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T20:56:08.7129504Z getitem: "Sym(s0)" = size[0] 2025-03-04T20:56:08.7129727Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T20:56:08.7129993Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T20:56:08.7130245Z getitem_2: "Sym(1230 - s0)" = size_1[0] 2025-03-04T20:56:08.7130479Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T20:56:08.7130693Z 2025-03-04T20:56:08.7131055Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:56:08.7132003Z proposal_boxes: "f32[3230, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T20:56:08.7132712Z 2025-03-04T20:56:08.7133168Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:56:08.7133741Z deltas: "f32[3230, 320][320, 1]cpu" = l_predictions_1_.float(); l_predictions_1_ = None 2025-03-04T20:56:08.7134017Z 2025-03-04T20:56:08.7134418Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:56:08.7134967Z boxes: "f32[3230, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T20:56:08.7135246Z 2025-03-04T20:56:08.7135638Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:56:08.7136134Z getitem_4: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:56:08.7136432Z getitem_5: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:56:08.7136751Z widths: "f32[3230][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T20:56:08.7137014Z 2025-03-04T20:56:08.7137423Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:56:08.7137918Z getitem_6: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:56:08.7138211Z getitem_7: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:56:08.7138517Z heights: "f32[3230][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T20:56:08.7138778Z 2025-03-04T20:56:08.7139181Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:56:08.7139665Z getitem_8: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:56:08.7139926Z mul: "f32[3230][1]cpu" = 0.5 * widths 2025-03-04T20:56:08.7140181Z ctr_x: "f32[3230][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T20:56:08.7140417Z 2025-03-04T20:56:08.7140818Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:56:08.7141333Z getitem_9: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:56:08.7141619Z mul_1: "f32[3230][1]cpu" = 0.5 * heights 2025-03-04T20:56:08.7141882Z ctr_y: "f32[3230][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T20:56:08.7142119Z 2025-03-04T20:56:08.7142526Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:56:08.7143059Z getitem_10: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:56:08.7143382Z dx: "f32[3230, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T20:56:08.7143610Z 2025-03-04T20:56:08.7143996Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:56:08.7144506Z getitem_11: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:56:08.7144837Z dy: "f32[3230, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T20:56:08.7145069Z 2025-03-04T20:56:08.7145467Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:56:08.7145972Z getitem_12: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:56:08.7146290Z dw: "f32[3230, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T20:56:08.7146516Z 2025-03-04T20:56:08.7146898Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:56:08.7147427Z getitem_13: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:56:08.7147784Z dh: "f32[3230, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T20:56:08.7148009Z 2025-03-04T20:56:08.7148441Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:56:08.7148971Z dw_1: "f32[3230, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:56:08.7149225Z 2025-03-04T20:56:08.7149643Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:56:08.7150162Z dh_1: "f32[3230, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:56:08.7150410Z 2025-03-04T20:56:08.7150837Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:56:08.7151376Z getitem_14: "f32[3230, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:56:08.7151685Z mul_2: "f32[3230, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T20:56:08.7152008Z getitem_15: "f32[3230, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:56:08.7152352Z pred_ctr_x: "f32[3230, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T20:56:08.7152604Z 2025-03-04T20:56:08.7153042Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:56:08.7153580Z getitem_16: "f32[3230, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:56:08.7153891Z mul_3: "f32[3230, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T20:56:08.7154304Z getitem_17: "f32[3230, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:56:08.7154656Z pred_ctr_y: "f32[3230, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T20:56:08.7154916Z 2025-03-04T20:56:08.7155357Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:56:08.7155883Z exp: "f32[3230, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:56:08.7156207Z getitem_18: "f32[3230, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:56:08.7156561Z pred_w: "f32[3230, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T20:56:08.7156812Z 2025-03-04T20:56:08.7157248Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:56:08.7157785Z exp_1: "f32[3230, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:56:08.7158123Z getitem_19: "f32[3230, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:56:08.7158498Z pred_h: "f32[3230, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T20:56:08.7158757Z 2025-03-04T20:56:08.7159171Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:56:08.7159642Z mul_6: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T20:56:08.7159905Z x1: "f32[3230, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:56:08.7160140Z 2025-03-04T20:56:08.7160541Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:56:08.7161030Z mul_7: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T20:56:08.7161294Z y1: "f32[3230, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:56:08.7161529Z 2025-03-04T20:56:08.7161934Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:56:08.7162428Z mul_8: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:56:08.7162727Z x2: "f32[3230, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:56:08.7162977Z 2025-03-04T20:56:08.7163378Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:56:08.7163868Z mul_9: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:56:08.7164143Z y2: "f32[3230, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:56:08.7164383Z 2025-03-04T20:56:08.7164810Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:56:08.7165378Z pred_boxes: "f32[3230, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:56:08.7165665Z 2025-03-04T20:56:08.7166076Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:56:08.7166620Z predict_boxes: "f32[3230, 320][320, 1]cpu" = pred_boxes.reshape((3230, 320)); pred_boxes = None 2025-03-04T20:56:08.7166900Z 2025-03-04T20:56:08.7167333Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T20:56:08.7167933Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T20:56:08.7168286Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T20:56:08.7168565Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T20:56:08.7168879Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T20:56:08.7169182Z getitem_23: "f32[1230 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T20:56:08.7169430Z 2025-03-04T20:56:08.7169794Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:56:08.7170330Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T20:56:08.7170670Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T20:56:08.7170935Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T20:56:08.7171284Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T20:56:08.7171638Z getitem_26: "Sym(1230 - s0)" = size_3[0] 2025-03-04T20:56:08.7171882Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T20:56:08.7172101Z 2025-03-04T20:56:08.7172515Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T20:56:08.7173098Z probs: "f32[3230, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1); l_predictions_0_ = None 2025-03-04T20:56:08.7173421Z 2025-03-04T20:56:08.7173859Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T20:56:08.7174479Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T20:56:08.7174830Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T20:56:08.7175115Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T20:56:08.7175409Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T20:56:08.7175713Z getitem_31: "f32[1230 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T20:56:08.7175961Z 2025-03-04T20:56:08.7176496Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T20:56:08.7177167Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T20:56:08.7177505Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:56:08.7177835Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T20:56:08.7178171Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T20:56:08.7178456Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T20:56:08.7178692Z 2025-03-04T20:56:08.7179120Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T20:56:08.7179629Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T20:56:08.7179855Z 2025-03-04T20:56:08.7179980Z 2025-03-04T20:56:08.7180071Z class GraphModule(torch.nn.Module): 2025-03-04T20:56:08.7180863Z def forward(self, L_predictions_0_: "f32[3230, 81][81, 1]cpu", L_predictions_1_: "f32[3230, 320][320, 1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1230 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1230 - s0, 4][4, 1]cpu"): 2025-03-04T20:56:08.7181648Z l_predictions_0_ = L_predictions_0_ 2025-03-04T20:56:08.7181867Z l_predictions_1_ = L_predictions_1_ 2025-03-04T20:56:08.7182165Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T20:56:08.7182555Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T20:56:08.7182940Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T20:56:08.7183319Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T20:56:08.7183592Z 2025-03-04T20:56:08.7183974Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:56:08.7184444Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T20:56:08.7184691Z getitem: "Sym(s0)" = size[0] 2025-03-04T20:56:08.7184913Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T20:56:08.7185176Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T20:56:08.7185428Z getitem_2: "Sym(1230 - s0)" = size_1[0] 2025-03-04T20:56:08.7185661Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T20:56:08.7185876Z 2025-03-04T20:56:08.7186242Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:56:08.7187189Z proposal_boxes: "f32[3230, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T20:56:08.7187893Z 2025-03-04T20:56:08.7188346Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:56:08.7188905Z deltas: "f32[3230, 320][320, 1]cpu" = l_predictions_1_.float(); l_predictions_1_ = None 2025-03-04T20:56:08.7189171Z 2025-03-04T20:56:08.7189559Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:56:08.7190072Z boxes: "f32[3230, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T20:56:08.7190345Z 2025-03-04T20:56:08.7190738Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:56:08.7191221Z getitem_4: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:56:08.7191516Z getitem_5: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:56:08.7191826Z widths: "f32[3230][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T20:56:08.7192076Z 2025-03-04T20:56:08.7192472Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:56:08.7192949Z getitem_6: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:56:08.7193234Z getitem_7: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:56:08.7193544Z heights: "f32[3230][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T20:56:08.7193797Z 2025-03-04T20:56:08.7194278Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:56:08.7194803Z getitem_8: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:56:08.7195071Z mul: "f32[3230][1]cpu" = 0.5 * widths 2025-03-04T20:56:08.7195322Z ctr_x: "f32[3230][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T20:56:08.7195549Z 2025-03-04T20:56:08.7195940Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:56:08.7196435Z getitem_9: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:56:08.7196716Z mul_1: "f32[3230][1]cpu" = 0.5 * heights 2025-03-04T20:56:08.7196984Z ctr_y: "f32[3230][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T20:56:08.7197215Z 2025-03-04T20:56:08.7197622Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:56:08.7198122Z getitem_10: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:56:08.7198439Z dx: "f32[3230, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T20:56:08.7198658Z 2025-03-04T20:56:08.7199045Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:56:08.7199534Z getitem_11: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:56:08.7199863Z dy: "f32[3230, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T20:56:08.7200088Z 2025-03-04T20:56:08.7200462Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:56:08.7200955Z getitem_12: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:56:08.7201267Z dw: "f32[3230, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T20:56:08.7201495Z 2025-03-04T20:56:08.7201874Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:56:08.7202560Z getitem_13: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:56:08.7202900Z dh: "f32[3230, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T20:56:08.7203130Z 2025-03-04T20:56:08.7203550Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:56:08.7204067Z dw_1: "f32[3230, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:56:08.7204324Z 2025-03-04T20:56:08.7204734Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:56:08.7216466Z dh_1: "f32[3230, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:56:08.7216842Z 2025-03-04T20:56:08.7217327Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:56:08.7217900Z getitem_14: "f32[3230, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:56:08.7218221Z mul_2: "f32[3230, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T20:56:08.7218552Z getitem_15: "f32[3230, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:56:08.7218906Z pred_ctr_x: "f32[3230, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T20:56:08.7219292Z 2025-03-04T20:56:08.7219734Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:56:08.7220269Z getitem_16: "f32[3230, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:56:08.7220574Z mul_3: "f32[3230, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T20:56:08.7220891Z getitem_17: "f32[3230, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:56:08.7221264Z pred_ctr_y: "f32[3230, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T20:56:08.7221521Z 2025-03-04T20:56:08.7221976Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:56:08.7222482Z exp: "f32[3230, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:56:08.7222807Z getitem_18: "f32[3230, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:56:08.7223147Z pred_w: "f32[3230, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T20:56:08.7223396Z 2025-03-04T20:56:08.7223822Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:56:08.7224370Z exp_1: "f32[3230, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:56:08.7224693Z getitem_19: "f32[3230, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:56:08.7225035Z pred_h: "f32[3230, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T20:56:08.7225284Z 2025-03-04T20:56:08.7225695Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:56:08.7226158Z mul_6: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T20:56:08.7226421Z x1: "f32[3230, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:56:08.7226655Z 2025-03-04T20:56:08.7227055Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:56:08.7227515Z mul_7: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T20:56:08.7227776Z y1: "f32[3230, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:56:08.7228005Z 2025-03-04T20:56:08.7228397Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:56:08.7228876Z mul_8: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:56:08.7229167Z x2: "f32[3230, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:56:08.7229411Z 2025-03-04T20:56:08.7229800Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:56:08.7230273Z mul_9: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:56:08.7230557Z y2: "f32[3230, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:56:08.7230800Z 2025-03-04T20:56:08.7231228Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:56:08.7231820Z pred_boxes: "f32[3230, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:56:08.7232143Z 2025-03-04T20:56:08.7232564Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:56:08.7233125Z predict_boxes: "f32[3230, 320][320, 1]cpu" = pred_boxes.reshape((3230, 320)); pred_boxes = None 2025-03-04T20:56:08.7233410Z 2025-03-04T20:56:08.7233856Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T20:56:08.7234580Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T20:56:08.7234950Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T20:56:08.7235258Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T20:56:08.7235570Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T20:56:08.7235891Z getitem_23: "f32[1230 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T20:56:08.7236152Z 2025-03-04T20:56:08.7236537Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:56:08.7237097Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T20:56:08.7237448Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T20:56:08.7237720Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T20:56:08.7238075Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T20:56:08.7238418Z getitem_26: "Sym(1230 - s0)" = size_3[0] 2025-03-04T20:56:08.7238656Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T20:56:08.7238872Z 2025-03-04T20:56:08.7239285Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T20:56:08.7239869Z probs: "f32[3230, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1); l_predictions_0_ = None 2025-03-04T20:56:08.7240186Z 2025-03-04T20:56:08.7240619Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T20:56:08.7241208Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T20:56:08.7241556Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T20:56:08.7241840Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T20:56:08.7242133Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T20:56:08.7242441Z getitem_31: "f32[1230 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T20:56:08.7242692Z 2025-03-04T20:56:08.7243227Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T20:56:08.7243910Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T20:56:08.7244252Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:56:08.7244581Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T20:56:08.7244919Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T20:56:08.7245225Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T20:56:08.7245456Z 2025-03-04T20:56:08.7245882Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T20:56:08.7246389Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T20:56:08.7246614Z 2025-03-04T20:56:10.8347013Z 2025-03-04T20:56:10.8353852Z class GraphModule(torch.nn.Module): 2025-03-04T20:56:10.8358435Z def forward(self, L_scores_0_: "f32[1000, 81][81, 1]cpu", L_boxes_0_: "f32[1000, 320][320, 1]cpu"): 2025-03-04T20:56:10.8360453Z l_scores_0_ = L_scores_0_ 2025-03-04T20:56:10.8360840Z l_boxes_0_ = L_boxes_0_ 2025-03-04T20:56:10.8366060Z 2025-03-04T20:56:10.8370135Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T20:56:10.8371075Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(l_boxes_0_); l_boxes_0_ = None 2025-03-04T20:56:10.8371405Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:56:10.8371760Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(l_scores_0_); l_scores_0_ = None 2025-03-04T20:56:10.8372083Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T20:56:10.8372375Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T20:56:10.8372684Z 2025-03-04T20:56:10.8373143Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T20:56:10.8373678Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T20:56:10.8373918Z 2025-03-04T20:56:10.8374012Z 2025-03-04T20:56:10.8374101Z class GraphModule(torch.nn.Module): 2025-03-04T20:56:10.8374405Z def forward(self, L_scores_0_: "f32[1000, 81][81, 1]cpu", L_boxes_0_: "f32[1000, 320][320, 1]cpu"): 2025-03-04T20:56:10.8374707Z l_scores_0_ = L_scores_0_ 2025-03-04T20:56:10.8374912Z l_boxes_0_ = L_boxes_0_ 2025-03-04T20:56:10.8375095Z 2025-03-04T20:56:10.8376903Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T20:56:10.8377605Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(l_boxes_0_); l_boxes_0_ = None 2025-03-04T20:56:10.8377922Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:56:10.8378230Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(l_scores_0_); l_scores_0_ = None 2025-03-04T20:56:10.8378555Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T20:56:10.8378843Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T20:56:10.8379084Z 2025-03-04T20:56:10.8379517Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T20:56:10.8380018Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T20:56:10.8380243Z 2025-03-04T20:56:26.7176129Z Compilation time (from dynamo_timed): 34.196316885 2025-03-04T20:56:26.7176637Z pass 2025-03-04T20:56:26.7181390Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T20:56:26.7183085Z TIMING: entire_frame_compile:34.19632 gc:0.03179 _recursive_pre_grad_passes:0.02796 async_compile.wait:8.18039 backend_compile:22.46397 _recursive_joint_graph_passes:0.43344 _recursive_post_grad_passes:0.07898 code_gen:11.13032 inductor_compile:12.60281 total_wall_time:34.19632 2025-03-04T20:56:26.7184635Z STATS: call_* op count: 611 | FakeTensorMode.__torch_dispatch__:18544 | FakeTensor.__torch_dispatch__:1848 | ProxyTorchDispatchMode.__torch_dispatch__:5928 | attempt fast:51 | slow no contiguity match:20 | fast is_contiguous:31 2025-03-04T20:56:26.7185381Z Dynamo produced 52 graphs covering 611 ops with 42 graph breaks (6 unique) 2025-03-04T20:56:32.0359422Z 2025-03-04T20:56:37.4742170Z loading model: 0it [00:00, ?it/s] 2025-03-04T20:56:37.4742510Z loading model: 0it [00:05, ?it/s] 2025-03-04T20:56:37.4749854Z cpu eval detectron2_fasterrcnn_r_50_fpn 2025-03-04T20:56:51.0118620Z WARNING:common:fp64 golden ref were not generated for detectron2_fasterrcnn_r_50_fpn. Setting accuracy check to cosine 2025-03-04T20:56:51.0336697Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T20:56:58.5235024Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T20:57:07.2043436Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T20:57:17.0500371Z 2025-03-04T20:57:17.0503748Z class GraphModule(torch.nn.Module): 2025-03-04T20:57:17.0569985Z def forward(self, L_stack0_tensor: "f32[4, 3, 1184, 1216][4319232, 1439744, 1216, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", 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L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_: "f32[256, 256, 3, 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L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_: "f32[512, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_: "f32[2048, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_lateral_convs_0_parameters_weight_: "f32[256, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_0_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_0_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_0_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_1_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_1_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_1_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_2_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_2_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_2_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_3_parameters_weight_: "f32[256, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_3_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_3_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_3_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[3, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[3][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[12, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[12][1]cpu"): 2025-03-04T20:57:17.0633181Z l_stack0_tensor = L_stack0_tensor 2025-03-04T20:57:17.0634006Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ 2025-03-04T20:57:17.0634970Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.0635866Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.0636696Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:57:17.0637592Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:57:17.0638381Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:57:17.0639223Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.0640166Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.0641106Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:57:17.0642024Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:57:17.0651437Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:57:17.0652446Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.0653607Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.0654572Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:57:17.0655460Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:57:17.0656356Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:57:17.0657280Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.0658274Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.0659207Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:57:17.0660115Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:57:17.0661145Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:57:17.0662054Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.0663019Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.0663972Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:57:17.0664883Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:57:17.0665730Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:57:17.0666593Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.0667496Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.0668357Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:57:17.0669185Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:57:17.0669947Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:57:17.0670758Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.0671637Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.0672487Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:57:17.0673306Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:57:17.0674090Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:57:17.0674936Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.0675809Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.0676676Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:57:17.0677639Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:57:17.0678495Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:57:17.0679288Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.0680131Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.0680963Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:57:17.0681763Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:57:17.0682527Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:57:17.0683346Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.0684194Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.0685040Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:57:17.0685858Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:57:17.0686628Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:57:17.0687426Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.0688292Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.0689115Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:57:17.0689913Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:57:17.0690678Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:57:17.0691476Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.0692334Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.0693193Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:57:17.0694000Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:57:17.0694783Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:57:17.0695598Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.0696497Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.0697358Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:57:17.0698212Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:57:17.0699016Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:57:17.0699839Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.0700716Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.0701567Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:57:17.0702576Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:57:17.0703385Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:57:17.0704238Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.0705142Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.0706026Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:57:17.0706883Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:57:17.0707691Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:57:17.0708513Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.0709391Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.0710231Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:57:17.0711096Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:57:17.0711890Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:57:17.0712737Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.0713629Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.0714475Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:57:17.0715299Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:57:17.0716126Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:57:17.0717015Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.0717902Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.0718734Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:57:17.0719539Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:57:17.0720307Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:57:17.0721106Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.0721960Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.0722787Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:57:17.0723591Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:57:17.0724383Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:57:17.0725182Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.0726053Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.0726914Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:57:17.0727717Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:57:17.0728485Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:57:17.0729284Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.0730180Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.0731007Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:57:17.0731811Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:57:17.0732579Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ 2025-03-04T20:57:17.0733377Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.0734250Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.0735074Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:57:17.0735875Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:57:17.0736649Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ 2025-03-04T20:57:17.0737447Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.0738332Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.0739169Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:57:17.0739997Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:57:17.0740785Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ 2025-03-04T20:57:17.0741585Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.0742445Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.0743284Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:57:17.0744108Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:57:17.0744884Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:57:17.0745686Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.0746537Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.0747371Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:57:17.0748170Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:57:17.0748939Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:57:17.0749736Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.0750595Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.0751441Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:57:17.0752241Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:57:17.0753023Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:57:17.0753863Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.0754762Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.0755627Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:57:17.0756455Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:57:17.0758502Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:57:17.0759357Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.0760245Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.0761103Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:57:17.0761944Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:57:17.0762731Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:57:17.0763535Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.0764392Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.0765224Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:57:17.0766029Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:57:17.0766857Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:57:17.0767681Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.0768570Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.0769420Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:57:17.0770229Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:57:17.0771000Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:57:17.0771801Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.0772686Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.0773527Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:57:17.0774341Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:57:17.0775117Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:57:17.0775935Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.0776799Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.0777651Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:57:17.0778499Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:57:17.0779304Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:57:17.0780131Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.0781024Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.0781874Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:57:17.0782729Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:57:17.0783526Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:57:17.0784350Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.0785225Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.0786097Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:57:17.0786915Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:57:17.0787705Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ 2025-03-04T20:57:17.0788524Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.0789399Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.0790251Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:57:17.0791075Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:57:17.0791863Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ 2025-03-04T20:57:17.0795944Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.0796963Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.0797915Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:57:17.0798765Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:57:17.0799563Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ 2025-03-04T20:57:17.0800416Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.0801279Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.0802342Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:57:17.0803174Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:57:17.0804026Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ 2025-03-04T20:57:17.0804823Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.0805680Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.0806504Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:57:17.0807309Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:57:17.0808080Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ 2025-03-04T20:57:17.0808884Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.0809730Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.0810558Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:57:17.0811356Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:57:17.0812156Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ 2025-03-04T20:57:17.0812961Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.0813838Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.0814696Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:57:17.0815499Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:57:17.0816276Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ 2025-03-04T20:57:17.0817087Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.0817954Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.0818796Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:57:17.0819615Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:57:17.0820400Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ 2025-03-04T20:57:17.0821213Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.0822079Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.0822916Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:57:17.0823726Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:57:17.0824505Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ 2025-03-04T20:57:17.0825316Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.0826165Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.0826992Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:57:17.0827807Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:57:17.0828596Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:57:17.0829402Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.0830258Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.0831103Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:57:17.0831909Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:57:17.0832685Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:57:17.0833498Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.0834379Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.0835232Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:57:17.0836064Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:57:17.0836905Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:57:17.0837862Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.0838746Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.0839628Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:57:17.0840457Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:57:17.0841275Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:57:17.0842146Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.0843048Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.0843924Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:57:17.0844783Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:57:17.0845598Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:57:17.0846424Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.0847308Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.0848165Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:57:17.0848993Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:57:17.0849775Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:57:17.0850578Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.0851446Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.0852283Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:57:17.0853094Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:57:17.0853882Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:57:17.0854688Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.0855559Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.0856402Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:57:17.0857207Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:57:17.0857979Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:57:17.0858790Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.0859656Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.0860500Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:57:17.0861325Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:57:17.0862113Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:57:17.0862934Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.0863785Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.0864611Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:57:17.0865415Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:57:17.0866189Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:57:17.0867001Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.0867860Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.0868705Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:57:17.0870205Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:57:17.0870881Z l_self_modules_backbone_lateral_convs_0_parameters_weight_ = L_self_modules_backbone_lateral_convs_0_parameters_weight_ 2025-03-04T20:57:17.0871380Z l_self_modules_backbone_lateral_convs_0_parameters_bias_ = L_self_modules_backbone_lateral_convs_0_parameters_bias_ 2025-03-04T20:57:17.0871879Z l_self_modules_backbone_output_convs_0_parameters_weight_ = L_self_modules_backbone_output_convs_0_parameters_weight_ 2025-03-04T20:57:17.0872372Z l_self_modules_backbone_output_convs_0_parameters_bias_ = L_self_modules_backbone_output_convs_0_parameters_bias_ 2025-03-04T20:57:17.0872895Z l_self_modules_backbone_lateral_convs_1_parameters_weight_ = L_self_modules_backbone_lateral_convs_1_parameters_weight_ 2025-03-04T20:57:17.0873394Z l_self_modules_backbone_lateral_convs_1_parameters_bias_ = L_self_modules_backbone_lateral_convs_1_parameters_bias_ 2025-03-04T20:57:17.0873889Z l_self_modules_backbone_output_convs_1_parameters_weight_ = L_self_modules_backbone_output_convs_1_parameters_weight_ 2025-03-04T20:57:17.0874385Z l_self_modules_backbone_output_convs_1_parameters_bias_ = L_self_modules_backbone_output_convs_1_parameters_bias_ 2025-03-04T20:57:17.0874894Z l_self_modules_backbone_lateral_convs_2_parameters_weight_ = L_self_modules_backbone_lateral_convs_2_parameters_weight_ 2025-03-04T20:57:17.0875391Z l_self_modules_backbone_lateral_convs_2_parameters_bias_ = L_self_modules_backbone_lateral_convs_2_parameters_bias_ 2025-03-04T20:57:17.0875882Z l_self_modules_backbone_output_convs_2_parameters_weight_ = L_self_modules_backbone_output_convs_2_parameters_weight_ 2025-03-04T20:57:17.0876373Z l_self_modules_backbone_output_convs_2_parameters_bias_ = L_self_modules_backbone_output_convs_2_parameters_bias_ 2025-03-04T20:57:17.0876940Z l_self_modules_backbone_lateral_convs_3_parameters_weight_ = L_self_modules_backbone_lateral_convs_3_parameters_weight_ 2025-03-04T20:57:17.0877444Z l_self_modules_backbone_lateral_convs_3_parameters_bias_ = L_self_modules_backbone_lateral_convs_3_parameters_bias_ 2025-03-04T20:57:17.0877953Z l_self_modules_backbone_output_convs_3_parameters_weight_ = L_self_modules_backbone_output_convs_3_parameters_weight_ 2025-03-04T20:57:17.0878452Z l_self_modules_backbone_output_convs_3_parameters_bias_ = L_self_modules_backbone_output_convs_3_parameters_bias_ 2025-03-04T20:57:17.0879102Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T20:57:17.0879879Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ 2025-03-04T20:57:17.0880642Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ 2025-03-04T20:57:17.0881462Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ 2025-03-04T20:57:17.0882211Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ 2025-03-04T20:57:17.0882942Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T20:57:17.0883666Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T20:57:17.0884423Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T20:57:17.0885223Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T20:57:17.0886003Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T20:57:17.0886769Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T20:57:17.0887270Z 2025-03-04T20:57:17.0887671Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.0888530Z x: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1); l_stack0_tensor = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = None 2025-03-04T20:57:17.0889171Z 2025-03-04T20:57:17.0889529Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.0891601Z x_1: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.0893438Z 2025-03-04T20:57:17.0893809Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x) 2025-03-04T20:57:17.0894290Z x_2: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.relu_(x_1); x_1 = None 2025-03-04T20:57:17.0894549Z 2025-03-04T20:57:17.0894989Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1) 2025-03-04T20:57:17.0895670Z x_3: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1); x_2 = None 2025-03-04T20:57:17.0896028Z 2025-03-04T20:57:17.0896367Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.0897200Z x_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:57:17.0897799Z 2025-03-04T20:57:17.0898171Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.0900330Z x_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.0902452Z 2025-03-04T20:57:17.0902839Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.0903326Z out: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_5); x_5 = None 2025-03-04T20:57:17.0903583Z 2025-03-04T20:57:17.0903926Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.0904741Z x_6: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:57:17.0905360Z 2025-03-04T20:57:17.0905705Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.0907778Z x_7: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_6 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.0909690Z 2025-03-04T20:57:17.0910068Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.0910536Z out_1: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_7); x_7 = None 2025-03-04T20:57:17.0910793Z 2025-03-04T20:57:17.0911125Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.0911985Z x_8: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_1 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:57:17.0912635Z 2025-03-04T20:57:17.0912991Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.0915128Z x_9: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.0919303Z 2025-03-04T20:57:17.0919644Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.0920465Z x_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); x_3 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:57:17.0921099Z 2025-03-04T20:57:17.0921449Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.0923662Z x_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_10 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.0925831Z 2025-03-04T20:57:17.0926197Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:57:17.0926680Z x_9 += x_11; out_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_9; x_9 = x_11 = None 2025-03-04T20:57:17.0926938Z 2025-03-04T20:57:17.0927307Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.0927797Z out_3: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_2); out_2 = None 2025-03-04T20:57:17.0928066Z 2025-03-04T20:57:17.0928424Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.0929249Z x_12: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:57:17.0929854Z 2025-03-04T20:57:17.0930206Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.0932365Z x_13: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_12 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.0934284Z 2025-03-04T20:57:17.0934657Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.0935142Z out_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_13); x_13 = None 2025-03-04T20:57:17.0935400Z 2025-03-04T20:57:17.0935738Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.0936538Z x_14: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:57:17.0937152Z 2025-03-04T20:57:17.0937493Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.0941846Z x_15: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_14 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.0943987Z 2025-03-04T20:57:17.0944364Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.0944880Z out_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_15); x_15 = None 2025-03-04T20:57:17.0945142Z 2025-03-04T20:57:17.0945513Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.0946322Z x_16: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_5 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:57:17.0946935Z 2025-03-04T20:57:17.0947276Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.0949445Z x_17: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_16 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.0951362Z 2025-03-04T20:57:17.0951723Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:57:17.0952214Z x_17 += out_3; out_6: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_17; x_17 = out_3 = None 2025-03-04T20:57:17.0952479Z 2025-03-04T20:57:17.0952848Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.0953344Z out_7: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_6); out_6 = None 2025-03-04T20:57:17.0953604Z 2025-03-04T20:57:17.0953942Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.0954745Z x_18: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:57:17.0955352Z 2025-03-04T20:57:17.0955701Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.0958218Z x_19: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_18 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.0960189Z 2025-03-04T20:57:17.0960559Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.0961039Z out_8: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_19); x_19 = None 2025-03-04T20:57:17.0961295Z 2025-03-04T20:57:17.0961635Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.0962457Z x_20: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:57:17.0963091Z 2025-03-04T20:57:17.0963442Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.0965580Z x_21: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_20 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.0967488Z 2025-03-04T20:57:17.0967875Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.0968384Z out_9: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_21); x_21 = None 2025-03-04T20:57:17.0968655Z 2025-03-04T20:57:17.0969007Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.0969842Z x_22: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_9 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:57:17.0970474Z 2025-03-04T20:57:17.0970824Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.0972980Z x_23: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_22 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.0974917Z 2025-03-04T20:57:17.0975283Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:57:17.0975764Z x_23 += out_7; out_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_23; x_23 = out_7 = None 2025-03-04T20:57:17.0976029Z 2025-03-04T20:57:17.0976414Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.0976911Z out_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_10); out_10 = None 2025-03-04T20:57:17.0977178Z 2025-03-04T20:57:17.0977515Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.0978319Z x_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:57:17.0978941Z 2025-03-04T20:57:17.0979296Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.0981543Z x_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.0983548Z 2025-03-04T20:57:17.0983943Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.0984460Z out_12: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_25); x_25 = None 2025-03-04T20:57:17.0984781Z 2025-03-04T20:57:17.0985129Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.0985981Z x_26: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_12 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:57:17.0986631Z 2025-03-04T20:57:17.0987019Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.0989295Z x_27: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_26 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.0991341Z 2025-03-04T20:57:17.0991737Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.0992255Z out_13: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_27); x_27 = None 2025-03-04T20:57:17.0992515Z 2025-03-04T20:57:17.0992849Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.0993662Z x_28: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_13 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:57:17.0994283Z 2025-03-04T20:57:17.0994635Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.0996974Z x_29: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_28 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.0999046Z 2025-03-04T20:57:17.0999381Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1000212Z x_30: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:57:17.1000831Z 2025-03-04T20:57:17.1001180Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1003647Z x_31: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_30 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1005812Z 2025-03-04T20:57:17.1006185Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:57:17.1006675Z x_29 += x_31; out_14: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_29; x_29 = x_31 = None 2025-03-04T20:57:17.1006941Z 2025-03-04T20:57:17.1007310Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1007807Z out_15: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_14); out_14 = None 2025-03-04T20:57:17.1008078Z 2025-03-04T20:57:17.1008415Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1009221Z x_32: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:57:17.1009834Z 2025-03-04T20:57:17.1010187Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1012340Z x_33: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_32 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1014273Z 2025-03-04T20:57:17.1014647Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1015134Z out_16: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_33); x_33 = None 2025-03-04T20:57:17.1015386Z 2025-03-04T20:57:17.1015710Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1016524Z x_34: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_16 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:57:17.1017130Z 2025-03-04T20:57:17.1017489Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1019624Z x_35: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_34 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1021480Z 2025-03-04T20:57:17.1021841Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1022311Z out_17: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_35); x_35 = None 2025-03-04T20:57:17.1022565Z 2025-03-04T20:57:17.1022889Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1023672Z x_36: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_17 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:57:17.1024269Z 2025-03-04T20:57:17.1024609Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1026669Z x_37: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_36 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1028556Z 2025-03-04T20:57:17.1028913Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:57:17.1029392Z x_37 += out_15; out_18: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_37; x_37 = out_15 = None 2025-03-04T20:57:17.1029657Z 2025-03-04T20:57:17.1030033Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1030571Z out_19: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_18); out_18 = None 2025-03-04T20:57:17.1030841Z 2025-03-04T20:57:17.1031173Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1031978Z x_38: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:57:17.1032580Z 2025-03-04T20:57:17.1032932Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1035099Z x_39: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_38 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1037110Z 2025-03-04T20:57:17.1037508Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1038068Z out_20: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_39); x_39 = None 2025-03-04T20:57:17.1038359Z 2025-03-04T20:57:17.1038736Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1039627Z x_40: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_20 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:57:17.1040251Z 2025-03-04T20:57:17.1040611Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1042778Z x_41: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_40 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1044702Z 2025-03-04T20:57:17.1045094Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1045584Z out_21: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_41); x_41 = None 2025-03-04T20:57:17.1045844Z 2025-03-04T20:57:17.1046178Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1046990Z x_42: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_21 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:57:17.1047607Z 2025-03-04T20:57:17.1047952Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1050042Z x_43: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_42 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1051985Z 2025-03-04T20:57:17.1052345Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:57:17.1052818Z x_43 += out_19; out_22: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_43; x_43 = out_19 = None 2025-03-04T20:57:17.1053076Z 2025-03-04T20:57:17.1053428Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1053904Z out_23: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_22); out_22 = None 2025-03-04T20:57:17.1054166Z 2025-03-04T20:57:17.1054492Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1055278Z x_44: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T20:57:17.1055922Z 2025-03-04T20:57:17.1056255Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1058411Z x_45: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_44 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1060281Z 2025-03-04T20:57:17.1060640Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1061126Z out_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_45); x_45 = None 2025-03-04T20:57:17.1061381Z 2025-03-04T20:57:17.1061702Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1062486Z x_46: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T20:57:17.1063081Z 2025-03-04T20:57:17.1063421Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1065493Z x_47: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_46 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1067351Z 2025-03-04T20:57:17.1067710Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1068181Z out_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_47); x_47 = None 2025-03-04T20:57:17.1068437Z 2025-03-04T20:57:17.1068779Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1069573Z x_48: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_25 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T20:57:17.1070178Z 2025-03-04T20:57:17.1070523Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1072712Z x_49: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_48 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1074650Z 2025-03-04T20:57:17.1075014Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:57:17.1075505Z x_49 += out_23; out_26: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_49; x_49 = out_23 = None 2025-03-04T20:57:17.1075778Z 2025-03-04T20:57:17.1076150Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1076650Z out_27: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_26); out_26 = None 2025-03-04T20:57:17.1076998Z 2025-03-04T20:57:17.1077376Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1078216Z x_50: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:57:17.1078809Z 2025-03-04T20:57:17.1079163Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1081273Z x_51: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_50 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1083195Z 2025-03-04T20:57:17.1083567Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1084044Z out_28: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_51); x_51 = None 2025-03-04T20:57:17.1084293Z 2025-03-04T20:57:17.1084633Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1085448Z x_52: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_28 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:57:17.1086049Z 2025-03-04T20:57:17.1086418Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1088542Z x_53: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_52 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1090486Z 2025-03-04T20:57:17.1090879Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1091384Z out_29: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_53); x_53 = None 2025-03-04T20:57:17.1091674Z 2025-03-04T20:57:17.1092029Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1092881Z x_54: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_29 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:57:17.1093507Z 2025-03-04T20:57:17.1093857Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1095987Z x_55: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_54 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1097913Z 2025-03-04T20:57:17.1098248Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1099065Z x_56: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:57:17.1099656Z 2025-03-04T20:57:17.1100025Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1102381Z x_57: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_56 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1104545Z 2025-03-04T20:57:17.1104917Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:57:17.1105390Z x_55 += x_57; out_30: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_55; x_55 = x_57 = None 2025-03-04T20:57:17.1105646Z 2025-03-04T20:57:17.1106006Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1106486Z out_31: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_30); out_30 = None 2025-03-04T20:57:17.1106747Z 2025-03-04T20:57:17.1107085Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1107880Z x_58: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:57:17.1108487Z 2025-03-04T20:57:17.1108832Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1110968Z x_59: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_58 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1112905Z 2025-03-04T20:57:17.1113278Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1113778Z out_32: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_59); x_59 = None 2025-03-04T20:57:17.1114034Z 2025-03-04T20:57:17.1114369Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1115190Z x_60: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_32 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:57:17.1115812Z 2025-03-04T20:57:17.1116180Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1118503Z x_61: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_60 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1120453Z 2025-03-04T20:57:17.1120830Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1121313Z out_33: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_61); x_61 = None 2025-03-04T20:57:17.1121573Z 2025-03-04T20:57:17.1121914Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1122729Z x_62: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_33 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:57:17.1123342Z 2025-03-04T20:57:17.1123699Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1125840Z x_63: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_62 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1127757Z 2025-03-04T20:57:17.1128137Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:57:17.1128619Z x_63 += out_31; out_34: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_63; x_63 = out_31 = None 2025-03-04T20:57:17.1128893Z 2025-03-04T20:57:17.1129265Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1129738Z out_35: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_34); out_34 = None 2025-03-04T20:57:17.1129992Z 2025-03-04T20:57:17.1130325Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1131135Z x_64: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:57:17.1131731Z 2025-03-04T20:57:17.1132077Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1134149Z x_65: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_64 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1136007Z 2025-03-04T20:57:17.1136372Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1136835Z out_36: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_65); x_65 = None 2025-03-04T20:57:17.1137085Z 2025-03-04T20:57:17.1137415Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1138199Z x_66: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_36 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:57:17.1138808Z 2025-03-04T20:57:17.1139167Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1141328Z x_67: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_66 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1143194Z 2025-03-04T20:57:17.1143553Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1144014Z out_37: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_67); x_67 = None 2025-03-04T20:57:17.1144261Z 2025-03-04T20:57:17.1144584Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1145374Z x_68: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_37 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:57:17.1145964Z 2025-03-04T20:57:17.1146306Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1148399Z x_69: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_68 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1150285Z 2025-03-04T20:57:17.1150653Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:57:17.1151138Z x_69 += out_35; out_38: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_69; x_69 = out_35 = None 2025-03-04T20:57:17.1151397Z 2025-03-04T20:57:17.1151765Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1152278Z out_39: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_38); out_38 = None 2025-03-04T20:57:17.1152541Z 2025-03-04T20:57:17.1152874Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1153692Z x_70: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T20:57:17.1154288Z 2025-03-04T20:57:17.1154644Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1156903Z x_71: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_70 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1158935Z 2025-03-04T20:57:17.1159309Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1159786Z out_40: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_71); x_71 = None 2025-03-04T20:57:17.1160041Z 2025-03-04T20:57:17.1160376Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1161183Z x_72: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_40 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T20:57:17.1161788Z 2025-03-04T20:57:17.1162144Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1164280Z x_73: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_72 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1166181Z 2025-03-04T20:57:17.1166551Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1167029Z out_41: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_73); x_73 = None 2025-03-04T20:57:17.1167304Z 2025-03-04T20:57:17.1167628Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1168414Z x_74: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_41 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T20:57:17.1169004Z 2025-03-04T20:57:17.1169360Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1171442Z x_75: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_74 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1173324Z 2025-03-04T20:57:17.1173679Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:57:17.1174142Z x_75 += out_39; out_42: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_75; x_75 = out_39 = None 2025-03-04T20:57:17.1174396Z 2025-03-04T20:57:17.1174756Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1175222Z out_43: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_42); out_42 = None 2025-03-04T20:57:17.1175468Z 2025-03-04T20:57:17.1175792Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1176568Z x_76: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = None 2025-03-04T20:57:17.1177159Z 2025-03-04T20:57:17.1177500Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1179582Z x_77: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_76 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1181454Z 2025-03-04T20:57:17.1181815Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1182279Z out_44: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_77); x_77 = None 2025-03-04T20:57:17.1182527Z 2025-03-04T20:57:17.1182899Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1183687Z x_78: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_44 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = None 2025-03-04T20:57:17.1184278Z 2025-03-04T20:57:17.1184620Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1186697Z x_79: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_78 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1188555Z 2025-03-04T20:57:17.1188915Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1189368Z out_45: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_79); x_79 = None 2025-03-04T20:57:17.1189617Z 2025-03-04T20:57:17.1189945Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1190744Z x_80: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_45 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = None 2025-03-04T20:57:17.1191351Z 2025-03-04T20:57:17.1191700Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1193835Z x_81: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_80 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1195764Z 2025-03-04T20:57:17.1196127Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:57:17.1196623Z x_81 += out_43; out_46: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_81; x_81 = out_43 = None 2025-03-04T20:57:17.1196938Z 2025-03-04T20:57:17.1197347Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1197857Z out_47: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_46); out_46 = None 2025-03-04T20:57:17.1198119Z 2025-03-04T20:57:17.1198453Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1199249Z x_82: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = None 2025-03-04T20:57:17.1199868Z 2025-03-04T20:57:17.1200226Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1202505Z x_83: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_82 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1204421Z 2025-03-04T20:57:17.1204800Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1205280Z out_48: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_83); x_83 = None 2025-03-04T20:57:17.1205536Z 2025-03-04T20:57:17.1205876Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1206679Z x_84: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_48 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = None 2025-03-04T20:57:17.1207292Z 2025-03-04T20:57:17.1207646Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1209835Z x_85: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_84 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1211728Z 2025-03-04T20:57:17.1212091Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1212550Z out_49: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_85); x_85 = None 2025-03-04T20:57:17.1212802Z 2025-03-04T20:57:17.1213131Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1213914Z x_86: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_49 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = None 2025-03-04T20:57:17.1214535Z 2025-03-04T20:57:17.1214880Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1216943Z x_87: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_86 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1218793Z 2025-03-04T20:57:17.1219146Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:57:17.1219616Z x_87 += out_47; out_50: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_87; x_87 = out_47 = None 2025-03-04T20:57:17.1219872Z 2025-03-04T20:57:17.1220228Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1220699Z out_51: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_50); out_50 = None 2025-03-04T20:57:17.1220949Z 2025-03-04T20:57:17.1221270Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1222037Z x_88: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:57:17.1222631Z 2025-03-04T20:57:17.1222974Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1225065Z x_89: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_88 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1226927Z 2025-03-04T20:57:17.1227285Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1227766Z out_52: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_89); x_89 = None 2025-03-04T20:57:17.1228009Z 2025-03-04T20:57:17.1228337Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1229122Z x_90: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_52 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:57:17.1229709Z 2025-03-04T20:57:17.1230052Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1232116Z x_91: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_90 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1234006Z 2025-03-04T20:57:17.1234380Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1234860Z out_53: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_91); x_91 = None 2025-03-04T20:57:17.1235117Z 2025-03-04T20:57:17.1235457Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1236274Z x_92: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_53 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:57:17.1236929Z 2025-03-04T20:57:17.1237302Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1239538Z x_93: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_92 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1241516Z 2025-03-04T20:57:17.1241874Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1242726Z x_94: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:57:17.1243375Z 2025-03-04T20:57:17.1243745Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1246016Z x_95: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_94 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1248121Z 2025-03-04T20:57:17.1248486Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:57:17.1248958Z x_93 += x_95; out_54: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_93; x_93 = x_95 = None 2025-03-04T20:57:17.1249210Z 2025-03-04T20:57:17.1249583Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1250088Z out_55: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_54); out_54 = None 2025-03-04T20:57:17.1250345Z 2025-03-04T20:57:17.1250691Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1251466Z x_96: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:57:17.1252046Z 2025-03-04T20:57:17.1252405Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1254507Z x_97: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_96 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1256386Z 2025-03-04T20:57:17.1256747Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1257206Z out_56: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_97); x_97 = None 2025-03-04T20:57:17.1257450Z 2025-03-04T20:57:17.1257774Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1258554Z x_98: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_56 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:57:17.1259139Z 2025-03-04T20:57:17.1259479Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1261542Z x_99: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_98 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1263393Z 2025-03-04T20:57:17.1263776Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1264233Z out_57: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_99); x_99 = None 2025-03-04T20:57:17.1264475Z 2025-03-04T20:57:17.1264799Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1265592Z x_100: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_57 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:57:17.1266192Z 2025-03-04T20:57:17.1266552Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1268655Z x_101: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_100 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1270547Z 2025-03-04T20:57:17.1270905Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:57:17.1271405Z x_101 += out_55; out_58: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_101; x_101 = out_55 = None 2025-03-04T20:57:17.1271672Z 2025-03-04T20:57:17.1272041Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1272524Z out_59: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_58); out_58 = None 2025-03-04T20:57:17.1272780Z 2025-03-04T20:57:17.1273113Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1273910Z x_102: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:57:17.1274512Z 2025-03-04T20:57:17.1274867Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1277067Z x_103: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_102 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1278989Z 2025-03-04T20:57:17.1279358Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1279866Z out_60: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_103); x_103 = None 2025-03-04T20:57:17.1280139Z 2025-03-04T20:57:17.1280511Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1281367Z x_104: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_60 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:57:17.1281977Z 2025-03-04T20:57:17.1282331Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1284487Z x_105: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_104 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1286414Z 2025-03-04T20:57:17.1286781Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1287250Z out_61: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_105); x_105 = None 2025-03-04T20:57:17.1287499Z 2025-03-04T20:57:17.1287838Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1288638Z x_106: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_61 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:57:17.1289239Z 2025-03-04T20:57:17.1289587Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1291717Z x_107: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_106 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1293596Z 2025-03-04T20:57:17.1293963Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:57:17.1294445Z x_107 += out_59; out_62: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_107; x_107 = out_59 = None 2025-03-04T20:57:17.1294706Z 2025-03-04T20:57:17.1295072Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1295547Z out_63: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_62); out_62 = None 2025-03-04T20:57:17.1295798Z 2025-03-04T20:57:17.1296136Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1297018Z x_108: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(out_63, l_self_modules_backbone_lateral_convs_0_parameters_weight_, l_self_modules_backbone_lateral_convs_0_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_63 = l_self_modules_backbone_lateral_convs_0_parameters_weight_ = l_self_modules_backbone_lateral_convs_0_parameters_bias_ = None 2025-03-04T20:57:17.1297710Z 2025-03-04T20:57:17.1298043Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1298885Z x_109: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_108, l_self_modules_backbone_output_convs_0_parameters_weight_, l_self_modules_backbone_output_convs_0_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_self_modules_backbone_output_convs_0_parameters_weight_ = l_self_modules_backbone_output_convs_0_parameters_bias_ = None 2025-03-04T20:57:17.1299536Z 2025-03-04T20:57:17.1300028Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest") 2025-03-04T20:57:17.1300746Z top_down_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.interpolate(x_108, scale_factor = 2.0, mode = 'nearest'); x_108 = None 2025-03-04T20:57:17.1301121Z 2025-03-04T20:57:17.1301451Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1302446Z x_110: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_lateral_convs_1_parameters_weight_, l_self_modules_backbone_lateral_convs_1_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_51 = l_self_modules_backbone_lateral_convs_1_parameters_weight_ = l_self_modules_backbone_lateral_convs_1_parameters_bias_ = None 2025-03-04T20:57:17.1303116Z 2025-03-04T20:57:17.1303542Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features 2025-03-04T20:57:17.1304124Z prev_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = x_110 + top_down_features; x_110 = top_down_features = None 2025-03-04T20:57:17.1304429Z 2025-03-04T20:57:17.1304791Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1305652Z x_111: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(prev_features, l_self_modules_backbone_output_convs_1_parameters_weight_, l_self_modules_backbone_output_convs_1_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_self_modules_backbone_output_convs_1_parameters_weight_ = l_self_modules_backbone_output_convs_1_parameters_bias_ = None 2025-03-04T20:57:17.1306324Z 2025-03-04T20:57:17.1306816Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest") 2025-03-04T20:57:17.1307600Z top_down_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.interpolate(prev_features, scale_factor = 2.0, mode = 'nearest'); prev_features = None 2025-03-04T20:57:17.1308030Z 2025-03-04T20:57:17.1308365Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1309239Z x_112: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_lateral_convs_2_parameters_weight_, l_self_modules_backbone_lateral_convs_2_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_27 = l_self_modules_backbone_lateral_convs_2_parameters_weight_ = l_self_modules_backbone_lateral_convs_2_parameters_bias_ = None 2025-03-04T20:57:17.1309952Z 2025-03-04T20:57:17.1310371Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features 2025-03-04T20:57:17.1310969Z prev_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = x_112 + top_down_features_1; x_112 = top_down_features_1 = None 2025-03-04T20:57:17.1311290Z 2025-03-04T20:57:17.1311618Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1312490Z x_113: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(prev_features_1, l_self_modules_backbone_output_convs_2_parameters_weight_, l_self_modules_backbone_output_convs_2_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_self_modules_backbone_output_convs_2_parameters_weight_ = l_self_modules_backbone_output_convs_2_parameters_bias_ = None 2025-03-04T20:57:17.1313181Z 2025-03-04T20:57:17.1313655Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest") 2025-03-04T20:57:17.1314427Z top_down_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.interpolate(prev_features_1, scale_factor = 2.0, mode = 'nearest'); prev_features_1 = None 2025-03-04T20:57:17.1314856Z 2025-03-04T20:57:17.1315182Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1316055Z x_114: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_lateral_convs_3_parameters_weight_, l_self_modules_backbone_lateral_convs_3_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_11 = l_self_modules_backbone_lateral_convs_3_parameters_weight_ = l_self_modules_backbone_lateral_convs_3_parameters_bias_ = None 2025-03-04T20:57:17.1316780Z 2025-03-04T20:57:17.1317236Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features 2025-03-04T20:57:17.1317880Z prev_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_114 + top_down_features_2; x_114 = top_down_features_2 = None 2025-03-04T20:57:17.1318232Z 2025-03-04T20:57:17.1318569Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1319510Z x_115: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(prev_features_2, l_self_modules_backbone_output_convs_3_parameters_weight_, l_self_modules_backbone_output_convs_3_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); prev_features_2 = l_self_modules_backbone_output_convs_3_parameters_weight_ = l_self_modules_backbone_output_convs_3_parameters_bias_ = None 2025-03-04T20:57:17.1320251Z 2025-03-04T20:57:17.1320712Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:200 in forward, code: return [F.max_pool2d(x, kernel_size=1, stride=2, padding=0)] 2025-03-04T20:57:17.1321357Z res: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.max_pool2d(x_109, kernel_size = 1, stride = 2, padding = 0) 2025-03-04T20:57:17.1321685Z 2025-03-04T20:57:17.1322222Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:57:17.1322884Z arange: "f32[304][1]cpu" = torch.arange(0.0, 1216, step = 4, dtype = torch.float32) 2025-03-04T20:57:17.1323154Z 2025-03-04T20:57:17.1323550Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:17.1324064Z shifts_x: "f32[304][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T20:57:17.1324325Z 2025-03-04T20:57:17.1324854Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:57:17.1325501Z arange_1: "f32[296][1]cpu" = torch.arange(0.0, 1184, step = 4, dtype = torch.float32) 2025-03-04T20:57:17.1325770Z 2025-03-04T20:57:17.1326146Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:17.1326634Z shifts_y: "f32[296][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T20:57:17.1326892Z 2025-03-04T20:57:17.1327357Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:57:17.1327967Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T20:57:17.1328298Z shift_y: "f32[296, 304][1, 0]cpu" = meshgrid[0] 2025-03-04T20:57:17.1328569Z shift_x: "f32[296, 304][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T20:57:17.1328802Z 2025-03-04T20:57:17.1329223Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:57:17.1329741Z shift_x_1: "f32[89984][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T20:57:17.1329981Z 2025-03-04T20:57:17.1330400Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:57:17.1330912Z shift_y_1: "f32[89984][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T20:57:17.1331153Z 2025-03-04T20:57:17.1331633Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:57:17.1332308Z shifts: "f32[89984, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T20:57:17.1332635Z 2025-03-04T20:57:17.1333144Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:57:17.1333722Z view: "f32[89984, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T20:57:17.1334331Z view_1: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T20:57:17.1334932Z add_3: "f32[89984, 3, 4][12, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T20:57:17.1335226Z x_116: "f32[269952, 4][4, 1]cpu" = add_3.reshape(-1, 4); add_3 = None 2025-03-04T20:57:17.1335458Z 2025-03-04T20:57:17.1335975Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:57:17.1336596Z arange_2: "f32[152][1]cpu" = torch.arange(0.0, 1216, step = 8, dtype = torch.float32) 2025-03-04T20:57:17.1336860Z 2025-03-04T20:57:17.1337237Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:17.1337736Z shifts_x_1: "f32[152][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T20:57:17.1337996Z 2025-03-04T20:57:17.1338514Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:57:17.1339147Z arange_3: "f32[148][1]cpu" = torch.arange(0.0, 1184, step = 8, dtype = torch.float32) 2025-03-04T20:57:17.1339400Z 2025-03-04T20:57:17.1339767Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:17.1340237Z shifts_y_1: "f32[148][1]cpu" = arange_3.to(device(type='cpu')); arange_3 = None 2025-03-04T20:57:17.1340486Z 2025-03-04T20:57:17.1340930Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:57:17.1341533Z meshgrid_1 = torch.functional.meshgrid(shifts_y_1, shifts_x_1); shifts_y_1 = shifts_x_1 = None 2025-03-04T20:57:17.1341876Z shift_y_2: "f32[148, 152][1, 0]cpu" = meshgrid_1[0] 2025-03-04T20:57:17.1342149Z shift_x_2: "f32[148, 152][0, 1]cpu" = meshgrid_1[1]; meshgrid_1 = None 2025-03-04T20:57:17.1342382Z 2025-03-04T20:57:17.1342786Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:57:17.1343287Z shift_x_3: "f32[22496][1]cpu" = shift_x_2.reshape(-1); shift_x_2 = None 2025-03-04T20:57:17.1343527Z 2025-03-04T20:57:17.1343920Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:57:17.1344413Z shift_y_3: "f32[22496][1]cpu" = shift_y_2.reshape(-1); shift_y_2 = None 2025-03-04T20:57:17.1344648Z 2025-03-04T20:57:17.1345103Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:57:17.1345756Z shifts_1: "f32[22496, 4][4, 1]cpu" = torch.stack((shift_x_3, shift_y_3, shift_x_3, shift_y_3), dim = 1); shift_x_3 = shift_y_3 = None 2025-03-04T20:57:17.1346079Z 2025-03-04T20:57:17.1346567Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:57:17.1347146Z view_2: "f32[22496, 1, 4][4, 4, 1]cpu" = shifts_1.view(-1, 1, 4); shifts_1 = None 2025-03-04T20:57:17.1347754Z view_3: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = None 2025-03-04T20:57:17.1348353Z add_4: "f32[22496, 3, 4][12, 4, 1]cpu" = view_2 + view_3; view_2 = view_3 = None 2025-03-04T20:57:17.1348637Z x_117: "f32[67488, 4][4, 1]cpu" = add_4.reshape(-1, 4); add_4 = None 2025-03-04T20:57:17.1348864Z 2025-03-04T20:57:17.1349375Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:57:17.1349995Z arange_4: "f32[76][1]cpu" = torch.arange(0.0, 1216, step = 16, dtype = torch.float32) 2025-03-04T20:57:17.1350249Z 2025-03-04T20:57:17.1350641Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:17.1351118Z shifts_x_2: "f32[76][1]cpu" = arange_4.to(device(type='cpu')); arange_4 = None 2025-03-04T20:57:17.1351369Z 2025-03-04T20:57:17.1351876Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:57:17.1352492Z arange_5: "f32[74][1]cpu" = torch.arange(0.0, 1184, step = 16, dtype = torch.float32) 2025-03-04T20:57:17.1352747Z 2025-03-04T20:57:17.1353117Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:17.1353589Z shifts_y_2: "f32[74][1]cpu" = arange_5.to(device(type='cpu')); arange_5 = None 2025-03-04T20:57:17.1353839Z 2025-03-04T20:57:17.1354286Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:57:17.1354902Z meshgrid_2 = torch.functional.meshgrid(shifts_y_2, shifts_x_2); shifts_y_2 = shifts_x_2 = None 2025-03-04T20:57:17.1355249Z shift_y_4: "f32[74, 76][1, 0]cpu" = meshgrid_2[0] 2025-03-04T20:57:17.1355517Z shift_x_4: "f32[74, 76][0, 1]cpu" = meshgrid_2[1]; meshgrid_2 = None 2025-03-04T20:57:17.1355750Z 2025-03-04T20:57:17.1356166Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:57:17.1356680Z shift_x_5: "f32[5624][1]cpu" = shift_x_4.reshape(-1); shift_x_4 = None 2025-03-04T20:57:17.1356994Z 2025-03-04T20:57:17.1357415Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:57:17.1357921Z shift_y_5: "f32[5624][1]cpu" = shift_y_4.reshape(-1); shift_y_4 = None 2025-03-04T20:57:17.1358163Z 2025-03-04T20:57:17.1358629Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:57:17.1359307Z shifts_2: "f32[5624, 4][4, 1]cpu" = torch.stack((shift_x_5, shift_y_5, shift_x_5, shift_y_5), dim = 1); shift_x_5 = shift_y_5 = None 2025-03-04T20:57:17.1359634Z 2025-03-04T20:57:17.1360142Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:57:17.1360758Z view_4: "f32[5624, 1, 4][4, 4, 1]cpu" = shifts_2.view(-1, 1, 4); shifts_2 = None 2025-03-04T20:57:17.1361385Z view_5: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = None 2025-03-04T20:57:17.1361974Z add_5: "f32[5624, 3, 4][12, 4, 1]cpu" = view_4 + view_5; view_4 = view_5 = None 2025-03-04T20:57:17.1362268Z x_118: "f32[16872, 4][4, 1]cpu" = add_5.reshape(-1, 4); add_5 = None 2025-03-04T20:57:17.1362506Z 2025-03-04T20:57:17.1363038Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:57:17.1363678Z arange_6: "f32[38][1]cpu" = torch.arange(0.0, 1216, step = 32, dtype = torch.float32) 2025-03-04T20:57:17.1363955Z 2025-03-04T20:57:17.1364333Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:17.1364817Z shifts_x_3: "f32[38][1]cpu" = arange_6.to(device(type='cpu')); arange_6 = None 2025-03-04T20:57:17.1365074Z 2025-03-04T20:57:17.1365591Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:57:17.1366213Z arange_7: "f32[37][1]cpu" = torch.arange(0.0, 1184, step = 32, dtype = torch.float32) 2025-03-04T20:57:17.1366473Z 2025-03-04T20:57:17.1366850Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:17.1367340Z shifts_y_3: "f32[37][1]cpu" = arange_7.to(device(type='cpu')); arange_7 = None 2025-03-04T20:57:17.1367595Z 2025-03-04T20:57:17.1368054Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:57:17.1368676Z meshgrid_3 = torch.functional.meshgrid(shifts_y_3, shifts_x_3); shifts_y_3 = shifts_x_3 = None 2025-03-04T20:57:17.1369024Z shift_y_6: "f32[37, 38][1, 0]cpu" = meshgrid_3[0] 2025-03-04T20:57:17.1369292Z shift_x_6: "f32[37, 38][0, 1]cpu" = meshgrid_3[1]; meshgrid_3 = None 2025-03-04T20:57:17.1369526Z 2025-03-04T20:57:17.1369939Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:57:17.1370446Z shift_x_7: "f32[1406][1]cpu" = shift_x_6.reshape(-1); shift_x_6 = None 2025-03-04T20:57:17.1370683Z 2025-03-04T20:57:17.1371090Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:57:17.1371597Z shift_y_7: "f32[1406][1]cpu" = shift_y_6.reshape(-1); shift_y_6 = None 2025-03-04T20:57:17.1371827Z 2025-03-04T20:57:17.1372291Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:57:17.1372910Z shifts_3: "f32[1406, 4][4, 1]cpu" = torch.stack((shift_x_7, shift_y_7, shift_x_7, shift_y_7), dim = 1); shift_x_7 = shift_y_7 = None 2025-03-04T20:57:17.1373221Z 2025-03-04T20:57:17.1373697Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:57:17.1374282Z view_6: "f32[1406, 1, 4][4, 4, 1]cpu" = shifts_3.view(-1, 1, 4); shifts_3 = None 2025-03-04T20:57:17.1374878Z view_7: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = None 2025-03-04T20:57:17.1375453Z add_6: "f32[1406, 3, 4][12, 4, 1]cpu" = view_6 + view_7; view_6 = view_7 = None 2025-03-04T20:57:17.1375737Z x_119: "f32[4218, 4][4, 1]cpu" = add_6.reshape(-1, 4); add_6 = None 2025-03-04T20:57:17.1375963Z 2025-03-04T20:57:17.1376466Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:57:17.1377082Z arange_8: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32) 2025-03-04T20:57:17.1377359Z 2025-03-04T20:57:17.1377728Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:17.1378208Z shifts_x_4: "f32[19][1]cpu" = arange_8.to(device(type='cpu')); arange_8 = None 2025-03-04T20:57:17.1378458Z 2025-03-04T20:57:17.1378964Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:57:17.1379576Z arange_9: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32) 2025-03-04T20:57:17.1379829Z 2025-03-04T20:57:17.1380198Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:17.1380672Z shifts_y_4: "f32[19][1]cpu" = arange_9.to(device(type='cpu')); arange_9 = None 2025-03-04T20:57:17.1380921Z 2025-03-04T20:57:17.1381373Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:57:17.1381977Z meshgrid_4 = torch.functional.meshgrid(shifts_y_4, shifts_x_4); shifts_y_4 = shifts_x_4 = None 2025-03-04T20:57:17.1382311Z shift_y_8: "f32[19, 19][1, 0]cpu" = meshgrid_4[0] 2025-03-04T20:57:17.1382568Z shift_x_8: "f32[19, 19][0, 1]cpu" = meshgrid_4[1]; meshgrid_4 = None 2025-03-04T20:57:17.1382798Z 2025-03-04T20:57:17.1383203Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:57:17.1383699Z shift_x_9: "f32[361][1]cpu" = shift_x_8.reshape(-1); shift_x_8 = None 2025-03-04T20:57:17.1383934Z 2025-03-04T20:57:17.1384335Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:57:17.1384824Z shift_y_9: "f32[361][1]cpu" = shift_y_8.reshape(-1); shift_y_8 = None 2025-03-04T20:57:17.1385081Z 2025-03-04T20:57:17.1385530Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:57:17.1386155Z shifts_4: "f32[361, 4][4, 1]cpu" = torch.stack((shift_x_9, shift_y_9, shift_x_9, shift_y_9), dim = 1); shift_x_9 = shift_y_9 = None 2025-03-04T20:57:17.1386467Z 2025-03-04T20:57:17.1386943Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:57:17.1387538Z view_8: "f32[361, 1, 4][4, 4, 1]cpu" = shifts_4.view(-1, 1, 4); shifts_4 = None 2025-03-04T20:57:17.1388136Z view_9: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = None 2025-03-04T20:57:17.1388706Z add_7: "f32[361, 3, 4][12, 4, 1]cpu" = view_8 + view_9; view_8 = view_9 = None 2025-03-04T20:57:17.1388988Z x_120: "f32[1083, 4][4, 1]cpu" = add_7.reshape(-1, 4); add_7 = None 2025-03-04T20:57:17.1389214Z 2025-03-04T20:57:17.1389592Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:57:17.1390065Z tensor: "f32[269952, 4][4, 1]cpu" = x_116.to(torch.float32); x_116 = None 2025-03-04T20:57:17.1390388Z tensor_1: "f32[67488, 4][4, 1]cpu" = x_117.to(torch.float32); x_117 = None 2025-03-04T20:57:17.1390684Z tensor_2: "f32[16872, 4][4, 1]cpu" = x_118.to(torch.float32); x_118 = None 2025-03-04T20:57:17.1390975Z tensor_3: "f32[4218, 4][4, 1]cpu" = x_119.to(torch.float32); x_119 = None 2025-03-04T20:57:17.1391262Z tensor_4: "f32[1083, 4][4, 1]cpu" = x_120.to(torch.float32); x_120 = None 2025-03-04T20:57:17.1391492Z 2025-03-04T20:57:17.1391828Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1392370Z x_121: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_115, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_115 = None 2025-03-04T20:57:17.1392445Z 2025-03-04T20:57:17.1392739Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:57:17.1392959Z x_122: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.relu(x_121, inplace = False); x_121 = None 2025-03-04T20:57:17.1393026Z 2025-03-04T20:57:17.1393444Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:57:17.1394031Z score: "f32[4, 3, 296, 304][269952, 89984, 304, 1]cpu" = torch.conv2d(x_122, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T20:57:17.1394100Z 2025-03-04T20:57:17.1394529Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:57:17.1395120Z x_131: "f32[4, 12, 296, 304][1079808, 89984, 304, 1]cpu" = torch.conv2d(x_122, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_122 = None 2025-03-04T20:57:17.1395244Z 2025-03-04T20:57:17.1395511Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1396033Z x_123: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(x_113, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_113 = None 2025-03-04T20:57:17.1396100Z 2025-03-04T20:57:17.1396411Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:57:17.1396635Z x_124: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.relu(x_123, inplace = False); x_123 = None 2025-03-04T20:57:17.1396774Z 2025-03-04T20:57:17.1397190Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:57:17.1397762Z score_1: "f32[4, 3, 148, 152][67488, 22496, 152, 1]cpu" = torch.conv2d(x_124, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T20:57:17.1397862Z 2025-03-04T20:57:17.1398264Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:57:17.1398852Z x_132: "f32[4, 12, 148, 152][269952, 22496, 152, 1]cpu" = torch.conv2d(x_124, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_124 = None 2025-03-04T20:57:17.1398932Z 2025-03-04T20:57:17.1399208Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1399715Z x_125: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(x_111, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_111 = None 2025-03-04T20:57:17.1399790Z 2025-03-04T20:57:17.1400078Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:57:17.1400282Z x_126: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.relu(x_125, inplace = False); x_125 = None 2025-03-04T20:57:17.1400349Z 2025-03-04T20:57:17.1400752Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:57:17.1401281Z score_2: "f32[4, 3, 74, 76][16872, 5624, 76, 1]cpu" = torch.conv2d(x_126, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T20:57:17.1401357Z 2025-03-04T20:57:17.1401739Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:57:17.1402411Z x_133: "f32[4, 12, 74, 76][67488, 5624, 76, 1]cpu" = torch.conv2d(x_126, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_126 = None 2025-03-04T20:57:17.1402527Z 2025-03-04T20:57:17.1402797Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1403304Z x_127: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_109, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_109 = None 2025-03-04T20:57:17.1403969Z 2025-03-04T20:57:17.1404283Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:57:17.1404495Z x_128: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.nn.functional.relu(x_127, inplace = False); x_127 = None 2025-03-04T20:57:17.1404568Z 2025-03-04T20:57:17.1404966Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:57:17.1405506Z score_3: "f32[4, 3, 37, 38][4218, 1406, 38, 1]cpu" = torch.conv2d(x_128, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T20:57:17.1405595Z 2025-03-04T20:57:17.1405971Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:57:17.1406499Z x_134: "f32[4, 12, 37, 38][16872, 1406, 38, 1]cpu" = torch.conv2d(x_128, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_128 = None 2025-03-04T20:57:17.1406562Z 2025-03-04T20:57:17.1406819Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1407577Z x_129: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.conv2d(res, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); res = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T20:57:17.1407645Z 2025-03-04T20:57:17.1407910Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:57:17.1408086Z x_130: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.relu(x_129, inplace = False); x_129 = None 2025-03-04T20:57:17.1408146Z 2025-03-04T20:57:17.1408518Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:57:17.1409355Z score_4: "f32[4, 3, 19, 19][1083, 361, 19, 1]cpu" = torch.conv2d(x_130, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T20:57:17.1409440Z 2025-03-04T20:57:17.1409795Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:57:17.1410617Z x_135: "f32[4, 12, 19, 19][4332, 361, 19, 1]cpu" = torch.conv2d(x_130, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_130 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T20:57:17.1410686Z 2025-03-04T20:57:17.1411028Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T20:57:17.1411198Z permute: "f32[4, 296, 304, 3][269952, 304, 1, 89984]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T20:57:17.1411337Z logits_i: "f32[4, 269952][269952, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T20:57:17.1411502Z permute_1: "f32[4, 148, 152, 3][67488, 152, 1, 22496]cpu" = score_1.permute(0, 2, 3, 1); score_1 = None 2025-03-04T20:57:17.1411641Z logits_i_1: "f32[4, 67488][67488, 1]cpu" = permute_1.flatten(1); permute_1 = None 2025-03-04T20:57:17.1411795Z permute_2: "f32[4, 74, 76, 3][16872, 76, 1, 5624]cpu" = score_2.permute(0, 2, 3, 1); score_2 = None 2025-03-04T20:57:17.1411947Z logits_i_2: "f32[4, 16872][16872, 1]cpu" = permute_2.flatten(1); permute_2 = None 2025-03-04T20:57:17.1412093Z permute_3: "f32[4, 37, 38, 3][4218, 38, 1, 1406]cpu" = score_3.permute(0, 2, 3, 1); score_3 = None 2025-03-04T20:57:17.1412222Z logits_i_3: "f32[4, 4218][4218, 1]cpu" = permute_3.flatten(1); permute_3 = None 2025-03-04T20:57:17.1412367Z permute_4: "f32[4, 19, 19, 3][1083, 19, 1, 361]cpu" = score_4.permute(0, 2, 3, 1); score_4 = None 2025-03-04T20:57:17.1412494Z logits_i_4: "f32[4, 1083][1083, 1]cpu" = permute_4.flatten(1); permute_4 = None 2025-03-04T20:57:17.1412560Z 2025-03-04T20:57:17.1412972Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T20:57:17.1413149Z view_10: "f32[4, 3, 4, 296, 304][1079808, 359936, 89984, 304, 1]cpu" = x_131.view(4, -1, 4, 296, 304); x_131 = None 2025-03-04T20:57:17.1413332Z permute_5: "f32[4, 296, 304, 3, 4][1079808, 304, 1, 359936, 89984]cpu" = view_10.permute(0, 3, 4, 1, 2); view_10 = None 2025-03-04T20:57:17.1413505Z pred_anchor_deltas_i: "f32[4, 269952, 4][1079808, 4, 1]cpu" = permute_5.flatten(1, -2); permute_5 = None 2025-03-04T20:57:17.1413666Z view_11: "f32[4, 3, 4, 148, 152][269952, 89984, 22496, 152, 1]cpu" = x_132.view(4, -1, 4, 148, 152); x_132 = None 2025-03-04T20:57:17.1413835Z permute_6: "f32[4, 148, 152, 3, 4][269952, 152, 1, 89984, 22496]cpu" = view_11.permute(0, 3, 4, 1, 2); view_11 = None 2025-03-04T20:57:17.1414007Z pred_anchor_deltas_i_2: "f32[4, 67488, 4][269952, 4, 1]cpu" = permute_6.flatten(1, -2); permute_6 = None 2025-03-04T20:57:17.1414149Z view_12: "f32[4, 3, 4, 74, 76][67488, 22496, 5624, 76, 1]cpu" = x_133.view(4, -1, 4, 74, 76); x_133 = None 2025-03-04T20:57:17.1414314Z permute_7: "f32[4, 74, 76, 3, 4][67488, 76, 1, 22496, 5624]cpu" = view_12.permute(0, 3, 4, 1, 2); view_12 = None 2025-03-04T20:57:17.1414479Z pred_anchor_deltas_i_4: "f32[4, 16872, 4][67488, 4, 1]cpu" = permute_7.flatten(1, -2); permute_7 = None 2025-03-04T20:57:17.1414621Z view_13: "f32[4, 3, 4, 37, 38][16872, 5624, 1406, 38, 1]cpu" = x_134.view(4, -1, 4, 37, 38); x_134 = None 2025-03-04T20:57:17.1414792Z permute_8: "f32[4, 37, 38, 3, 4][16872, 38, 1, 5624, 1406]cpu" = view_13.permute(0, 3, 4, 1, 2); view_13 = None 2025-03-04T20:57:17.1414959Z pred_anchor_deltas_i_6: "f32[4, 4218, 4][16872, 4, 1]cpu" = permute_8.flatten(1, -2); permute_8 = None 2025-03-04T20:57:17.1415091Z view_14: "f32[4, 3, 4, 19, 19][4332, 1444, 361, 19, 1]cpu" = x_135.view(4, -1, 4, 19, 19); x_135 = None 2025-03-04T20:57:17.1415248Z permute_9: "f32[4, 19, 19, 3, 4][4332, 19, 1, 1444, 361]cpu" = view_14.permute(0, 3, 4, 1, 2); view_14 = None 2025-03-04T20:57:17.1415404Z pred_anchor_deltas_i_8: "f32[4, 1083, 4][4332, 4, 1]cpu" = permute_9.flatten(1, -2); permute_9 = None 2025-03-04T20:57:17.1415477Z 2025-03-04T20:57:17.1415887Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:57:17.1416109Z pred_anchor_deltas_i_1: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T20:57:17.1416171Z 2025-03-04T20:57:17.1416599Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:57:17.1416748Z unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T20:57:17.1416897Z expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T20:57:17.1417054Z anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T20:57:17.1417117Z 2025-03-04T20:57:17.1417494Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:57:17.1417660Z deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T20:57:17.1417727Z 2025-03-04T20:57:17.1418041Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:57:17.1418188Z boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T20:57:17.1418249Z 2025-03-04T20:57:17.1418571Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:57:17.1418705Z getitem_10: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:57:17.1418839Z getitem_11: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:57:17.1418991Z widths: "f32[1079808][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T20:57:17.1419059Z 2025-03-04T20:57:17.1419379Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:57:17.1419509Z getitem_12: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:57:17.1419629Z getitem_13: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:57:17.1419786Z heights: "f32[1079808][1]cpu" = getitem_12 - getitem_13; getitem_12 = getitem_13 = None 2025-03-04T20:57:17.1419848Z 2025-03-04T20:57:17.1420165Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:57:17.1420288Z getitem_14: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:57:17.1420389Z mul: "f32[1079808][1]cpu" = 0.5 * widths 2025-03-04T20:57:17.1420531Z ctr_x: "f32[1079808][1]cpu" = getitem_14 + mul; getitem_14 = mul = None 2025-03-04T20:57:17.1420599Z 2025-03-04T20:57:17.1420911Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:57:17.1421072Z getitem_15: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:57:17.1421156Z mul_1: "f32[1079808][1]cpu" = 0.5 * heights 2025-03-04T20:57:17.1421287Z ctr_y: "f32[1079808][1]cpu" = getitem_15 + mul_1; getitem_15 = mul_1 = None 2025-03-04T20:57:17.1421348Z 2025-03-04T20:57:17.1421721Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:57:17.1421891Z getitem_16: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:57:17.1422015Z dx: "f32[1079808, 1][1, 1]cpu" = getitem_16 / 1.0; getitem_16 = None 2025-03-04T20:57:17.1422077Z 2025-03-04T20:57:17.1422388Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:57:17.1422540Z getitem_17: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:57:17.1422659Z dy: "f32[1079808, 1][1, 1]cpu" = getitem_17 / 1.0; getitem_17 = None 2025-03-04T20:57:17.1422720Z 2025-03-04T20:57:17.1423045Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:57:17.1423198Z getitem_18: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:57:17.1423315Z dw: "f32[1079808, 1][1, 1]cpu" = getitem_18 / 1.0; getitem_18 = None 2025-03-04T20:57:17.1423376Z 2025-03-04T20:57:17.1423686Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:57:17.1423877Z getitem_19: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:57:17.1423984Z dh: "f32[1079808, 1][1, 1]cpu" = getitem_19 / 1.0; getitem_19 = None 2025-03-04T20:57:17.1424052Z 2025-03-04T20:57:17.1424395Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:57:17.1424547Z dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:57:17.1424608Z 2025-03-04T20:57:17.1424953Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:57:17.1425090Z dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:57:17.1425156Z 2025-03-04T20:57:17.1425504Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:57:17.1425648Z getitem_20: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:57:17.1425772Z mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_20; dx = getitem_20 = None 2025-03-04T20:57:17.1425934Z getitem_21: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:57:17.1426071Z pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_21; mul_2 = getitem_21 = None 2025-03-04T20:57:17.1426137Z 2025-03-04T20:57:17.1426485Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:57:17.1426652Z getitem_22: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:57:17.1426771Z mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_22; dy = getitem_22 = None 2025-03-04T20:57:17.1426924Z getitem_23: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:57:17.1427060Z pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_23; mul_3 = getitem_23 = None 2025-03-04T20:57:17.1427127Z 2025-03-04T20:57:17.1427476Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:57:17.1427614Z exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:57:17.1427775Z getitem_24: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:57:17.1427913Z pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_24; exp = getitem_24 = None 2025-03-04T20:57:17.1427972Z 2025-03-04T20:57:17.1428316Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:57:17.1428432Z exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:57:17.1428603Z getitem_25: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:57:17.1428756Z pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_25; exp_1 = getitem_25 = None 2025-03-04T20:57:17.1428824Z 2025-03-04T20:57:17.1429142Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:57:17.1429245Z mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T20:57:17.1429360Z x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:57:17.1429428Z 2025-03-04T20:57:17.1429742Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:57:17.1429845Z mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T20:57:17.1429958Z y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:57:17.1430026Z 2025-03-04T20:57:17.1430331Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:57:17.1430448Z mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:57:17.1430577Z x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:57:17.1430646Z 2025-03-04T20:57:17.1430948Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:57:17.1431068Z mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:57:17.1431192Z y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:57:17.1431260Z 2025-03-04T20:57:17.1431608Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:57:17.1431799Z pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:57:17.1431861Z 2025-03-04T20:57:17.1432209Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:57:17.1432392Z proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4)); pred_boxes = None 2025-03-04T20:57:17.1432452Z 2025-03-04T20:57:17.1432842Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:57:17.1433018Z proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T20:57:17.1433085Z 2025-03-04T20:57:17.1433498Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:57:17.1433727Z pred_anchor_deltas_i_3: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_2.reshape(-1, 4); pred_anchor_deltas_i_2 = None 2025-03-04T20:57:17.1433791Z 2025-03-04T20:57:17.1434235Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:57:17.1434387Z unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = tensor_1.unsqueeze(0); tensor_1 = None 2025-03-04T20:57:17.1434541Z expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1); unsqueeze_1 = None 2025-03-04T20:57:17.1434693Z anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4); expand_1 = None 2025-03-04T20:57:17.1434790Z 2025-03-04T20:57:17.1435167Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:57:17.1435350Z deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float(); pred_anchor_deltas_i_3 = None 2025-03-04T20:57:17.1435416Z 2025-03-04T20:57:17.1435738Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:57:17.1435879Z boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32); anchors_i_1 = None 2025-03-04T20:57:17.1435950Z 2025-03-04T20:57:17.1436260Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:57:17.1436401Z getitem_26: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)] 2025-03-04T20:57:17.1436526Z getitem_27: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T20:57:17.1436683Z widths_1: "f32[269952][1]cpu" = getitem_26 - getitem_27; getitem_26 = getitem_27 = None 2025-03-04T20:57:17.1436817Z 2025-03-04T20:57:17.1437177Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:57:17.1437309Z getitem_28: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)] 2025-03-04T20:57:17.1437449Z getitem_29: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)] 2025-03-04T20:57:17.1437616Z heights_1: "f32[269952][1]cpu" = getitem_28 - getitem_29; getitem_28 = getitem_29 = None 2025-03-04T20:57:17.1437695Z 2025-03-04T20:57:17.1438066Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:57:17.1438209Z getitem_30: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T20:57:17.1438311Z mul_10: "f32[269952][1]cpu" = 0.5 * widths_1 2025-03-04T20:57:17.1438490Z ctr_x_1: "f32[269952][1]cpu" = getitem_30 + mul_10; getitem_30 = mul_10 = None 2025-03-04T20:57:17.1438557Z 2025-03-04T20:57:17.1438927Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:57:17.1439075Z getitem_31: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]; boxes_1 = None 2025-03-04T20:57:17.1439175Z mul_11: "f32[269952][1]cpu" = 0.5 * heights_1 2025-03-04T20:57:17.1439311Z ctr_y_1: "f32[269952][1]cpu" = getitem_31 + mul_11; getitem_31 = mul_11 = None 2025-03-04T20:57:17.1439373Z 2025-03-04T20:57:17.1439704Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:57:17.1439873Z getitem_32: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:57:17.1440004Z dx_1: "f32[269952, 1][1, 1]cpu" = getitem_32 / 1.0; getitem_32 = None 2025-03-04T20:57:17.1440073Z 2025-03-04T20:57:17.1440415Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:57:17.1440582Z getitem_33: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:57:17.1440710Z dy_1: "f32[269952, 1][1, 1]cpu" = getitem_33 / 1.0; getitem_33 = None 2025-03-04T20:57:17.1440777Z 2025-03-04T20:57:17.1441150Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:57:17.1441313Z getitem_34: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:57:17.1441442Z dw_2: "f32[269952, 1][1, 1]cpu" = getitem_34 / 1.0; getitem_34 = None 2025-03-04T20:57:17.1441511Z 2025-03-04T20:57:17.1441848Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:57:17.1442051Z getitem_35: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))]; deltas_1 = None 2025-03-04T20:57:17.1442180Z dh_2: "f32[269952, 1][1, 1]cpu" = getitem_35 / 1.0; getitem_35 = None 2025-03-04T20:57:17.1442246Z 2025-03-04T20:57:17.1442627Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:57:17.1442779Z dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356); dw_2 = None 2025-03-04T20:57:17.1442853Z 2025-03-04T20:57:17.1443223Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:57:17.1443383Z dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356); dh_2 = None 2025-03-04T20:57:17.1443450Z 2025-03-04T20:57:17.1443840Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:57:17.1443986Z getitem_36: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)] 2025-03-04T20:57:17.1444138Z mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_36; dx_1 = getitem_36 = None 2025-03-04T20:57:17.1444315Z getitem_37: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)]; ctr_x_1 = None 2025-03-04T20:57:17.1444478Z pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_37; mul_12 = getitem_37 = None 2025-03-04T20:57:17.1444547Z 2025-03-04T20:57:17.1444957Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:57:17.1445114Z getitem_38: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)] 2025-03-04T20:57:17.1445250Z mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_38; dy_1 = getitem_38 = None 2025-03-04T20:57:17.1445424Z getitem_39: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)]; ctr_y_1 = None 2025-03-04T20:57:17.1445576Z pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_39; mul_13 = getitem_39 = None 2025-03-04T20:57:17.1445670Z 2025-03-04T20:57:17.1446082Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:57:17.1446213Z exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3); dw_3 = None 2025-03-04T20:57:17.1446371Z getitem_40: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]; widths_1 = None 2025-03-04T20:57:17.1446508Z pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_40; exp_2 = getitem_40 = None 2025-03-04T20:57:17.1446567Z 2025-03-04T20:57:17.1446900Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:57:17.1447009Z exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3); dh_3 = None 2025-03-04T20:57:17.1447208Z getitem_41: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]; heights_1 = None 2025-03-04T20:57:17.1447341Z pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_41; exp_3 = getitem_41 = None 2025-03-04T20:57:17.1447419Z 2025-03-04T20:57:17.1447730Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:57:17.1447831Z mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1 2025-03-04T20:57:17.1447945Z x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16; mul_16 = None 2025-03-04T20:57:17.1448010Z 2025-03-04T20:57:17.1448315Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:57:17.1448412Z mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1 2025-03-04T20:57:17.1448525Z y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17; mul_17 = None 2025-03-04T20:57:17.1448592Z 2025-03-04T20:57:17.1448889Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:57:17.1449008Z mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1; pred_w_1 = None 2025-03-04T20:57:17.1449140Z x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18; pred_ctr_x_1 = mul_18 = None 2025-03-04T20:57:17.1449207Z 2025-03-04T20:57:17.1449503Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:57:17.1449623Z mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1; pred_h_1 = None 2025-03-04T20:57:17.1449751Z y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19; pred_ctr_y_1 = mul_19 = None 2025-03-04T20:57:17.1449824Z 2025-03-04T20:57:17.1450166Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:57:17.1450361Z pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1); x1_1 = y1_1 = x2_1 = y2_1 = None 2025-03-04T20:57:17.1450440Z 2025-03-04T20:57:17.1450773Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:57:17.1450931Z proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4)); pred_boxes_1 = None 2025-03-04T20:57:17.1450998Z 2025-03-04T20:57:17.1451369Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:57:17.1451563Z proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4); proposals_i_1 = None 2025-03-04T20:57:17.1451624Z 2025-03-04T20:57:17.1452046Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:57:17.1452261Z pred_anchor_deltas_i_5: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_4.reshape(-1, 4); pred_anchor_deltas_i_4 = None 2025-03-04T20:57:17.1452328Z 2025-03-04T20:57:17.1452755Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:57:17.1452904Z unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = tensor_2.unsqueeze(0); tensor_2 = None 2025-03-04T20:57:17.1453072Z expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1); unsqueeze_2 = None 2025-03-04T20:57:17.1453204Z anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4); expand_2 = None 2025-03-04T20:57:17.1453269Z 2025-03-04T20:57:17.1453630Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:57:17.1453798Z deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_5.float(); pred_anchor_deltas_i_5 = None 2025-03-04T20:57:17.1453857Z 2025-03-04T20:57:17.1454166Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:57:17.1454301Z boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32); anchors_i_2 = None 2025-03-04T20:57:17.1454368Z 2025-03-04T20:57:17.1454673Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:57:17.1454802Z getitem_42: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)] 2025-03-04T20:57:17.1454923Z getitem_43: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T20:57:17.1455070Z widths_2: "f32[67488][1]cpu" = getitem_42 - getitem_43; getitem_42 = getitem_43 = None 2025-03-04T20:57:17.1455128Z 2025-03-04T20:57:17.1455443Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:57:17.1455561Z getitem_44: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)] 2025-03-04T20:57:17.1455680Z getitem_45: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)] 2025-03-04T20:57:17.1455825Z heights_2: "f32[67488][1]cpu" = getitem_44 - getitem_45; getitem_44 = getitem_45 = None 2025-03-04T20:57:17.1455890Z 2025-03-04T20:57:17.1456190Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:57:17.1456314Z getitem_46: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T20:57:17.1456417Z mul_20: "f32[67488][1]cpu" = 0.5 * widths_2 2025-03-04T20:57:17.1456549Z ctr_x_2: "f32[67488][1]cpu" = getitem_46 + mul_20; getitem_46 = mul_20 = None 2025-03-04T20:57:17.1456607Z 2025-03-04T20:57:17.1456913Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:57:17.1457052Z getitem_47: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]; boxes_2 = None 2025-03-04T20:57:17.1457148Z mul_21: "f32[67488][1]cpu" = 0.5 * heights_2 2025-03-04T20:57:17.1457286Z ctr_y_2: "f32[67488][1]cpu" = getitem_47 + mul_21; getitem_47 = mul_21 = None 2025-03-04T20:57:17.1457354Z 2025-03-04T20:57:17.1457676Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:57:17.1457834Z getitem_48: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:57:17.1457944Z dx_2: "f32[67488, 1][1, 1]cpu" = getitem_48 / 1.0; getitem_48 = None 2025-03-04T20:57:17.1458013Z 2025-03-04T20:57:17.1458314Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:57:17.1458471Z getitem_49: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:57:17.1458578Z dy_2: "f32[67488, 1][1, 1]cpu" = getitem_49 / 1.0; getitem_49 = None 2025-03-04T20:57:17.1458660Z 2025-03-04T20:57:17.1458968Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:57:17.1459117Z getitem_50: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:57:17.1459234Z dw_4: "f32[67488, 1][1, 1]cpu" = getitem_50 / 1.0; getitem_50 = None 2025-03-04T20:57:17.1459295Z 2025-03-04T20:57:17.1459608Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:57:17.1459785Z getitem_51: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))]; deltas_2 = None 2025-03-04T20:57:17.1459895Z dh_4: "f32[67488, 1][1, 1]cpu" = getitem_51 / 1.0; getitem_51 = None 2025-03-04T20:57:17.1459955Z 2025-03-04T20:57:17.1460289Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:57:17.1460423Z dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356); dw_4 = None 2025-03-04T20:57:17.1460491Z 2025-03-04T20:57:17.1460826Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:57:17.1460966Z dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356); dh_4 = None 2025-03-04T20:57:17.1461028Z 2025-03-04T20:57:17.1461385Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:57:17.1461513Z getitem_52: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)] 2025-03-04T20:57:17.1461640Z mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_52; dx_2 = getitem_52 = None 2025-03-04T20:57:17.1461785Z getitem_53: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)]; ctr_x_2 = None 2025-03-04T20:57:17.1461930Z pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_53; mul_22 = getitem_53 = None 2025-03-04T20:57:17.1462007Z 2025-03-04T20:57:17.1462360Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:57:17.1462492Z getitem_54: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)] 2025-03-04T20:57:17.1462615Z mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_54; dy_2 = getitem_54 = None 2025-03-04T20:57:17.1462762Z getitem_55: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)]; ctr_y_2 = None 2025-03-04T20:57:17.1462920Z pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_55; mul_23 = getitem_55 = None 2025-03-04T20:57:17.1462982Z 2025-03-04T20:57:17.1463324Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:57:17.1463436Z exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5); dw_5 = None 2025-03-04T20:57:17.1463597Z getitem_56: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]; widths_2 = None 2025-03-04T20:57:17.1463724Z pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_56; exp_4 = getitem_56 = None 2025-03-04T20:57:17.1463799Z 2025-03-04T20:57:17.1464124Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:57:17.1464255Z exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5); dh_5 = None 2025-03-04T20:57:17.1464415Z getitem_57: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]; heights_2 = None 2025-03-04T20:57:17.1464552Z pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_57; exp_5 = getitem_57 = None 2025-03-04T20:57:17.1464614Z 2025-03-04T20:57:17.1464926Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:57:17.1465025Z mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2 2025-03-04T20:57:17.1465135Z x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26; mul_26 = None 2025-03-04T20:57:17.1465200Z 2025-03-04T20:57:17.1465500Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:57:17.1465598Z mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2 2025-03-04T20:57:17.1465707Z y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27; mul_27 = None 2025-03-04T20:57:17.1465774Z 2025-03-04T20:57:17.1466069Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:57:17.1466188Z mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2; pred_w_2 = None 2025-03-04T20:57:17.1466319Z x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28; pred_ctr_x_2 = mul_28 = None 2025-03-04T20:57:17.1466383Z 2025-03-04T20:57:17.1466684Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:57:17.1466799Z mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2; pred_h_2 = None 2025-03-04T20:57:17.1466926Z y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29; pred_ctr_y_2 = mul_29 = None 2025-03-04T20:57:17.1466994Z 2025-03-04T20:57:17.1467343Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:57:17.1467536Z pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1); x1_2 = y1_2 = x2_2 = y2_2 = None 2025-03-04T20:57:17.1467617Z 2025-03-04T20:57:17.1467960Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:57:17.1468118Z proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4)); pred_boxes_2 = None 2025-03-04T20:57:17.1468185Z 2025-03-04T20:57:17.1468567Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:57:17.1468757Z proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4); proposals_i_2 = None 2025-03-04T20:57:17.1468818Z 2025-03-04T20:57:17.1469234Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:57:17.1469434Z pred_anchor_deltas_i_7: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_6.reshape(-1, 4); pred_anchor_deltas_i_6 = None 2025-03-04T20:57:17.1469504Z 2025-03-04T20:57:17.1469933Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:57:17.1470086Z unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = tensor_3.unsqueeze(0); tensor_3 = None 2025-03-04T20:57:17.1470251Z expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1); unsqueeze_3 = None 2025-03-04T20:57:17.1470394Z anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4); expand_3 = None 2025-03-04T20:57:17.1470455Z 2025-03-04T20:57:17.1470832Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:57:17.1470997Z deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_7.float(); pred_anchor_deltas_i_7 = None 2025-03-04T20:57:17.1471065Z 2025-03-04T20:57:17.1471373Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:57:17.1471535Z boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32); anchors_i_3 = None 2025-03-04T20:57:17.1471599Z 2025-03-04T20:57:17.1471921Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:57:17.1472050Z getitem_58: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)] 2025-03-04T20:57:17.1472184Z getitem_59: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T20:57:17.1472330Z widths_3: "f32[16872][1]cpu" = getitem_58 - getitem_59; getitem_58 = getitem_59 = None 2025-03-04T20:57:17.1472412Z 2025-03-04T20:57:17.1472742Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:57:17.1472863Z getitem_60: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)] 2025-03-04T20:57:17.1472986Z getitem_61: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)] 2025-03-04T20:57:17.1473136Z heights_3: "f32[16872][1]cpu" = getitem_60 - getitem_61; getitem_60 = getitem_61 = None 2025-03-04T20:57:17.1473203Z 2025-03-04T20:57:17.1473514Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:57:17.1473661Z getitem_62: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T20:57:17.1473753Z mul_30: "f32[16872][1]cpu" = 0.5 * widths_3 2025-03-04T20:57:17.1473890Z ctr_x_3: "f32[16872][1]cpu" = getitem_62 + mul_30; getitem_62 = mul_30 = None 2025-03-04T20:57:17.1473953Z 2025-03-04T20:57:17.1474274Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:57:17.1474419Z getitem_63: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]; boxes_3 = None 2025-03-04T20:57:17.1474543Z mul_31: "f32[16872][1]cpu" = 0.5 * heights_3 2025-03-04T20:57:17.1474673Z ctr_y_3: "f32[16872][1]cpu" = getitem_63 + mul_31; getitem_63 = mul_31 = None 2025-03-04T20:57:17.1474741Z 2025-03-04T20:57:17.1475056Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:57:17.1475218Z getitem_64: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:57:17.1475331Z dx_3: "f32[16872, 1][1, 1]cpu" = getitem_64 / 1.0; getitem_64 = None 2025-03-04T20:57:17.1475399Z 2025-03-04T20:57:17.1475701Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:57:17.1475855Z getitem_65: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:57:17.1475995Z dy_3: "f32[16872, 1][1, 1]cpu" = getitem_65 / 1.0; getitem_65 = None 2025-03-04T20:57:17.1476063Z 2025-03-04T20:57:17.1476364Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:57:17.1476521Z getitem_66: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:57:17.1476625Z dw_6: "f32[16872, 1][1, 1]cpu" = getitem_66 / 1.0; getitem_66 = None 2025-03-04T20:57:17.1476696Z 2025-03-04T20:57:17.1477076Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:57:17.1477273Z getitem_67: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))]; deltas_3 = None 2025-03-04T20:57:17.1477382Z dh_6: "f32[16872, 1][1, 1]cpu" = getitem_67 / 1.0; getitem_67 = None 2025-03-04T20:57:17.1477453Z 2025-03-04T20:57:17.1477801Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:57:17.1477951Z dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356); dw_6 = None 2025-03-04T20:57:17.1478014Z 2025-03-04T20:57:17.1478367Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:57:17.1478511Z dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356); dh_6 = None 2025-03-04T20:57:17.1478575Z 2025-03-04T20:57:17.1478939Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:57:17.1479078Z getitem_68: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)] 2025-03-04T20:57:17.1479208Z mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_68; dx_3 = getitem_68 = None 2025-03-04T20:57:17.1479363Z getitem_69: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)]; ctr_x_3 = None 2025-03-04T20:57:17.1479535Z pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_69; mul_32 = getitem_69 = None 2025-03-04T20:57:17.1479598Z 2025-03-04T20:57:17.1479967Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:57:17.1480114Z getitem_70: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)] 2025-03-04T20:57:17.1480241Z mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_70; dy_3 = getitem_70 = None 2025-03-04T20:57:17.1480405Z getitem_71: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)]; ctr_y_3 = None 2025-03-04T20:57:17.1480549Z pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_71; mul_33 = getitem_71 = None 2025-03-04T20:57:17.1480609Z 2025-03-04T20:57:17.1480961Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:57:17.1481076Z exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7); dw_7 = None 2025-03-04T20:57:17.1481239Z getitem_72: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]; widths_3 = None 2025-03-04T20:57:17.1481369Z pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_72; exp_6 = getitem_72 = None 2025-03-04T20:57:17.1481438Z 2025-03-04T20:57:17.1481769Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:57:17.1481901Z exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7); dh_7 = None 2025-03-04T20:57:17.1482063Z getitem_73: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]; heights_3 = None 2025-03-04T20:57:17.1482201Z pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_73; exp_7 = getitem_73 = None 2025-03-04T20:57:17.1482263Z 2025-03-04T20:57:17.1482587Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:57:17.1482680Z mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3 2025-03-04T20:57:17.1482802Z x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36; mul_36 = None 2025-03-04T20:57:17.1482862Z 2025-03-04T20:57:17.1483180Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:57:17.1483275Z mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3 2025-03-04T20:57:17.1483393Z y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37; mul_37 = None 2025-03-04T20:57:17.1483452Z 2025-03-04T20:57:17.1483772Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:57:17.1483884Z mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3; pred_w_3 = None 2025-03-04T20:57:17.1484022Z x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38; pred_ctr_x_3 = mul_38 = None 2025-03-04T20:57:17.1484085Z 2025-03-04T20:57:17.1484402Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:57:17.1484512Z mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3; pred_h_3 = None 2025-03-04T20:57:17.1484648Z y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39; pred_ctr_y_3 = mul_39 = None 2025-03-04T20:57:17.1484707Z 2025-03-04T20:57:17.1485071Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:57:17.1485276Z pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1); x1_3 = y1_3 = x2_3 = y2_3 = None 2025-03-04T20:57:17.1485345Z 2025-03-04T20:57:17.1485679Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:57:17.1485846Z proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4)); pred_boxes_3 = None 2025-03-04T20:57:17.1485906Z 2025-03-04T20:57:17.1486316Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:57:17.1486493Z proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4); proposals_i_3 = None 2025-03-04T20:57:17.1486553Z 2025-03-04T20:57:17.1486973Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:57:17.1487178Z pred_anchor_deltas_i_9: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_8.reshape(-1, 4); pred_anchor_deltas_i_8 = None 2025-03-04T20:57:17.1487247Z 2025-03-04T20:57:17.1487679Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:57:17.1487880Z unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = tensor_4.unsqueeze(0); tensor_4 = None 2025-03-04T20:57:17.1488030Z expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1); unsqueeze_4 = None 2025-03-04T20:57:17.1488174Z anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4); expand_4 = None 2025-03-04T20:57:17.1488238Z 2025-03-04T20:57:17.1488627Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:57:17.1488795Z deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_9.float(); pred_anchor_deltas_i_9 = None 2025-03-04T20:57:17.1488865Z 2025-03-04T20:57:17.1489183Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:57:17.1489328Z boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32); anchors_i_4 = None 2025-03-04T20:57:17.1489389Z 2025-03-04T20:57:17.1489706Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:57:17.1489831Z getitem_74: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)] 2025-03-04T20:57:17.1489956Z getitem_75: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T20:57:17.1490099Z widths_4: "f32[4332][1]cpu" = getitem_74 - getitem_75; getitem_74 = getitem_75 = None 2025-03-04T20:57:17.1490169Z 2025-03-04T20:57:17.1490482Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:57:17.1490609Z getitem_76: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)] 2025-03-04T20:57:17.1490729Z getitem_77: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)] 2025-03-04T20:57:17.1490885Z heights_4: "f32[4332][1]cpu" = getitem_76 - getitem_77; getitem_76 = getitem_77 = None 2025-03-04T20:57:17.1490945Z 2025-03-04T20:57:17.1491269Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:57:17.1491406Z getitem_78: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T20:57:17.1491502Z mul_40: "f32[4332][1]cpu" = 0.5 * widths_4 2025-03-04T20:57:17.1491631Z ctr_x_4: "f32[4332][1]cpu" = getitem_78 + mul_40; getitem_78 = mul_40 = None 2025-03-04T20:57:17.1491701Z 2025-03-04T20:57:17.1492021Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:57:17.1492179Z getitem_79: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]; boxes_4 = None 2025-03-04T20:57:17.1492291Z mul_41: "f32[4332][1]cpu" = 0.5 * heights_4 2025-03-04T20:57:17.1492428Z ctr_y_4: "f32[4332][1]cpu" = getitem_79 + mul_41; getitem_79 = mul_41 = None 2025-03-04T20:57:17.1492491Z 2025-03-04T20:57:17.1492826Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:57:17.1492983Z getitem_80: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:57:17.1493102Z dx_4: "f32[4332, 1][1, 1]cpu" = getitem_80 / 1.0; getitem_80 = None 2025-03-04T20:57:17.1493166Z 2025-03-04T20:57:17.1493486Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:57:17.1493644Z getitem_81: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:57:17.1493773Z dy_4: "f32[4332, 1][1, 1]cpu" = getitem_81 / 1.0; getitem_81 = None 2025-03-04T20:57:17.1493845Z 2025-03-04T20:57:17.1494150Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:57:17.1494307Z getitem_82: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:57:17.1494413Z dw_8: "f32[4332, 1][1, 1]cpu" = getitem_82 / 1.0; getitem_82 = None 2025-03-04T20:57:17.1494483Z 2025-03-04T20:57:17.1494791Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:57:17.1494982Z getitem_83: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))]; deltas_4 = None 2025-03-04T20:57:17.1495091Z dh_8: "f32[4332, 1][1, 1]cpu" = getitem_83 / 1.0; getitem_83 = None 2025-03-04T20:57:17.1495160Z 2025-03-04T20:57:17.1495509Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:57:17.1495657Z dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356); dw_8 = None 2025-03-04T20:57:17.1495720Z 2025-03-04T20:57:17.1496071Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:57:17.1496204Z dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356); dh_8 = None 2025-03-04T20:57:17.1496273Z 2025-03-04T20:57:17.1496629Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:57:17.1496773Z getitem_84: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)] 2025-03-04T20:57:17.1496896Z mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_84; dx_4 = getitem_84 = None 2025-03-04T20:57:17.1497056Z getitem_85: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)]; ctr_x_4 = None 2025-03-04T20:57:17.1497217Z pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_85; mul_42 = getitem_85 = None 2025-03-04T20:57:17.1497288Z 2025-03-04T20:57:17.1497656Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:57:17.1497798Z getitem_86: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)] 2025-03-04T20:57:17.1497920Z mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_86; dy_4 = getitem_86 = None 2025-03-04T20:57:17.1498095Z getitem_87: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)]; ctr_y_4 = None 2025-03-04T20:57:17.1498233Z pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_87; mul_43 = getitem_87 = None 2025-03-04T20:57:17.1498303Z 2025-03-04T20:57:17.1498664Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:57:17.1498789Z exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9); dw_9 = None 2025-03-04T20:57:17.1498949Z getitem_88: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]; widths_4 = None 2025-03-04T20:57:17.1499090Z pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_88; exp_8 = getitem_88 = None 2025-03-04T20:57:17.1499154Z 2025-03-04T20:57:17.1499509Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:57:17.1499645Z exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9); dh_9 = None 2025-03-04T20:57:17.1499812Z getitem_89: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]; heights_4 = None 2025-03-04T20:57:17.1499949Z pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_89; exp_9 = getitem_89 = None 2025-03-04T20:57:17.1500013Z 2025-03-04T20:57:17.1500339Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:57:17.1500436Z mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4 2025-03-04T20:57:17.1500555Z x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46; mul_46 = None 2025-03-04T20:57:17.1500618Z 2025-03-04T20:57:17.1500941Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:57:17.1501039Z mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4 2025-03-04T20:57:17.1501158Z y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47; mul_47 = None 2025-03-04T20:57:17.1501219Z 2025-03-04T20:57:17.1501542Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:57:17.1501656Z mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4; pred_w_4 = None 2025-03-04T20:57:17.1501792Z x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48; pred_ctr_x_4 = mul_48 = None 2025-03-04T20:57:17.1501852Z 2025-03-04T20:57:17.1502298Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:57:17.1502414Z mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4; pred_h_4 = None 2025-03-04T20:57:17.1502553Z y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49; pred_ctr_y_4 = mul_49 = None 2025-03-04T20:57:17.1502614Z 2025-03-04T20:57:17.1502988Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:57:17.1503213Z pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1); x1_4 = y1_4 = x2_4 = y2_4 = None 2025-03-04T20:57:17.1503280Z 2025-03-04T20:57:17.1503612Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:57:17.1503776Z proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4)); pred_boxes_4 = None 2025-03-04T20:57:17.1503836Z 2025-03-04T20:57:17.1504250Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:57:17.1504421Z proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4); proposals_i_4 = None 2025-03-04T20:57:17.1504513Z 2025-03-04T20:57:17.1505005Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T20:57:17.1505144Z arange_10: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T20:57:17.1505205Z 2025-03-04T20:57:17.1505512Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:17.1505652Z batch_idx: "i64[4][1]cpu" = arange_10.to(device(type='cpu')); arange_10 = None 2025-03-04T20:57:17.1505743Z 2025-03-04T20:57:17.1506198Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:57:17.1506316Z topk = logits_i.topk(1000, dim = 1); logits_i = None 2025-03-04T20:57:17.1506417Z topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0] 2025-03-04T20:57:17.1506536Z topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1]; topk = None 2025-03-04T20:57:17.1506598Z 2025-03-04T20:57:17.1507066Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:57:17.1507197Z getitem_92: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T20:57:17.1507433Z topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_92, topk_idx)]; proposals_i_5 = getitem_92 = topk_idx = None 2025-03-04T20:57:17.1507502Z 2025-03-04T20:57:17.1507959Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:57:17.1508131Z full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:57:17.1508191Z 2025-03-04T20:57:17.1508494Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:17.1508614Z to_21: "i64[1000][1]cpu" = full.to(device(type='cpu')); full = None 2025-03-04T20:57:17.1508683Z 2025-03-04T20:57:17.1509118Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:57:17.1509242Z topk_1 = logits_i_1.topk(1000, dim = 1); logits_i_1 = None 2025-03-04T20:57:17.1509348Z topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0] 2025-03-04T20:57:17.1509485Z topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1]; topk_1 = None 2025-03-04T20:57:17.1509546Z 2025-03-04T20:57:17.1510011Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:57:17.1510139Z getitem_96: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T20:57:17.1510392Z topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_96, topk_idx_1)]; proposals_i_6 = getitem_96 = topk_idx_1 = None 2025-03-04T20:57:17.1510454Z 2025-03-04T20:57:17.1510930Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:57:17.1511095Z full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:57:17.1511165Z 2025-03-04T20:57:17.1511455Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:17.1511583Z to_22: "i64[1000][1]cpu" = full_1.to(device(type='cpu')); full_1 = None 2025-03-04T20:57:17.1511643Z 2025-03-04T20:57:17.1512083Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:57:17.1512211Z topk_2 = logits_i_2.topk(1000, dim = 1); logits_i_2 = None 2025-03-04T20:57:17.1512320Z topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0] 2025-03-04T20:57:17.1512436Z topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1]; topk_2 = None 2025-03-04T20:57:17.1512506Z 2025-03-04T20:57:17.1512969Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:57:17.1513106Z getitem_100: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T20:57:17.1513341Z topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_100, topk_idx_2)]; proposals_i_7 = getitem_100 = topk_idx_2 = None 2025-03-04T20:57:17.1513410Z 2025-03-04T20:57:17.1513880Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:57:17.1514040Z full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:57:17.1514110Z 2025-03-04T20:57:17.1514405Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:17.1514532Z to_23: "i64[1000][1]cpu" = full_2.to(device(type='cpu')); full_2 = None 2025-03-04T20:57:17.1514593Z 2025-03-04T20:57:17.1515034Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:57:17.1515146Z topk_3 = logits_i_3.topk(1000, dim = 1); logits_i_3 = None 2025-03-04T20:57:17.1515252Z topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0] 2025-03-04T20:57:17.1515366Z topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1]; topk_3 = None 2025-03-04T20:57:17.1515456Z 2025-03-04T20:57:17.1515917Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:57:17.1516056Z getitem_104: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T20:57:17.1516290Z topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_104, topk_idx_3)]; proposals_i_8 = getitem_104 = topk_idx_3 = None 2025-03-04T20:57:17.1516359Z 2025-03-04T20:57:17.1516899Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:57:17.1517090Z full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:57:17.1517158Z 2025-03-04T20:57:17.1517487Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:17.1517613Z to_24: "i64[1000][1]cpu" = full_3.to(device(type='cpu')); full_3 = None 2025-03-04T20:57:17.1517687Z 2025-03-04T20:57:17.1518143Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:57:17.1518277Z topk_4 = logits_i_4.topk(1000, dim = 1); logits_i_4 = None 2025-03-04T20:57:17.1518378Z topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0] 2025-03-04T20:57:17.1518497Z topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1]; topk_4 = None 2025-03-04T20:57:17.1518559Z 2025-03-04T20:57:17.1519028Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:57:17.1519192Z getitem_108: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T20:57:17.1519434Z topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_108, topk_idx_4)]; proposals_i_9 = getitem_108 = topk_idx_4 = None 2025-03-04T20:57:17.1519495Z 2025-03-04T20:57:17.1519964Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:57:17.1520127Z full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:57:17.1520189Z 2025-03-04T20:57:17.1520484Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:17.1520601Z to_25: "i64[1000][1]cpu" = full_4.to(device(type='cpu')); full_4 = None 2025-03-04T20:57:17.1520667Z 2025-03-04T20:57:17.1520939Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:57:17.1521315Z topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1); topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None 2025-03-04T20:57:17.1521376Z 2025-03-04T20:57:17.1521658Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:57:17.1522113Z topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1); topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None 2025-03-04T20:57:17.1522197Z 2025-03-04T20:57:17.1522468Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:57:17.1522668Z level_ids: "i64[5000][1]cpu" = torch.cat([to_21, to_22, to_23, to_24, to_25], 0); to_21 = to_22 = to_23 = to_24 = to_25 = level_ids = None 2025-03-04T20:57:17.1522729Z 2025-03-04T20:57:17.1523130Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T20:57:17.1523282Z getitem_110: "f32[5000, 4][4, 1]cpu" = topk_proposals[0]; topk_proposals = None 2025-03-04T20:57:17.1523351Z 2025-03-04T20:57:17.1523640Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:57:17.1523787Z tensor_5: "f32[5000, 4][4, 1]cpu" = getitem_110.to(torch.float32); getitem_110 = None 2025-03-04T20:57:17.1523848Z 2025-03-04T20:57:17.1524221Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T20:57:17.1524363Z scores_per_img: "f32[5000][1]cpu" = topk_scores[0]; topk_scores = None 2025-03-04T20:57:17.1524430Z 2025-03-04T20:57:17.1524906Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T20:57:17.1525049Z isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor_5); tensor_5 = None 2025-03-04T20:57:17.1525172Z all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:57:17.1525318Z isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T20:57:17.1525450Z valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T20:57:17.1525510Z 2025-03-04T20:57:17.1525877Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T20:57:17.1525990Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T20:57:17.1526058Z 2025-03-04T20:57:17.1526572Z 2025-03-04T20:57:17.1526661Z class GraphModule(torch.nn.Module): 2025-03-04T20:57:17.1590143Z def forward(self, L_stack0_tensor: "f32[4, 3, 1184, 1216][4319232, 1439744, 1216, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: 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L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[3, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[3][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[12, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[12][1]cpu"): 2025-03-04T20:57:17.1590675Z l_stack0_tensor = L_stack0_tensor 2025-03-04T20:57:17.1591092Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ 2025-03-04T20:57:17.1591554Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.1592026Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.1592465Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:57:17.1592889Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:57:17.1593332Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:57:17.1593846Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.1594311Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.1594818Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:57:17.1595318Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:57:17.1595752Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:57:17.1596243Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.1596754Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.1597268Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:57:17.1597783Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:57:17.1598280Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:57:17.1598878Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.1599363Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.1599830Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:57:17.1600296Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:57:17.1600738Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:57:17.1601200Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.1601644Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.1602141Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:57:17.1602534Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:57:17.1602966Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:57:17.1603483Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.1603984Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.1604385Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:57:17.1604777Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:57:17.1605131Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:57:17.1605531Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.1605932Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.1606317Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:57:17.1606697Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:57:17.1607050Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:57:17.1607452Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.1607912Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.1608345Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:57:17.1608786Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:57:17.1609127Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:57:17.1609548Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.1609962Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.1610340Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:57:17.1610716Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:57:17.1611058Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:57:17.1611481Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.1611877Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.1612259Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:57:17.1612633Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:57:17.1612977Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:57:17.1613379Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.1613767Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.1614148Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:57:17.1614516Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:57:17.1614863Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:57:17.1615293Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.1615687Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.1616087Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:57:17.1616468Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:57:17.1616823Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:57:17.1617261Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.1617724Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.1618153Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:57:17.1618571Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:57:17.1618956Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:57:17.1619405Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.1619852Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.1620273Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:57:17.1620696Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:57:17.1621107Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:57:17.1621574Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.1622067Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.1622508Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:57:17.1622962Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:57:17.1623359Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:57:17.1623810Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.1624255Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.1624673Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:57:17.1625110Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:57:17.1625492Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:57:17.1625939Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.1626331Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.1626715Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:57:17.1627090Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:57:17.1627433Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:57:17.1627836Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.1628234Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.1628615Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:57:17.1629008Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:57:17.1629346Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:57:17.1629763Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.1630171Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.1630555Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:57:17.1630925Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:57:17.1631291Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:57:17.1631702Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.1632093Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.1632477Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:57:17.1632848Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:57:17.1633239Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:57:17.1633683Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.1634128Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.1634555Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:57:17.1634968Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:57:17.1635370Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ 2025-03-04T20:57:17.1635816Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.1636276Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.1636782Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:57:17.1637218Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:57:17.1637609Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ 2025-03-04T20:57:17.1638023Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.1638447Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.1638824Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:57:17.1639202Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:57:17.1639544Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ 2025-03-04T20:57:17.1639957Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.1640358Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.1640739Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:57:17.1641116Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:57:17.1641460Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:57:17.1641871Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.1642285Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.1642671Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:57:17.1643062Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:57:17.1643416Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:57:17.1643825Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.1644218Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.1644602Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:57:17.1644993Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:57:17.1645341Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:57:17.1645747Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.1646140Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.1646522Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:57:17.1646890Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:57:17.1647250Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:57:17.1647662Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.1648076Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.1648494Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:57:17.1648878Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:57:17.1649226Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:57:17.1649644Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.1650060Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.1650435Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:57:17.1650812Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:57:17.1651178Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:57:17.1651580Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.1651978Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.1652350Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:57:17.1652728Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:57:17.1653070Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:57:17.1653478Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.1653881Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.1654260Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:57:17.1654638Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:57:17.1655509Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:57:17.1655914Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.1656324Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.1656731Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:57:17.1657184Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:57:17.1657615Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:57:17.1658080Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.1658522Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.1658928Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:57:17.1659357Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:57:17.1659775Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:57:17.1660243Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.1660708Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.1661159Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:57:17.1661591Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:57:17.1661993Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ 2025-03-04T20:57:17.1662448Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.1662938Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.1663383Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:57:17.1663822Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:57:17.1664240Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ 2025-03-04T20:57:17.1664702Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.1665177Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.1665640Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:57:17.1666064Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:57:17.1666476Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ 2025-03-04T20:57:17.1666914Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.1667353Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.1667749Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:57:17.1668156Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:57:17.1668523Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ 2025-03-04T20:57:17.1668954Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.1669394Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.1669818Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:57:17.1670226Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:57:17.1670596Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ 2025-03-04T20:57:17.1671027Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.1671431Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.1671809Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:57:17.1672186Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:57:17.1672547Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ 2025-03-04T20:57:17.1672958Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.1673354Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.1673740Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:57:17.1674121Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:57:17.1674473Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ 2025-03-04T20:57:17.1674892Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.1675297Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.1675696Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:57:17.1676073Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:57:17.1676449Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ 2025-03-04T20:57:17.1676918Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.1677354Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.1677807Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:57:17.1678232Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:57:17.1678634Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ 2025-03-04T20:57:17.1679080Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.1679496Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.1679877Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:57:17.1680249Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:57:17.1680603Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:57:17.1681002Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.1681404Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.1681780Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:57:17.1682158Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:57:17.1682505Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:57:17.1682906Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.1683332Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.1683720Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:57:17.1684110Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:57:17.1684465Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:57:17.1684868Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.1685269Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.1685662Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:57:17.1686039Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:57:17.1686399Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:57:17.1686828Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.1687250Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.1687661Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:57:17.1688061Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:57:17.1688410Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:57:17.1688830Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.1689231Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.1689646Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:57:17.1690074Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:57:17.1690492Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:57:17.1690976Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.1691442Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.1691894Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:57:17.1692277Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:57:17.1692651Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:57:17.1693073Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.1693476Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.1693869Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:57:17.1694249Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:57:17.1694610Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:57:17.1695018Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.1695429Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.1695823Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:57:17.1696213Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:57:17.1696585Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:57:17.1696993Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.1697430Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.1697833Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:57:17.1698219Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:57:17.1698586Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:57:17.1699009Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.1699417Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.1699805Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:57:17.1700189Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:57:17.1700419Z l_self_modules_backbone_lateral_convs_0_parameters_weight_ = L_self_modules_backbone_lateral_convs_0_parameters_weight_ 2025-03-04T20:57:17.1700642Z l_self_modules_backbone_lateral_convs_0_parameters_bias_ = L_self_modules_backbone_lateral_convs_0_parameters_bias_ 2025-03-04T20:57:17.1700867Z l_self_modules_backbone_output_convs_0_parameters_weight_ = L_self_modules_backbone_output_convs_0_parameters_weight_ 2025-03-04T20:57:17.1701089Z l_self_modules_backbone_output_convs_0_parameters_bias_ = L_self_modules_backbone_output_convs_0_parameters_bias_ 2025-03-04T20:57:17.1701319Z l_self_modules_backbone_lateral_convs_1_parameters_weight_ = L_self_modules_backbone_lateral_convs_1_parameters_weight_ 2025-03-04T20:57:17.1701533Z l_self_modules_backbone_lateral_convs_1_parameters_bias_ = L_self_modules_backbone_lateral_convs_1_parameters_bias_ 2025-03-04T20:57:17.1701758Z l_self_modules_backbone_output_convs_1_parameters_weight_ = L_self_modules_backbone_output_convs_1_parameters_weight_ 2025-03-04T20:57:17.1702063Z l_self_modules_backbone_output_convs_1_parameters_bias_ = L_self_modules_backbone_output_convs_1_parameters_bias_ 2025-03-04T20:57:17.1702298Z l_self_modules_backbone_lateral_convs_2_parameters_weight_ = L_self_modules_backbone_lateral_convs_2_parameters_weight_ 2025-03-04T20:57:17.1702511Z l_self_modules_backbone_lateral_convs_2_parameters_bias_ = L_self_modules_backbone_lateral_convs_2_parameters_bias_ 2025-03-04T20:57:17.1702776Z l_self_modules_backbone_output_convs_2_parameters_weight_ = L_self_modules_backbone_output_convs_2_parameters_weight_ 2025-03-04T20:57:17.1702989Z l_self_modules_backbone_output_convs_2_parameters_bias_ = L_self_modules_backbone_output_convs_2_parameters_bias_ 2025-03-04T20:57:17.1703220Z l_self_modules_backbone_lateral_convs_3_parameters_weight_ = L_self_modules_backbone_lateral_convs_3_parameters_weight_ 2025-03-04T20:57:17.1703437Z l_self_modules_backbone_lateral_convs_3_parameters_bias_ = L_self_modules_backbone_lateral_convs_3_parameters_bias_ 2025-03-04T20:57:17.1703717Z l_self_modules_backbone_output_convs_3_parameters_weight_ = L_self_modules_backbone_output_convs_3_parameters_weight_ 2025-03-04T20:57:17.1703953Z l_self_modules_backbone_output_convs_3_parameters_bias_ = L_self_modules_backbone_output_convs_3_parameters_bias_ 2025-03-04T20:57:17.1704323Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T20:57:17.1704671Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ 2025-03-04T20:57:17.1705029Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ 2025-03-04T20:57:17.1705404Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ 2025-03-04T20:57:17.1705753Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ 2025-03-04T20:57:17.1706088Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T20:57:17.1706408Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T20:57:17.1706777Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T20:57:17.1707134Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T20:57:17.1707491Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T20:57:17.1707828Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T20:57:17.1707896Z 2025-03-04T20:57:17.1708171Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1708722Z x: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1); l_stack0_tensor = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = None 2025-03-04T20:57:17.1708795Z 2025-03-04T20:57:17.1709078Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1710828Z x_1: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1710900Z 2025-03-04T20:57:17.1711181Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x) 2025-03-04T20:57:17.1711318Z x_2: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.relu_(x_1); x_1 = None 2025-03-04T20:57:17.1711378Z 2025-03-04T20:57:17.1711740Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1) 2025-03-04T20:57:17.1711986Z x_3: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1); x_2 = None 2025-03-04T20:57:17.1712053Z 2025-03-04T20:57:17.1712305Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1712794Z x_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:57:17.1712855Z 2025-03-04T20:57:17.1713137Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1714946Z x_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1715013Z 2025-03-04T20:57:17.1715311Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1715446Z out: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_5); x_5 = None 2025-03-04T20:57:17.1715517Z 2025-03-04T20:57:17.1715770Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1716289Z x_6: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:57:17.1716351Z 2025-03-04T20:57:17.1716634Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1718601Z x_7: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_6 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1718684Z 2025-03-04T20:57:17.1718973Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1719106Z out_1: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_7); x_7 = None 2025-03-04T20:57:17.1719174Z 2025-03-04T20:57:17.1719422Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1719925Z x_8: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_1 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:57:17.1719985Z 2025-03-04T20:57:17.1720256Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1722024Z x_9: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1722087Z 2025-03-04T20:57:17.1722343Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1722837Z x_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); x_3 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:57:17.1722928Z 2025-03-04T20:57:17.1723184Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1725030Z x_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_10 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1725100Z 2025-03-04T20:57:17.1725392Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:57:17.1725541Z x_9 += x_11; out_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_9; x_9 = x_11 = None 2025-03-04T20:57:17.1725601Z 2025-03-04T20:57:17.1725887Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1726038Z out_3: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_2); out_2 = None 2025-03-04T20:57:17.1726098Z 2025-03-04T20:57:17.1726347Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1726827Z x_12: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:57:17.1726895Z 2025-03-04T20:57:17.1727155Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1728950Z x_13: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_12 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1729032Z 2025-03-04T20:57:17.1729310Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1729453Z out_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_13); x_13 = None 2025-03-04T20:57:17.1729512Z 2025-03-04T20:57:17.1729763Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1730266Z x_14: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:57:17.1730333Z 2025-03-04T20:57:17.1730632Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1732404Z x_15: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_14 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1732485Z 2025-03-04T20:57:17.1732765Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1732907Z out_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_15); x_15 = None 2025-03-04T20:57:17.1732965Z 2025-03-04T20:57:17.1733212Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1733703Z x_16: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_5 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:57:17.1733771Z 2025-03-04T20:57:17.1734028Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1735806Z x_17: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_16 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1735894Z 2025-03-04T20:57:17.1736169Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:57:17.1736324Z x_17 += out_3; out_6: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_17; x_17 = out_3 = None 2025-03-04T20:57:17.1736383Z 2025-03-04T20:57:17.1736680Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1736824Z out_7: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_6); out_6 = None 2025-03-04T20:57:17.1736890Z 2025-03-04T20:57:17.1737150Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1737633Z x_18: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:57:17.1737701Z 2025-03-04T20:57:17.1737960Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1739756Z x_19: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_18 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1739817Z 2025-03-04T20:57:17.1740101Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1740242Z out_8: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_19); x_19 = None 2025-03-04T20:57:17.1740303Z 2025-03-04T20:57:17.1740555Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1741036Z x_20: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:57:17.1741103Z 2025-03-04T20:57:17.1741364Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1743118Z x_21: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_20 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1743217Z 2025-03-04T20:57:17.1743496Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1743648Z out_9: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_21); x_21 = None 2025-03-04T20:57:17.1743708Z 2025-03-04T20:57:17.1743958Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1744449Z x_22: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_9 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:57:17.1744539Z 2025-03-04T20:57:17.1744799Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1746620Z x_23: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_22 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1746691Z 2025-03-04T20:57:17.1746971Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:57:17.1747133Z x_23 += out_7; out_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_23; x_23 = out_7 = None 2025-03-04T20:57:17.1747194Z 2025-03-04T20:57:17.1747479Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1747626Z out_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_10); out_10 = None 2025-03-04T20:57:17.1747693Z 2025-03-04T20:57:17.1747949Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1748435Z x_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:57:17.1748512Z 2025-03-04T20:57:17.1748778Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1750637Z x_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1750702Z 2025-03-04T20:57:17.1751004Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1751142Z out_12: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_25); x_25 = None 2025-03-04T20:57:17.1751225Z 2025-03-04T20:57:17.1751487Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1752000Z x_26: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_12 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:57:17.1752069Z 2025-03-04T20:57:17.1752333Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1754152Z x_27: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_26 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1754216Z 2025-03-04T20:57:17.1754506Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1754651Z out_13: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_27); x_27 = None 2025-03-04T20:57:17.1754715Z 2025-03-04T20:57:17.1754971Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1755467Z x_28: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_13 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:57:17.1755553Z 2025-03-04T20:57:17.1755818Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1757846Z x_29: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_28 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1757932Z 2025-03-04T20:57:17.1758223Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1758848Z x_30: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:57:17.1758910Z 2025-03-04T20:57:17.1759184Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1761046Z x_31: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_30 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1761119Z 2025-03-04T20:57:17.1761408Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:57:17.1761551Z x_29 += x_31; out_14: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_29; x_29 = x_31 = None 2025-03-04T20:57:17.1761621Z 2025-03-04T20:57:17.1761904Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1762061Z out_15: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_14); out_14 = None 2025-03-04T20:57:17.1762124Z 2025-03-04T20:57:17.1762399Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1762887Z x_32: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:57:17.1762956Z 2025-03-04T20:57:17.1763219Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1765077Z x_33: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_32 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1765165Z 2025-03-04T20:57:17.1765450Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1765594Z out_16: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_33); x_33 = None 2025-03-04T20:57:17.1765656Z 2025-03-04T20:57:17.1765908Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1766420Z x_34: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_16 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:57:17.1766488Z 2025-03-04T20:57:17.1766754Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1768511Z x_35: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_34 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1768580Z 2025-03-04T20:57:17.1768857Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1769012Z out_17: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_35); x_35 = None 2025-03-04T20:57:17.1769078Z 2025-03-04T20:57:17.1769320Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1769806Z x_36: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_17 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:57:17.1769867Z 2025-03-04T20:57:17.1770145Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1771918Z x_37: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_36 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1772005Z 2025-03-04T20:57:17.1772286Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:57:17.1772437Z x_37 += out_15; out_18: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_37; x_37 = out_15 = None 2025-03-04T20:57:17.1772504Z 2025-03-04T20:57:17.1772783Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1772935Z out_19: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_18); out_18 = None 2025-03-04T20:57:17.1772996Z 2025-03-04T20:57:17.1773247Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1773731Z x_38: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:57:17.1773801Z 2025-03-04T20:57:17.1774061Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1775837Z x_39: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_38 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1775922Z 2025-03-04T20:57:17.1776200Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1776340Z out_20: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_39); x_39 = None 2025-03-04T20:57:17.1776401Z 2025-03-04T20:57:17.1776663Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1777159Z x_40: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_20 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:57:17.1777228Z 2025-03-04T20:57:17.1777488Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1779269Z x_41: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_40 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1779355Z 2025-03-04T20:57:17.1779635Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1779777Z out_21: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_41); x_41 = None 2025-03-04T20:57:17.1779836Z 2025-03-04T20:57:17.1780087Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1780582Z x_42: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_21 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:57:17.1780649Z 2025-03-04T20:57:17.1780914Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1782684Z x_43: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_42 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1782770Z 2025-03-04T20:57:17.1783059Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:57:17.1783214Z x_43 += out_19; out_22: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_43; x_43 = out_19 = None 2025-03-04T20:57:17.1783280Z 2025-03-04T20:57:17.1783570Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1783725Z out_23: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_22); out_22 = None 2025-03-04T20:57:17.1783784Z 2025-03-04T20:57:17.1784030Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1784508Z x_44: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T20:57:17.1784586Z 2025-03-04T20:57:17.1784844Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1786626Z x_45: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_44 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1786694Z 2025-03-04T20:57:17.1786971Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1787111Z out_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_45); x_45 = None 2025-03-04T20:57:17.1787169Z 2025-03-04T20:57:17.1787416Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1787899Z x_46: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T20:57:17.1787968Z 2025-03-04T20:57:17.1788226Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1790031Z x_47: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_46 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1790102Z 2025-03-04T20:57:17.1790379Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1790517Z out_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_47); x_47 = None 2025-03-04T20:57:17.1790575Z 2025-03-04T20:57:17.1790822Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1791321Z x_48: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_25 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T20:57:17.1791391Z 2025-03-04T20:57:17.1791649Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1793456Z x_49: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_48 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1793525Z 2025-03-04T20:57:17.1793801Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:57:17.1793955Z x_49 += out_23; out_26: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_49; x_49 = out_23 = None 2025-03-04T20:57:17.1794015Z 2025-03-04T20:57:17.1794306Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1794450Z out_27: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_26); out_26 = None 2025-03-04T20:57:17.1794517Z 2025-03-04T20:57:17.1794766Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1795272Z x_50: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:57:17.1795340Z 2025-03-04T20:57:17.1795603Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1797504Z x_51: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_50 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1797599Z 2025-03-04T20:57:17.1797913Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1798061Z out_28: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_51); x_51 = None 2025-03-04T20:57:17.1798126Z 2025-03-04T20:57:17.1798395Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1798917Z x_52: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_28 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:57:17.1798985Z 2025-03-04T20:57:17.1799251Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1801088Z x_53: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_52 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1801161Z 2025-03-04T20:57:17.1801447Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1801590Z out_29: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_53); x_53 = None 2025-03-04T20:57:17.1801667Z 2025-03-04T20:57:17.1802058Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1802556Z x_54: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_29 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:57:17.1802632Z 2025-03-04T20:57:17.1802927Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1804762Z x_55: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_54 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1804855Z 2025-03-04T20:57:17.1805107Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1805606Z x_56: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:57:17.1805668Z 2025-03-04T20:57:17.1805942Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1807810Z x_57: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_56 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1807876Z 2025-03-04T20:57:17.1808166Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:57:17.1808306Z x_55 += x_57; out_30: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_55; x_55 = x_57 = None 2025-03-04T20:57:17.1808379Z 2025-03-04T20:57:17.1808709Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1808856Z out_31: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_30); out_30 = None 2025-03-04T20:57:17.1808918Z 2025-03-04T20:57:17.1809174Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1809669Z x_58: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:57:17.1809740Z 2025-03-04T20:57:17.1810034Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1811868Z x_59: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_58 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1811953Z 2025-03-04T20:57:17.1812256Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1812387Z out_32: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_59); x_59 = None 2025-03-04T20:57:17.1812453Z 2025-03-04T20:57:17.1812704Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1813206Z x_60: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_32 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:57:17.1813268Z 2025-03-04T20:57:17.1813544Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1815361Z x_61: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_60 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1815444Z 2025-03-04T20:57:17.1815729Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1815857Z out_33: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_61); x_61 = None 2025-03-04T20:57:17.1815922Z 2025-03-04T20:57:17.1816166Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1816682Z x_62: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_33 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:57:17.1816744Z 2025-03-04T20:57:17.1817006Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1818802Z x_63: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_62 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1818882Z 2025-03-04T20:57:17.1819165Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:57:17.1819306Z x_63 += out_31; out_34: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_63; x_63 = out_31 = None 2025-03-04T20:57:17.1819372Z 2025-03-04T20:57:17.1819651Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1819799Z out_35: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_34); out_34 = None 2025-03-04T20:57:17.1819860Z 2025-03-04T20:57:17.1820117Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1820603Z x_64: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:57:17.1820673Z 2025-03-04T20:57:17.1820939Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1822722Z x_65: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_64 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1822818Z 2025-03-04T20:57:17.1823110Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1823260Z out_36: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_65); x_65 = None 2025-03-04T20:57:17.1823320Z 2025-03-04T20:57:17.1823572Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1824044Z x_66: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_36 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:57:17.1824108Z 2025-03-04T20:57:17.1824373Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1826134Z x_67: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_66 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1826204Z 2025-03-04T20:57:17.1826477Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1826608Z out_37: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_67); x_67 = None 2025-03-04T20:57:17.1826674Z 2025-03-04T20:57:17.1826923Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1827414Z x_68: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_37 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:57:17.1827476Z 2025-03-04T20:57:17.1827750Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1829569Z x_69: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_68 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1829652Z 2025-03-04T20:57:17.1829955Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:57:17.1830097Z x_69 += out_35; out_38: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_69; x_69 = out_35 = None 2025-03-04T20:57:17.1830164Z 2025-03-04T20:57:17.1830448Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1830592Z out_39: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_38); out_38 = None 2025-03-04T20:57:17.1830653Z 2025-03-04T20:57:17.1830911Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1831410Z x_70: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T20:57:17.1831478Z 2025-03-04T20:57:17.1831744Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1833548Z x_71: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_70 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1833621Z 2025-03-04T20:57:17.1833905Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1834044Z out_40: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_71); x_71 = None 2025-03-04T20:57:17.1834106Z 2025-03-04T20:57:17.1834362Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1834846Z x_72: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_40 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T20:57:17.1834932Z 2025-03-04T20:57:17.1835196Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1837090Z x_73: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_72 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1837174Z 2025-03-04T20:57:17.1837472Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1837618Z out_41: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_73); x_73 = None 2025-03-04T20:57:17.1837701Z 2025-03-04T20:57:17.1837970Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1838488Z x_74: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_41 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T20:57:17.1838559Z 2025-03-04T20:57:17.1838860Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1840717Z x_75: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_74 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1840789Z 2025-03-04T20:57:17.1841069Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:57:17.1841224Z x_75 += out_39; out_42: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_75; x_75 = out_39 = None 2025-03-04T20:57:17.1841295Z 2025-03-04T20:57:17.1841596Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1841781Z out_43: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_42); out_42 = None 2025-03-04T20:57:17.1841848Z 2025-03-04T20:57:17.1842135Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1842673Z x_76: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = None 2025-03-04T20:57:17.1842750Z 2025-03-04T20:57:17.1843063Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1845091Z x_77: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_76 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1845196Z 2025-03-04T20:57:17.1845520Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1845674Z out_44: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_77); x_77 = None 2025-03-04T20:57:17.1845743Z 2025-03-04T20:57:17.1846034Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1846528Z x_78: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_44 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = None 2025-03-04T20:57:17.1846598Z 2025-03-04T20:57:17.1846869Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1848866Z x_79: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_78 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1848945Z 2025-03-04T20:57:17.1849282Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1849433Z out_45: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_79); x_79 = None 2025-03-04T20:57:17.1849500Z 2025-03-04T20:57:17.1849784Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1850347Z x_80: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_45 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = None 2025-03-04T20:57:17.1850426Z 2025-03-04T20:57:17.1850742Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1852721Z x_81: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_80 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1852807Z 2025-03-04T20:57:17.1853087Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:57:17.1853235Z x_81 += out_43; out_46: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_81; x_81 = out_43 = None 2025-03-04T20:57:17.1853295Z 2025-03-04T20:57:17.1853586Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1853724Z out_47: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_46); out_46 = None 2025-03-04T20:57:17.1853794Z 2025-03-04T20:57:17.1854045Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1854534Z x_82: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = None 2025-03-04T20:57:17.1854599Z 2025-03-04T20:57:17.1854864Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1856675Z x_83: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_82 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1856751Z 2025-03-04T20:57:17.1857045Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1857196Z out_48: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_83); x_83 = None 2025-03-04T20:57:17.1857256Z 2025-03-04T20:57:17.1857527Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1858017Z x_84: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_48 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = None 2025-03-04T20:57:17.1858084Z 2025-03-04T20:57:17.1858350Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1860163Z x_85: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_84 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1860248Z 2025-03-04T20:57:17.1860539Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1860675Z out_49: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_85); x_85 = None 2025-03-04T20:57:17.1860737Z 2025-03-04T20:57:17.1860995Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1861483Z x_86: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_49 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = None 2025-03-04T20:57:17.1861552Z 2025-03-04T20:57:17.1861814Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1863640Z x_87: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_86 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1863728Z 2025-03-04T20:57:17.1864024Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:57:17.1864188Z x_87 += out_47; out_50: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_87; x_87 = out_47 = None 2025-03-04T20:57:17.1864251Z 2025-03-04T20:57:17.1864539Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1864678Z out_51: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_50); out_50 = None 2025-03-04T20:57:17.1864745Z 2025-03-04T20:57:17.1864994Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1865490Z x_88: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:57:17.1865576Z 2025-03-04T20:57:17.1865849Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1867668Z x_89: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_88 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1867732Z 2025-03-04T20:57:17.1868022Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1868151Z out_52: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_89); x_89 = None 2025-03-04T20:57:17.1868220Z 2025-03-04T20:57:17.1868475Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1868951Z x_90: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_52 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:57:17.1869018Z 2025-03-04T20:57:17.1869297Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1871080Z x_91: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_90 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1871144Z 2025-03-04T20:57:17.1871432Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1871566Z out_53: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_91); x_91 = None 2025-03-04T20:57:17.1871625Z 2025-03-04T20:57:17.1871871Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1872381Z x_92: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_53 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:57:17.1872449Z 2025-03-04T20:57:17.1872715Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1874534Z x_93: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_92 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1874604Z 2025-03-04T20:57:17.1874855Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1875353Z x_94: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:57:17.1875416Z 2025-03-04T20:57:17.1875690Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1877641Z x_95: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_94 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1877719Z 2025-03-04T20:57:17.1878005Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:57:17.1878155Z x_93 += x_95; out_54: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_93; x_93 = x_95 = None 2025-03-04T20:57:17.1878223Z 2025-03-04T20:57:17.1878498Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1878657Z out_55: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_54); out_54 = None 2025-03-04T20:57:17.1878719Z 2025-03-04T20:57:17.1878970Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1879436Z x_96: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:57:17.1879505Z 2025-03-04T20:57:17.1879763Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1881512Z x_97: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_96 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1881584Z 2025-03-04T20:57:17.1881861Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1882000Z out_56: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_97); x_97 = None 2025-03-04T20:57:17.1882058Z 2025-03-04T20:57:17.1882309Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1882798Z x_98: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_56 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:57:17.1882865Z 2025-03-04T20:57:17.1883123Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1884912Z x_99: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_98 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1884982Z 2025-03-04T20:57:17.1885275Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1885406Z out_57: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_99); x_99 = None 2025-03-04T20:57:17.1885465Z 2025-03-04T20:57:17.1885716Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1886206Z x_100: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_57 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:57:17.1886264Z 2025-03-04T20:57:17.1886529Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1888307Z x_101: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_100 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1888374Z 2025-03-04T20:57:17.1888654Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:57:17.1888801Z x_101 += out_55; out_58: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_101; x_101 = out_55 = None 2025-03-04T20:57:17.1888884Z 2025-03-04T20:57:17.1889161Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1889302Z out_59: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_58); out_58 = None 2025-03-04T20:57:17.1889360Z 2025-03-04T20:57:17.1889612Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1890099Z x_102: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:57:17.1890167Z 2025-03-04T20:57:17.1890439Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1892214Z x_103: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_102 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1892300Z 2025-03-04T20:57:17.1892581Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1892717Z out_60: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_103); x_103 = None 2025-03-04T20:57:17.1892778Z 2025-03-04T20:57:17.1893026Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1893505Z x_104: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_60 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:57:17.1893575Z 2025-03-04T20:57:17.1893835Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1895599Z x_105: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_104 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1895684Z 2025-03-04T20:57:17.1895964Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1896104Z out_61: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_105); x_105 = None 2025-03-04T20:57:17.1896165Z 2025-03-04T20:57:17.1896415Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1896937Z x_106: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_61 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:57:17.1897007Z 2025-03-04T20:57:17.1897265Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.1899030Z x_107: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_106 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.1899114Z 2025-03-04T20:57:17.1899386Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:57:17.1899536Z x_107 += out_59; out_62: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_107; x_107 = out_59 = None 2025-03-04T20:57:17.1899597Z 2025-03-04T20:57:17.1899887Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.1900030Z out_63: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_62); out_62 = None 2025-03-04T20:57:17.1900092Z 2025-03-04T20:57:17.1900336Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1900904Z x_108: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(out_63, l_self_modules_backbone_lateral_convs_0_parameters_weight_, l_self_modules_backbone_lateral_convs_0_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_63 = l_self_modules_backbone_lateral_convs_0_parameters_weight_ = l_self_modules_backbone_lateral_convs_0_parameters_bias_ = None 2025-03-04T20:57:17.1900971Z 2025-03-04T20:57:17.1901214Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1901761Z x_109: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_108, l_self_modules_backbone_output_convs_0_parameters_weight_, l_self_modules_backbone_output_convs_0_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_self_modules_backbone_output_convs_0_parameters_weight_ = l_self_modules_backbone_output_convs_0_parameters_bias_ = None 2025-03-04T20:57:17.1901838Z 2025-03-04T20:57:17.1902338Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest") 2025-03-04T20:57:17.1902608Z top_down_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.interpolate(x_108, scale_factor = 2.0, mode = 'nearest'); x_108 = None 2025-03-04T20:57:17.1902676Z 2025-03-04T20:57:17.1902958Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1903548Z x_110: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_lateral_convs_1_parameters_weight_, l_self_modules_backbone_lateral_convs_1_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_51 = l_self_modules_backbone_lateral_convs_1_parameters_weight_ = l_self_modules_backbone_lateral_convs_1_parameters_bias_ = None 2025-03-04T20:57:17.1903617Z 2025-03-04T20:57:17.1903961Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features 2025-03-04T20:57:17.1904156Z prev_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = x_110 + top_down_features; x_110 = top_down_features = None 2025-03-04T20:57:17.1904215Z 2025-03-04T20:57:17.1904469Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1905052Z x_111: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(prev_features, l_self_modules_backbone_output_convs_1_parameters_weight_, l_self_modules_backbone_output_convs_1_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_self_modules_backbone_output_convs_1_parameters_weight_ = l_self_modules_backbone_output_convs_1_parameters_bias_ = None 2025-03-04T20:57:17.1905121Z 2025-03-04T20:57:17.1905519Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest") 2025-03-04T20:57:17.1905837Z top_down_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.interpolate(prev_features, scale_factor = 2.0, mode = 'nearest'); prev_features = None 2025-03-04T20:57:17.1905899Z 2025-03-04T20:57:17.1906156Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1906717Z x_112: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_lateral_convs_2_parameters_weight_, l_self_modules_backbone_lateral_convs_2_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_27 = l_self_modules_backbone_lateral_convs_2_parameters_weight_ = l_self_modules_backbone_lateral_convs_2_parameters_bias_ = None 2025-03-04T20:57:17.1906783Z 2025-03-04T20:57:17.1907122Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features 2025-03-04T20:57:17.1907330Z prev_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = x_112 + top_down_features_1; x_112 = top_down_features_1 = None 2025-03-04T20:57:17.1907399Z 2025-03-04T20:57:17.1907646Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1908219Z x_113: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(prev_features_1, l_self_modules_backbone_output_convs_2_parameters_weight_, l_self_modules_backbone_output_convs_2_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_self_modules_backbone_output_convs_2_parameters_weight_ = l_self_modules_backbone_output_convs_2_parameters_bias_ = None 2025-03-04T20:57:17.1908302Z 2025-03-04T20:57:17.1908701Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest") 2025-03-04T20:57:17.1909018Z top_down_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.interpolate(prev_features_1, scale_factor = 2.0, mode = 'nearest'); prev_features_1 = None 2025-03-04T20:57:17.1909086Z 2025-03-04T20:57:17.1909346Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1909925Z x_114: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_lateral_convs_3_parameters_weight_, l_self_modules_backbone_lateral_convs_3_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_11 = l_self_modules_backbone_lateral_convs_3_parameters_weight_ = l_self_modules_backbone_lateral_convs_3_parameters_bias_ = None 2025-03-04T20:57:17.1909986Z 2025-03-04T20:57:17.1910329Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features 2025-03-04T20:57:17.1910529Z prev_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_114 + top_down_features_2; x_114 = top_down_features_2 = None 2025-03-04T20:57:17.1910649Z 2025-03-04T20:57:17.1910893Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1911498Z x_115: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(prev_features_2, l_self_modules_backbone_output_convs_3_parameters_weight_, l_self_modules_backbone_output_convs_3_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); prev_features_2 = l_self_modules_backbone_output_convs_3_parameters_weight_ = l_self_modules_backbone_output_convs_3_parameters_bias_ = None 2025-03-04T20:57:17.1911566Z 2025-03-04T20:57:17.1911914Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:200 in forward, code: return [F.max_pool2d(x, kernel_size=1, stride=2, padding=0)] 2025-03-04T20:57:17.1912122Z res: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.max_pool2d(x_109, kernel_size = 1, stride = 2, padding = 0) 2025-03-04T20:57:17.1912182Z 2025-03-04T20:57:17.1912612Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:57:17.1912759Z arange: "f32[304][1]cpu" = torch.arange(0.0, 1216, step = 4, dtype = torch.float32) 2025-03-04T20:57:17.1912824Z 2025-03-04T20:57:17.1913112Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:17.1913255Z shifts_x: "f32[304][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T20:57:17.1913315Z 2025-03-04T20:57:17.1913755Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:57:17.1913904Z arange_1: "f32[296][1]cpu" = torch.arange(0.0, 1184, step = 4, dtype = torch.float32) 2025-03-04T20:57:17.1913973Z 2025-03-04T20:57:17.1914273Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:17.1914434Z shifts_y: "f32[296][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T20:57:17.1914492Z 2025-03-04T20:57:17.1914873Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:57:17.1915048Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T20:57:17.1915150Z shift_y: "f32[296, 304][1, 0]cpu" = meshgrid[0] 2025-03-04T20:57:17.1915268Z shift_x: "f32[296, 304][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T20:57:17.1915352Z 2025-03-04T20:57:17.1915684Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:57:17.1915837Z shift_x_1: "f32[89984][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T20:57:17.1915901Z 2025-03-04T20:57:17.1916239Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:57:17.1916359Z shift_y_1: "f32[89984][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T20:57:17.1916429Z 2025-03-04T20:57:17.1916856Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:57:17.1917101Z shifts: "f32[89984, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T20:57:17.1917171Z 2025-03-04T20:57:17.1917652Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:57:17.1917808Z view: "f32[89984, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T20:57:17.1918236Z view_1: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T20:57:17.1918365Z add_3: "f32[89984, 3, 4][12, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T20:57:17.1918482Z x_116: "f32[269952, 4][4, 1]cpu" = add_3.reshape(-1, 4); add_3 = None 2025-03-04T20:57:17.1918550Z 2025-03-04T20:57:17.1918983Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:57:17.1919136Z arange_2: "f32[152][1]cpu" = torch.arange(0.0, 1216, step = 8, dtype = torch.float32) 2025-03-04T20:57:17.1919196Z 2025-03-04T20:57:17.1919497Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:17.1919635Z shifts_x_1: "f32[152][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T20:57:17.1919705Z 2025-03-04T20:57:17.1920141Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:57:17.1920296Z arange_3: "f32[148][1]cpu" = torch.arange(0.0, 1184, step = 8, dtype = torch.float32) 2025-03-04T20:57:17.1920357Z 2025-03-04T20:57:17.1920659Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:17.1920820Z shifts_y_1: "f32[148][1]cpu" = arange_3.to(device(type='cpu')); arange_3 = None 2025-03-04T20:57:17.1920887Z 2025-03-04T20:57:17.1921258Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:57:17.1921457Z meshgrid_1 = torch.functional.meshgrid(shifts_y_1, shifts_x_1); shifts_y_1 = shifts_x_1 = None 2025-03-04T20:57:17.1921556Z shift_y_2: "f32[148, 152][1, 0]cpu" = meshgrid_1[0] 2025-03-04T20:57:17.1921701Z shift_x_2: "f32[148, 152][0, 1]cpu" = meshgrid_1[1]; meshgrid_1 = None 2025-03-04T20:57:17.1921764Z 2025-03-04T20:57:17.1922111Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:57:17.1922238Z shift_x_3: "f32[22496][1]cpu" = shift_x_2.reshape(-1); shift_x_2 = None 2025-03-04T20:57:17.1922307Z 2025-03-04T20:57:17.1922629Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:57:17.1922759Z shift_y_3: "f32[22496][1]cpu" = shift_y_2.reshape(-1); shift_y_2 = None 2025-03-04T20:57:17.1922821Z 2025-03-04T20:57:17.1923213Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:57:17.1923450Z shifts_1: "f32[22496, 4][4, 1]cpu" = torch.stack((shift_x_3, shift_y_3, shift_x_3, shift_y_3), dim = 1); shift_x_3 = shift_y_3 = None 2025-03-04T20:57:17.1923510Z 2025-03-04T20:57:17.1923934Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:57:17.1924064Z view_2: "f32[22496, 1, 4][4, 4, 1]cpu" = shifts_1.view(-1, 1, 4); shifts_1 = None 2025-03-04T20:57:17.1924503Z view_3: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = None 2025-03-04T20:57:17.1924627Z add_4: "f32[22496, 3, 4][12, 4, 1]cpu" = view_2 + view_3; view_2 = view_3 = None 2025-03-04T20:57:17.1924749Z x_117: "f32[67488, 4][4, 1]cpu" = add_4.reshape(-1, 4); add_4 = None 2025-03-04T20:57:17.1924809Z 2025-03-04T20:57:17.1925246Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:57:17.1925390Z arange_4: "f32[76][1]cpu" = torch.arange(0.0, 1216, step = 16, dtype = torch.float32) 2025-03-04T20:57:17.1925456Z 2025-03-04T20:57:17.1925749Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:17.1925891Z shifts_x_2: "f32[76][1]cpu" = arange_4.to(device(type='cpu')); arange_4 = None 2025-03-04T20:57:17.1925951Z 2025-03-04T20:57:17.1926386Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:57:17.1926524Z arange_5: "f32[74][1]cpu" = torch.arange(0.0, 1184, step = 16, dtype = torch.float32) 2025-03-04T20:57:17.1926593Z 2025-03-04T20:57:17.1926884Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:17.1927049Z shifts_y_2: "f32[74][1]cpu" = arange_5.to(device(type='cpu')); arange_5 = None 2025-03-04T20:57:17.1927109Z 2025-03-04T20:57:17.1927487Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:57:17.1927676Z meshgrid_2 = torch.functional.meshgrid(shifts_y_2, shifts_x_2); shifts_y_2 = shifts_x_2 = None 2025-03-04T20:57:17.1927782Z shift_y_4: "f32[74, 76][1, 0]cpu" = meshgrid_2[0] 2025-03-04T20:57:17.1927919Z shift_x_4: "f32[74, 76][0, 1]cpu" = meshgrid_2[1]; meshgrid_2 = None 2025-03-04T20:57:17.1927987Z 2025-03-04T20:57:17.1928325Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:57:17.1928454Z shift_x_5: "f32[5624][1]cpu" = shift_x_4.reshape(-1); shift_x_4 = None 2025-03-04T20:57:17.1928515Z 2025-03-04T20:57:17.1928844Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:57:17.1928961Z shift_y_5: "f32[5624][1]cpu" = shift_y_4.reshape(-1); shift_y_4 = None 2025-03-04T20:57:17.1929029Z 2025-03-04T20:57:17.1929407Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:57:17.1929649Z shifts_2: "f32[5624, 4][4, 1]cpu" = torch.stack((shift_x_5, shift_y_5, shift_x_5, shift_y_5), dim = 1); shift_x_5 = shift_y_5 = None 2025-03-04T20:57:17.1929718Z 2025-03-04T20:57:17.1930122Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:57:17.1930251Z view_4: "f32[5624, 1, 4][4, 4, 1]cpu" = shifts_2.view(-1, 1, 4); shifts_2 = None 2025-03-04T20:57:17.1930655Z view_5: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = None 2025-03-04T20:57:17.1930780Z add_5: "f32[5624, 3, 4][12, 4, 1]cpu" = view_4 + view_5; view_4 = view_5 = None 2025-03-04T20:57:17.1930891Z x_118: "f32[16872, 4][4, 1]cpu" = add_5.reshape(-1, 4); add_5 = None 2025-03-04T20:57:17.1930956Z 2025-03-04T20:57:17.1931381Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:57:17.1931524Z arange_6: "f32[38][1]cpu" = torch.arange(0.0, 1216, step = 32, dtype = torch.float32) 2025-03-04T20:57:17.1931584Z 2025-03-04T20:57:17.1931878Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:17.1932007Z shifts_x_3: "f32[38][1]cpu" = arange_6.to(device(type='cpu')); arange_6 = None 2025-03-04T20:57:17.1932072Z 2025-03-04T20:57:17.1932489Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:57:17.1932634Z arange_7: "f32[37][1]cpu" = torch.arange(0.0, 1184, step = 32, dtype = torch.float32) 2025-03-04T20:57:17.1932693Z 2025-03-04T20:57:17.1933004Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:17.1933134Z shifts_y_3: "f32[37][1]cpu" = arange_7.to(device(type='cpu')); arange_7 = None 2025-03-04T20:57:17.1933200Z 2025-03-04T20:57:17.1933565Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:57:17.1933759Z meshgrid_3 = torch.functional.meshgrid(shifts_y_3, shifts_x_3); shifts_y_3 = shifts_x_3 = None 2025-03-04T20:57:17.1933872Z shift_y_6: "f32[37, 38][1, 0]cpu" = meshgrid_3[0] 2025-03-04T20:57:17.1933998Z shift_x_6: "f32[37, 38][0, 1]cpu" = meshgrid_3[1]; meshgrid_3 = None 2025-03-04T20:57:17.1934058Z 2025-03-04T20:57:17.1934411Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:57:17.1934531Z shift_x_7: "f32[1406][1]cpu" = shift_x_6.reshape(-1); shift_x_6 = None 2025-03-04T20:57:17.1934596Z 2025-03-04T20:57:17.1934914Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:57:17.1935037Z shift_y_7: "f32[1406][1]cpu" = shift_y_6.reshape(-1); shift_y_6 = None 2025-03-04T20:57:17.1935097Z 2025-03-04T20:57:17.1935489Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:57:17.1935712Z shifts_3: "f32[1406, 4][4, 1]cpu" = torch.stack((shift_x_7, shift_y_7, shift_x_7, shift_y_7), dim = 1); shift_x_7 = shift_y_7 = None 2025-03-04T20:57:17.1935771Z 2025-03-04T20:57:17.1936176Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:57:17.1936295Z view_6: "f32[1406, 1, 4][4, 4, 1]cpu" = shifts_3.view(-1, 1, 4); shifts_3 = None 2025-03-04T20:57:17.1936707Z view_7: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = None 2025-03-04T20:57:17.1936826Z add_6: "f32[1406, 3, 4][12, 4, 1]cpu" = view_6 + view_7; view_6 = view_7 = None 2025-03-04T20:57:17.1936941Z x_119: "f32[4218, 4][4, 1]cpu" = add_6.reshape(-1, 4); add_6 = None 2025-03-04T20:57:17.1937002Z 2025-03-04T20:57:17.1937428Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:57:17.1937567Z arange_8: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32) 2025-03-04T20:57:17.1937634Z 2025-03-04T20:57:17.1937918Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:17.1938053Z shifts_x_4: "f32[19][1]cpu" = arange_8.to(device(type='cpu')); arange_8 = None 2025-03-04T20:57:17.1938113Z 2025-03-04T20:57:17.1938539Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:57:17.1938676Z arange_9: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32) 2025-03-04T20:57:17.1938762Z 2025-03-04T20:57:17.1939045Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:17.1939181Z shifts_y_4: "f32[19][1]cpu" = arange_9.to(device(type='cpu')); arange_9 = None 2025-03-04T20:57:17.1939242Z 2025-03-04T20:57:17.1939609Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:57:17.1939811Z meshgrid_4 = torch.functional.meshgrid(shifts_y_4, shifts_x_4); shifts_y_4 = shifts_x_4 = None 2025-03-04T20:57:17.1939913Z shift_y_8: "f32[19, 19][1, 0]cpu" = meshgrid_4[0] 2025-03-04T20:57:17.1940023Z shift_x_8: "f32[19, 19][0, 1]cpu" = meshgrid_4[1]; meshgrid_4 = None 2025-03-04T20:57:17.1940090Z 2025-03-04T20:57:17.1940425Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:57:17.1940552Z shift_x_9: "f32[361][1]cpu" = shift_x_8.reshape(-1); shift_x_8 = None 2025-03-04T20:57:17.1940612Z 2025-03-04T20:57:17.1940934Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:57:17.1941048Z shift_y_9: "f32[361][1]cpu" = shift_y_8.reshape(-1); shift_y_8 = None 2025-03-04T20:57:17.1941139Z 2025-03-04T20:57:17.1941512Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:57:17.1941724Z shifts_4: "f32[361, 4][4, 1]cpu" = torch.stack((shift_x_9, shift_y_9, shift_x_9, shift_y_9), dim = 1); shift_x_9 = shift_y_9 = None 2025-03-04T20:57:17.1941789Z 2025-03-04T20:57:17.1942193Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:57:17.1942320Z view_8: "f32[361, 1, 4][4, 4, 1]cpu" = shifts_4.view(-1, 1, 4); shifts_4 = None 2025-03-04T20:57:17.1942728Z view_9: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = None 2025-03-04T20:57:17.1942852Z add_7: "f32[361, 3, 4][12, 4, 1]cpu" = view_8 + view_9; view_8 = view_9 = None 2025-03-04T20:57:17.1942958Z x_120: "f32[1083, 4][4, 1]cpu" = add_7.reshape(-1, 4); add_7 = None 2025-03-04T20:57:17.1943024Z 2025-03-04T20:57:17.1943324Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:57:17.1943454Z tensor: "f32[269952, 4][4, 1]cpu" = x_116.to(torch.float32); x_116 = None 2025-03-04T20:57:17.1943578Z tensor_1: "f32[67488, 4][4, 1]cpu" = x_117.to(torch.float32); x_117 = None 2025-03-04T20:57:17.1943707Z tensor_2: "f32[16872, 4][4, 1]cpu" = x_118.to(torch.float32); x_118 = None 2025-03-04T20:57:17.1943828Z tensor_3: "f32[4218, 4][4, 1]cpu" = x_119.to(torch.float32); x_119 = None 2025-03-04T20:57:17.1943950Z tensor_4: "f32[1083, 4][4, 1]cpu" = x_120.to(torch.float32); x_120 = None 2025-03-04T20:57:17.1944008Z 2025-03-04T20:57:17.1944275Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1944774Z x_121: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_115, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_115 = None 2025-03-04T20:57:17.1944857Z 2025-03-04T20:57:17.1945126Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:57:17.1945328Z x_122: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.relu(x_121, inplace = False); x_121 = None 2025-03-04T20:57:17.1945387Z 2025-03-04T20:57:17.1945793Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:57:17.1946318Z score: "f32[4, 3, 296, 304][269952, 89984, 304, 1]cpu" = torch.conv2d(x_122, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T20:57:17.1946379Z 2025-03-04T20:57:17.1946739Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:57:17.1947248Z x_131: "f32[4, 12, 296, 304][1079808, 89984, 304, 1]cpu" = torch.conv2d(x_122, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_122 = None 2025-03-04T20:57:17.1947330Z 2025-03-04T20:57:17.1947578Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1948053Z x_123: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(x_113, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_113 = None 2025-03-04T20:57:17.1948117Z 2025-03-04T20:57:17.1948390Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:57:17.1948578Z x_124: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.relu(x_123, inplace = False); x_123 = None 2025-03-04T20:57:17.1948650Z 2025-03-04T20:57:17.1949018Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:57:17.1949528Z score_1: "f32[4, 3, 148, 152][67488, 22496, 152, 1]cpu" = torch.conv2d(x_124, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T20:57:17.1949590Z 2025-03-04T20:57:17.1949946Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:57:17.1950457Z x_132: "f32[4, 12, 148, 152][269952, 22496, 152, 1]cpu" = torch.conv2d(x_124, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_124 = None 2025-03-04T20:57:17.1950518Z 2025-03-04T20:57:17.1950772Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1951238Z x_125: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(x_111, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_111 = None 2025-03-04T20:57:17.1951325Z 2025-03-04T20:57:17.1951588Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:57:17.1951775Z x_126: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.relu(x_125, inplace = False); x_125 = None 2025-03-04T20:57:17.1951835Z 2025-03-04T20:57:17.1952233Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:57:17.1952759Z score_2: "f32[4, 3, 74, 76][16872, 5624, 76, 1]cpu" = torch.conv2d(x_126, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T20:57:17.1952831Z 2025-03-04T20:57:17.1953183Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:57:17.1953684Z x_133: "f32[4, 12, 74, 76][67488, 5624, 76, 1]cpu" = torch.conv2d(x_126, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_126 = None 2025-03-04T20:57:17.1953768Z 2025-03-04T20:57:17.1954022Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1954503Z x_127: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_109, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_109 = None 2025-03-04T20:57:17.1954564Z 2025-03-04T20:57:17.1975983Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:57:17.1976332Z x_128: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.nn.functional.relu(x_127, inplace = False); x_127 = None 2025-03-04T20:57:17.1976434Z 2025-03-04T20:57:17.1976870Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:57:17.1977395Z score_3: "f32[4, 3, 37, 38][4218, 1406, 38, 1]cpu" = torch.conv2d(x_128, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T20:57:17.1977464Z 2025-03-04T20:57:17.1977839Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:57:17.1978345Z x_134: "f32[4, 12, 37, 38][16872, 1406, 38, 1]cpu" = torch.conv2d(x_128, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_128 = None 2025-03-04T20:57:17.1978422Z 2025-03-04T20:57:17.1978693Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.1979549Z x_129: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.conv2d(res, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); res = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T20:57:17.1979620Z 2025-03-04T20:57:17.1979894Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:57:17.1980108Z x_130: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.relu(x_129, inplace = False); x_129 = None 2025-03-04T20:57:17.1980170Z 2025-03-04T20:57:17.1980576Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:57:17.1981425Z score_4: "f32[4, 3, 19, 19][1083, 361, 19, 1]cpu" = torch.conv2d(x_130, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T20:57:17.1981514Z 2025-03-04T20:57:17.1981876Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:57:17.1982673Z x_135: "f32[4, 12, 19, 19][4332, 361, 19, 1]cpu" = torch.conv2d(x_130, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_130 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T20:57:17.1982739Z 2025-03-04T20:57:17.1983076Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T20:57:17.1983246Z permute: "f32[4, 296, 304, 3][269952, 304, 1, 89984]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T20:57:17.1983388Z logits_i: "f32[4, 269952][269952, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T20:57:17.1983555Z permute_1: "f32[4, 148, 152, 3][67488, 152, 1, 22496]cpu" = score_1.permute(0, 2, 3, 1); score_1 = None 2025-03-04T20:57:17.1983697Z logits_i_1: "f32[4, 67488][67488, 1]cpu" = permute_1.flatten(1); permute_1 = None 2025-03-04T20:57:17.1983856Z permute_2: "f32[4, 74, 76, 3][16872, 76, 1, 5624]cpu" = score_2.permute(0, 2, 3, 1); score_2 = None 2025-03-04T20:57:17.1983991Z logits_i_2: "f32[4, 16872][16872, 1]cpu" = permute_2.flatten(1); permute_2 = None 2025-03-04T20:57:17.1984139Z permute_3: "f32[4, 37, 38, 3][4218, 38, 1, 1406]cpu" = score_3.permute(0, 2, 3, 1); score_3 = None 2025-03-04T20:57:17.1984269Z logits_i_3: "f32[4, 4218][4218, 1]cpu" = permute_3.flatten(1); permute_3 = None 2025-03-04T20:57:17.1984416Z permute_4: "f32[4, 19, 19, 3][1083, 19, 1, 361]cpu" = score_4.permute(0, 2, 3, 1); score_4 = None 2025-03-04T20:57:17.1984543Z logits_i_4: "f32[4, 1083][1083, 1]cpu" = permute_4.flatten(1); permute_4 = None 2025-03-04T20:57:17.1984614Z 2025-03-04T20:57:17.1985033Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T20:57:17.1985234Z view_10: "f32[4, 3, 4, 296, 304][1079808, 359936, 89984, 304, 1]cpu" = x_131.view(4, -1, 4, 296, 304); x_131 = None 2025-03-04T20:57:17.1985411Z permute_5: "f32[4, 296, 304, 3, 4][1079808, 304, 1, 359936, 89984]cpu" = view_10.permute(0, 3, 4, 1, 2); view_10 = None 2025-03-04T20:57:17.1985593Z pred_anchor_deltas_i: "f32[4, 269952, 4][1079808, 4, 1]cpu" = permute_5.flatten(1, -2); permute_5 = None 2025-03-04T20:57:17.1985758Z view_11: "f32[4, 3, 4, 148, 152][269952, 89984, 22496, 152, 1]cpu" = x_132.view(4, -1, 4, 148, 152); x_132 = None 2025-03-04T20:57:17.1985945Z permute_6: "f32[4, 148, 152, 3, 4][269952, 152, 1, 89984, 22496]cpu" = view_11.permute(0, 3, 4, 1, 2); view_11 = None 2025-03-04T20:57:17.1986146Z pred_anchor_deltas_i_2: "f32[4, 67488, 4][269952, 4, 1]cpu" = permute_6.flatten(1, -2); permute_6 = None 2025-03-04T20:57:17.1986295Z view_12: "f32[4, 3, 4, 74, 76][67488, 22496, 5624, 76, 1]cpu" = x_133.view(4, -1, 4, 74, 76); x_133 = None 2025-03-04T20:57:17.1986462Z permute_7: "f32[4, 74, 76, 3, 4][67488, 76, 1, 22496, 5624]cpu" = view_12.permute(0, 3, 4, 1, 2); view_12 = None 2025-03-04T20:57:17.1986624Z pred_anchor_deltas_i_4: "f32[4, 16872, 4][67488, 4, 1]cpu" = permute_7.flatten(1, -2); permute_7 = None 2025-03-04T20:57:17.1986768Z view_13: "f32[4, 3, 4, 37, 38][16872, 5624, 1406, 38, 1]cpu" = x_134.view(4, -1, 4, 37, 38); x_134 = None 2025-03-04T20:57:17.1986921Z permute_8: "f32[4, 37, 38, 3, 4][16872, 38, 1, 5624, 1406]cpu" = view_13.permute(0, 3, 4, 1, 2); view_13 = None 2025-03-04T20:57:17.1987106Z pred_anchor_deltas_i_6: "f32[4, 4218, 4][16872, 4, 1]cpu" = permute_8.flatten(1, -2); permute_8 = None 2025-03-04T20:57:17.1987241Z view_14: "f32[4, 3, 4, 19, 19][4332, 1444, 361, 19, 1]cpu" = x_135.view(4, -1, 4, 19, 19); x_135 = None 2025-03-04T20:57:17.1987404Z permute_9: "f32[4, 19, 19, 3, 4][4332, 19, 1, 1444, 361]cpu" = view_14.permute(0, 3, 4, 1, 2); view_14 = None 2025-03-04T20:57:17.1987564Z pred_anchor_deltas_i_8: "f32[4, 1083, 4][4332, 4, 1]cpu" = permute_9.flatten(1, -2); permute_9 = None 2025-03-04T20:57:17.1987632Z 2025-03-04T20:57:17.1988030Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:57:17.1988236Z pred_anchor_deltas_i_1: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T20:57:17.1988297Z 2025-03-04T20:57:17.1988739Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:57:17.1988891Z unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T20:57:17.1989039Z expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T20:57:17.1989175Z anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T20:57:17.1989243Z 2025-03-04T20:57:17.1989610Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:57:17.1989783Z deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T20:57:17.1989854Z 2025-03-04T20:57:17.1990159Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:57:17.1990303Z boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T20:57:17.1990379Z 2025-03-04T20:57:17.1990689Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:57:17.1990816Z getitem_10: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:57:17.1990951Z getitem_11: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:57:17.1991097Z widths: "f32[1079808][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T20:57:17.1991165Z 2025-03-04T20:57:17.1991490Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:57:17.1991619Z getitem_12: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:57:17.1991751Z getitem_13: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:57:17.1991907Z heights: "f32[1079808][1]cpu" = getitem_12 - getitem_13; getitem_12 = getitem_13 = None 2025-03-04T20:57:17.1991967Z 2025-03-04T20:57:17.1992279Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:57:17.1992403Z getitem_14: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:57:17.1992488Z mul: "f32[1079808][1]cpu" = 0.5 * widths 2025-03-04T20:57:17.1992634Z ctr_x: "f32[1079808][1]cpu" = getitem_14 + mul; getitem_14 = mul = None 2025-03-04T20:57:17.1992694Z 2025-03-04T20:57:17.1992999Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:57:17.1993154Z getitem_15: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:57:17.1993252Z mul_1: "f32[1079808][1]cpu" = 0.5 * heights 2025-03-04T20:57:17.1993383Z ctr_y: "f32[1079808][1]cpu" = getitem_15 + mul_1; getitem_15 = mul_1 = None 2025-03-04T20:57:17.1993453Z 2025-03-04T20:57:17.1993814Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:57:17.1993984Z getitem_16: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:57:17.1994107Z dx: "f32[1079808, 1][1, 1]cpu" = getitem_16 / 1.0; getitem_16 = None 2025-03-04T20:57:17.1994177Z 2025-03-04T20:57:17.1994497Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:57:17.1994667Z getitem_17: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:57:17.1994784Z dy: "f32[1079808, 1][1, 1]cpu" = getitem_17 / 1.0; getitem_17 = None 2025-03-04T20:57:17.1994853Z 2025-03-04T20:57:17.1995168Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:57:17.1995333Z getitem_18: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:57:17.1995447Z dw: "f32[1079808, 1][1, 1]cpu" = getitem_18 / 1.0; getitem_18 = None 2025-03-04T20:57:17.1995518Z 2025-03-04T20:57:17.1995870Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:57:17.1996073Z getitem_19: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:57:17.1996189Z dh: "f32[1079808, 1][1, 1]cpu" = getitem_19 / 1.0; getitem_19 = None 2025-03-04T20:57:17.1996280Z 2025-03-04T20:57:17.1996639Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:57:17.1996886Z dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:57:17.1996955Z 2025-03-04T20:57:17.1997315Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:57:17.1997479Z dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:57:17.1997553Z 2025-03-04T20:57:17.1997937Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:57:17.1998093Z getitem_20: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:57:17.1998224Z mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_20; dx = getitem_20 = None 2025-03-04T20:57:17.1998392Z getitem_21: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:57:17.1998536Z pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_21; mul_2 = getitem_21 = None 2025-03-04T20:57:17.1998608Z 2025-03-04T20:57:17.1998976Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:57:17.1999149Z getitem_22: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:57:17.1999284Z mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_22; dy = getitem_22 = None 2025-03-04T20:57:17.1999442Z getitem_23: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:57:17.1999588Z pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_23; mul_3 = getitem_23 = None 2025-03-04T20:57:17.1999652Z 2025-03-04T20:57:17.2000010Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:57:17.2000133Z exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:57:17.2000305Z getitem_24: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:57:17.2000444Z pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_24; exp = getitem_24 = None 2025-03-04T20:57:17.2000514Z 2025-03-04T20:57:17.2000867Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:57:17.2000994Z exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:57:17.2001168Z getitem_25: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:57:17.2001316Z pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_25; exp_1 = getitem_25 = None 2025-03-04T20:57:17.2001380Z 2025-03-04T20:57:17.2001717Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:57:17.2001818Z mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T20:57:17.2002087Z x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:57:17.2002159Z 2025-03-04T20:57:17.2002492Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:57:17.2002651Z mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T20:57:17.2002777Z y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:57:17.2002841Z 2025-03-04T20:57:17.2003177Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:57:17.2003294Z mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:57:17.2003436Z x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:57:17.2003502Z 2025-03-04T20:57:17.2003852Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:57:17.2003996Z mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:57:17.2004137Z y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:57:17.2004202Z 2025-03-04T20:57:17.2004577Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:57:17.2004766Z pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:57:17.2004838Z 2025-03-04T20:57:17.2005191Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:57:17.2005397Z proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4)); pred_boxes = None 2025-03-04T20:57:17.2005470Z 2025-03-04T20:57:17.2005852Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:57:17.2006024Z proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T20:57:17.2006092Z 2025-03-04T20:57:17.2006478Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:57:17.2006688Z pred_anchor_deltas_i_3: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_2.reshape(-1, 4); pred_anchor_deltas_i_2 = None 2025-03-04T20:57:17.2006749Z 2025-03-04T20:57:17.2007180Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:57:17.2007338Z unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = tensor_1.unsqueeze(0); tensor_1 = None 2025-03-04T20:57:17.2007484Z expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1); unsqueeze_1 = None 2025-03-04T20:57:17.2007625Z anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4); expand_1 = None 2025-03-04T20:57:17.2007684Z 2025-03-04T20:57:17.2008053Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:57:17.2008213Z deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float(); pred_anchor_deltas_i_3 = None 2025-03-04T20:57:17.2008281Z 2025-03-04T20:57:17.2008581Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:57:17.2008728Z boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32); anchors_i_1 = None 2025-03-04T20:57:17.2008803Z 2025-03-04T20:57:17.2009114Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:57:17.2009242Z getitem_26: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)] 2025-03-04T20:57:17.2009371Z getitem_27: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T20:57:17.2009518Z widths_1: "f32[269952][1]cpu" = getitem_26 - getitem_27; getitem_26 = getitem_27 = None 2025-03-04T20:57:17.2009585Z 2025-03-04T20:57:17.2009921Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:57:17.2010048Z getitem_28: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)] 2025-03-04T20:57:17.2010179Z getitem_29: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)] 2025-03-04T20:57:17.2010337Z heights_1: "f32[269952][1]cpu" = getitem_28 - getitem_29; getitem_28 = getitem_29 = None 2025-03-04T20:57:17.2010399Z 2025-03-04T20:57:17.2010703Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:57:17.2010818Z getitem_30: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T20:57:17.2010911Z mul_10: "f32[269952][1]cpu" = 0.5 * widths_1 2025-03-04T20:57:17.2011055Z ctr_x_1: "f32[269952][1]cpu" = getitem_30 + mul_10; getitem_30 = mul_10 = None 2025-03-04T20:57:17.2011124Z 2025-03-04T20:57:17.2011423Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:57:17.2011574Z getitem_31: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]; boxes_1 = None 2025-03-04T20:57:17.2011665Z mul_11: "f32[269952][1]cpu" = 0.5 * heights_1 2025-03-04T20:57:17.2011795Z ctr_y_1: "f32[269952][1]cpu" = getitem_31 + mul_11; getitem_31 = mul_11 = None 2025-03-04T20:57:17.2011865Z 2025-03-04T20:57:17.2012172Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:57:17.2012319Z getitem_32: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:57:17.2012435Z dx_1: "f32[269952, 1][1, 1]cpu" = getitem_32 / 1.0; getitem_32 = None 2025-03-04T20:57:17.2012494Z 2025-03-04T20:57:17.2012791Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:57:17.2012943Z getitem_33: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:57:17.2013053Z dy_1: "f32[269952, 1][1, 1]cpu" = getitem_33 / 1.0; getitem_33 = None 2025-03-04T20:57:17.2013120Z 2025-03-04T20:57:17.2013407Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:57:17.2013557Z getitem_34: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:57:17.2013662Z dw_2: "f32[269952, 1][1, 1]cpu" = getitem_34 / 1.0; getitem_34 = None 2025-03-04T20:57:17.2013728Z 2025-03-04T20:57:17.2014023Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:57:17.2014207Z getitem_35: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))]; deltas_1 = None 2025-03-04T20:57:17.2014328Z dh_2: "f32[269952, 1][1, 1]cpu" = getitem_35 / 1.0; getitem_35 = None 2025-03-04T20:57:17.2014393Z 2025-03-04T20:57:17.2014719Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:57:17.2014863Z dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356); dw_2 = None 2025-03-04T20:57:17.2014921Z 2025-03-04T20:57:17.2015248Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:57:17.2015394Z dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356); dh_2 = None 2025-03-04T20:57:17.2015462Z 2025-03-04T20:57:17.2015810Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:57:17.2015951Z getitem_36: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)] 2025-03-04T20:57:17.2016072Z mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_36; dx_1 = getitem_36 = None 2025-03-04T20:57:17.2016233Z getitem_37: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)]; ctr_x_1 = None 2025-03-04T20:57:17.2016368Z pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_37; mul_12 = getitem_37 = None 2025-03-04T20:57:17.2016435Z 2025-03-04T20:57:17.2016790Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:57:17.2016928Z getitem_38: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)] 2025-03-04T20:57:17.2017048Z mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_38; dy_1 = getitem_38 = None 2025-03-04T20:57:17.2017205Z getitem_39: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)]; ctr_y_1 = None 2025-03-04T20:57:17.2017337Z pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_39; mul_13 = getitem_39 = None 2025-03-04T20:57:17.2017404Z 2025-03-04T20:57:17.2017727Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:57:17.2017841Z exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3); dw_3 = None 2025-03-04T20:57:17.2017998Z getitem_40: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]; widths_1 = None 2025-03-04T20:57:17.2018134Z pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_40; exp_2 = getitem_40 = None 2025-03-04T20:57:17.2018194Z 2025-03-04T20:57:17.2018523Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:57:17.2018637Z exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3); dh_3 = None 2025-03-04T20:57:17.2018800Z getitem_41: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]; heights_1 = None 2025-03-04T20:57:17.2018934Z pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_41; exp_3 = getitem_41 = None 2025-03-04T20:57:17.2018992Z 2025-03-04T20:57:17.2019299Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:57:17.2019394Z mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1 2025-03-04T20:57:17.2019513Z x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16; mul_16 = None 2025-03-04T20:57:17.2019571Z 2025-03-04T20:57:17.2019879Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:57:17.2019986Z mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1 2025-03-04T20:57:17.2020104Z y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17; mul_17 = None 2025-03-04T20:57:17.2020163Z 2025-03-04T20:57:17.2020464Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:57:17.2020576Z mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1; pred_w_1 = None 2025-03-04T20:57:17.2020743Z x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18; pred_ctr_x_1 = mul_18 = None 2025-03-04T20:57:17.2020806Z 2025-03-04T20:57:17.2021120Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:57:17.2021232Z mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1; pred_h_1 = None 2025-03-04T20:57:17.2021363Z y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19; pred_ctr_y_1 = mul_19 = None 2025-03-04T20:57:17.2021422Z 2025-03-04T20:57:17.2021767Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:57:17.2021957Z pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1); x1_1 = y1_1 = x2_1 = y2_1 = None 2025-03-04T20:57:17.2022037Z 2025-03-04T20:57:17.2022365Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:57:17.2022531Z proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4)); pred_boxes_1 = None 2025-03-04T20:57:17.2022592Z 2025-03-04T20:57:17.2022968Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:57:17.2023137Z proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4); proposals_i_1 = None 2025-03-04T20:57:17.2023204Z 2025-03-04T20:57:17.2023590Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:57:17.2023799Z pred_anchor_deltas_i_5: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_4.reshape(-1, 4); pred_anchor_deltas_i_4 = None 2025-03-04T20:57:17.2023861Z 2025-03-04T20:57:17.2024293Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:57:17.2024440Z unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = tensor_2.unsqueeze(0); tensor_2 = None 2025-03-04T20:57:17.2024593Z expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1); unsqueeze_2 = None 2025-03-04T20:57:17.2024725Z anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4); expand_2 = None 2025-03-04T20:57:17.2024792Z 2025-03-04T20:57:17.2025152Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:57:17.2025326Z deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_5.float(); pred_anchor_deltas_i_5 = None 2025-03-04T20:57:17.2025384Z 2025-03-04T20:57:17.2025693Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:57:17.2025851Z boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32); anchors_i_2 = None 2025-03-04T20:57:17.2025910Z 2025-03-04T20:57:17.2026222Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:57:17.2026347Z getitem_42: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)] 2025-03-04T20:57:17.2026471Z getitem_43: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T20:57:17.2026627Z widths_2: "f32[67488][1]cpu" = getitem_42 - getitem_43; getitem_42 = getitem_43 = None 2025-03-04T20:57:17.2026694Z 2025-03-04T20:57:17.2027006Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:57:17.2027176Z getitem_44: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)] 2025-03-04T20:57:17.2027292Z getitem_45: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)] 2025-03-04T20:57:17.2027439Z heights_2: "f32[67488][1]cpu" = getitem_44 - getitem_45; getitem_44 = getitem_45 = None 2025-03-04T20:57:17.2027499Z 2025-03-04T20:57:17.2027804Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:57:17.2027918Z getitem_46: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T20:57:17.2028025Z mul_20: "f32[67488][1]cpu" = 0.5 * widths_2 2025-03-04T20:57:17.2028152Z ctr_x_2: "f32[67488][1]cpu" = getitem_46 + mul_20; getitem_46 = mul_20 = None 2025-03-04T20:57:17.2028218Z 2025-03-04T20:57:17.2028524Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:57:17.2028673Z getitem_47: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]; boxes_2 = None 2025-03-04T20:57:17.2028761Z mul_21: "f32[67488][1]cpu" = 0.5 * heights_2 2025-03-04T20:57:17.2028887Z ctr_y_2: "f32[67488][1]cpu" = getitem_47 + mul_21; getitem_47 = mul_21 = None 2025-03-04T20:57:17.2028946Z 2025-03-04T20:57:17.2029248Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:57:17.2029396Z getitem_48: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:57:17.2029511Z dx_2: "f32[67488, 1][1, 1]cpu" = getitem_48 / 1.0; getitem_48 = None 2025-03-04T20:57:17.2029572Z 2025-03-04T20:57:17.2029871Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:57:17.2030017Z getitem_49: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:57:17.2030133Z dy_2: "f32[67488, 1][1, 1]cpu" = getitem_49 / 1.0; getitem_49 = None 2025-03-04T20:57:17.2030192Z 2025-03-04T20:57:17.2030488Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:57:17.2030631Z getitem_50: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:57:17.2030743Z dw_4: "f32[67488, 1][1, 1]cpu" = getitem_50 / 1.0; getitem_50 = None 2025-03-04T20:57:17.2030802Z 2025-03-04T20:57:17.2031101Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:57:17.2031279Z getitem_51: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))]; deltas_2 = None 2025-03-04T20:57:17.2031415Z dh_4: "f32[67488, 1][1, 1]cpu" = getitem_51 / 1.0; getitem_51 = None 2025-03-04T20:57:17.2031473Z 2025-03-04T20:57:17.2031808Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:57:17.2031949Z dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356); dw_4 = None 2025-03-04T20:57:17.2032009Z 2025-03-04T20:57:17.2032373Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:57:17.2032508Z dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356); dh_4 = None 2025-03-04T20:57:17.2032574Z 2025-03-04T20:57:17.2032928Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:57:17.2033065Z getitem_52: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)] 2025-03-04T20:57:17.2033187Z mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_52; dx_2 = getitem_52 = None 2025-03-04T20:57:17.2033343Z getitem_53: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)]; ctr_x_2 = None 2025-03-04T20:57:17.2033480Z pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_53; mul_22 = getitem_53 = None 2025-03-04T20:57:17.2033563Z 2025-03-04T20:57:17.2033911Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:57:17.2034052Z getitem_54: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)] 2025-03-04T20:57:17.2034174Z mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_54; dy_2 = getitem_54 = None 2025-03-04T20:57:17.2034325Z getitem_55: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)]; ctr_y_2 = None 2025-03-04T20:57:17.2034459Z pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_55; mul_23 = getitem_55 = None 2025-03-04T20:57:17.2034524Z 2025-03-04T20:57:17.2034851Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:57:17.2034969Z exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5); dw_5 = None 2025-03-04T20:57:17.2035125Z getitem_56: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]; widths_2 = None 2025-03-04T20:57:17.2035264Z pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_56; exp_4 = getitem_56 = None 2025-03-04T20:57:17.2035326Z 2025-03-04T20:57:17.2035662Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:57:17.2035770Z exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5); dh_5 = None 2025-03-04T20:57:17.2035941Z getitem_57: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]; heights_2 = None 2025-03-04T20:57:17.2036071Z pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_57; exp_5 = getitem_57 = None 2025-03-04T20:57:17.2036140Z 2025-03-04T20:57:17.2036451Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:57:17.2036552Z mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2 2025-03-04T20:57:17.2036667Z x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26; mul_26 = None 2025-03-04T20:57:17.2036822Z 2025-03-04T20:57:17.2037139Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:57:17.2037236Z mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2 2025-03-04T20:57:17.2037349Z y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27; mul_27 = None 2025-03-04T20:57:17.2037420Z 2025-03-04T20:57:17.2037724Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:57:17.2037848Z mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2; pred_w_2 = None 2025-03-04T20:57:17.2037998Z x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28; pred_ctr_x_2 = mul_28 = None 2025-03-04T20:57:17.2038070Z 2025-03-04T20:57:17.2038388Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:57:17.2038511Z mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2; pred_h_2 = None 2025-03-04T20:57:17.2038638Z y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29; pred_ctr_y_2 = mul_29 = None 2025-03-04T20:57:17.2038707Z 2025-03-04T20:57:17.2039057Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:57:17.2039252Z pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1); x1_2 = y1_2 = x2_2 = y2_2 = None 2025-03-04T20:57:17.2039327Z 2025-03-04T20:57:17.2039669Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:57:17.2039835Z proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4)); pred_boxes_2 = None 2025-03-04T20:57:17.2039897Z 2025-03-04T20:57:17.2040283Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:57:17.2040456Z proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4); proposals_i_2 = None 2025-03-04T20:57:17.2040522Z 2025-03-04T20:57:17.2040919Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:57:17.2041130Z pred_anchor_deltas_i_7: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_6.reshape(-1, 4); pred_anchor_deltas_i_6 = None 2025-03-04T20:57:17.2041191Z 2025-03-04T20:57:17.2041630Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:57:17.2041776Z unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = tensor_3.unsqueeze(0); tensor_3 = None 2025-03-04T20:57:17.2041928Z expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1); unsqueeze_3 = None 2025-03-04T20:57:17.2042061Z anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4); expand_3 = None 2025-03-04T20:57:17.2042127Z 2025-03-04T20:57:17.2042495Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:57:17.2042667Z deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_7.float(); pred_anchor_deltas_i_7 = None 2025-03-04T20:57:17.2042727Z 2025-03-04T20:57:17.2043042Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:57:17.2043196Z boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32); anchors_i_3 = None 2025-03-04T20:57:17.2043263Z 2025-03-04T20:57:17.2043575Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:57:17.2043712Z getitem_58: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)] 2025-03-04T20:57:17.2043834Z getitem_59: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T20:57:17.2043999Z widths_3: "f32[16872][1]cpu" = getitem_58 - getitem_59; getitem_58 = getitem_59 = None 2025-03-04T20:57:17.2044061Z 2025-03-04T20:57:17.2044401Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:57:17.2044526Z getitem_60: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)] 2025-03-04T20:57:17.2044648Z getitem_61: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)] 2025-03-04T20:57:17.2044795Z heights_3: "f32[16872][1]cpu" = getitem_60 - getitem_61; getitem_60 = getitem_61 = None 2025-03-04T20:57:17.2044864Z 2025-03-04T20:57:17.2045175Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:57:17.2045316Z getitem_62: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T20:57:17.2045401Z mul_30: "f32[16872][1]cpu" = 0.5 * widths_3 2025-03-04T20:57:17.2045536Z ctr_x_3: "f32[16872][1]cpu" = getitem_62 + mul_30; getitem_62 = mul_30 = None 2025-03-04T20:57:17.2045595Z 2025-03-04T20:57:17.2045910Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:57:17.2046054Z getitem_63: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]; boxes_3 = None 2025-03-04T20:57:17.2046151Z mul_31: "f32[16872][1]cpu" = 0.5 * heights_3 2025-03-04T20:57:17.2046277Z ctr_y_3: "f32[16872][1]cpu" = getitem_63 + mul_31; getitem_63 = mul_31 = None 2025-03-04T20:57:17.2046344Z 2025-03-04T20:57:17.2046644Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:57:17.2046800Z getitem_64: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:57:17.2046913Z dx_3: "f32[16872, 1][1, 1]cpu" = getitem_64 / 1.0; getitem_64 = None 2025-03-04T20:57:17.2046973Z 2025-03-04T20:57:17.2047281Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:57:17.2047429Z getitem_65: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:57:17.2047543Z dy_3: "f32[16872, 1][1, 1]cpu" = getitem_65 / 1.0; getitem_65 = None 2025-03-04T20:57:17.2047603Z 2025-03-04T20:57:17.2047903Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:57:17.2048048Z getitem_66: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:57:17.2048173Z dw_6: "f32[16872, 1][1, 1]cpu" = getitem_66 / 1.0; getitem_66 = None 2025-03-04T20:57:17.2048231Z 2025-03-04T20:57:17.2048533Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:57:17.2048725Z getitem_67: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))]; deltas_3 = None 2025-03-04T20:57:17.2048835Z dh_6: "f32[16872, 1][1, 1]cpu" = getitem_67 / 1.0; getitem_67 = None 2025-03-04T20:57:17.2048892Z 2025-03-04T20:57:17.2049224Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:57:17.2049356Z dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356); dw_6 = None 2025-03-04T20:57:17.2049424Z 2025-03-04T20:57:17.2049760Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:57:17.2049913Z dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356); dh_6 = None 2025-03-04T20:57:17.2049974Z 2025-03-04T20:57:17.2050316Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:57:17.2050442Z getitem_68: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)] 2025-03-04T20:57:17.2050565Z mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_68; dx_3 = getitem_68 = None 2025-03-04T20:57:17.2050710Z getitem_69: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)]; ctr_x_3 = None 2025-03-04T20:57:17.2050872Z pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_69; mul_32 = getitem_69 = None 2025-03-04T20:57:17.2050933Z 2025-03-04T20:57:17.2051278Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:57:17.2051408Z getitem_70: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)] 2025-03-04T20:57:17.2051530Z mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_70; dy_3 = getitem_70 = None 2025-03-04T20:57:17.2051673Z getitem_71: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)]; ctr_y_3 = None 2025-03-04T20:57:17.2051812Z pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_71; mul_33 = getitem_71 = None 2025-03-04T20:57:17.2051871Z 2025-03-04T20:57:17.2052198Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:57:17.2052312Z exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7); dw_7 = None 2025-03-04T20:57:17.2052464Z getitem_72: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]; widths_3 = None 2025-03-04T20:57:17.2052598Z pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_72; exp_6 = getitem_72 = None 2025-03-04T20:57:17.2052660Z 2025-03-04T20:57:17.2052982Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:57:17.2053088Z exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7); dh_7 = None 2025-03-04T20:57:17.2053251Z getitem_73: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]; heights_3 = None 2025-03-04T20:57:17.2053375Z pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_73; exp_7 = getitem_73 = None 2025-03-04T20:57:17.2053443Z 2025-03-04T20:57:17.2053742Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:57:17.2053837Z mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3 2025-03-04T20:57:17.2053949Z x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36; mul_36 = None 2025-03-04T20:57:17.2054031Z 2025-03-04T20:57:17.2054329Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:57:17.2054423Z mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3 2025-03-04T20:57:17.2054530Z y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37; mul_37 = None 2025-03-04T20:57:17.2054595Z 2025-03-04T20:57:17.2054889Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:57:17.2055020Z mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3; pred_w_3 = None 2025-03-04T20:57:17.2055147Z x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38; pred_ctr_x_3 = mul_38 = None 2025-03-04T20:57:17.2055212Z 2025-03-04T20:57:17.2055519Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:57:17.2055634Z mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3; pred_h_3 = None 2025-03-04T20:57:17.2055756Z y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39; pred_ctr_y_3 = mul_39 = None 2025-03-04T20:57:17.2055820Z 2025-03-04T20:57:17.2056154Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:57:17.2056360Z pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1); x1_3 = y1_3 = x2_3 = y2_3 = None 2025-03-04T20:57:17.2056419Z 2025-03-04T20:57:17.2056752Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:57:17.2056906Z proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4)); pred_boxes_3 = None 2025-03-04T20:57:17.2056972Z 2025-03-04T20:57:17.2057342Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:57:17.2057513Z proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4); proposals_i_3 = None 2025-03-04T20:57:17.2057573Z 2025-03-04T20:57:17.2057969Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:57:17.2058170Z pred_anchor_deltas_i_9: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_8.reshape(-1, 4); pred_anchor_deltas_i_8 = None 2025-03-04T20:57:17.2058239Z 2025-03-04T20:57:17.2058659Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:57:17.2058802Z unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = tensor_4.unsqueeze(0); tensor_4 = None 2025-03-04T20:57:17.2058942Z expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1); unsqueeze_4 = None 2025-03-04T20:57:17.2059077Z anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4); expand_4 = None 2025-03-04T20:57:17.2059140Z 2025-03-04T20:57:17.2059514Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:57:17.2059681Z deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_9.float(); pred_anchor_deltas_i_9 = None 2025-03-04T20:57:17.2059755Z 2025-03-04T20:57:17.2060057Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:57:17.2060190Z boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32); anchors_i_4 = None 2025-03-04T20:57:17.2060257Z 2025-03-04T20:57:17.2060560Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:57:17.2060688Z getitem_74: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)] 2025-03-04T20:57:17.2060821Z getitem_75: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T20:57:17.2060969Z widths_4: "f32[4332][1]cpu" = getitem_74 - getitem_75; getitem_74 = getitem_75 = None 2025-03-04T20:57:17.2061028Z 2025-03-04T20:57:17.2061357Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:57:17.2061474Z getitem_76: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)] 2025-03-04T20:57:17.2061591Z getitem_77: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)] 2025-03-04T20:57:17.2061729Z heights_4: "f32[4332][1]cpu" = getitem_76 - getitem_77; getitem_76 = getitem_77 = None 2025-03-04T20:57:17.2061792Z 2025-03-04T20:57:17.2062093Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:57:17.2062227Z getitem_78: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T20:57:17.2062313Z mul_40: "f32[4332][1]cpu" = 0.5 * widths_4 2025-03-04T20:57:17.2062440Z ctr_x_4: "f32[4332][1]cpu" = getitem_78 + mul_40; getitem_78 = mul_40 = None 2025-03-04T20:57:17.2062501Z 2025-03-04T20:57:17.2062815Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:57:17.2062954Z getitem_79: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]; boxes_4 = None 2025-03-04T20:57:17.2063043Z mul_41: "f32[4332][1]cpu" = 0.5 * heights_4 2025-03-04T20:57:17.2063163Z ctr_y_4: "f32[4332][1]cpu" = getitem_79 + mul_41; getitem_79 = mul_41 = None 2025-03-04T20:57:17.2063229Z 2025-03-04T20:57:17.2063525Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:57:17.2063681Z getitem_80: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:57:17.2063787Z dx_4: "f32[4332, 1][1, 1]cpu" = getitem_80 / 1.0; getitem_80 = None 2025-03-04T20:57:17.2063853Z 2025-03-04T20:57:17.2064146Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:57:17.2064297Z getitem_81: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:57:17.2064399Z dy_4: "f32[4332, 1][1, 1]cpu" = getitem_81 / 1.0; getitem_81 = None 2025-03-04T20:57:17.2064465Z 2025-03-04T20:57:17.2064753Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:57:17.2064907Z getitem_82: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:57:17.2065008Z dw_8: "f32[4332, 1][1, 1]cpu" = getitem_82 / 1.0; getitem_82 = None 2025-03-04T20:57:17.2065073Z 2025-03-04T20:57:17.2065370Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:57:17.2065570Z getitem_83: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))]; deltas_4 = None 2025-03-04T20:57:17.2065679Z dh_8: "f32[4332, 1][1, 1]cpu" = getitem_83 / 1.0; getitem_83 = None 2025-03-04T20:57:17.2065737Z 2025-03-04T20:57:17.2066071Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:57:17.2066200Z dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356); dw_8 = None 2025-03-04T20:57:17.2066267Z 2025-03-04T20:57:17.2066609Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:57:17.2066797Z dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356); dh_8 = None 2025-03-04T20:57:17.2066859Z 2025-03-04T20:57:17.2067198Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:57:17.2067324Z getitem_84: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)] 2025-03-04T20:57:17.2067446Z mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_84; dx_4 = getitem_84 = None 2025-03-04T20:57:17.2067592Z getitem_85: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)]; ctr_x_4 = None 2025-03-04T20:57:17.2067749Z pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_85; mul_42 = getitem_85 = None 2025-03-04T20:57:17.2067818Z 2025-03-04T20:57:17.2068163Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:57:17.2068292Z getitem_86: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)] 2025-03-04T20:57:17.2068413Z mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_86; dy_4 = getitem_86 = None 2025-03-04T20:57:17.2068553Z getitem_87: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)]; ctr_y_4 = None 2025-03-04T20:57:17.2068686Z pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_87; mul_43 = getitem_87 = None 2025-03-04T20:57:17.2068745Z 2025-03-04T20:57:17.2069068Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:57:17.2069179Z exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9); dw_9 = None 2025-03-04T20:57:17.2069338Z getitem_88: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]; widths_4 = None 2025-03-04T20:57:17.2069465Z pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_88; exp_8 = getitem_88 = None 2025-03-04T20:57:17.2069532Z 2025-03-04T20:57:17.2069850Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:57:17.2069962Z exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9); dh_9 = None 2025-03-04T20:57:17.2070119Z getitem_89: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]; heights_4 = None 2025-03-04T20:57:17.2070249Z pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_89; exp_9 = getitem_89 = None 2025-03-04T20:57:17.2070308Z 2025-03-04T20:57:17.2070616Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:57:17.2070707Z mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4 2025-03-04T20:57:17.2070822Z x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46; mul_46 = None 2025-03-04T20:57:17.2070896Z 2025-03-04T20:57:17.2071204Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:57:17.2071293Z mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4 2025-03-04T20:57:17.2071408Z y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47; mul_47 = None 2025-03-04T20:57:17.2071467Z 2025-03-04T20:57:17.2071768Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:57:17.2071891Z mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4; pred_w_4 = None 2025-03-04T20:57:17.2072026Z x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48; pred_ctr_x_4 = mul_48 = None 2025-03-04T20:57:17.2072106Z 2025-03-04T20:57:17.2072411Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:57:17.2072517Z mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4; pred_h_4 = None 2025-03-04T20:57:17.2072645Z y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49; pred_ctr_y_4 = mul_49 = None 2025-03-04T20:57:17.2072704Z 2025-03-04T20:57:17.2073053Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:57:17.2073248Z pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1); x1_4 = y1_4 = x2_4 = y2_4 = None 2025-03-04T20:57:17.2073318Z 2025-03-04T20:57:17.2073658Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:57:17.2073815Z proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4)); pred_boxes_4 = None 2025-03-04T20:57:17.2073881Z 2025-03-04T20:57:17.2074266Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:57:17.2074440Z proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4); proposals_i_4 = None 2025-03-04T20:57:17.2074500Z 2025-03-04T20:57:17.2074996Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T20:57:17.2075127Z arange_10: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T20:57:17.2075196Z 2025-03-04T20:57:17.2075494Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:17.2075641Z batch_idx: "i64[4][1]cpu" = arange_10.to(device(type='cpu')); arange_10 = None 2025-03-04T20:57:17.2075701Z 2025-03-04T20:57:17.2076147Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:57:17.2076258Z topk = logits_i.topk(1000, dim = 1); logits_i = None 2025-03-04T20:57:17.2076367Z topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0] 2025-03-04T20:57:17.2076478Z topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1]; topk = None 2025-03-04T20:57:17.2076545Z 2025-03-04T20:57:17.2077086Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:57:17.2077256Z getitem_92: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T20:57:17.2077497Z topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_92, topk_idx)]; proposals_i_5 = getitem_92 = topk_idx = None 2025-03-04T20:57:17.2077570Z 2025-03-04T20:57:17.2078050Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:57:17.2078241Z full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:57:17.2078302Z 2025-03-04T20:57:17.2078625Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:17.2078750Z to_21: "i64[1000][1]cpu" = full.to(device(type='cpu')); full = None 2025-03-04T20:57:17.2078821Z 2025-03-04T20:57:17.2079258Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:57:17.2079383Z topk_1 = logits_i_1.topk(1000, dim = 1); logits_i_1 = None 2025-03-04T20:57:17.2079488Z topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0] 2025-03-04T20:57:17.2079630Z topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1]; topk_1 = None 2025-03-04T20:57:17.2079690Z 2025-03-04T20:57:17.2080157Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:57:17.2080292Z getitem_96: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T20:57:17.2080525Z topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_96, topk_idx_1)]; proposals_i_6 = getitem_96 = topk_idx_1 = None 2025-03-04T20:57:17.2080596Z 2025-03-04T20:57:17.2081051Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:57:17.2081224Z full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:57:17.2081286Z 2025-03-04T20:57:17.2081586Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:17.2081710Z to_22: "i64[1000][1]cpu" = full_1.to(device(type='cpu')); full_1 = None 2025-03-04T20:57:17.2081777Z 2025-03-04T20:57:17.2082208Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:57:17.2082327Z topk_2 = logits_i_2.topk(1000, dim = 1); logits_i_2 = None 2025-03-04T20:57:17.2082429Z topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0] 2025-03-04T20:57:17.2082549Z topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1]; topk_2 = None 2025-03-04T20:57:17.2082611Z 2025-03-04T20:57:17.2083074Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:57:17.2083205Z getitem_100: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T20:57:17.2083457Z topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_100, topk_idx_2)]; proposals_i_7 = getitem_100 = topk_idx_2 = None 2025-03-04T20:57:17.2083519Z 2025-03-04T20:57:17.2083973Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:57:17.2084133Z full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:57:17.2084204Z 2025-03-04T20:57:17.2084507Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:17.2084649Z to_23: "i64[1000][1]cpu" = full_2.to(device(type='cpu')); full_2 = None 2025-03-04T20:57:17.2084712Z 2025-03-04T20:57:17.2085154Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:57:17.2085264Z topk_3 = logits_i_3.topk(1000, dim = 1); logits_i_3 = None 2025-03-04T20:57:17.2085372Z topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0] 2025-03-04T20:57:17.2085484Z topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1]; topk_3 = None 2025-03-04T20:57:17.2085553Z 2025-03-04T20:57:17.2086018Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:57:17.2086154Z getitem_104: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T20:57:17.2086390Z topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_104, topk_idx_3)]; proposals_i_8 = getitem_104 = topk_idx_3 = None 2025-03-04T20:57:17.2086451Z 2025-03-04T20:57:17.2086903Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:57:17.2087061Z full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:57:17.2087129Z 2025-03-04T20:57:17.2087418Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:17.2087542Z to_24: "i64[1000][1]cpu" = full_3.to(device(type='cpu')); full_3 = None 2025-03-04T20:57:17.2087602Z 2025-03-04T20:57:17.2088036Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:57:17.2088145Z topk_4 = logits_i_4.topk(1000, dim = 1); logits_i_4 = None 2025-03-04T20:57:17.2088252Z topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0] 2025-03-04T20:57:17.2088363Z topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1]; topk_4 = None 2025-03-04T20:57:17.2088431Z 2025-03-04T20:57:17.2088883Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:57:17.2089050Z getitem_108: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T20:57:17.2089278Z topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_108, topk_idx_4)]; proposals_i_9 = getitem_108 = topk_idx_4 = None 2025-03-04T20:57:17.2089363Z 2025-03-04T20:57:17.2089814Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:57:17.2089978Z full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:57:17.2090037Z 2025-03-04T20:57:17.2090350Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:17.2090468Z to_25: "i64[1000][1]cpu" = full_4.to(device(type='cpu')); full_4 = None 2025-03-04T20:57:17.2090536Z 2025-03-04T20:57:17.2090829Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:57:17.2091228Z topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1); topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None 2025-03-04T20:57:17.2091287Z 2025-03-04T20:57:17.2091564Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:57:17.2092024Z topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1); topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None 2025-03-04T20:57:17.2092100Z 2025-03-04T20:57:17.2092377Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:57:17.2092572Z level_ids: "i64[5000][1]cpu" = torch.cat([to_21, to_22, to_23, to_24, to_25], 0); to_21 = to_22 = to_23 = to_24 = to_25 = level_ids = None 2025-03-04T20:57:17.2092639Z 2025-03-04T20:57:17.2093019Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T20:57:17.2093160Z getitem_110: "f32[5000, 4][4, 1]cpu" = topk_proposals[0]; topk_proposals = None 2025-03-04T20:57:17.2093220Z 2025-03-04T20:57:17.2093518Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:57:17.2093657Z tensor_5: "f32[5000, 4][4, 1]cpu" = getitem_110.to(torch.float32); getitem_110 = None 2025-03-04T20:57:17.2093723Z 2025-03-04T20:57:17.2094095Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T20:57:17.2094230Z scores_per_img: "f32[5000][1]cpu" = topk_scores[0]; topk_scores = None 2025-03-04T20:57:17.2094289Z 2025-03-04T20:57:17.2094772Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T20:57:17.2094903Z isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor_5); tensor_5 = None 2025-03-04T20:57:17.2095028Z all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:57:17.2095177Z isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T20:57:17.2095313Z valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T20:57:17.2095387Z 2025-03-04T20:57:17.2095759Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T20:57:17.2095871Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T20:57:17.2095938Z 2025-03-04T20:57:17.2095947Z 2025-03-04T20:57:17.2096035Z class GraphModule(torch.nn.Module): 2025-03-04T20:57:17.2159556Z def forward(self, L_stack0_tensor: "f32[4, 3, 1184, 1216][4319232, 1439744, 1216, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: 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L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_: "f32[512, 128, 1, 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1][2048, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_lateral_convs_0_parameters_weight_: "f32[256, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_0_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_0_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_0_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_1_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_1_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_1_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_2_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_2_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_2_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_3_parameters_weight_: "f32[256, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_3_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_3_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_3_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[3, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[3][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[12, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[12][1]cpu"): 2025-03-04T20:57:17.2160113Z l_stack0_tensor = L_stack0_tensor 2025-03-04T20:57:17.2160536Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ 2025-03-04T20:57:17.2161019Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.2161420Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.2161844Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:57:17.2162244Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:57:17.2162609Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:57:17.2163067Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.2163564Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.2164040Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:57:17.2164440Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:57:17.2164797Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:57:17.2165202Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.2165611Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.2165987Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:57:17.2166369Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:57:17.2166714Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:57:17.2167135Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.2167610Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.2168037Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:57:17.2168435Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:57:17.2168796Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:57:17.2169215Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.2169627Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.2170043Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:57:17.2170441Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:57:17.2170783Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:57:17.2171187Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.2171589Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.2171975Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:57:17.2172345Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:57:17.2172696Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:57:17.2173103Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.2173508Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.2173891Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:57:17.2174262Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:57:17.2174629Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:57:17.2175315Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.2175723Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.2176104Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:57:17.2176494Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:57:17.2176842Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:57:17.2177241Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.2177647Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.2178074Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:57:17.2178501Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:57:17.2178888Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:57:17.2179305Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.2179706Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.2180081Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:57:17.2180471Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:57:17.2180812Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:57:17.2181231Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.2181646Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.2182023Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:57:17.2182398Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:57:17.2182738Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:57:17.2183159Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.2183555Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.2183940Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:57:17.2184316Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:57:17.2184657Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:57:17.2185065Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.2185460Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.2185840Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:57:17.2186217Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:57:17.2186561Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:57:17.2186977Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.2187369Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.2187770Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:57:17.2188153Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:57:17.2188518Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:57:17.2188937Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.2189363Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.2189767Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:57:17.2190153Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:57:17.2190498Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:57:17.2190899Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.2191303Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.2191690Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:57:17.2192061Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:57:17.2192411Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:57:17.2192848Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.2193319Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.2193744Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:57:17.2194179Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:57:17.2194581Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:57:17.2195035Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.2195492Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.2195916Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:57:17.2196360Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:57:17.2196788Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:57:17.2197244Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.2197701Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.2198136Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:57:17.2198563Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:57:17.2198946Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:57:17.2199461Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.2199899Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.2200339Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:57:17.2200784Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:57:17.2201170Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:57:17.2201660Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.2202276Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.2202715Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:57:17.2203103Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:57:17.2203492Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ 2025-03-04T20:57:17.2203921Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.2204338Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.2204744Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:57:17.2205131Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:57:17.2205504Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ 2025-03-04T20:57:17.2205925Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.2206353Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.2206740Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:57:17.2207109Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:57:17.2207479Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ 2025-03-04T20:57:17.2207879Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.2208303Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.2208696Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:57:17.2209077Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:57:17.2209425Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:57:17.2209826Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.2210241Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.2210616Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:57:17.2210991Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:57:17.2211328Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:57:17.2211733Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.2212131Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.2212508Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:57:17.2212882Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:57:17.2213226Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:57:17.2213629Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.2214037Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.2214418Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:57:17.2214810Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:57:17.2215181Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:57:17.2215603Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.2216010Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.2216410Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:57:17.2216814Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:57:17.2217157Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:57:17.2217557Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.2217952Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.2218338Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:57:17.2218707Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:57:17.2219051Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:57:17.2219453Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.2219848Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.2220243Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:57:17.2220610Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:57:17.2220951Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:57:17.2221359Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.2221773Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.2222156Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:57:17.2222526Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:57:17.2222889Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:57:17.2223289Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.2223686Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.2224060Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:57:17.2224438Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:57:17.2224783Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:57:17.2225179Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.2225579Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.2225958Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:57:17.2226332Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:57:17.2226697Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:57:17.2227109Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.2227516Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.2227915Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:57:17.2228289Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:57:17.2228628Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ 2025-03-04T20:57:17.2229032Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.2229440Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.2229823Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:57:17.2230191Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:57:17.2230537Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ 2025-03-04T20:57:17.2230943Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.2231334Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.2231714Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:57:17.2232082Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:57:17.2232433Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ 2025-03-04T20:57:17.2232832Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.2233278Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.2233659Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:57:17.2234042Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:57:17.2234406Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ 2025-03-04T20:57:17.2234806Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.2235205Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.2235597Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:57:17.2235981Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:57:17.2236338Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ 2025-03-04T20:57:17.2236783Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.2237200Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.2237586Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:57:17.2237982Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:57:17.2238321Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ 2025-03-04T20:57:17.2238723Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.2239126Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.2239538Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:57:17.2239931Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:57:17.2240310Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ 2025-03-04T20:57:17.2240755Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.2241167Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.2241568Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:57:17.2241960Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:57:17.2242328Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ 2025-03-04T20:57:17.2242755Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.2243170Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.2243570Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:57:17.2243949Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:57:17.2244301Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ 2025-03-04T20:57:17.2244717Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.2245117Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.2245510Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:57:17.2245885Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:57:17.2246257Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ 2025-03-04T20:57:17.2246664Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.2247090Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.2247498Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:57:17.2247877Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:57:17.2248232Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ 2025-03-04T20:57:17.2248638Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.2249074Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.2249457Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:57:17.2249841Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:57:17.2250197Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ 2025-03-04T20:57:17.2250605Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.2251016Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.2251398Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:57:17.2251782Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:57:17.2252161Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T20:57:17.2252593Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.2253011Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.2253419Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T20:57:17.2253819Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T20:57:17.2254164Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ 2025-03-04T20:57:17.2254573Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.2254976Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.2255368Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:57:17.2255743Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:57:17.2256081Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ 2025-03-04T20:57:17.2256484Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.2256877Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.2257261Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:57:17.2257638Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:57:17.2257975Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ 2025-03-04T20:57:17.2258378Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.2258770Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.2259169Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:57:17.2259536Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:57:17.2259897Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ 2025-03-04T20:57:17.2260317Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.2260714Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.2261098Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T20:57:17.2261466Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T20:57:17.2261829Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ 2025-03-04T20:57:17.2262227Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.2262628Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.2263012Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T20:57:17.2263382Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T20:57:17.2263733Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ 2025-03-04T20:57:17.2264134Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T20:57:17.2264542Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T20:57:17.2264918Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T20:57:17.2265298Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T20:57:17.2265544Z l_self_modules_backbone_lateral_convs_0_parameters_weight_ = L_self_modules_backbone_lateral_convs_0_parameters_weight_ 2025-03-04T20:57:17.2265761Z l_self_modules_backbone_lateral_convs_0_parameters_bias_ = L_self_modules_backbone_lateral_convs_0_parameters_bias_ 2025-03-04T20:57:17.2265984Z l_self_modules_backbone_output_convs_0_parameters_weight_ = L_self_modules_backbone_output_convs_0_parameters_weight_ 2025-03-04T20:57:17.2266209Z l_self_modules_backbone_output_convs_0_parameters_bias_ = L_self_modules_backbone_output_convs_0_parameters_bias_ 2025-03-04T20:57:17.2266435Z l_self_modules_backbone_lateral_convs_1_parameters_weight_ = L_self_modules_backbone_lateral_convs_1_parameters_weight_ 2025-03-04T20:57:17.2266658Z l_self_modules_backbone_lateral_convs_1_parameters_bias_ = L_self_modules_backbone_lateral_convs_1_parameters_bias_ 2025-03-04T20:57:17.2266881Z l_self_modules_backbone_output_convs_1_parameters_weight_ = L_self_modules_backbone_output_convs_1_parameters_weight_ 2025-03-04T20:57:17.2267085Z l_self_modules_backbone_output_convs_1_parameters_bias_ = L_self_modules_backbone_output_convs_1_parameters_bias_ 2025-03-04T20:57:17.2267307Z l_self_modules_backbone_lateral_convs_2_parameters_weight_ = L_self_modules_backbone_lateral_convs_2_parameters_weight_ 2025-03-04T20:57:17.2267515Z l_self_modules_backbone_lateral_convs_2_parameters_bias_ = L_self_modules_backbone_lateral_convs_2_parameters_bias_ 2025-03-04T20:57:17.2267750Z l_self_modules_backbone_output_convs_2_parameters_weight_ = L_self_modules_backbone_output_convs_2_parameters_weight_ 2025-03-04T20:57:17.2267955Z l_self_modules_backbone_output_convs_2_parameters_bias_ = L_self_modules_backbone_output_convs_2_parameters_bias_ 2025-03-04T20:57:17.2268174Z l_self_modules_backbone_lateral_convs_3_parameters_weight_ = L_self_modules_backbone_lateral_convs_3_parameters_weight_ 2025-03-04T20:57:17.2268382Z l_self_modules_backbone_lateral_convs_3_parameters_bias_ = L_self_modules_backbone_lateral_convs_3_parameters_bias_ 2025-03-04T20:57:17.2268598Z l_self_modules_backbone_output_convs_3_parameters_weight_ = L_self_modules_backbone_output_convs_3_parameters_weight_ 2025-03-04T20:57:17.2268799Z l_self_modules_backbone_output_convs_3_parameters_bias_ = L_self_modules_backbone_output_convs_3_parameters_bias_ 2025-03-04T20:57:17.2269155Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T20:57:17.2269504Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ 2025-03-04T20:57:17.2269844Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ 2025-03-04T20:57:17.2270185Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ 2025-03-04T20:57:17.2270524Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ 2025-03-04T20:57:17.2270846Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T20:57:17.2271153Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T20:57:17.2271540Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T20:57:17.2271895Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T20:57:17.2272262Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T20:57:17.2272606Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T20:57:17.2272689Z 2025-03-04T20:57:17.2272978Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.2273505Z x: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1); l_stack0_tensor = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = None 2025-03-04T20:57:17.2273573Z 2025-03-04T20:57:17.2273849Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.2275663Z x_1: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.2275736Z 2025-03-04T20:57:17.2276028Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x) 2025-03-04T20:57:17.2276172Z x_2: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.relu_(x_1); x_1 = None 2025-03-04T20:57:17.2276236Z 2025-03-04T20:57:17.2276608Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1) 2025-03-04T20:57:17.2276907Z x_3: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1); x_2 = None 2025-03-04T20:57:17.2276987Z 2025-03-04T20:57:17.2277258Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.2277800Z x_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:57:17.2277866Z 2025-03-04T20:57:17.2278179Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.2280054Z x_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.2280120Z 2025-03-04T20:57:17.2280422Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.2280559Z out: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_5); x_5 = None 2025-03-04T20:57:17.2280630Z 2025-03-04T20:57:17.2280885Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.2281412Z x_6: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:57:17.2281484Z 2025-03-04T20:57:17.2281769Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.2283602Z x_7: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_6 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.2283664Z 2025-03-04T20:57:17.2283959Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.2284103Z out_1: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_7); x_7 = None 2025-03-04T20:57:17.2284166Z 2025-03-04T20:57:17.2284426Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.2284934Z x_8: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_1 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:57:17.2285019Z 2025-03-04T20:57:17.2285288Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.2287152Z x_9: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.2287226Z 2025-03-04T20:57:17.2287476Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.2287985Z x_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); x_3 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:57:17.2288062Z 2025-03-04T20:57:17.2288335Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.2290241Z x_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_10 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.2290311Z 2025-03-04T20:57:17.2290599Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:57:17.2290741Z x_9 += x_11; out_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_9; x_9 = x_11 = None 2025-03-04T20:57:17.2290811Z 2025-03-04T20:57:17.2291103Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.2291257Z out_3: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_2); out_2 = None 2025-03-04T20:57:17.2291316Z 2025-03-04T20:57:17.2291565Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.2292058Z x_12: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:57:17.2292125Z 2025-03-04T20:57:17.2292383Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.2294202Z x_13: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_12 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.2294321Z 2025-03-04T20:57:17.2294605Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.2294756Z out_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_13); x_13 = None 2025-03-04T20:57:17.2294818Z 2025-03-04T20:57:17.2295071Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.2295566Z x_14: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:57:17.2295635Z 2025-03-04T20:57:17.2295906Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.2297701Z x_15: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_14 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.2297769Z 2025-03-04T20:57:17.2298046Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.2298186Z out_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_15); x_15 = None 2025-03-04T20:57:17.2298261Z 2025-03-04T20:57:17.2298515Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.2299015Z x_16: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_5 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:57:17.2299077Z 2025-03-04T20:57:17.2299377Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.2301174Z x_17: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_16 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.2301258Z 2025-03-04T20:57:17.2301552Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:57:17.2301707Z x_17 += out_3; out_6: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_17; x_17 = out_3 = None 2025-03-04T20:57:17.2301771Z 2025-03-04T20:57:17.2302163Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.2302317Z out_7: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_6); out_6 = None 2025-03-04T20:57:17.2302378Z 2025-03-04T20:57:17.2302628Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.2303118Z x_18: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:57:17.2303188Z 2025-03-04T20:57:17.2303456Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.2305247Z x_19: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_18 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.2305356Z 2025-03-04T20:57:17.2305634Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.2305772Z out_8: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_19); x_19 = None 2025-03-04T20:57:17.2305832Z 2025-03-04T20:57:17.2306105Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.2306608Z x_20: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:57:17.2306680Z 2025-03-04T20:57:17.2306940Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.2308717Z x_21: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_20 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.2308810Z 2025-03-04T20:57:17.2309088Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.2309228Z out_9: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_21); x_21 = None 2025-03-04T20:57:17.2309289Z 2025-03-04T20:57:17.2309544Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.2310032Z x_22: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_9 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:57:17.2310101Z 2025-03-04T20:57:17.2310368Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.2312145Z x_23: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_22 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.2312232Z 2025-03-04T20:57:17.2312506Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:57:17.2312681Z x_23 += out_7; out_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_23; x_23 = out_7 = None 2025-03-04T20:57:17.2312750Z 2025-03-04T20:57:17.2313032Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.2313205Z out_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_10); out_10 = None 2025-03-04T20:57:17.2313270Z 2025-03-04T20:57:17.2313526Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.2314016Z x_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:57:17.2314101Z 2025-03-04T20:57:17.2314369Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.2316170Z x_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.2316241Z 2025-03-04T20:57:17.2316532Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.2316683Z out_12: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_25); x_25 = None 2025-03-04T20:57:17.2316779Z 2025-03-04T20:57:17.2317042Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.2317560Z x_26: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_12 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:57:17.2317631Z 2025-03-04T20:57:17.2317920Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.2319775Z x_27: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_26 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.2319879Z 2025-03-04T20:57:17.2320165Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.2320315Z out_13: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_27); x_27 = None 2025-03-04T20:57:17.2320375Z 2025-03-04T20:57:17.2320640Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.2321142Z x_28: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_13 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:57:17.2321227Z 2025-03-04T20:57:17.2321506Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.2323334Z x_29: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_28 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.2323406Z 2025-03-04T20:57:17.2323668Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.2324183Z x_30: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:57:17.2324246Z 2025-03-04T20:57:17.2324530Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.2326452Z x_31: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_30 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.2326531Z 2025-03-04T20:57:17.2326835Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:57:17.2326991Z x_29 += x_31; out_14: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_29; x_29 = x_31 = None 2025-03-04T20:57:17.2327051Z 2025-03-04T20:57:17.2327344Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.2327494Z out_15: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_14); out_14 = None 2025-03-04T20:57:17.2327560Z 2025-03-04T20:57:17.2327813Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.2328321Z x_32: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:57:17.2328382Z 2025-03-04T20:57:17.2328656Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.2330489Z x_33: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_32 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.2330558Z 2025-03-04T20:57:17.2330848Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.2330986Z out_16: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_33); x_33 = None 2025-03-04T20:57:17.2331053Z 2025-03-04T20:57:17.2331303Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.2331801Z x_34: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_16 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:57:17.2331875Z 2025-03-04T20:57:17.2332148Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.2333949Z x_35: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_34 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.2334012Z 2025-03-04T20:57:17.2334291Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.2334472Z out_17: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_35); x_35 = None 2025-03-04T20:57:17.2334538Z 2025-03-04T20:57:17.2334779Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.2335273Z x_36: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_17 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:57:17.2335333Z 2025-03-04T20:57:17.2335599Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.2337372Z x_37: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_36 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.2337434Z 2025-03-04T20:57:17.2337714Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:57:17.2337864Z x_37 += out_15; out_18: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_37; x_37 = out_15 = None 2025-03-04T20:57:17.2337932Z 2025-03-04T20:57:17.2338205Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.2338374Z out_19: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_18); out_18 = None 2025-03-04T20:57:17.2338434Z 2025-03-04T20:57:17.2338688Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.2339169Z x_38: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:57:17.2339253Z 2025-03-04T20:57:17.2339524Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.2341293Z x_39: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_38 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.2341379Z 2025-03-04T20:57:17.2341663Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.2341797Z out_20: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_39); x_39 = None 2025-03-04T20:57:17.2341866Z 2025-03-04T20:57:17.2342111Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.2342603Z x_40: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_20 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:57:17.2342664Z 2025-03-04T20:57:17.2342931Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.2344688Z x_41: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_40 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.2344771Z 2025-03-04T20:57:17.2345063Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.2345197Z out_21: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_41); x_41 = None 2025-03-04T20:57:17.2345265Z 2025-03-04T20:57:17.2345511Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.2346021Z x_42: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_21 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:57:17.2346083Z 2025-03-04T20:57:17.2346361Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.2348123Z x_43: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_42 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.2348207Z 2025-03-04T20:57:17.2348487Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:57:17.2348634Z x_43 += out_19; out_22: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_43; x_43 = out_19 = None 2025-03-04T20:57:17.2348698Z 2025-03-04T20:57:17.2348968Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.2349121Z out_23: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_22); out_22 = None 2025-03-04T20:57:17.2349182Z 2025-03-04T20:57:17.2349432Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.2349913Z x_44: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T20:57:17.2349975Z 2025-03-04T20:57:17.2350231Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.2351993Z x_45: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_44 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.2352079Z 2025-03-04T20:57:17.2352373Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.2352515Z out_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_45); x_45 = None 2025-03-04T20:57:17.2352577Z 2025-03-04T20:57:17.2352847Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.2353350Z x_46: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T20:57:17.2353408Z 2025-03-04T20:57:17.2353677Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.2355500Z x_47: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_46 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.2355570Z 2025-03-04T20:57:17.2355859Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.2355995Z out_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_47); x_47 = None 2025-03-04T20:57:17.2356057Z 2025-03-04T20:57:17.2356306Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.2356896Z x_48: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_25 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T20:57:17.2356963Z 2025-03-04T20:57:17.2357254Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.2359122Z x_49: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_48 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.2359223Z 2025-03-04T20:57:17.2359529Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:57:17.2359680Z x_49 += out_23; out_26: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_49; x_49 = out_23 = None 2025-03-04T20:57:17.2359747Z 2025-03-04T20:57:17.2360034Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.2360188Z out_27: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_26); out_26 = None 2025-03-04T20:57:17.2360249Z 2025-03-04T20:57:17.2360505Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.2361004Z x_50: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:57:17.2361071Z 2025-03-04T20:57:17.2361336Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.2363129Z x_51: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_50 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.2363195Z 2025-03-04T20:57:17.2363475Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.2363613Z out_28: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_51); x_51 = None 2025-03-04T20:57:17.2363674Z 2025-03-04T20:57:17.2363931Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.2364415Z x_52: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_28 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:57:17.2366715Z 2025-03-04T20:57:17.2366987Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.2368853Z x_53: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_52 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.2368954Z 2025-03-04T20:57:17.2369241Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.2369369Z out_29: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_53); x_53 = None 2025-03-04T20:57:17.2369439Z 2025-03-04T20:57:17.2369683Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.2370174Z x_54: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_29 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:57:17.2370236Z 2025-03-04T20:57:17.2370498Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.2372262Z x_55: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_54 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.2373481Z 2025-03-04T20:57:17.2373740Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.2374232Z x_56: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:57:17.2374302Z 2025-03-04T20:57:17.2374566Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.2376491Z x_57: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_56 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.2376569Z 2025-03-04T20:57:17.2376849Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:57:17.2376982Z x_55 += x_57; out_30: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_55; x_55 = x_57 = None 2025-03-04T20:57:17.2377050Z 2025-03-04T20:57:17.2377326Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.2377469Z out_31: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_30); out_30 = None 2025-03-04T20:57:17.2377528Z 2025-03-04T20:57:17.2377784Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.2378255Z x_58: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:57:17.2378323Z 2025-03-04T20:57:17.2378591Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.2380355Z x_59: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_58 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.2380453Z 2025-03-04T20:57:17.2380732Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.2380869Z out_32: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_59); x_59 = None 2025-03-04T20:57:17.2380930Z 2025-03-04T20:57:17.2381184Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.2381698Z x_60: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_32 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:57:17.2381761Z 2025-03-04T20:57:17.2382029Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.2383814Z x_61: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_60 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.2383889Z 2025-03-04T20:57:17.2384172Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.2384301Z out_33: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_61); x_61 = None 2025-03-04T20:57:17.2384367Z 2025-03-04T20:57:17.2384614Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.2385101Z x_62: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_33 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:57:17.2385162Z 2025-03-04T20:57:17.2385429Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.2387197Z x_63: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_62 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.2387291Z 2025-03-04T20:57:17.2387578Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:57:17.2387721Z x_63 += out_31; out_34: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_63; x_63 = out_31 = None 2025-03-04T20:57:17.2387809Z 2025-03-04T20:57:17.2388097Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.2388246Z out_35: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_34); out_34 = None 2025-03-04T20:57:17.2388309Z 2025-03-04T20:57:17.2388564Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.2389071Z x_64: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:57:17.2389142Z 2025-03-04T20:57:17.2389405Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.2391207Z x_65: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_64 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.2391279Z 2025-03-04T20:57:17.2391560Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.2391698Z out_36: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_65); x_65 = None 2025-03-04T20:57:17.2391759Z 2025-03-04T20:57:17.2392015Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.2392497Z x_66: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_36 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:57:17.2392565Z 2025-03-04T20:57:17.2392830Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.2394629Z x_67: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_66 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.2394730Z 2025-03-04T20:57:17.2395011Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.2395150Z out_37: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_67); x_67 = None 2025-03-04T20:57:17.2395213Z 2025-03-04T20:57:17.2395488Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.2395998Z x_68: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_37 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:57:17.2396061Z 2025-03-04T20:57:17.2396331Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.2398311Z x_69: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_68 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.2398391Z 2025-03-04T20:57:17.2398680Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:57:17.2398826Z x_69 += out_35; out_38: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_69; x_69 = out_35 = None 2025-03-04T20:57:17.2398895Z 2025-03-04T20:57:17.2399176Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.2399324Z out_39: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_38); out_38 = None 2025-03-04T20:57:17.2399387Z 2025-03-04T20:57:17.2399644Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.2400123Z x_70: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T20:57:17.2400214Z 2025-03-04T20:57:17.2400483Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.2402549Z x_71: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_70 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.2402654Z 2025-03-04T20:57:17.2402962Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.2403097Z out_40: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_71); x_71 = None 2025-03-04T20:57:17.2403158Z 2025-03-04T20:57:17.2403408Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.2403877Z x_72: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_40 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T20:57:17.2403946Z 2025-03-04T20:57:17.2404201Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.2405952Z x_73: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_72 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.2406024Z 2025-03-04T20:57:17.2406306Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.2406439Z out_41: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_73); x_73 = None 2025-03-04T20:57:17.2406501Z 2025-03-04T20:57:17.2406754Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.2407248Z x_74: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_41 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T20:57:17.2407315Z 2025-03-04T20:57:17.2407573Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.2409355Z x_75: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_74 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.2409438Z 2025-03-04T20:57:17.2409709Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:57:17.2409859Z x_75 += out_39; out_42: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_75; x_75 = out_39 = None 2025-03-04T20:57:17.2409920Z 2025-03-04T20:57:17.2410201Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.2410338Z out_43: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_42); out_42 = None 2025-03-04T20:57:17.2410406Z 2025-03-04T20:57:17.2410647Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.2411117Z x_76: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = None 2025-03-04T20:57:17.2411185Z 2025-03-04T20:57:17.2411445Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.2413213Z x_77: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_76 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.2413300Z 2025-03-04T20:57:17.2413574Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.2413709Z out_44: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_77); x_77 = None 2025-03-04T20:57:17.2413768Z 2025-03-04T20:57:17.2414016Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.2414483Z x_78: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_44 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = None 2025-03-04T20:57:17.2414567Z 2025-03-04T20:57:17.2414822Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.2416593Z x_79: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_78 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.2416664Z 2025-03-04T20:57:17.2416943Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.2417076Z out_45: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_79); x_79 = None 2025-03-04T20:57:17.2417136Z 2025-03-04T20:57:17.2417386Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.2417861Z x_80: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_45 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = None 2025-03-04T20:57:17.2417932Z 2025-03-04T20:57:17.2418191Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.2419955Z x_81: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_80 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.2420038Z 2025-03-04T20:57:17.2420311Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:57:17.2420458Z x_81 += out_43; out_46: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_81; x_81 = out_43 = None 2025-03-04T20:57:17.2420516Z 2025-03-04T20:57:17.2420795Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.2420945Z out_47: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_46); out_46 = None 2025-03-04T20:57:17.2421013Z 2025-03-04T20:57:17.2421257Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.2421746Z x_82: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = None 2025-03-04T20:57:17.2421805Z 2025-03-04T20:57:17.2422084Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.2423845Z x_83: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_82 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.2423909Z 2025-03-04T20:57:17.2424193Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.2424320Z out_48: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_83); x_83 = None 2025-03-04T20:57:17.2424387Z 2025-03-04T20:57:17.2424632Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.2425108Z x_84: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_48 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = None 2025-03-04T20:57:17.2425177Z 2025-03-04T20:57:17.2425435Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.2427200Z x_85: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_84 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.2427304Z 2025-03-04T20:57:17.2427584Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.2427714Z out_49: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_85); x_85 = None 2025-03-04T20:57:17.2427775Z 2025-03-04T20:57:17.2428024Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.2428526Z x_86: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_49 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = None 2025-03-04T20:57:17.2428593Z 2025-03-04T20:57:17.2428849Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.2430612Z x_87: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_86 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.2430681Z 2025-03-04T20:57:17.2430948Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:57:17.2431092Z x_87 += out_47; out_50: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_87; x_87 = out_47 = None 2025-03-04T20:57:17.2431151Z 2025-03-04T20:57:17.2431431Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.2431563Z out_51: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_50); out_50 = None 2025-03-04T20:57:17.2431628Z 2025-03-04T20:57:17.2431871Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.2432340Z x_88: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T20:57:17.2432413Z 2025-03-04T20:57:17.2432681Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.2434492Z x_89: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_88 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.2434769Z 2025-03-04T20:57:17.2435081Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.2435224Z out_52: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_89); x_89 = None 2025-03-04T20:57:17.2435294Z 2025-03-04T20:57:17.2435543Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.2436037Z x_90: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_52 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T20:57:17.2436100Z 2025-03-04T20:57:17.2436375Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.2438277Z x_91: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_90 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.2438350Z 2025-03-04T20:57:17.2438665Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.2438792Z out_53: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_91); x_91 = None 2025-03-04T20:57:17.2438858Z 2025-03-04T20:57:17.2439107Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.2439603Z x_92: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_53 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T20:57:17.2439692Z 2025-03-04T20:57:17.2439960Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.2441783Z x_93: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_92 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.2441902Z 2025-03-04T20:57:17.2442167Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.2442667Z x_94: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T20:57:17.2442732Z 2025-03-04T20:57:17.2443009Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.2444941Z x_95: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_94 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.2445016Z 2025-03-04T20:57:17.2445319Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:57:17.2445463Z x_93 += x_95; out_54: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_93; x_93 = x_95 = None 2025-03-04T20:57:17.2445537Z 2025-03-04T20:57:17.2445845Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.2445990Z out_55: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_54); out_54 = None 2025-03-04T20:57:17.2446051Z 2025-03-04T20:57:17.2446309Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.2446809Z x_96: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T20:57:17.2446881Z 2025-03-04T20:57:17.2447153Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.2449017Z x_97: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_96 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.2449110Z 2025-03-04T20:57:17.2449395Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.2449533Z out_56: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_97); x_97 = None 2025-03-04T20:57:17.2449594Z 2025-03-04T20:57:17.2449850Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.2450340Z x_98: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_56 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T20:57:17.2450407Z 2025-03-04T20:57:17.2450673Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.2452474Z x_99: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_98 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.2452545Z 2025-03-04T20:57:17.2452821Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.2452954Z out_57: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_99); x_99 = None 2025-03-04T20:57:17.2453032Z 2025-03-04T20:57:17.2453282Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.2453768Z x_100: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_57 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T20:57:17.2453828Z 2025-03-04T20:57:17.2454090Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.2455912Z x_101: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_100 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.2455982Z 2025-03-04T20:57:17.2456259Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:57:17.2456407Z x_101 += out_55; out_58: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_101; x_101 = out_55 = None 2025-03-04T20:57:17.2456473Z 2025-03-04T20:57:17.2456747Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.2456886Z out_59: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_58); out_58 = None 2025-03-04T20:57:17.2456946Z 2025-03-04T20:57:17.2457194Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.2457658Z x_102: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T20:57:17.2457726Z 2025-03-04T20:57:17.2457983Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.2459750Z x_103: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_102 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.2459835Z 2025-03-04T20:57:17.2460113Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.2460251Z out_60: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_103); x_103 = None 2025-03-04T20:57:17.2460310Z 2025-03-04T20:57:17.2460562Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.2461049Z x_104: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_60 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T20:57:17.2461117Z 2025-03-04T20:57:17.2461393Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.2463162Z x_105: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_104 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.2463234Z 2025-03-04T20:57:17.2463517Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.2463651Z out_61: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_105); x_105 = None 2025-03-04T20:57:17.2463711Z 2025-03-04T20:57:17.2463966Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.2464442Z x_106: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_61 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T20:57:17.2464509Z 2025-03-04T20:57:17.2464770Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T20:57:17.2466556Z x_107: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_106 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T20:57:17.2466644Z 2025-03-04T20:57:17.2466914Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T20:57:17.2467062Z x_107 += out_59; out_62: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_107; x_107 = out_59 = None 2025-03-04T20:57:17.2467138Z 2025-03-04T20:57:17.2467421Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T20:57:17.2467556Z out_63: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_62); out_62 = None 2025-03-04T20:57:17.2467622Z 2025-03-04T20:57:17.2467877Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.2468455Z x_108: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(out_63, l_self_modules_backbone_lateral_convs_0_parameters_weight_, l_self_modules_backbone_lateral_convs_0_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_63 = l_self_modules_backbone_lateral_convs_0_parameters_weight_ = l_self_modules_backbone_lateral_convs_0_parameters_bias_ = None 2025-03-04T20:57:17.2468524Z 2025-03-04T20:57:17.2468765Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.2469300Z x_109: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_108, l_self_modules_backbone_output_convs_0_parameters_weight_, l_self_modules_backbone_output_convs_0_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_self_modules_backbone_output_convs_0_parameters_weight_ = l_self_modules_backbone_output_convs_0_parameters_bias_ = None 2025-03-04T20:57:17.2469379Z 2025-03-04T20:57:17.2469782Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest") 2025-03-04T20:57:17.2470044Z top_down_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.interpolate(x_108, scale_factor = 2.0, mode = 'nearest'); x_108 = None 2025-03-04T20:57:17.2470112Z 2025-03-04T20:57:17.2470356Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.2470915Z x_110: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_lateral_convs_1_parameters_weight_, l_self_modules_backbone_lateral_convs_1_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_51 = l_self_modules_backbone_lateral_convs_1_parameters_weight_ = l_self_modules_backbone_lateral_convs_1_parameters_bias_ = None 2025-03-04T20:57:17.2470975Z 2025-03-04T20:57:17.2471324Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features 2025-03-04T20:57:17.2471510Z prev_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = x_110 + top_down_features; x_110 = top_down_features = None 2025-03-04T20:57:17.2471579Z 2025-03-04T20:57:17.2471822Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.2472407Z x_111: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(prev_features, l_self_modules_backbone_output_convs_1_parameters_weight_, l_self_modules_backbone_output_convs_1_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_self_modules_backbone_output_convs_1_parameters_weight_ = l_self_modules_backbone_output_convs_1_parameters_bias_ = None 2025-03-04T20:57:17.2472492Z 2025-03-04T20:57:17.2472886Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest") 2025-03-04T20:57:17.2473205Z top_down_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.interpolate(prev_features, scale_factor = 2.0, mode = 'nearest'); prev_features = None 2025-03-04T20:57:17.2473283Z 2025-03-04T20:57:17.2473533Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.2474115Z x_112: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_lateral_convs_2_parameters_weight_, l_self_modules_backbone_lateral_convs_2_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_27 = l_self_modules_backbone_lateral_convs_2_parameters_weight_ = l_self_modules_backbone_lateral_convs_2_parameters_bias_ = None 2025-03-04T20:57:17.2474187Z 2025-03-04T20:57:17.2474537Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features 2025-03-04T20:57:17.2474749Z prev_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = x_112 + top_down_features_1; x_112 = top_down_features_1 = None 2025-03-04T20:57:17.2474809Z 2025-03-04T20:57:17.2475061Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.2475626Z x_113: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(prev_features_1, l_self_modules_backbone_output_convs_2_parameters_weight_, l_self_modules_backbone_output_convs_2_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_self_modules_backbone_output_convs_2_parameters_weight_ = l_self_modules_backbone_output_convs_2_parameters_bias_ = None 2025-03-04T20:57:17.2475694Z 2025-03-04T20:57:17.2476085Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest") 2025-03-04T20:57:17.2476410Z top_down_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.interpolate(prev_features_1, scale_factor = 2.0, mode = 'nearest'); prev_features_1 = None 2025-03-04T20:57:17.2476479Z 2025-03-04T20:57:17.2476776Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.2477374Z x_114: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_lateral_convs_3_parameters_weight_, l_self_modules_backbone_lateral_convs_3_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_11 = l_self_modules_backbone_lateral_convs_3_parameters_weight_ = l_self_modules_backbone_lateral_convs_3_parameters_bias_ = None 2025-03-04T20:57:17.2477439Z 2025-03-04T20:57:17.2477814Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features 2025-03-04T20:57:17.2478041Z prev_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_114 + top_down_features_2; x_114 = top_down_features_2 = None 2025-03-04T20:57:17.2478110Z 2025-03-04T20:57:17.2478358Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.2479005Z x_115: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(prev_features_2, l_self_modules_backbone_output_convs_3_parameters_weight_, l_self_modules_backbone_output_convs_3_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); prev_features_2 = l_self_modules_backbone_output_convs_3_parameters_weight_ = l_self_modules_backbone_output_convs_3_parameters_bias_ = None 2025-03-04T20:57:17.2479066Z 2025-03-04T20:57:17.2479440Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:200 in forward, code: return [F.max_pool2d(x, kernel_size=1, stride=2, padding=0)] 2025-03-04T20:57:17.2479658Z res: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.max_pool2d(x_109, kernel_size = 1, stride = 2, padding = 0) 2025-03-04T20:57:17.2479758Z 2025-03-04T20:57:17.2480201Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:57:17.2480356Z arange: "f32[304][1]cpu" = torch.arange(0.0, 1216, step = 4, dtype = torch.float32) 2025-03-04T20:57:17.2480425Z 2025-03-04T20:57:17.2480739Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:17.2480902Z shifts_x: "f32[304][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T20:57:17.2480966Z 2025-03-04T20:57:17.2481408Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:57:17.2481557Z arange_1: "f32[296][1]cpu" = torch.arange(0.0, 1184, step = 4, dtype = torch.float32) 2025-03-04T20:57:17.2481624Z 2025-03-04T20:57:17.2481915Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:17.2482058Z shifts_y: "f32[296][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T20:57:17.2482119Z 2025-03-04T20:57:17.2482495Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:57:17.2482672Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T20:57:17.2482774Z shift_y: "f32[296, 304][1, 0]cpu" = meshgrid[0] 2025-03-04T20:57:17.2482894Z shift_x: "f32[296, 304][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T20:57:17.2482962Z 2025-03-04T20:57:17.2483294Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:57:17.2483428Z shift_x_1: "f32[89984][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T20:57:17.2483488Z 2025-03-04T20:57:17.2483817Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:57:17.2483936Z shift_y_1: "f32[89984][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T20:57:17.2484003Z 2025-03-04T20:57:17.2484380Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:57:17.2484601Z shifts: "f32[89984, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T20:57:17.2484661Z 2025-03-04T20:57:17.2485120Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:57:17.2485246Z view: "f32[89984, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T20:57:17.2485681Z view_1: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T20:57:17.2485804Z add_3: "f32[89984, 3, 4][12, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T20:57:17.2485944Z x_116: "f32[269952, 4][4, 1]cpu" = add_3.reshape(-1, 4); add_3 = None 2025-03-04T20:57:17.2486007Z 2025-03-04T20:57:17.2486447Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:57:17.2486606Z arange_2: "f32[152][1]cpu" = torch.arange(0.0, 1216, step = 8, dtype = torch.float32) 2025-03-04T20:57:17.2486668Z 2025-03-04T20:57:17.2486991Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:17.2487147Z shifts_x_1: "f32[152][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T20:57:17.2487216Z 2025-03-04T20:57:17.2487645Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:57:17.2487799Z arange_3: "f32[148][1]cpu" = torch.arange(0.0, 1184, step = 8, dtype = torch.float32) 2025-03-04T20:57:17.2487862Z 2025-03-04T20:57:17.2488162Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:17.2488298Z shifts_y_1: "f32[148][1]cpu" = arange_3.to(device(type='cpu')); arange_3 = None 2025-03-04T20:57:17.2488366Z 2025-03-04T20:57:17.2488738Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:57:17.2488939Z meshgrid_1 = torch.functional.meshgrid(shifts_y_1, shifts_x_1); shifts_y_1 = shifts_x_1 = None 2025-03-04T20:57:17.2489038Z shift_y_2: "f32[148, 152][1, 0]cpu" = meshgrid_1[0] 2025-03-04T20:57:17.2489168Z shift_x_2: "f32[148, 152][0, 1]cpu" = meshgrid_1[1]; meshgrid_1 = None 2025-03-04T20:57:17.2489227Z 2025-03-04T20:57:17.2489560Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:57:17.2489688Z shift_x_3: "f32[22496][1]cpu" = shift_x_2.reshape(-1); shift_x_2 = None 2025-03-04T20:57:17.2489753Z 2025-03-04T20:57:17.2490077Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:57:17.2490207Z shift_y_3: "f32[22496][1]cpu" = shift_y_2.reshape(-1); shift_y_2 = None 2025-03-04T20:57:17.2490265Z 2025-03-04T20:57:17.2490654Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:57:17.2490867Z shifts_1: "f32[22496, 4][4, 1]cpu" = torch.stack((shift_x_3, shift_y_3, shift_x_3, shift_y_3), dim = 1); shift_x_3 = shift_y_3 = None 2025-03-04T20:57:17.2490949Z 2025-03-04T20:57:17.2491353Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:57:17.2491484Z view_2: "f32[22496, 1, 4][4, 4, 1]cpu" = shifts_1.view(-1, 1, 4); shifts_1 = None 2025-03-04T20:57:17.2491893Z view_3: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = None 2025-03-04T20:57:17.2492035Z add_4: "f32[22496, 3, 4][12, 4, 1]cpu" = view_2 + view_3; view_2 = view_3 = None 2025-03-04T20:57:17.2492145Z x_117: "f32[67488, 4][4, 1]cpu" = add_4.reshape(-1, 4); add_4 = None 2025-03-04T20:57:17.2492214Z 2025-03-04T20:57:17.2492639Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:57:17.2492797Z arange_4: "f32[76][1]cpu" = torch.arange(0.0, 1216, step = 16, dtype = torch.float32) 2025-03-04T20:57:17.2492865Z 2025-03-04T20:57:17.2493174Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:17.2493315Z shifts_x_2: "f32[76][1]cpu" = arange_4.to(device(type='cpu')); arange_4 = None 2025-03-04T20:57:17.2493375Z 2025-03-04T20:57:17.2493803Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:57:17.2493941Z arange_5: "f32[74][1]cpu" = torch.arange(0.0, 1184, step = 16, dtype = torch.float32) 2025-03-04T20:57:17.2494009Z 2025-03-04T20:57:17.2494293Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:17.2494429Z shifts_y_2: "f32[74][1]cpu" = arange_5.to(device(type='cpu')); arange_5 = None 2025-03-04T20:57:17.2494486Z 2025-03-04T20:57:17.2494861Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:57:17.2495047Z meshgrid_2 = torch.functional.meshgrid(shifts_y_2, shifts_x_2); shifts_y_2 = shifts_x_2 = None 2025-03-04T20:57:17.2495148Z shift_y_4: "f32[74, 76][1, 0]cpu" = meshgrid_2[0] 2025-03-04T20:57:17.2495262Z shift_x_4: "f32[74, 76][0, 1]cpu" = meshgrid_2[1]; meshgrid_2 = None 2025-03-04T20:57:17.2495325Z 2025-03-04T20:57:17.2495645Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:57:17.2495771Z shift_x_5: "f32[5624][1]cpu" = shift_x_4.reshape(-1); shift_x_4 = None 2025-03-04T20:57:17.2495828Z 2025-03-04T20:57:17.2496151Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:57:17.2496265Z shift_y_5: "f32[5624][1]cpu" = shift_y_4.reshape(-1); shift_y_4 = None 2025-03-04T20:57:17.2496332Z 2025-03-04T20:57:17.2496705Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:57:17.2496919Z shifts_2: "f32[5624, 4][4, 1]cpu" = torch.stack((shift_x_5, shift_y_5, shift_x_5, shift_y_5), dim = 1); shift_x_5 = shift_y_5 = None 2025-03-04T20:57:17.2496994Z 2025-03-04T20:57:17.2497406Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:57:17.2497527Z view_4: "f32[5624, 1, 4][4, 4, 1]cpu" = shifts_2.view(-1, 1, 4); shifts_2 = None 2025-03-04T20:57:17.2497947Z view_5: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = None 2025-03-04T20:57:17.2498086Z add_5: "f32[5624, 3, 4][12, 4, 1]cpu" = view_4 + view_5; view_4 = view_5 = None 2025-03-04T20:57:17.2498202Z x_118: "f32[16872, 4][4, 1]cpu" = add_5.reshape(-1, 4); add_5 = None 2025-03-04T20:57:17.2498262Z 2025-03-04T20:57:17.2498688Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:57:17.2498848Z arange_6: "f32[38][1]cpu" = torch.arange(0.0, 1216, step = 32, dtype = torch.float32) 2025-03-04T20:57:17.2498909Z 2025-03-04T20:57:17.2499219Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:17.2499352Z shifts_x_3: "f32[38][1]cpu" = arange_6.to(device(type='cpu')); arange_6 = None 2025-03-04T20:57:17.2499419Z 2025-03-04T20:57:17.2499833Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:57:17.2499978Z arange_7: "f32[37][1]cpu" = torch.arange(0.0, 1184, step = 32, dtype = torch.float32) 2025-03-04T20:57:17.2500037Z 2025-03-04T20:57:17.2500326Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:17.2500454Z shifts_y_3: "f32[37][1]cpu" = arange_7.to(device(type='cpu')); arange_7 = None 2025-03-04T20:57:17.2500522Z 2025-03-04T20:57:17.2500883Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:57:17.2501075Z meshgrid_3 = torch.functional.meshgrid(shifts_y_3, shifts_x_3); shifts_y_3 = shifts_x_3 = None 2025-03-04T20:57:17.2501170Z shift_y_6: "f32[37, 38][1, 0]cpu" = meshgrid_3[0] 2025-03-04T20:57:17.2501287Z shift_x_6: "f32[37, 38][0, 1]cpu" = meshgrid_3[1]; meshgrid_3 = None 2025-03-04T20:57:17.2501349Z 2025-03-04T20:57:17.2501672Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:57:17.2501789Z shift_x_7: "f32[1406][1]cpu" = shift_x_6.reshape(-1); shift_x_6 = None 2025-03-04T20:57:17.2501856Z 2025-03-04T20:57:17.2502299Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:57:17.2502425Z shift_y_7: "f32[1406][1]cpu" = shift_y_6.reshape(-1); shift_y_6 = None 2025-03-04T20:57:17.2502487Z 2025-03-04T20:57:17.2502861Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:57:17.2503064Z shifts_3: "f32[1406, 4][4, 1]cpu" = torch.stack((shift_x_7, shift_y_7, shift_x_7, shift_y_7), dim = 1); shift_x_7 = shift_y_7 = None 2025-03-04T20:57:17.2503170Z 2025-03-04T20:57:17.2503575Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:57:17.2503705Z view_6: "f32[1406, 1, 4][4, 4, 1]cpu" = shifts_3.view(-1, 1, 4); shifts_3 = None 2025-03-04T20:57:17.2504115Z view_7: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = None 2025-03-04T20:57:17.2504262Z add_6: "f32[1406, 3, 4][12, 4, 1]cpu" = view_6 + view_7; view_6 = view_7 = None 2025-03-04T20:57:17.2504371Z x_119: "f32[4218, 4][4, 1]cpu" = add_6.reshape(-1, 4); add_6 = None 2025-03-04T20:57:17.2504440Z 2025-03-04T20:57:17.2504894Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:57:17.2505036Z arange_8: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32) 2025-03-04T20:57:17.2505105Z 2025-03-04T20:57:17.2505414Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:17.2505551Z shifts_x_4: "f32[19][1]cpu" = arange_8.to(device(type='cpu')); arange_8 = None 2025-03-04T20:57:17.2505611Z 2025-03-04T20:57:17.2506032Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:57:17.2506169Z arange_9: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32) 2025-03-04T20:57:17.2506235Z 2025-03-04T20:57:17.2506518Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:17.2506652Z shifts_y_4: "f32[19][1]cpu" = arange_9.to(device(type='cpu')); arange_9 = None 2025-03-04T20:57:17.2506713Z 2025-03-04T20:57:17.2507078Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:57:17.2507262Z meshgrid_4 = torch.functional.meshgrid(shifts_y_4, shifts_x_4); shifts_y_4 = shifts_x_4 = None 2025-03-04T20:57:17.2507359Z shift_y_8: "f32[19, 19][1, 0]cpu" = meshgrid_4[0] 2025-03-04T20:57:17.2507472Z shift_x_8: "f32[19, 19][0, 1]cpu" = meshgrid_4[1]; meshgrid_4 = None 2025-03-04T20:57:17.2507537Z 2025-03-04T20:57:17.2507855Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:57:17.2507977Z shift_x_9: "f32[361][1]cpu" = shift_x_8.reshape(-1); shift_x_8 = None 2025-03-04T20:57:17.2508035Z 2025-03-04T20:57:17.2508358Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:57:17.2508470Z shift_y_9: "f32[361][1]cpu" = shift_y_8.reshape(-1); shift_y_8 = None 2025-03-04T20:57:17.2508537Z 2025-03-04T20:57:17.2508904Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:57:17.2509129Z shifts_4: "f32[361, 4][4, 1]cpu" = torch.stack((shift_x_9, shift_y_9, shift_x_9, shift_y_9), dim = 1); shift_x_9 = shift_y_9 = None 2025-03-04T20:57:17.2509187Z 2025-03-04T20:57:17.2509592Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:57:17.2509710Z view_8: "f32[361, 1, 4][4, 4, 1]cpu" = shifts_4.view(-1, 1, 4); shifts_4 = None 2025-03-04T20:57:17.2510121Z view_9: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = None 2025-03-04T20:57:17.2510251Z add_7: "f32[361, 3, 4][12, 4, 1]cpu" = view_8 + view_9; view_8 = view_9 = None 2025-03-04T20:57:17.2510368Z x_120: "f32[1083, 4][4, 1]cpu" = add_7.reshape(-1, 4); add_7 = None 2025-03-04T20:57:17.2510428Z 2025-03-04T20:57:17.2510741Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:57:17.2510871Z tensor: "f32[269952, 4][4, 1]cpu" = x_116.to(torch.float32); x_116 = None 2025-03-04T20:57:17.2510993Z tensor_1: "f32[67488, 4][4, 1]cpu" = x_117.to(torch.float32); x_117 = None 2025-03-04T20:57:17.2511131Z tensor_2: "f32[16872, 4][4, 1]cpu" = x_118.to(torch.float32); x_118 = None 2025-03-04T20:57:17.2511246Z tensor_3: "f32[4218, 4][4, 1]cpu" = x_119.to(torch.float32); x_119 = None 2025-03-04T20:57:17.2511365Z tensor_4: "f32[1083, 4][4, 1]cpu" = x_120.to(torch.float32); x_120 = None 2025-03-04T20:57:17.2511423Z 2025-03-04T20:57:17.2511677Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.2512173Z x_121: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_115, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_115 = None 2025-03-04T20:57:17.2512239Z 2025-03-04T20:57:17.2512505Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:57:17.2512703Z x_122: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.relu(x_121, inplace = False); x_121 = None 2025-03-04T20:57:17.2512766Z 2025-03-04T20:57:17.2513150Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:57:17.2513675Z score: "f32[4, 3, 296, 304][269952, 89984, 304, 1]cpu" = torch.conv2d(x_122, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T20:57:17.2513742Z 2025-03-04T20:57:17.2514110Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:57:17.2514644Z x_131: "f32[4, 12, 296, 304][1079808, 89984, 304, 1]cpu" = torch.conv2d(x_122, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_122 = None 2025-03-04T20:57:17.2514706Z 2025-03-04T20:57:17.2514965Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.2515478Z x_123: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(x_113, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_113 = None 2025-03-04T20:57:17.2515538Z 2025-03-04T20:57:17.2515821Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:57:17.2516014Z x_124: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.relu(x_123, inplace = False); x_123 = None 2025-03-04T20:57:17.2516097Z 2025-03-04T20:57:17.2516477Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:57:17.2517110Z score_1: "f32[4, 3, 148, 152][67488, 22496, 152, 1]cpu" = torch.conv2d(x_124, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T20:57:17.2517180Z 2025-03-04T20:57:17.2517587Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:57:17.2518141Z x_132: "f32[4, 12, 148, 152][269952, 22496, 152, 1]cpu" = torch.conv2d(x_124, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_124 = None 2025-03-04T20:57:17.2518210Z 2025-03-04T20:57:17.2518463Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.2518957Z x_125: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(x_111, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_111 = None 2025-03-04T20:57:17.2519026Z 2025-03-04T20:57:17.2519301Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:57:17.2519489Z x_126: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.relu(x_125, inplace = False); x_125 = None 2025-03-04T20:57:17.2519554Z 2025-03-04T20:57:17.2519934Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:57:17.2520446Z score_2: "f32[4, 3, 74, 76][16872, 5624, 76, 1]cpu" = torch.conv2d(x_126, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T20:57:17.2520513Z 2025-03-04T20:57:17.2520874Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:57:17.2521390Z x_133: "f32[4, 12, 74, 76][67488, 5624, 76, 1]cpu" = torch.conv2d(x_126, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_126 = None 2025-03-04T20:57:17.2521452Z 2025-03-04T20:57:17.2521712Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.2522206Z x_127: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_109, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_109 = None 2025-03-04T20:57:17.2522276Z 2025-03-04T20:57:17.2522548Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:57:17.2522732Z x_128: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.nn.functional.relu(x_127, inplace = False); x_127 = None 2025-03-04T20:57:17.2522807Z 2025-03-04T20:57:17.2523184Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:57:17.2523705Z score_3: "f32[4, 3, 37, 38][4218, 1406, 38, 1]cpu" = torch.conv2d(x_128, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T20:57:17.2523766Z 2025-03-04T20:57:17.2524141Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:57:17.2524638Z x_134: "f32[4, 12, 37, 38][16872, 1406, 38, 1]cpu" = torch.conv2d(x_128, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_128 = None 2025-03-04T20:57:17.2524706Z 2025-03-04T20:57:17.2524958Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:17.2525727Z x_129: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.conv2d(res, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); res = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T20:57:17.2525789Z 2025-03-04T20:57:17.2526066Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:57:17.2526245Z x_130: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.relu(x_129, inplace = False); x_129 = None 2025-03-04T20:57:17.2526306Z 2025-03-04T20:57:17.2526685Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:57:17.2527546Z score_4: "f32[4, 3, 19, 19][1083, 361, 19, 1]cpu" = torch.conv2d(x_130, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T20:57:17.2527615Z 2025-03-04T20:57:17.2527968Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:57:17.2528814Z x_135: "f32[4, 12, 19, 19][4332, 361, 19, 1]cpu" = torch.conv2d(x_130, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_130 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T20:57:17.2528874Z 2025-03-04T20:57:17.2529211Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T20:57:17.2529392Z permute: "f32[4, 296, 304, 3][269952, 304, 1, 89984]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T20:57:17.2529526Z logits_i: "f32[4, 269952][269952, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T20:57:17.2529692Z permute_1: "f32[4, 148, 152, 3][67488, 152, 1, 22496]cpu" = score_1.permute(0, 2, 3, 1); score_1 = None 2025-03-04T20:57:17.2529827Z logits_i_1: "f32[4, 67488][67488, 1]cpu" = permute_1.flatten(1); permute_1 = None 2025-03-04T20:57:17.2529991Z permute_2: "f32[4, 74, 76, 3][16872, 76, 1, 5624]cpu" = score_2.permute(0, 2, 3, 1); score_2 = None 2025-03-04T20:57:17.2530124Z logits_i_2: "f32[4, 16872][16872, 1]cpu" = permute_2.flatten(1); permute_2 = None 2025-03-04T20:57:17.2530286Z permute_3: "f32[4, 37, 38, 3][4218, 38, 1, 1406]cpu" = score_3.permute(0, 2, 3, 1); score_3 = None 2025-03-04T20:57:17.2530417Z logits_i_3: "f32[4, 4218][4218, 1]cpu" = permute_3.flatten(1); permute_3 = None 2025-03-04T20:57:17.2530564Z permute_4: "f32[4, 19, 19, 3][1083, 19, 1, 361]cpu" = score_4.permute(0, 2, 3, 1); score_4 = None 2025-03-04T20:57:17.2530690Z logits_i_4: "f32[4, 1083][1083, 1]cpu" = permute_4.flatten(1); permute_4 = None 2025-03-04T20:57:17.2530760Z 2025-03-04T20:57:17.2531170Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T20:57:17.2531347Z view_10: "f32[4, 3, 4, 296, 304][1079808, 359936, 89984, 304, 1]cpu" = x_131.view(4, -1, 4, 296, 304); x_131 = None 2025-03-04T20:57:17.2531526Z permute_5: "f32[4, 296, 304, 3, 4][1079808, 304, 1, 359936, 89984]cpu" = view_10.permute(0, 3, 4, 1, 2); view_10 = None 2025-03-04T20:57:17.2531705Z pred_anchor_deltas_i: "f32[4, 269952, 4][1079808, 4, 1]cpu" = permute_5.flatten(1, -2); permute_5 = None 2025-03-04T20:57:17.2531864Z view_11: "f32[4, 3, 4, 148, 152][269952, 89984, 22496, 152, 1]cpu" = x_132.view(4, -1, 4, 148, 152); x_132 = None 2025-03-04T20:57:17.2532039Z permute_6: "f32[4, 148, 152, 3, 4][269952, 152, 1, 89984, 22496]cpu" = view_11.permute(0, 3, 4, 1, 2); view_11 = None 2025-03-04T20:57:17.2532205Z pred_anchor_deltas_i_2: "f32[4, 67488, 4][269952, 4, 1]cpu" = permute_6.flatten(1, -2); permute_6 = None 2025-03-04T20:57:17.2532353Z view_12: "f32[4, 3, 4, 74, 76][67488, 22496, 5624, 76, 1]cpu" = x_133.view(4, -1, 4, 74, 76); x_133 = None 2025-03-04T20:57:17.2532510Z permute_7: "f32[4, 74, 76, 3, 4][67488, 76, 1, 22496, 5624]cpu" = view_12.permute(0, 3, 4, 1, 2); view_12 = None 2025-03-04T20:57:17.2532678Z pred_anchor_deltas_i_4: "f32[4, 16872, 4][67488, 4, 1]cpu" = permute_7.flatten(1, -2); permute_7 = None 2025-03-04T20:57:17.2532814Z view_13: "f32[4, 3, 4, 37, 38][16872, 5624, 1406, 38, 1]cpu" = x_134.view(4, -1, 4, 37, 38); x_134 = None 2025-03-04T20:57:17.2532978Z permute_8: "f32[4, 37, 38, 3, 4][16872, 38, 1, 5624, 1406]cpu" = view_13.permute(0, 3, 4, 1, 2); view_13 = None 2025-03-04T20:57:17.2533137Z pred_anchor_deltas_i_6: "f32[4, 4218, 4][16872, 4, 1]cpu" = permute_8.flatten(1, -2); permute_8 = None 2025-03-04T20:57:17.2533294Z view_14: "f32[4, 3, 4, 19, 19][4332, 1444, 361, 19, 1]cpu" = x_135.view(4, -1, 4, 19, 19); x_135 = None 2025-03-04T20:57:17.2533453Z permute_9: "f32[4, 19, 19, 3, 4][4332, 19, 1, 1444, 361]cpu" = view_14.permute(0, 3, 4, 1, 2); view_14 = None 2025-03-04T20:57:17.2533614Z pred_anchor_deltas_i_8: "f32[4, 1083, 4][4332, 4, 1]cpu" = permute_9.flatten(1, -2); permute_9 = None 2025-03-04T20:57:17.2533679Z 2025-03-04T20:57:17.2534078Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:57:17.2534301Z pred_anchor_deltas_i_1: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T20:57:17.2534361Z 2025-03-04T20:57:17.2534789Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:57:17.2534939Z unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T20:57:17.2535099Z expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T20:57:17.2535233Z anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T20:57:17.2535300Z 2025-03-04T20:57:17.2535679Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:57:17.2535851Z deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T20:57:17.2535911Z 2025-03-04T20:57:17.2536221Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:57:17.2536357Z boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T20:57:17.2536422Z 2025-03-04T20:57:17.2536729Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:57:17.2536866Z getitem_10: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:57:17.2536985Z getitem_11: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:57:17.2537138Z widths: "f32[1079808][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T20:57:17.2537197Z 2025-03-04T20:57:17.2537519Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:57:17.2537642Z getitem_12: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:57:17.2537766Z getitem_13: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:57:17.2537915Z heights: "f32[1079808][1]cpu" = getitem_12 - getitem_13; getitem_12 = getitem_13 = None 2025-03-04T20:57:17.2537983Z 2025-03-04T20:57:17.2538289Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:57:17.2538415Z getitem_14: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:57:17.2538500Z mul: "f32[1079808][1]cpu" = 0.5 * widths 2025-03-04T20:57:17.2538630Z ctr_x: "f32[1079808][1]cpu" = getitem_14 + mul; getitem_14 = mul = None 2025-03-04T20:57:17.2538689Z 2025-03-04T20:57:17.2539001Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:57:17.2539160Z getitem_15: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:57:17.2539253Z mul_1: "f32[1079808][1]cpu" = 0.5 * heights 2025-03-04T20:57:17.2539376Z ctr_y: "f32[1079808][1]cpu" = getitem_15 + mul_1; getitem_15 = mul_1 = None 2025-03-04T20:57:17.2539446Z 2025-03-04T20:57:17.2539774Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:57:17.2539930Z getitem_16: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:57:17.2540087Z dx: "f32[1079808, 1][1, 1]cpu" = getitem_16 / 1.0; getitem_16 = None 2025-03-04T20:57:17.2540155Z 2025-03-04T20:57:17.2540454Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:57:17.2540611Z getitem_17: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:57:17.2540743Z dy: "f32[1079808, 1][1, 1]cpu" = getitem_17 / 1.0; getitem_17 = None 2025-03-04T20:57:17.2540805Z 2025-03-04T20:57:17.2541104Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:57:17.2541273Z getitem_18: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:57:17.2541388Z dw: "f32[1079808, 1][1, 1]cpu" = getitem_18 / 1.0; getitem_18 = None 2025-03-04T20:57:17.2541449Z 2025-03-04T20:57:17.2541747Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:57:17.2541924Z getitem_19: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:57:17.2542037Z dh: "f32[1079808, 1][1, 1]cpu" = getitem_19 / 1.0; getitem_19 = None 2025-03-04T20:57:17.2542095Z 2025-03-04T20:57:17.2542433Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:57:17.2542572Z dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:57:17.2542637Z 2025-03-04T20:57:17.2542961Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:57:17.2543100Z dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:57:17.2543159Z 2025-03-04T20:57:17.2543503Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:57:17.2543638Z getitem_20: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:57:17.2543765Z mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_20; dx = getitem_20 = None 2025-03-04T20:57:17.2543913Z getitem_21: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:57:17.2544054Z pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_21; mul_2 = getitem_21 = None 2025-03-04T20:57:17.2544113Z 2025-03-04T20:57:17.2544460Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:57:17.2544593Z getitem_22: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:57:17.2544733Z mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_22; dy = getitem_22 = None 2025-03-04T20:57:17.2544878Z getitem_23: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:57:17.2545020Z pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_23; mul_3 = getitem_23 = None 2025-03-04T20:57:17.2545082Z 2025-03-04T20:57:17.2545410Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:57:17.2545526Z exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:57:17.2545701Z getitem_24: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:57:17.2545834Z pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_24; exp = getitem_24 = None 2025-03-04T20:57:17.2545892Z 2025-03-04T20:57:17.2546221Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:57:17.2546346Z exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:57:17.2546509Z getitem_25: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:57:17.2546652Z pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_25; exp_1 = getitem_25 = None 2025-03-04T20:57:17.2546717Z 2025-03-04T20:57:17.2547028Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:57:17.2547128Z mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T20:57:17.2547240Z x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:57:17.2547304Z 2025-03-04T20:57:17.2547614Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:57:17.2547709Z mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T20:57:17.2547820Z y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:57:17.2547883Z 2025-03-04T20:57:17.2548185Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:57:17.2548302Z mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:57:17.2548427Z x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:57:17.2548493Z 2025-03-04T20:57:17.2548794Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:57:17.2548909Z mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:57:17.2549033Z y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:57:17.2549098Z 2025-03-04T20:57:17.2549441Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:57:17.2549625Z pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:57:17.2549684Z 2025-03-04T20:57:17.2550023Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:57:17.2550185Z proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4)); pred_boxes = None 2025-03-04T20:57:17.2550271Z 2025-03-04T20:57:17.2550639Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:57:17.2550818Z proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T20:57:17.2550877Z 2025-03-04T20:57:17.2551271Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:57:17.2551472Z pred_anchor_deltas_i_3: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_2.reshape(-1, 4); pred_anchor_deltas_i_2 = None 2025-03-04T20:57:17.2551558Z 2025-03-04T20:57:17.2551983Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:57:17.2552145Z unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = tensor_1.unsqueeze(0); tensor_1 = None 2025-03-04T20:57:17.2552305Z expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1); unsqueeze_1 = None 2025-03-04T20:57:17.2552450Z anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4); expand_1 = None 2025-03-04T20:57:17.2552509Z 2025-03-04T20:57:17.2552887Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:57:17.2553055Z deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float(); pred_anchor_deltas_i_3 = None 2025-03-04T20:57:17.2553120Z 2025-03-04T20:57:17.2553421Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:57:17.2553568Z boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32); anchors_i_1 = None 2025-03-04T20:57:17.2553634Z 2025-03-04T20:57:17.2553942Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:57:17.2554076Z getitem_26: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)] 2025-03-04T20:57:17.2554198Z getitem_27: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T20:57:17.2554348Z widths_1: "f32[269952][1]cpu" = getitem_26 - getitem_27; getitem_26 = getitem_27 = None 2025-03-04T20:57:17.2554408Z 2025-03-04T20:57:17.2554721Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:57:17.2554842Z getitem_28: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)] 2025-03-04T20:57:17.2554966Z getitem_29: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)] 2025-03-04T20:57:17.2555113Z heights_1: "f32[269952][1]cpu" = getitem_28 - getitem_29; getitem_28 = getitem_29 = None 2025-03-04T20:57:17.2555177Z 2025-03-04T20:57:17.2555480Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:57:17.2555605Z getitem_30: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T20:57:17.2555695Z mul_10: "f32[269952][1]cpu" = 0.5 * widths_1 2025-03-04T20:57:17.2555828Z ctr_x_1: "f32[269952][1]cpu" = getitem_30 + mul_10; getitem_30 = mul_10 = None 2025-03-04T20:57:17.2555887Z 2025-03-04T20:57:17.2556198Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:57:17.2556356Z getitem_31: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]; boxes_1 = None 2025-03-04T20:57:17.2556451Z mul_11: "f32[269952][1]cpu" = 0.5 * heights_1 2025-03-04T20:57:17.2556575Z ctr_y_1: "f32[269952][1]cpu" = getitem_31 + mul_11; getitem_31 = mul_11 = None 2025-03-04T20:57:17.2556642Z 2025-03-04T20:57:17.2557012Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:57:17.2557179Z getitem_32: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:57:17.2557313Z dx_1: "f32[269952, 1][1, 1]cpu" = getitem_32 / 1.0; getitem_32 = None 2025-03-04T20:57:17.2557384Z 2025-03-04T20:57:17.2557696Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:57:17.2557858Z getitem_33: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:57:17.2557988Z dy_1: "f32[269952, 1][1, 1]cpu" = getitem_33 / 1.0; getitem_33 = None 2025-03-04T20:57:17.2558059Z 2025-03-04T20:57:17.2558379Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:57:17.2558553Z getitem_34: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:57:17.2558659Z dw_2: "f32[269952, 1][1, 1]cpu" = getitem_34 / 1.0; getitem_34 = None 2025-03-04T20:57:17.2558726Z 2025-03-04T20:57:17.2559022Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:57:17.2559209Z getitem_35: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))]; deltas_1 = None 2025-03-04T20:57:17.2559314Z dh_2: "f32[269952, 1][1, 1]cpu" = getitem_35 / 1.0; getitem_35 = None 2025-03-04T20:57:17.2559382Z 2025-03-04T20:57:17.2559711Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:57:17.2559853Z dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356); dw_2 = None 2025-03-04T20:57:17.2559919Z 2025-03-04T20:57:17.2560243Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:57:17.2560381Z dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356); dh_2 = None 2025-03-04T20:57:17.2560439Z 2025-03-04T20:57:17.2560784Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:57:17.2560918Z getitem_36: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)] 2025-03-04T20:57:17.2561043Z mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_36; dx_1 = getitem_36 = None 2025-03-04T20:57:17.2561192Z getitem_37: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)]; ctr_x_1 = None 2025-03-04T20:57:17.2561337Z pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_37; mul_12 = getitem_37 = None 2025-03-04T20:57:17.2561396Z 2025-03-04T20:57:17.2561744Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:57:17.2561876Z getitem_38: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)] 2025-03-04T20:57:17.2562016Z mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_38; dy_1 = getitem_38 = None 2025-03-04T20:57:17.2562163Z getitem_39: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)]; ctr_y_1 = None 2025-03-04T20:57:17.2562301Z pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_39; mul_13 = getitem_39 = None 2025-03-04T20:57:17.2562359Z 2025-03-04T20:57:17.2562688Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:57:17.2562815Z exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3); dw_3 = None 2025-03-04T20:57:17.2562978Z getitem_40: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]; widths_1 = None 2025-03-04T20:57:17.2563109Z pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_40; exp_2 = getitem_40 = None 2025-03-04T20:57:17.2563176Z 2025-03-04T20:57:17.2563511Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:57:17.2563651Z exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3); dh_3 = None 2025-03-04T20:57:17.2563817Z getitem_41: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]; heights_1 = None 2025-03-04T20:57:17.2563969Z pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_41; exp_3 = getitem_41 = None 2025-03-04T20:57:17.2564041Z 2025-03-04T20:57:17.2564352Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:57:17.2564446Z mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1 2025-03-04T20:57:17.2564566Z x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16; mul_16 = None 2025-03-04T20:57:17.2564628Z 2025-03-04T20:57:17.2564935Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:57:17.2565028Z mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1 2025-03-04T20:57:17.2565147Z y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17; mul_17 = None 2025-03-04T20:57:17.2565206Z 2025-03-04T20:57:17.2565512Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:57:17.2565626Z mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1; pred_w_1 = None 2025-03-04T20:57:17.2565767Z x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18; pred_ctr_x_1 = mul_18 = None 2025-03-04T20:57:17.2565828Z 2025-03-04T20:57:17.2566138Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:57:17.2566253Z mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1; pred_h_1 = None 2025-03-04T20:57:17.2566393Z y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19; pred_ctr_y_1 = mul_19 = None 2025-03-04T20:57:17.2566455Z 2025-03-04T20:57:17.2566815Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:57:17.2567009Z pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1); x1_1 = y1_1 = x2_1 = y2_1 = None 2025-03-04T20:57:17.2567081Z 2025-03-04T20:57:17.2567413Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:57:17.2567602Z proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4)); pred_boxes_1 = None 2025-03-04T20:57:17.2567668Z 2025-03-04T20:57:17.2568052Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:57:17.2568230Z proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4); proposals_i_1 = None 2025-03-04T20:57:17.2568291Z 2025-03-04T20:57:17.2568691Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:57:17.2568910Z pred_anchor_deltas_i_5: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_4.reshape(-1, 4); pred_anchor_deltas_i_4 = None 2025-03-04T20:57:17.2568976Z 2025-03-04T20:57:17.2569409Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:57:17.2569577Z unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = tensor_2.unsqueeze(0); tensor_2 = None 2025-03-04T20:57:17.2569728Z expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1); unsqueeze_2 = None 2025-03-04T20:57:17.2569885Z anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4); expand_2 = None 2025-03-04T20:57:17.2569947Z 2025-03-04T20:57:17.2570328Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:57:17.2570498Z deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_5.float(); pred_anchor_deltas_i_5 = None 2025-03-04T20:57:17.2570567Z 2025-03-04T20:57:17.2570877Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:57:17.2571025Z boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32); anchors_i_2 = None 2025-03-04T20:57:17.2571085Z 2025-03-04T20:57:17.2571406Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:57:17.2571531Z getitem_42: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)] 2025-03-04T20:57:17.2571661Z getitem_43: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T20:57:17.2571807Z widths_2: "f32[67488][1]cpu" = getitem_42 - getitem_43; getitem_42 = getitem_43 = None 2025-03-04T20:57:17.2571874Z 2025-03-04T20:57:17.2572192Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:57:17.2572320Z getitem_44: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)] 2025-03-04T20:57:17.2572438Z getitem_45: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)] 2025-03-04T20:57:17.2572593Z heights_2: "f32[67488][1]cpu" = getitem_44 - getitem_45; getitem_44 = getitem_45 = None 2025-03-04T20:57:17.2572654Z 2025-03-04T20:57:17.2572973Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:57:17.2573094Z getitem_46: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T20:57:17.2573189Z mul_20: "f32[67488][1]cpu" = 0.5 * widths_2 2025-03-04T20:57:17.2573317Z ctr_x_2: "f32[67488][1]cpu" = getitem_46 + mul_20; getitem_46 = mul_20 = None 2025-03-04T20:57:17.2573401Z 2025-03-04T20:57:17.2573714Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:57:17.2573868Z getitem_47: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]; boxes_2 = None 2025-03-04T20:57:17.2573958Z mul_21: "f32[67488][1]cpu" = 0.5 * heights_2 2025-03-04T20:57:17.2574091Z ctr_y_2: "f32[67488][1]cpu" = getitem_47 + mul_21; getitem_47 = mul_21 = None 2025-03-04T20:57:17.2574154Z 2025-03-04T20:57:17.2574463Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:57:17.2574645Z getitem_48: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:57:17.2574754Z dx_2: "f32[67488, 1][1, 1]cpu" = getitem_48 / 1.0; getitem_48 = None 2025-03-04T20:57:17.2574824Z 2025-03-04T20:57:17.2575123Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:57:17.2575292Z getitem_49: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:57:17.2575402Z dy_2: "f32[67488, 1][1, 1]cpu" = getitem_49 / 1.0; getitem_49 = None 2025-03-04T20:57:17.2575466Z 2025-03-04T20:57:17.2575782Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:57:17.2575936Z getitem_50: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:57:17.2576042Z dw_4: "f32[67488, 1][1, 1]cpu" = getitem_50 / 1.0; getitem_50 = None 2025-03-04T20:57:17.2576108Z 2025-03-04T20:57:17.2576410Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:57:17.2576596Z getitem_51: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))]; deltas_2 = None 2025-03-04T20:57:17.2576703Z dh_4: "f32[67488, 1][1, 1]cpu" = getitem_51 / 1.0; getitem_51 = None 2025-03-04T20:57:17.2576768Z 2025-03-04T20:57:17.2577112Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:57:17.2577255Z dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356); dw_4 = None 2025-03-04T20:57:17.2577315Z 2025-03-04T20:57:17.2577645Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:57:17.2577773Z dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356); dh_4 = None 2025-03-04T20:57:17.2577842Z 2025-03-04T20:57:17.2578178Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:57:17.2578315Z getitem_52: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)] 2025-03-04T20:57:17.2578435Z mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_52; dx_2 = getitem_52 = None 2025-03-04T20:57:17.2578588Z getitem_53: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)]; ctr_x_2 = None 2025-03-04T20:57:17.2578721Z pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_53; mul_22 = getitem_53 = None 2025-03-04T20:57:17.2578788Z 2025-03-04T20:57:17.2579131Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:57:17.2579284Z getitem_54: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)] 2025-03-04T20:57:17.2579397Z mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_54; dy_2 = getitem_54 = None 2025-03-04T20:57:17.2579550Z getitem_55: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)]; ctr_y_2 = None 2025-03-04T20:57:17.2579682Z pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_55; mul_23 = getitem_55 = None 2025-03-04T20:57:17.2579749Z 2025-03-04T20:57:17.2580079Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:57:17.2580205Z exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5); dw_5 = None 2025-03-04T20:57:17.2580364Z getitem_56: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]; widths_2 = None 2025-03-04T20:57:17.2580494Z pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_56; exp_4 = getitem_56 = None 2025-03-04T20:57:17.2580560Z 2025-03-04T20:57:17.2580898Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:57:17.2581013Z exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5); dh_5 = None 2025-03-04T20:57:17.2581186Z getitem_57: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]; heights_2 = None 2025-03-04T20:57:17.2581318Z pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_57; exp_5 = getitem_57 = None 2025-03-04T20:57:17.2581379Z 2025-03-04T20:57:17.2581687Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:57:17.2581778Z mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2 2025-03-04T20:57:17.2581896Z x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26; mul_26 = None 2025-03-04T20:57:17.2581954Z 2025-03-04T20:57:17.2582261Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:57:17.2582350Z mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2 2025-03-04T20:57:17.2582468Z y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27; mul_27 = None 2025-03-04T20:57:17.2582527Z 2025-03-04T20:57:17.2582828Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:57:17.2582938Z mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2; pred_w_2 = None 2025-03-04T20:57:17.2583073Z x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28; pred_ctr_x_2 = mul_28 = None 2025-03-04T20:57:17.2583134Z 2025-03-04T20:57:17.2583433Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:57:17.2583540Z mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2; pred_h_2 = None 2025-03-04T20:57:17.2583672Z y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29; pred_ctr_y_2 = mul_29 = None 2025-03-04T20:57:17.2583731Z 2025-03-04T20:57:17.2584089Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:57:17.2584277Z pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1); x1_2 = y1_2 = x2_2 = y2_2 = None 2025-03-04T20:57:17.2584343Z 2025-03-04T20:57:17.2584675Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:57:17.2584868Z proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4)); pred_boxes_2 = None 2025-03-04T20:57:17.2584928Z 2025-03-04T20:57:17.2585333Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:57:17.2585503Z proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4); proposals_i_2 = None 2025-03-04T20:57:17.2585569Z 2025-03-04T20:57:17.2585959Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:57:17.2586189Z pred_anchor_deltas_i_7: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_6.reshape(-1, 4); pred_anchor_deltas_i_6 = None 2025-03-04T20:57:17.2586248Z 2025-03-04T20:57:17.2586693Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:57:17.2586838Z unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = tensor_3.unsqueeze(0); tensor_3 = None 2025-03-04T20:57:17.2587005Z expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1); unsqueeze_3 = None 2025-03-04T20:57:17.2587144Z anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4); expand_3 = None 2025-03-04T20:57:17.2587203Z 2025-03-04T20:57:17.2587567Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:57:17.2587729Z deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_7.float(); pred_anchor_deltas_i_7 = None 2025-03-04T20:57:17.2587797Z 2025-03-04T20:57:17.2588097Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:57:17.2588240Z boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32); anchors_i_3 = None 2025-03-04T20:57:17.2588298Z 2025-03-04T20:57:17.2588617Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:57:17.2588741Z getitem_58: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)] 2025-03-04T20:57:17.2588871Z getitem_59: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T20:57:17.2589014Z widths_3: "f32[16872][1]cpu" = getitem_58 - getitem_59; getitem_58 = getitem_59 = None 2025-03-04T20:57:17.2589079Z 2025-03-04T20:57:17.2589399Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:57:17.2589529Z getitem_60: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)] 2025-03-04T20:57:17.2589646Z getitem_61: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)] 2025-03-04T20:57:17.2589803Z heights_3: "f32[16872][1]cpu" = getitem_60 - getitem_61; getitem_60 = getitem_61 = None 2025-03-04T20:57:17.2589868Z 2025-03-04T20:57:17.2590186Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:57:17.2590307Z getitem_62: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T20:57:17.2590402Z mul_30: "f32[16872][1]cpu" = 0.5 * widths_3 2025-03-04T20:57:17.2590533Z ctr_x_3: "f32[16872][1]cpu" = getitem_62 + mul_30; getitem_62 = mul_30 = None 2025-03-04T20:57:17.2590617Z 2025-03-04T20:57:17.2590939Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:57:17.2591097Z getitem_63: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]; boxes_3 = None 2025-03-04T20:57:17.2591190Z mul_31: "f32[16872][1]cpu" = 0.5 * heights_3 2025-03-04T20:57:17.2591329Z ctr_y_3: "f32[16872][1]cpu" = getitem_63 + mul_31; getitem_63 = mul_31 = None 2025-03-04T20:57:17.2591391Z 2025-03-04T20:57:17.2591708Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:57:17.2591884Z getitem_64: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:57:17.2592003Z dx_3: "f32[16872, 1][1, 1]cpu" = getitem_64 / 1.0; getitem_64 = None 2025-03-04T20:57:17.2592065Z 2025-03-04T20:57:17.2592383Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:57:17.2592559Z getitem_65: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:57:17.2592680Z dy_3: "f32[16872, 1][1, 1]cpu" = getitem_65 / 1.0; getitem_65 = None 2025-03-04T20:57:17.2592757Z 2025-03-04T20:57:17.2593105Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:57:17.2593291Z getitem_66: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:57:17.2593417Z dw_6: "f32[16872, 1][1, 1]cpu" = getitem_66 / 1.0; getitem_66 = None 2025-03-04T20:57:17.2593485Z 2025-03-04T20:57:17.2593849Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:57:17.2594076Z getitem_67: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))]; deltas_3 = None 2025-03-04T20:57:17.2594193Z dh_6: "f32[16872, 1][1, 1]cpu" = getitem_67 / 1.0; getitem_67 = None 2025-03-04T20:57:17.2594268Z 2025-03-04T20:57:17.2594664Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:57:17.2594824Z dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356); dw_6 = None 2025-03-04T20:57:17.2594893Z 2025-03-04T20:57:17.2595334Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:57:17.2595486Z dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356); dh_6 = None 2025-03-04T20:57:17.2595559Z 2025-03-04T20:57:17.2595966Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:57:17.2596129Z getitem_68: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)] 2025-03-04T20:57:17.2596259Z mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_68; dx_3 = getitem_68 = None 2025-03-04T20:57:17.2596425Z getitem_69: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)]; ctr_x_3 = None 2025-03-04T20:57:17.2596570Z pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_69; mul_32 = getitem_69 = None 2025-03-04T20:57:17.2596639Z 2025-03-04T20:57:17.2597082Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:57:17.2597262Z getitem_70: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)] 2025-03-04T20:57:17.2597395Z mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_70; dy_3 = getitem_70 = None 2025-03-04T20:57:17.2597568Z getitem_71: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)]; ctr_y_3 = None 2025-03-04T20:57:17.2597718Z pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_71; mul_33 = getitem_71 = None 2025-03-04T20:57:17.2597801Z 2025-03-04T20:57:17.2598250Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:57:17.2598395Z exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7); dw_7 = None 2025-03-04T20:57:17.2598558Z getitem_72: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]; widths_3 = None 2025-03-04T20:57:17.2598704Z pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_72; exp_6 = getitem_72 = None 2025-03-04T20:57:17.2598768Z 2025-03-04T20:57:17.2599136Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:57:17.2599249Z exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7); dh_7 = None 2025-03-04T20:57:17.2599439Z getitem_73: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]; heights_3 = None 2025-03-04T20:57:17.2599573Z pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_73; exp_7 = getitem_73 = None 2025-03-04T20:57:17.2599644Z 2025-03-04T20:57:17.2599961Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:57:17.2600065Z mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3 2025-03-04T20:57:17.2600179Z x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36; mul_36 = None 2025-03-04T20:57:17.2600248Z 2025-03-04T20:57:17.2600567Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:57:17.2600665Z mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3 2025-03-04T20:57:17.2600779Z y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37; mul_37 = None 2025-03-04T20:57:17.2600850Z 2025-03-04T20:57:17.2601161Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:57:17.2601285Z mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3; pred_w_3 = None 2025-03-04T20:57:17.2601418Z x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38; pred_ctr_x_3 = mul_38 = None 2025-03-04T20:57:17.2601488Z 2025-03-04T20:57:17.2601800Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:57:17.2602065Z mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3; pred_h_3 = None 2025-03-04T20:57:17.2602202Z y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39; pred_ctr_y_3 = mul_39 = None 2025-03-04T20:57:17.2602274Z 2025-03-04T20:57:17.2602633Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:57:17.2602831Z pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1); x1_3 = y1_3 = x2_3 = y2_3 = None 2025-03-04T20:57:17.2602902Z 2025-03-04T20:57:17.2603245Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:57:17.2603456Z proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4)); pred_boxes_3 = None 2025-03-04T20:57:17.2603519Z 2025-03-04T20:57:17.2603920Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:57:17.2604093Z proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4); proposals_i_3 = None 2025-03-04T20:57:17.2604186Z 2025-03-04T20:57:17.2604598Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:57:17.2604815Z pred_anchor_deltas_i_9: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_8.reshape(-1, 4); pred_anchor_deltas_i_8 = None 2025-03-04T20:57:17.2604877Z 2025-03-04T20:57:17.2605347Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:57:17.2605500Z unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = tensor_4.unsqueeze(0); tensor_4 = None 2025-03-04T20:57:17.2605682Z expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1); unsqueeze_4 = None 2025-03-04T20:57:17.2605819Z anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4); expand_4 = None 2025-03-04T20:57:17.2605888Z 2025-03-04T20:57:17.2606274Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:57:17.2606459Z deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_9.float(); pred_anchor_deltas_i_9 = None 2025-03-04T20:57:17.2606521Z 2025-03-04T20:57:17.2606843Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:57:17.2606982Z boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32); anchors_i_4 = None 2025-03-04T20:57:17.2607049Z 2025-03-04T20:57:17.2607364Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:57:17.2607498Z getitem_74: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)] 2025-03-04T20:57:17.2607618Z getitem_75: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T20:57:17.2607771Z widths_4: "f32[4332][1]cpu" = getitem_74 - getitem_75; getitem_74 = getitem_75 = None 2025-03-04T20:57:17.2607832Z 2025-03-04T20:57:17.2608156Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:57:17.2608275Z getitem_76: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)] 2025-03-04T20:57:17.2608400Z getitem_77: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)] 2025-03-04T20:57:17.2608547Z heights_4: "f32[4332][1]cpu" = getitem_76 - getitem_77; getitem_76 = getitem_77 = None 2025-03-04T20:57:17.2608616Z 2025-03-04T20:57:17.2608928Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:57:17.2609056Z getitem_78: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T20:57:17.2609143Z mul_40: "f32[4332][1]cpu" = 0.5 * widths_4 2025-03-04T20:57:17.2609325Z ctr_x_4: "f32[4332][1]cpu" = getitem_78 + mul_40; getitem_78 = mul_40 = None 2025-03-04T20:57:17.2609385Z 2025-03-04T20:57:17.2609699Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:57:17.2609848Z getitem_79: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]; boxes_4 = None 2025-03-04T20:57:17.2609936Z mul_41: "f32[4332][1]cpu" = 0.5 * heights_4 2025-03-04T20:57:17.2610070Z ctr_y_4: "f32[4332][1]cpu" = getitem_79 + mul_41; getitem_79 = mul_41 = None 2025-03-04T20:57:17.2610132Z 2025-03-04T20:57:17.2610461Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:57:17.2610610Z getitem_80: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:57:17.2610729Z dx_4: "f32[4332, 1][1, 1]cpu" = getitem_80 / 1.0; getitem_80 = None 2025-03-04T20:57:17.2610789Z 2025-03-04T20:57:17.2611112Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:57:17.2611261Z getitem_81: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:57:17.2611390Z dy_4: "f32[4332, 1][1, 1]cpu" = getitem_81 / 1.0; getitem_81 = None 2025-03-04T20:57:17.2611452Z 2025-03-04T20:57:17.2611753Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:57:17.2611898Z getitem_82: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:57:17.2612011Z dw_8: "f32[4332, 1][1, 1]cpu" = getitem_82 / 1.0; getitem_82 = None 2025-03-04T20:57:17.2612072Z 2025-03-04T20:57:17.2612378Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:57:17.2612556Z getitem_83: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))]; deltas_4 = None 2025-03-04T20:57:17.2612667Z dh_8: "f32[4332, 1][1, 1]cpu" = getitem_83 / 1.0; getitem_83 = None 2025-03-04T20:57:17.2612728Z 2025-03-04T20:57:17.2613068Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:57:17.2613201Z dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356); dw_8 = None 2025-03-04T20:57:17.2613267Z 2025-03-04T20:57:17.2613597Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:57:17.2613736Z dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356); dh_8 = None 2025-03-04T20:57:17.2613796Z 2025-03-04T20:57:17.2614147Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:57:17.2614281Z getitem_84: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)] 2025-03-04T20:57:17.2614408Z mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_84; dx_4 = getitem_84 = None 2025-03-04T20:57:17.2614556Z getitem_85: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)]; ctr_x_4 = None 2025-03-04T20:57:17.2614700Z pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_85; mul_42 = getitem_85 = None 2025-03-04T20:57:17.2614759Z 2025-03-04T20:57:17.2615112Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:57:17.2615259Z getitem_86: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)] 2025-03-04T20:57:17.2615384Z mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_86; dy_4 = getitem_86 = None 2025-03-04T20:57:17.2615538Z getitem_87: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)]; ctr_y_4 = None 2025-03-04T20:57:17.2615672Z pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_87; mul_43 = getitem_87 = None 2025-03-04T20:57:17.2615738Z 2025-03-04T20:57:17.2616079Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:57:17.2616197Z exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9); dw_9 = None 2025-03-04T20:57:17.2616354Z getitem_88: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]; widths_4 = None 2025-03-04T20:57:17.2616491Z pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_88; exp_8 = getitem_88 = None 2025-03-04T20:57:17.2616551Z 2025-03-04T20:57:17.2616905Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:57:17.2617030Z exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9); dh_9 = None 2025-03-04T20:57:17.2617197Z getitem_89: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]; heights_4 = None 2025-03-04T20:57:17.2617324Z pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_89; exp_9 = getitem_89 = None 2025-03-04T20:57:17.2617392Z 2025-03-04T20:57:17.2617702Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:57:17.2617803Z mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4 2025-03-04T20:57:17.2617912Z x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46; mul_46 = None 2025-03-04T20:57:17.2617979Z 2025-03-04T20:57:17.2618286Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:57:17.2618381Z mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4 2025-03-04T20:57:17.2618490Z y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47; mul_47 = None 2025-03-04T20:57:17.2618555Z 2025-03-04T20:57:17.2618861Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:57:17.2618977Z mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4; pred_w_4 = None 2025-03-04T20:57:17.2619102Z x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48; pred_ctr_x_4 = mul_48 = None 2025-03-04T20:57:17.2619170Z 2025-03-04T20:57:17.2619484Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:57:17.2619596Z mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4; pred_h_4 = None 2025-03-04T20:57:17.2619717Z y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49; pred_ctr_y_4 = mul_49 = None 2025-03-04T20:57:17.2619781Z 2025-03-04T20:57:17.2620118Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:57:17.2620299Z pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1); x1_4 = y1_4 = x2_4 = y2_4 = None 2025-03-04T20:57:17.2620358Z 2025-03-04T20:57:17.2620705Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:57:17.2620857Z proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4)); pred_boxes_4 = None 2025-03-04T20:57:17.2620923Z 2025-03-04T20:57:17.2621294Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:57:17.2621462Z proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4); proposals_i_4 = None 2025-03-04T20:57:17.2621540Z 2025-03-04T20:57:17.2622021Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T20:57:17.2622151Z arange_10: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T20:57:17.2622216Z 2025-03-04T20:57:17.2622527Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:17.2622671Z batch_idx: "i64[4][1]cpu" = arange_10.to(device(type='cpu')); arange_10 = None 2025-03-04T20:57:17.2622729Z 2025-03-04T20:57:17.2623176Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:57:17.2623293Z topk = logits_i.topk(1000, dim = 1); logits_i = None 2025-03-04T20:57:17.2623388Z topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0] 2025-03-04T20:57:17.2623504Z topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1]; topk = None 2025-03-04T20:57:17.2623566Z 2025-03-04T20:57:17.2624030Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:57:17.2624159Z getitem_92: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T20:57:17.2624391Z topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_92, topk_idx)]; proposals_i_5 = getitem_92 = topk_idx = None 2025-03-04T20:57:17.2624452Z 2025-03-04T20:57:17.2624914Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:57:17.2625076Z full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:57:17.2625145Z 2025-03-04T20:57:17.2625438Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:17.2625565Z to_21: "i64[1000][1]cpu" = full.to(device(type='cpu')); full = None 2025-03-04T20:57:17.2625627Z 2025-03-04T20:57:17.2626083Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:57:17.2626197Z topk_1 = logits_i_1.topk(1000, dim = 1); logits_i_1 = None 2025-03-04T20:57:17.2626307Z topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0] 2025-03-04T20:57:17.2626417Z topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1]; topk_1 = None 2025-03-04T20:57:17.2626485Z 2025-03-04T20:57:17.2626941Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:57:17.2627092Z getitem_96: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T20:57:17.2627319Z topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_96, topk_idx_1)]; proposals_i_6 = getitem_96 = topk_idx_1 = None 2025-03-04T20:57:17.2627386Z 2025-03-04T20:57:17.2627834Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:57:17.2628014Z full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:57:17.2628073Z 2025-03-04T20:57:17.2628367Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:17.2628488Z to_22: "i64[1000][1]cpu" = full_1.to(device(type='cpu')); full_1 = None 2025-03-04T20:57:17.2628556Z 2025-03-04T20:57:17.2628989Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:57:17.2629128Z topk_2 = logits_i_2.topk(1000, dim = 1); logits_i_2 = None 2025-03-04T20:57:17.2629230Z topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0] 2025-03-04T20:57:17.2629350Z topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1]; topk_2 = None 2025-03-04T20:57:17.2629410Z 2025-03-04T20:57:17.2629863Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:57:17.2629997Z getitem_100: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T20:57:17.2630224Z topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_100, topk_idx_2)]; proposals_i_7 = getitem_100 = topk_idx_2 = None 2025-03-04T20:57:17.2630291Z 2025-03-04T20:57:17.2630731Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:57:17.2630899Z full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:57:17.2630961Z 2025-03-04T20:57:17.2631261Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:17.2631384Z to_23: "i64[1000][1]cpu" = full_2.to(device(type='cpu')); full_2 = None 2025-03-04T20:57:17.2631454Z 2025-03-04T20:57:17.2631885Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:57:17.2632003Z topk_3 = logits_i_3.topk(1000, dim = 1); logits_i_3 = None 2025-03-04T20:57:17.2632105Z topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0] 2025-03-04T20:57:17.2632222Z topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1]; topk_3 = None 2025-03-04T20:57:17.2632285Z 2025-03-04T20:57:17.2632751Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:57:17.2632922Z getitem_104: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T20:57:17.2633160Z topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_104, topk_idx_3)]; proposals_i_8 = getitem_104 = topk_idx_3 = None 2025-03-04T20:57:17.2633220Z 2025-03-04T20:57:17.2633675Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:57:17.2633832Z full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:57:17.2633916Z 2025-03-04T20:57:17.2634209Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:17.2634338Z to_24: "i64[1000][1]cpu" = full_3.to(device(type='cpu')); full_3 = None 2025-03-04T20:57:17.2634401Z 2025-03-04T20:57:17.2634868Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:57:17.2634981Z topk_4 = logits_i_4.topk(1000, dim = 1); logits_i_4 = None 2025-03-04T20:57:17.2635107Z topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0] 2025-03-04T20:57:17.2635221Z topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1]; topk_4 = None 2025-03-04T20:57:17.2635293Z 2025-03-04T20:57:17.2635764Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:57:17.2635940Z getitem_108: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T20:57:17.2636184Z topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_108, topk_idx_4)]; proposals_i_9 = getitem_108 = topk_idx_4 = None 2025-03-04T20:57:17.2636245Z 2025-03-04T20:57:17.2636780Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:57:17.2636949Z full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:57:17.2637022Z 2025-03-04T20:57:17.2637324Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:17.2637455Z to_25: "i64[1000][1]cpu" = full_4.to(device(type='cpu')); full_4 = None 2025-03-04T20:57:17.2637522Z 2025-03-04T20:57:17.2637822Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:57:17.2638219Z topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1); topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None 2025-03-04T20:57:17.2638293Z 2025-03-04T20:57:17.2638587Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:57:17.2639084Z topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1); topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None 2025-03-04T20:57:17.2639169Z 2025-03-04T20:57:17.2639462Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:57:17.2639667Z level_ids: "i64[5000][1]cpu" = torch.cat([to_21, to_22, to_23, to_24, to_25], 0); to_21 = to_22 = to_23 = to_24 = to_25 = level_ids = None 2025-03-04T20:57:17.2639738Z 2025-03-04T20:57:17.2640134Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T20:57:17.2640283Z getitem_110: "f32[5000, 4][4, 1]cpu" = topk_proposals[0]; topk_proposals = None 2025-03-04T20:57:17.2640362Z 2025-03-04T20:57:17.2640680Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:57:17.2640829Z tensor_5: "f32[5000, 4][4, 1]cpu" = getitem_110.to(torch.float32); getitem_110 = None 2025-03-04T20:57:17.2640901Z 2025-03-04T20:57:17.2641319Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T20:57:17.2641469Z scores_per_img: "f32[5000][1]cpu" = topk_scores[0]; topk_scores = None 2025-03-04T20:57:17.2641540Z 2025-03-04T20:57:17.2642075Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T20:57:17.2642227Z isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor_5); tensor_5 = None 2025-03-04T20:57:17.2642351Z all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:57:17.2642519Z isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T20:57:17.2642658Z valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T20:57:17.2642729Z 2025-03-04T20:57:17.2643122Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T20:57:17.2643248Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T20:57:17.2643312Z 2025-03-04T20:57:28.2308402Z 2025-03-04T20:57:28.2313649Z class GraphModule(torch.nn.Module): 2025-03-04T20:57:28.2316224Z def forward(self, L_features_p2_: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu", L_features_p3_: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu", L_features_p4_: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu", L_features_p5_: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu", L_features_p6_: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_1_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_2_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_3_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_4_: "f32[3, 4][4, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_bias_: "f32[256][1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[3, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[3][1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[12, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[12][1]cpu"): 2025-03-04T20:57:28.2319432Z l_features_p2_ = L_features_p2_ 2025-03-04T20:57:28.2319671Z l_features_p3_ = L_features_p3_ 2025-03-04T20:57:28.2319890Z l_features_p4_ = L_features_p4_ 2025-03-04T20:57:28.2320101Z l_features_p5_ = L_features_p5_ 2025-03-04T20:57:28.2320324Z l_features_p6_ = L_features_p6_ 2025-03-04T20:57:28.2320731Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T20:57:28.2321332Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_1_ 2025-03-04T20:57:28.2321988Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_2_ 2025-03-04T20:57:28.2322579Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_3_ 2025-03-04T20:57:28.2323171Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_4_ 2025-03-04T20:57:28.2323804Z l_self_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T20:57:28.2324338Z l_self_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T20:57:28.2324975Z l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T20:57:28.2325573Z l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T20:57:28.2326144Z l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T20:57:28.2326693Z l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T20:57:28.2327058Z 2025-03-04T20:57:28.2327632Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:57:28.2328302Z arange: "f32[304][1]cpu" = torch.arange(0.0, 1216, step = 4, dtype = torch.float32) 2025-03-04T20:57:28.2328567Z 2025-03-04T20:57:28.2328962Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:28.2329485Z shifts_x: "f32[304][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T20:57:28.2329755Z 2025-03-04T20:57:28.2330317Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:57:28.2330980Z arange_1: "f32[296][1]cpu" = torch.arange(0.0, 1184, step = 4, dtype = torch.float32) 2025-03-04T20:57:28.2331248Z 2025-03-04T20:57:28.2331768Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:28.2332265Z shifts_y: "f32[296][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T20:57:28.2332523Z 2025-03-04T20:57:28.2332988Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:57:28.2333606Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T20:57:28.2333942Z shift_y: "f32[296, 304][1, 0]cpu" = meshgrid[0] 2025-03-04T20:57:28.2334234Z shift_x: "f32[296, 304][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T20:57:28.2334469Z 2025-03-04T20:57:28.2334884Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:57:28.2335393Z shift_x_1: "f32[89984][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T20:57:28.2335633Z 2025-03-04T20:57:28.2336044Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:57:28.2336567Z shift_y_1: "f32[89984][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T20:57:28.2336804Z 2025-03-04T20:57:28.2337265Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:57:28.2337908Z shifts: "f32[89984, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T20:57:28.2338229Z 2025-03-04T20:57:28.2338774Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:57:28.2339372Z view: "f32[89984, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T20:57:28.2339893Z view_1: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T20:57:28.2340393Z add: "f32[89984, 3, 4][12, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T20:57:28.2340685Z x: "f32[269952, 4][4, 1]cpu" = add.reshape(-1, 4); add = None 2025-03-04T20:57:28.2340911Z 2025-03-04T20:57:28.2341431Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:57:28.2342070Z arange_2: "f32[152][1]cpu" = torch.arange(0.0, 1216, step = 8, dtype = torch.float32) 2025-03-04T20:57:28.2342336Z 2025-03-04T20:57:28.2342716Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:28.2343205Z shifts_x_1: "f32[152][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T20:57:28.2343464Z 2025-03-04T20:57:28.2343982Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:57:28.2344619Z arange_3: "f32[148][1]cpu" = torch.arange(0.0, 1184, step = 8, dtype = torch.float32) 2025-03-04T20:57:28.2344883Z 2025-03-04T20:57:28.2345262Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:28.2345752Z shifts_y_1: "f32[148][1]cpu" = arange_3.to(device(type='cpu')); arange_3 = None 2025-03-04T20:57:28.2346009Z 2025-03-04T20:57:28.2346465Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:57:28.2347084Z meshgrid_1 = torch.functional.meshgrid(shifts_y_1, shifts_x_1); shifts_y_1 = shifts_x_1 = None 2025-03-04T20:57:28.2347437Z shift_y_2: "f32[148, 152][1, 0]cpu" = meshgrid_1[0] 2025-03-04T20:57:28.2347727Z shift_x_2: "f32[148, 152][0, 1]cpu" = meshgrid_1[1]; meshgrid_1 = None 2025-03-04T20:57:28.2347965Z 2025-03-04T20:57:28.2348377Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:57:28.2348883Z shift_x_3: "f32[22496][1]cpu" = shift_x_2.reshape(-1); shift_x_2 = None 2025-03-04T20:57:28.2349122Z 2025-03-04T20:57:28.2349530Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:57:28.2350053Z shift_y_3: "f32[22496][1]cpu" = shift_y_2.reshape(-1); shift_y_2 = None 2025-03-04T20:57:28.2350306Z 2025-03-04T20:57:28.2350792Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:57:28.2351476Z shifts_1: "f32[22496, 4][4, 1]cpu" = torch.stack((shift_x_3, shift_y_3, shift_x_3, shift_y_3), dim = 1); shift_x_3 = shift_y_3 = None 2025-03-04T20:57:28.2351821Z 2025-03-04T20:57:28.2352371Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:57:28.2353002Z view_2: "f32[22496, 1, 4][4, 4, 1]cpu" = shifts_1.view(-1, 1, 4); shifts_1 = None 2025-03-04T20:57:28.2353531Z view_3: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_ = None 2025-03-04T20:57:28.2354047Z add_1: "f32[22496, 3, 4][12, 4, 1]cpu" = view_2 + view_3; view_2 = view_3 = None 2025-03-04T20:57:28.2354359Z x_1: "f32[67488, 4][4, 1]cpu" = add_1.reshape(-1, 4); add_1 = None 2025-03-04T20:57:28.2354613Z 2025-03-04T20:57:28.2355200Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:57:28.2355881Z arange_4: "f32[76][1]cpu" = torch.arange(0.0, 1216, step = 16, dtype = torch.float32) 2025-03-04T20:57:28.2356172Z 2025-03-04T20:57:28.2356659Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:28.2357198Z shifts_x_2: "f32[76][1]cpu" = arange_4.to(device(type='cpu')); arange_4 = None 2025-03-04T20:57:28.2357482Z 2025-03-04T20:57:28.2358054Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:57:28.2358707Z arange_5: "f32[74][1]cpu" = torch.arange(0.0, 1184, step = 16, dtype = torch.float32) 2025-03-04T20:57:28.2358966Z 2025-03-04T20:57:28.2359349Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:28.2359888Z shifts_y_2: "f32[74][1]cpu" = arange_5.to(device(type='cpu')); arange_5 = None 2025-03-04T20:57:28.2360169Z 2025-03-04T20:57:28.2360676Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:57:28.2361360Z meshgrid_2 = torch.functional.meshgrid(shifts_y_2, shifts_x_2); shifts_y_2 = shifts_x_2 = None 2025-03-04T20:57:28.2361745Z shift_y_4: "f32[74, 76][1, 0]cpu" = meshgrid_2[0] 2025-03-04T20:57:28.2362068Z shift_x_4: "f32[74, 76][0, 1]cpu" = meshgrid_2[1]; meshgrid_2 = None 2025-03-04T20:57:28.2362335Z 2025-03-04T20:57:28.2362798Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:57:28.2363364Z shift_x_5: "f32[5624][1]cpu" = shift_x_4.reshape(-1); shift_x_4 = None 2025-03-04T20:57:28.2363632Z 2025-03-04T20:57:28.2364087Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:57:28.2364672Z shift_y_5: "f32[5624][1]cpu" = shift_y_4.reshape(-1); shift_y_4 = None 2025-03-04T20:57:28.2364938Z 2025-03-04T20:57:28.2365456Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:57:28.2366107Z shifts_2: "f32[5624, 4][4, 1]cpu" = torch.stack((shift_x_5, shift_y_5, shift_x_5, shift_y_5), dim = 1); shift_x_5 = shift_y_5 = None 2025-03-04T20:57:28.2366454Z 2025-03-04T20:57:28.2366990Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:57:28.2367572Z view_4: "f32[5624, 1, 4][4, 4, 1]cpu" = shifts_2.view(-1, 1, 4); shifts_2 = None 2025-03-04T20:57:28.2368057Z view_5: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_ = None 2025-03-04T20:57:28.2368535Z add_2: "f32[5624, 3, 4][12, 4, 1]cpu" = view_4 + view_5; view_4 = view_5 = None 2025-03-04T20:57:28.2368823Z x_2: "f32[16872, 4][4, 1]cpu" = add_2.reshape(-1, 4); add_2 = None 2025-03-04T20:57:28.2369049Z 2025-03-04T20:57:28.2369555Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:57:28.2370173Z arange_6: "f32[38][1]cpu" = torch.arange(0.0, 1216, step = 32, dtype = torch.float32) 2025-03-04T20:57:28.2370436Z 2025-03-04T20:57:28.2370802Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:28.2371285Z shifts_x_3: "f32[38][1]cpu" = arange_6.to(device(type='cpu')); arange_6 = None 2025-03-04T20:57:28.2371536Z 2025-03-04T20:57:28.2372044Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:57:28.2372658Z arange_7: "f32[37][1]cpu" = torch.arange(0.0, 1184, step = 32, dtype = torch.float32) 2025-03-04T20:57:28.2372916Z 2025-03-04T20:57:28.2373287Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:28.2373764Z shifts_y_3: "f32[37][1]cpu" = arange_7.to(device(type='cpu')); arange_7 = None 2025-03-04T20:57:28.2374021Z 2025-03-04T20:57:28.2374487Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:57:28.2375112Z meshgrid_3 = torch.functional.meshgrid(shifts_y_3, shifts_x_3); shifts_y_3 = shifts_x_3 = None 2025-03-04T20:57:28.2375457Z shift_y_6: "f32[37, 38][1, 0]cpu" = meshgrid_3[0] 2025-03-04T20:57:28.2376529Z shift_x_6: "f32[37, 38][0, 1]cpu" = meshgrid_3[1]; meshgrid_3 = None 2025-03-04T20:57:28.2376783Z 2025-03-04T20:57:28.2377220Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:57:28.2377749Z shift_x_7: "f32[1406][1]cpu" = shift_x_6.reshape(-1); shift_x_6 = None 2025-03-04T20:57:28.2377997Z 2025-03-04T20:57:28.2378414Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:57:28.2378960Z shift_y_7: "f32[1406][1]cpu" = shift_y_6.reshape(-1); shift_y_6 = None 2025-03-04T20:57:28.2379199Z 2025-03-04T20:57:28.2379662Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:57:28.2380312Z shifts_3: "f32[1406, 4][4, 1]cpu" = torch.stack((shift_x_7, shift_y_7, shift_x_7, shift_y_7), dim = 1); shift_x_7 = shift_y_7 = None 2025-03-04T20:57:28.2380630Z 2025-03-04T20:57:28.2381144Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:57:28.2381739Z view_6: "f32[1406, 1, 4][4, 4, 1]cpu" = shifts_3.view(-1, 1, 4); shifts_3 = None 2025-03-04T20:57:28.2382236Z view_7: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_ = None 2025-03-04T20:57:28.2382724Z add_3: "f32[1406, 3, 4][12, 4, 1]cpu" = view_6 + view_7; view_6 = view_7 = None 2025-03-04T20:57:28.2383011Z x_3: "f32[4218, 4][4, 1]cpu" = add_3.reshape(-1, 4); add_3 = None 2025-03-04T20:57:28.2383237Z 2025-03-04T20:57:28.2383754Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:57:28.2384392Z arange_8: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32) 2025-03-04T20:57:28.2384658Z 2025-03-04T20:57:28.2385045Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:28.2385537Z shifts_x_4: "f32[19][1]cpu" = arange_8.to(device(type='cpu')); arange_8 = None 2025-03-04T20:57:28.2385791Z 2025-03-04T20:57:28.2386312Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:57:28.2386943Z arange_9: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32) 2025-03-04T20:57:28.2387205Z 2025-03-04T20:57:28.2387585Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:28.2388071Z shifts_y_4: "f32[19][1]cpu" = arange_9.to(device(type='cpu')); arange_9 = None 2025-03-04T20:57:28.2388329Z 2025-03-04T20:57:28.2388797Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:57:28.2389423Z meshgrid_4 = torch.functional.meshgrid(shifts_y_4, shifts_x_4); shifts_y_4 = shifts_x_4 = None 2025-03-04T20:57:28.2389773Z shift_y_8: "f32[19, 19][1, 0]cpu" = meshgrid_4[0] 2025-03-04T20:57:28.2390059Z shift_x_8: "f32[19, 19][0, 1]cpu" = meshgrid_4[1]; meshgrid_4 = None 2025-03-04T20:57:28.2390295Z 2025-03-04T20:57:28.2390704Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:57:28.2391207Z shift_x_9: "f32[361][1]cpu" = shift_x_8.reshape(-1); shift_x_8 = None 2025-03-04T20:57:28.2391446Z 2025-03-04T20:57:28.2391852Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:57:28.2392368Z shift_y_9: "f32[361][1]cpu" = shift_y_8.reshape(-1); shift_y_8 = None 2025-03-04T20:57:28.2392605Z 2025-03-04T20:57:28.2393067Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:57:28.2393706Z shifts_4: "f32[361, 4][4, 1]cpu" = torch.stack((shift_x_9, shift_y_9, shift_x_9, shift_y_9), dim = 1); shift_x_9 = shift_y_9 = None 2025-03-04T20:57:28.2394028Z 2025-03-04T20:57:28.2394552Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:57:28.2395156Z view_8: "f32[361, 1, 4][4, 4, 1]cpu" = shifts_4.view(-1, 1, 4); shifts_4 = None 2025-03-04T20:57:28.2395659Z view_9: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_ = None 2025-03-04T20:57:28.2396150Z add_4: "f32[361, 3, 4][12, 4, 1]cpu" = view_8 + view_9; view_8 = view_9 = None 2025-03-04T20:57:28.2396444Z x_4: "f32[1083, 4][4, 1]cpu" = add_4.reshape(-1, 4); add_4 = None 2025-03-04T20:57:28.2396757Z 2025-03-04T20:57:28.2397165Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:57:28.2397657Z tensor: "f32[269952, 4][4, 1]cpu" = x.to(torch.float32); x = None 2025-03-04T20:57:28.2397962Z tensor_1: "f32[67488, 4][4, 1]cpu" = x_1.to(torch.float32); x_1 = None 2025-03-04T20:57:28.2398284Z tensor_2: "f32[16872, 4][4, 1]cpu" = x_2.to(torch.float32); x_2 = None 2025-03-04T20:57:28.2398580Z tensor_3: "f32[4218, 4][4, 1]cpu" = x_3.to(torch.float32); x_3 = None 2025-03-04T20:57:28.2398879Z tensor_4: "f32[1083, 4][4, 1]cpu" = x_4.to(torch.float32); x_4 = None 2025-03-04T20:57:28.2399116Z 2025-03-04T20:57:28.2399465Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:28.2400208Z x_5: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(l_features_p2_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_p2_ = None 2025-03-04T20:57:28.2400749Z 2025-03-04T20:57:28.2401105Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:57:28.2401619Z x_6: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.relu(x_5, inplace = False); x_5 = None 2025-03-04T20:57:28.2402077Z 2025-03-04T20:57:28.2402549Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:57:28.2403385Z score: "f32[4, 3, 296, 304][269952, 89984, 304, 1]cpu" = torch.conv2d(x_6, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T20:57:28.2403967Z 2025-03-04T20:57:28.2404408Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:57:28.2405213Z x_15: "f32[4, 12, 296, 304][1079808, 89984, 304, 1]cpu" = torch.conv2d(x_6, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_6 = None 2025-03-04T20:57:28.2405749Z 2025-03-04T20:57:28.2406085Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:28.2406789Z x_7: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(l_features_p3_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_p3_ = None 2025-03-04T20:57:28.2407304Z 2025-03-04T20:57:28.2407681Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:57:28.2408203Z x_8: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.relu(x_7, inplace = False); x_7 = None 2025-03-04T20:57:28.2408500Z 2025-03-04T20:57:28.2408948Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:57:28.2409772Z score_1: "f32[4, 3, 148, 152][67488, 22496, 152, 1]cpu" = torch.conv2d(x_8, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T20:57:28.2410289Z 2025-03-04T20:57:28.2410731Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:57:28.2411560Z x_16: "f32[4, 12, 148, 152][269952, 22496, 152, 1]cpu" = torch.conv2d(x_8, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_8 = None 2025-03-04T20:57:28.2412084Z 2025-03-04T20:57:28.2412415Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:28.2413127Z x_9: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(l_features_p4_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_p4_ = None 2025-03-04T20:57:28.2413630Z 2025-03-04T20:57:28.2413979Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:57:28.2414473Z x_10: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.relu(x_9, inplace = False); x_9 = None 2025-03-04T20:57:28.2414755Z 2025-03-04T20:57:28.2415194Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:57:28.2416002Z score_2: "f32[4, 3, 74, 76][16872, 5624, 76, 1]cpu" = torch.conv2d(x_10, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T20:57:28.2416510Z 2025-03-04T20:57:28.2416975Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:57:28.2417777Z x_17: "f32[4, 12, 74, 76][67488, 5624, 76, 1]cpu" = torch.conv2d(x_10, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_10 = None 2025-03-04T20:57:28.2418300Z 2025-03-04T20:57:28.2418630Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:28.2419321Z x_11: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(l_features_p5_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_p5_ = None 2025-03-04T20:57:28.2419850Z 2025-03-04T20:57:28.2420196Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:57:28.2420688Z x_12: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.nn.functional.relu(x_11, inplace = False); x_11 = None 2025-03-04T20:57:28.2420994Z 2025-03-04T20:57:28.2421459Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:57:28.2422260Z score_3: "f32[4, 3, 37, 38][4218, 1406, 38, 1]cpu" = torch.conv2d(x_12, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T20:57:28.2422757Z 2025-03-04T20:57:28.2423188Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:57:28.2423980Z x_18: "f32[4, 12, 37, 38][16872, 1406, 38, 1]cpu" = torch.conv2d(x_12, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_12 = None 2025-03-04T20:57:28.2424473Z 2025-03-04T20:57:28.2424801Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:28.2425667Z x_13: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.conv2d(l_features_p6_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_p6_ = l_self_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T20:57:28.2426344Z 2025-03-04T20:57:28.2426690Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:57:28.2427178Z x_14: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.relu(x_13, inplace = False); x_13 = None 2025-03-04T20:57:28.2427459Z 2025-03-04T20:57:28.2427908Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:57:28.2428954Z score_4: "f32[4, 3, 19, 19][1083, 361, 19, 1]cpu" = torch.conv2d(x_14, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T20:57:28.2429690Z 2025-03-04T20:57:28.2430132Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:57:28.2431141Z x_19: "f32[4, 12, 19, 19][4332, 361, 19, 1]cpu" = torch.conv2d(x_14, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_14 = l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T20:57:28.2431839Z 2025-03-04T20:57:28.2432263Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T20:57:28.2432837Z permute: "f32[4, 296, 304, 3][269952, 304, 1, 89984]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T20:57:28.2433194Z logits_i: "f32[4, 269952][269952, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T20:57:28.2433555Z permute_1: "f32[4, 148, 152, 3][67488, 152, 1, 22496]cpu" = score_1.permute(0, 2, 3, 1); score_1 = None 2025-03-04T20:57:28.2433933Z logits_i_1: "f32[4, 67488][67488, 1]cpu" = permute_1.flatten(1); permute_1 = None 2025-03-04T20:57:28.2434285Z permute_2: "f32[4, 74, 76, 3][16872, 76, 1, 5624]cpu" = score_2.permute(0, 2, 3, 1); score_2 = None 2025-03-04T20:57:28.2434648Z logits_i_2: "f32[4, 16872][16872, 1]cpu" = permute_2.flatten(1); permute_2 = None 2025-03-04T20:57:28.2434989Z permute_3: "f32[4, 37, 38, 3][4218, 38, 1, 1406]cpu" = score_3.permute(0, 2, 3, 1); score_3 = None 2025-03-04T20:57:28.2435323Z logits_i_3: "f32[4, 4218][4218, 1]cpu" = permute_3.flatten(1); permute_3 = None 2025-03-04T20:57:28.2435657Z permute_4: "f32[4, 19, 19, 3][1083, 19, 1, 361]cpu" = score_4.permute(0, 2, 3, 1); score_4 = None 2025-03-04T20:57:28.2435987Z logits_i_4: "f32[4, 1083][1083, 1]cpu" = permute_4.flatten(1); permute_4 = None 2025-03-04T20:57:28.2436242Z 2025-03-04T20:57:28.2436829Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T20:57:28.2437494Z view_10: "f32[4, 3, 4, 296, 304][1079808, 359936, 89984, 304, 1]cpu" = x_15.view(4, -1, 4, 296, 304); x_15 = None 2025-03-04T20:57:28.2437911Z permute_5: "f32[4, 296, 304, 3, 4][1079808, 304, 1, 359936, 89984]cpu" = view_10.permute(0, 3, 4, 1, 2); view_10 = None 2025-03-04T20:57:28.2438335Z pred_anchor_deltas_i: "f32[4, 269952, 4][1079808, 4, 1]cpu" = permute_5.flatten(1, -2); permute_5 = None 2025-03-04T20:57:28.2438738Z view_11: "f32[4, 3, 4, 148, 152][269952, 89984, 22496, 152, 1]cpu" = x_16.view(4, -1, 4, 148, 152); x_16 = None 2025-03-04T20:57:28.2439140Z permute_6: "f32[4, 148, 152, 3, 4][269952, 152, 1, 89984, 22496]cpu" = view_11.permute(0, 3, 4, 1, 2); view_11 = None 2025-03-04T20:57:28.2439551Z pred_anchor_deltas_i_2: "f32[4, 67488, 4][269952, 4, 1]cpu" = permute_6.flatten(1, -2); permute_6 = None 2025-03-04T20:57:28.2439936Z view_12: "f32[4, 3, 4, 74, 76][67488, 22496, 5624, 76, 1]cpu" = x_17.view(4, -1, 4, 74, 76); x_17 = None 2025-03-04T20:57:28.2440316Z permute_7: "f32[4, 74, 76, 3, 4][67488, 76, 1, 22496, 5624]cpu" = view_12.permute(0, 3, 4, 1, 2); view_12 = None 2025-03-04T20:57:28.2440711Z pred_anchor_deltas_i_4: "f32[4, 16872, 4][67488, 4, 1]cpu" = permute_7.flatten(1, -2); permute_7 = None 2025-03-04T20:57:28.2441092Z view_13: "f32[4, 3, 4, 37, 38][16872, 5624, 1406, 38, 1]cpu" = x_18.view(4, -1, 4, 37, 38); x_18 = None 2025-03-04T20:57:28.2441451Z permute_8: "f32[4, 37, 38, 3, 4][16872, 38, 1, 5624, 1406]cpu" = view_13.permute(0, 3, 4, 1, 2); view_13 = None 2025-03-04T20:57:28.2441829Z pred_anchor_deltas_i_6: "f32[4, 4218, 4][16872, 4, 1]cpu" = permute_8.flatten(1, -2); permute_8 = None 2025-03-04T20:57:28.2442213Z view_14: "f32[4, 3, 4, 19, 19][4332, 1444, 361, 19, 1]cpu" = x_19.view(4, -1, 4, 19, 19); x_19 = None 2025-03-04T20:57:28.2442571Z permute_9: "f32[4, 19, 19, 3, 4][4332, 19, 1, 1444, 361]cpu" = view_14.permute(0, 3, 4, 1, 2); view_14 = None 2025-03-04T20:57:28.2442953Z pred_anchor_deltas_i_8: "f32[4, 1083, 4][4332, 4, 1]cpu" = permute_9.flatten(1, -2); permute_9 = None 2025-03-04T20:57:28.2443244Z 2025-03-04T20:57:28.2443756Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:57:28.2444435Z pred_anchor_deltas_i_1: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T20:57:28.2444757Z 2025-03-04T20:57:28.2445285Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:57:28.2445964Z unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T20:57:28.2446326Z expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T20:57:28.2446687Z anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T20:57:28.2446947Z 2025-03-04T20:57:28.2447413Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:57:28.2448012Z deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T20:57:28.2448297Z 2025-03-04T20:57:28.2448698Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:57:28.2449208Z boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T20:57:28.2449464Z 2025-03-04T20:57:28.2449854Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:57:28.2450346Z getitem_10: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:57:28.2450652Z getitem_11: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:57:28.2450981Z widths: "f32[1079808][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T20:57:28.2451238Z 2025-03-04T20:57:28.2451635Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:57:28.2452126Z getitem_12: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:57:28.2452422Z getitem_13: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:57:28.2452744Z heights: "f32[1079808][1]cpu" = getitem_12 - getitem_13; getitem_12 = getitem_13 = None 2025-03-04T20:57:28.2453008Z 2025-03-04T20:57:28.2453399Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:57:28.2453880Z getitem_14: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:57:28.2454144Z mul: "f32[1079808][1]cpu" = 0.5 * widths 2025-03-04T20:57:28.2454405Z ctr_x: "f32[1079808][1]cpu" = getitem_14 + mul; getitem_14 = mul = None 2025-03-04T20:57:28.2454640Z 2025-03-04T20:57:28.2455028Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:57:28.2455550Z getitem_15: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:57:28.2455836Z mul_1: "f32[1079808][1]cpu" = 0.5 * heights 2025-03-04T20:57:28.2456106Z ctr_y: "f32[1079808][1]cpu" = getitem_15 + mul_1; getitem_15 = mul_1 = None 2025-03-04T20:57:28.2456352Z 2025-03-04T20:57:28.2456779Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:57:28.2457294Z getitem_16: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:57:28.2457615Z dx: "f32[1079808, 1][1, 1]cpu" = getitem_16 / 1.0; getitem_16 = None 2025-03-04T20:57:28.2457845Z 2025-03-04T20:57:28.2458222Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:57:28.2458715Z getitem_17: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:57:28.2459053Z dy: "f32[1079808, 1][1, 1]cpu" = getitem_17 / 1.0; getitem_17 = None 2025-03-04T20:57:28.2459289Z 2025-03-04T20:57:28.2459700Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:57:28.2460192Z getitem_18: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:57:28.2460513Z dw: "f32[1079808, 1][1, 1]cpu" = getitem_18 / 1.0; getitem_18 = None 2025-03-04T20:57:28.2460743Z 2025-03-04T20:57:28.2461124Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:57:28.2461650Z getitem_19: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:57:28.2461990Z dh: "f32[1079808, 1][1, 1]cpu" = getitem_19 / 1.0; getitem_19 = None 2025-03-04T20:57:28.2462223Z 2025-03-04T20:57:28.2462641Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:57:28.2463166Z dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:57:28.2463420Z 2025-03-04T20:57:28.2463829Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:57:28.2464362Z dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:57:28.2464630Z 2025-03-04T20:57:28.2465048Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:57:28.2465572Z getitem_20: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:57:28.2465892Z mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_20; dx = getitem_20 = None 2025-03-04T20:57:28.2466229Z getitem_21: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:57:28.2466580Z pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_21; mul_2 = getitem_21 = None 2025-03-04T20:57:28.2466841Z 2025-03-04T20:57:28.2467277Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:57:28.2467816Z getitem_22: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:57:28.2468161Z mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_22; dy = getitem_22 = None 2025-03-04T20:57:28.2468491Z getitem_23: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:57:28.2468836Z pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_23; mul_3 = getitem_23 = None 2025-03-04T20:57:28.2469089Z 2025-03-04T20:57:28.2469506Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:57:28.2470026Z exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:57:28.2470353Z getitem_24: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:57:28.2470700Z pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_24; exp = getitem_24 = None 2025-03-04T20:57:28.2470948Z 2025-03-04T20:57:28.2471369Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:57:28.2471884Z exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:57:28.2472221Z getitem_25: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:57:28.2472589Z pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_25; exp_1 = getitem_25 = None 2025-03-04T20:57:28.2472848Z 2025-03-04T20:57:28.2473247Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:57:28.2473714Z mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T20:57:28.2473977Z x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:57:28.2474217Z 2025-03-04T20:57:28.2474612Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:57:28.2475074Z mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T20:57:28.2475337Z y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:57:28.2475571Z 2025-03-04T20:57:28.2475963Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:57:28.2476439Z mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:57:28.2476815Z x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:57:28.2477067Z 2025-03-04T20:57:28.2477459Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:57:28.2477939Z mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:57:28.2478237Z y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:57:28.2478487Z 2025-03-04T20:57:28.2478924Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:57:28.2479521Z pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:57:28.2479814Z 2025-03-04T20:57:28.2480227Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:57:28.2480772Z proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4)); pred_boxes = None 2025-03-04T20:57:28.2481068Z 2025-03-04T20:57:28.2481525Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:57:28.2482122Z proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T20:57:28.2482406Z 2025-03-04T20:57:28.2482876Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:57:28.2483549Z pred_anchor_deltas_i_3: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_2.reshape(-1, 4); pred_anchor_deltas_i_2 = None 2025-03-04T20:57:28.2483866Z 2025-03-04T20:57:28.2484379Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:57:28.2485008Z unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = tensor_1.unsqueeze(0); tensor_1 = None 2025-03-04T20:57:28.2485377Z expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1); unsqueeze_1 = None 2025-03-04T20:57:28.2485734Z anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4); expand_1 = None 2025-03-04T20:57:28.2485989Z 2025-03-04T20:57:28.2486435Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:57:28.2487016Z deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float(); pred_anchor_deltas_i_3 = None 2025-03-04T20:57:28.2487295Z 2025-03-04T20:57:28.2487682Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:57:28.2488190Z boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32); anchors_i_1 = None 2025-03-04T20:57:28.2488450Z 2025-03-04T20:57:28.2488841Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:57:28.2489331Z getitem_26: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)] 2025-03-04T20:57:28.2489635Z getitem_27: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T20:57:28.2489964Z widths_1: "f32[269952][1]cpu" = getitem_26 - getitem_27; getitem_26 = getitem_27 = None 2025-03-04T20:57:28.2490222Z 2025-03-04T20:57:28.2490615Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:57:28.2491104Z getitem_28: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)] 2025-03-04T20:57:28.2491402Z getitem_29: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)] 2025-03-04T20:57:28.2491723Z heights_1: "f32[269952][1]cpu" = getitem_28 - getitem_29; getitem_28 = getitem_29 = None 2025-03-04T20:57:28.2491990Z 2025-03-04T20:57:28.2492383Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:57:28.2492863Z getitem_30: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T20:57:28.2493131Z mul_10: "f32[269952][1]cpu" = 0.5 * widths_1 2025-03-04T20:57:28.2493406Z ctr_x_1: "f32[269952][1]cpu" = getitem_30 + mul_10; getitem_30 = mul_10 = None 2025-03-04T20:57:28.2493654Z 2025-03-04T20:57:28.2494047Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:57:28.2494577Z getitem_31: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]; boxes_1 = None 2025-03-04T20:57:28.2494871Z mul_11: "f32[269952][1]cpu" = 0.5 * heights_1 2025-03-04T20:57:28.2495142Z ctr_y_1: "f32[269952][1]cpu" = getitem_31 + mul_11; getitem_31 = mul_11 = None 2025-03-04T20:57:28.2495388Z 2025-03-04T20:57:28.2495784Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:57:28.2496308Z getitem_32: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:57:28.2496631Z dx_1: "f32[269952, 1][1, 1]cpu" = getitem_32 / 1.0; getitem_32 = None 2025-03-04T20:57:28.2496869Z 2025-03-04T20:57:28.2497256Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:57:28.2497754Z getitem_33: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:57:28.2498086Z dy_1: "f32[269952, 1][1, 1]cpu" = getitem_33 / 1.0; getitem_33 = None 2025-03-04T20:57:28.2498314Z 2025-03-04T20:57:28.2498707Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:57:28.2499197Z getitem_34: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:57:28.2499513Z dw_2: "f32[269952, 1][1, 1]cpu" = getitem_34 / 1.0; getitem_34 = None 2025-03-04T20:57:28.2499742Z 2025-03-04T20:57:28.2500123Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:57:28.2500651Z getitem_35: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))]; deltas_1 = None 2025-03-04T20:57:28.2500996Z dh_2: "f32[269952, 1][1, 1]cpu" = getitem_35 / 1.0; getitem_35 = None 2025-03-04T20:57:28.2501222Z 2025-03-04T20:57:28.2501633Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:57:28.2502323Z dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356); dw_2 = None 2025-03-04T20:57:28.2502585Z 2025-03-04T20:57:28.2502996Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:57:28.2503513Z dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356); dh_2 = None 2025-03-04T20:57:28.2503766Z 2025-03-04T20:57:28.2504186Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:57:28.2504718Z getitem_36: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)] 2025-03-04T20:57:28.2505032Z mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_36; dx_1 = getitem_36 = None 2025-03-04T20:57:28.2505367Z getitem_37: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)]; ctr_x_1 = None 2025-03-04T20:57:28.2505716Z pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_37; mul_12 = getitem_37 = None 2025-03-04T20:57:28.2505974Z 2025-03-04T20:57:28.2506406Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:57:28.2506982Z getitem_38: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)] 2025-03-04T20:57:28.2507295Z mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_38; dy_1 = getitem_38 = None 2025-03-04T20:57:28.2507621Z getitem_39: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)]; ctr_y_1 = None 2025-03-04T20:57:28.2507966Z pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_39; mul_13 = getitem_39 = None 2025-03-04T20:57:28.2508220Z 2025-03-04T20:57:28.2508621Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:57:28.2509147Z exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3); dw_3 = None 2025-03-04T20:57:28.2509472Z getitem_40: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]; widths_1 = None 2025-03-04T20:57:28.2509821Z pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_40; exp_2 = getitem_40 = None 2025-03-04T20:57:28.2510084Z 2025-03-04T20:57:28.2510520Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:57:28.2511014Z exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3); dh_3 = None 2025-03-04T20:57:28.2511386Z getitem_41: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]; heights_1 = None 2025-03-04T20:57:28.2511741Z pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_41; exp_3 = getitem_41 = None 2025-03-04T20:57:28.2511997Z 2025-03-04T20:57:28.2512394Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:57:28.2512852Z mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1 2025-03-04T20:57:28.2513125Z x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16; mul_16 = None 2025-03-04T20:57:28.2513363Z 2025-03-04T20:57:28.2513758Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:57:28.2514211Z mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1 2025-03-04T20:57:28.2514478Z y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17; mul_17 = None 2025-03-04T20:57:28.2514721Z 2025-03-04T20:57:28.2515120Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:57:28.2515609Z mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1; pred_w_1 = None 2025-03-04T20:57:28.2515928Z x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18; pred_ctr_x_1 = mul_18 = None 2025-03-04T20:57:28.2516189Z 2025-03-04T20:57:28.2516641Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:57:28.2517134Z mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1; pred_h_1 = None 2025-03-04T20:57:28.2517438Z y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19; pred_ctr_y_1 = mul_19 = None 2025-03-04T20:57:28.2517694Z 2025-03-04T20:57:28.2518133Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:57:28.2518736Z pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1); x1_1 = y1_1 = x2_1 = y2_1 = None 2025-03-04T20:57:28.2519046Z 2025-03-04T20:57:28.2519466Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:57:28.2520054Z proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4)); pred_boxes_1 = None 2025-03-04T20:57:28.2520353Z 2025-03-04T20:57:28.2520837Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:57:28.2521457Z proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4); proposals_i_1 = None 2025-03-04T20:57:28.2521755Z 2025-03-04T20:57:28.2522243Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:57:28.2522928Z pred_anchor_deltas_i_5: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_4.reshape(-1, 4); pred_anchor_deltas_i_4 = None 2025-03-04T20:57:28.2523258Z 2025-03-04T20:57:28.2523780Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:57:28.2524437Z unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = tensor_2.unsqueeze(0); tensor_2 = None 2025-03-04T20:57:28.2524909Z expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1); unsqueeze_2 = None 2025-03-04T20:57:28.2525256Z anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4); expand_2 = None 2025-03-04T20:57:28.2525518Z 2025-03-04T20:57:28.2525981Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:57:28.2526593Z deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_5.float(); pred_anchor_deltas_i_5 = None 2025-03-04T20:57:28.2526900Z 2025-03-04T20:57:28.2527330Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:57:28.2527862Z boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32); anchors_i_2 = None 2025-03-04T20:57:28.2528142Z 2025-03-04T20:57:28.2528581Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:57:28.2529099Z getitem_42: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)] 2025-03-04T20:57:28.2529424Z getitem_43: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T20:57:28.2529765Z widths_2: "f32[67488][1]cpu" = getitem_42 - getitem_43; getitem_42 = getitem_43 = None 2025-03-04T20:57:28.2530037Z 2025-03-04T20:57:28.2530446Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:57:28.2530944Z getitem_44: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)] 2025-03-04T20:57:28.2531248Z getitem_45: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)] 2025-03-04T20:57:28.2531578Z heights_2: "f32[67488][1]cpu" = getitem_44 - getitem_45; getitem_44 = getitem_45 = None 2025-03-04T20:57:28.2531850Z 2025-03-04T20:57:28.2532249Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:57:28.2532744Z getitem_46: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T20:57:28.2533015Z mul_20: "f32[67488][1]cpu" = 0.5 * widths_2 2025-03-04T20:57:28.2533296Z ctr_x_2: "f32[67488][1]cpu" = getitem_46 + mul_20; getitem_46 = mul_20 = None 2025-03-04T20:57:28.2533572Z 2025-03-04T20:57:28.2533972Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:57:28.2534492Z getitem_47: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]; boxes_2 = None 2025-03-04T20:57:28.2534791Z mul_21: "f32[67488][1]cpu" = 0.5 * heights_2 2025-03-04T20:57:28.2535068Z ctr_y_2: "f32[67488][1]cpu" = getitem_47 + mul_21; getitem_47 = mul_21 = None 2025-03-04T20:57:28.2535320Z 2025-03-04T20:57:28.2535719Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:57:28.2536243Z getitem_48: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:57:28.2536569Z dx_2: "f32[67488, 1][1, 1]cpu" = getitem_48 / 1.0; getitem_48 = None 2025-03-04T20:57:28.2536806Z 2025-03-04T20:57:28.2537191Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:57:28.2537734Z getitem_49: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:57:28.2538056Z dy_2: "f32[67488, 1][1, 1]cpu" = getitem_49 / 1.0; getitem_49 = None 2025-03-04T20:57:28.2538289Z 2025-03-04T20:57:28.2538686Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:57:28.2539188Z getitem_50: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:57:28.2539509Z dw_4: "f32[67488, 1][1, 1]cpu" = getitem_50 / 1.0; getitem_50 = None 2025-03-04T20:57:28.2539739Z 2025-03-04T20:57:28.2540130Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:57:28.2540675Z getitem_51: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))]; deltas_2 = None 2025-03-04T20:57:28.2541037Z dh_4: "f32[67488, 1][1, 1]cpu" = getitem_51 / 1.0; getitem_51 = None 2025-03-04T20:57:28.2541274Z 2025-03-04T20:57:28.2541705Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:57:28.2542241Z dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356); dw_4 = None 2025-03-04T20:57:28.2542498Z 2025-03-04T20:57:28.2542909Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:57:28.2543430Z dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356); dh_4 = None 2025-03-04T20:57:28.2543683Z 2025-03-04T20:57:28.2544110Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:57:28.2544653Z getitem_52: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)] 2025-03-04T20:57:28.2544967Z mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_52; dx_2 = getitem_52 = None 2025-03-04T20:57:28.2545307Z getitem_53: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)]; ctr_x_2 = None 2025-03-04T20:57:28.2545652Z pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_53; mul_22 = getitem_53 = None 2025-03-04T20:57:28.2545910Z 2025-03-04T20:57:28.2546348Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:57:28.2546905Z getitem_54: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)] 2025-03-04T20:57:28.2547223Z mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_54; dy_2 = getitem_54 = None 2025-03-04T20:57:28.2547551Z getitem_55: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)]; ctr_y_2 = None 2025-03-04T20:57:28.2547896Z pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_55; mul_23 = getitem_55 = None 2025-03-04T20:57:28.2548153Z 2025-03-04T20:57:28.2548569Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:57:28.2549088Z exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5); dw_5 = None 2025-03-04T20:57:28.2549406Z getitem_56: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]; widths_2 = None 2025-03-04T20:57:28.2549755Z pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_56; exp_4 = getitem_56 = None 2025-03-04T20:57:28.2550011Z 2025-03-04T20:57:28.2550447Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:57:28.2550948Z exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5); dh_5 = None 2025-03-04T20:57:28.2551295Z getitem_57: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]; heights_2 = None 2025-03-04T20:57:28.2551652Z pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_57; exp_5 = getitem_57 = None 2025-03-04T20:57:28.2551911Z 2025-03-04T20:57:28.2552304Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:57:28.2552762Z mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2 2025-03-04T20:57:28.2553027Z x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26; mul_26 = None 2025-03-04T20:57:28.2553261Z 2025-03-04T20:57:28.2553654Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:57:28.2554108Z mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2 2025-03-04T20:57:28.2554379Z y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27; mul_27 = None 2025-03-04T20:57:28.2554619Z 2025-03-04T20:57:28.2555017Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:57:28.2555503Z mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2; pred_w_2 = None 2025-03-04T20:57:28.2555814Z x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28; pred_ctr_x_2 = mul_28 = None 2025-03-04T20:57:28.2556069Z 2025-03-04T20:57:28.2556468Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:57:28.2557029Z mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2; pred_h_2 = None 2025-03-04T20:57:28.2557346Z y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29; pred_ctr_y_2 = mul_29 = None 2025-03-04T20:57:28.2557607Z 2025-03-04T20:57:28.2558058Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:57:28.2558654Z pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1); x1_2 = y1_2 = x2_2 = y2_2 = None 2025-03-04T20:57:28.2558957Z 2025-03-04T20:57:28.2559379Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:57:28.2559949Z proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4)); pred_boxes_2 = None 2025-03-04T20:57:28.2560233Z 2025-03-04T20:57:28.2560698Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:57:28.2561303Z proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4); proposals_i_2 = None 2025-03-04T20:57:28.2561595Z 2025-03-04T20:57:28.2562092Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:57:28.2562747Z pred_anchor_deltas_i_7: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_6.reshape(-1, 4); pred_anchor_deltas_i_6 = None 2025-03-04T20:57:28.2563070Z 2025-03-04T20:57:28.2563614Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:57:28.2564253Z unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = tensor_3.unsqueeze(0); tensor_3 = None 2025-03-04T20:57:28.2564620Z expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1); unsqueeze_3 = None 2025-03-04T20:57:28.2564964Z anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4); expand_3 = None 2025-03-04T20:57:28.2565227Z 2025-03-04T20:57:28.2565679Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:57:28.2566268Z deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_7.float(); pred_anchor_deltas_i_7 = None 2025-03-04T20:57:28.2566556Z 2025-03-04T20:57:28.2566953Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:57:28.2567466Z boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32); anchors_i_3 = None 2025-03-04T20:57:28.2567733Z 2025-03-04T20:57:28.2568139Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:57:28.2568635Z getitem_58: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)] 2025-03-04T20:57:28.2568940Z getitem_59: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T20:57:28.2569271Z widths_3: "f32[16872][1]cpu" = getitem_58 - getitem_59; getitem_58 = getitem_59 = None 2025-03-04T20:57:28.2569536Z 2025-03-04T20:57:28.2569944Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:57:28.2570453Z getitem_60: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)] 2025-03-04T20:57:28.2570751Z getitem_61: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)] 2025-03-04T20:57:28.2571075Z heights_3: "f32[16872][1]cpu" = getitem_60 - getitem_61; getitem_60 = getitem_61 = None 2025-03-04T20:57:28.2571343Z 2025-03-04T20:57:28.2571733Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:57:28.2572214Z getitem_62: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T20:57:28.2572493Z mul_30: "f32[16872][1]cpu" = 0.5 * widths_3 2025-03-04T20:57:28.2572782Z ctr_x_3: "f32[16872][1]cpu" = getitem_62 + mul_30; getitem_62 = mul_30 = None 2025-03-04T20:57:28.2573026Z 2025-03-04T20:57:28.2573415Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:57:28.2573915Z getitem_63: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]; boxes_3 = None 2025-03-04T20:57:28.2574208Z mul_31: "f32[16872][1]cpu" = 0.5 * heights_3 2025-03-04T20:57:28.2574478Z ctr_y_3: "f32[16872][1]cpu" = getitem_63 + mul_31; getitem_63 = mul_31 = None 2025-03-04T20:57:28.2574723Z 2025-03-04T20:57:28.2575134Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:57:28.2575637Z getitem_64: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:57:28.2575957Z dx_3: "f32[16872, 1][1, 1]cpu" = getitem_64 / 1.0; getitem_64 = None 2025-03-04T20:57:28.2576184Z 2025-03-04T20:57:28.2576578Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:57:28.2577070Z getitem_65: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:57:28.2577397Z dy_3: "f32[16872, 1][1, 1]cpu" = getitem_65 / 1.0; getitem_65 = None 2025-03-04T20:57:28.2577626Z 2025-03-04T20:57:28.2578001Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:57:28.2578491Z getitem_66: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:57:28.2578797Z dw_6: "f32[16872, 1][1, 1]cpu" = getitem_66 / 1.0; getitem_66 = None 2025-03-04T20:57:28.2579023Z 2025-03-04T20:57:28.2579398Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:57:28.2579919Z getitem_67: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))]; deltas_3 = None 2025-03-04T20:57:28.2580251Z dh_6: "f32[16872, 1][1, 1]cpu" = getitem_67 / 1.0; getitem_67 = None 2025-03-04T20:57:28.2580470Z 2025-03-04T20:57:28.2580879Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:57:28.2581394Z dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356); dw_6 = None 2025-03-04T20:57:28.2581644Z 2025-03-04T20:57:28.2582049Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:57:28.2582554Z dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356); dh_6 = None 2025-03-04T20:57:28.2582801Z 2025-03-04T20:57:28.2583221Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:57:28.2583738Z getitem_68: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)] 2025-03-04T20:57:28.2584050Z mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_68; dx_3 = getitem_68 = None 2025-03-04T20:57:28.2584376Z getitem_69: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)]; ctr_x_3 = None 2025-03-04T20:57:28.2584713Z pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_69; mul_32 = getitem_69 = None 2025-03-04T20:57:28.2584965Z 2025-03-04T20:57:28.2585404Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:57:28.2585927Z getitem_70: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)] 2025-03-04T20:57:28.2586237Z mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_70; dy_3 = getitem_70 = None 2025-03-04T20:57:28.2586562Z getitem_71: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)]; ctr_y_3 = None 2025-03-04T20:57:28.2586900Z pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_71; mul_33 = getitem_71 = None 2025-03-04T20:57:28.2587216Z 2025-03-04T20:57:28.2587615Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:57:28.2588098Z exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7); dw_7 = None 2025-03-04T20:57:28.2588417Z getitem_72: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]; widths_3 = None 2025-03-04T20:57:28.2588763Z pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_72; exp_6 = getitem_72 = None 2025-03-04T20:57:28.2589013Z 2025-03-04T20:57:28.2589443Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:57:28.2589956Z exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7); dh_7 = None 2025-03-04T20:57:28.2590285Z getitem_73: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]; heights_3 = None 2025-03-04T20:57:28.2590636Z pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_73; exp_7 = getitem_73 = None 2025-03-04T20:57:28.2590885Z 2025-03-04T20:57:28.2591274Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:57:28.2591725Z mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3 2025-03-04T20:57:28.2591983Z x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36; mul_36 = None 2025-03-04T20:57:28.2592214Z 2025-03-04T20:57:28.2592598Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:57:28.2593040Z mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3 2025-03-04T20:57:28.2593302Z y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37; mul_37 = None 2025-03-04T20:57:28.2593535Z 2025-03-04T20:57:28.2593922Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:57:28.2594407Z mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3; pred_w_3 = None 2025-03-04T20:57:28.2594711Z x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38; pred_ctr_x_3 = mul_38 = None 2025-03-04T20:57:28.2594963Z 2025-03-04T20:57:28.2595352Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:57:28.2595824Z mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3; pred_h_3 = None 2025-03-04T20:57:28.2596125Z y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39; pred_ctr_y_3 = mul_39 = None 2025-03-04T20:57:28.2596372Z 2025-03-04T20:57:28.2596892Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:57:28.2597534Z pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1); x1_3 = y1_3 = x2_3 = y2_3 = None 2025-03-04T20:57:28.2597875Z 2025-03-04T20:57:28.2598293Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:57:28.2598850Z proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4)); pred_boxes_3 = None 2025-03-04T20:57:28.2599135Z 2025-03-04T20:57:28.2599615Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:57:28.2600225Z proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4); proposals_i_3 = None 2025-03-04T20:57:28.2600539Z 2025-03-04T20:57:28.2601023Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:57:28.2601683Z pred_anchor_deltas_i_9: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_8.reshape(-1, 4); pred_anchor_deltas_i_8 = None 2025-03-04T20:57:28.2602160Z 2025-03-04T20:57:28.2602735Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:57:28.2603410Z unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = tensor_4.unsqueeze(0); tensor_4 = None 2025-03-04T20:57:28.2603765Z expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1); unsqueeze_4 = None 2025-03-04T20:57:28.2604106Z anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4); expand_4 = None 2025-03-04T20:57:28.2604366Z 2025-03-04T20:57:28.2604827Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:57:28.2605421Z deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_9.float(); pred_anchor_deltas_i_9 = None 2025-03-04T20:57:28.2605709Z 2025-03-04T20:57:28.2606110Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:57:28.2606619Z boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32); anchors_i_4 = None 2025-03-04T20:57:28.2606880Z 2025-03-04T20:57:28.2607279Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:57:28.2607775Z getitem_74: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)] 2025-03-04T20:57:28.2608086Z getitem_75: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T20:57:28.2608407Z widths_4: "f32[4332][1]cpu" = getitem_74 - getitem_75; getitem_74 = getitem_75 = None 2025-03-04T20:57:28.2608668Z 2025-03-04T20:57:28.2609062Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:57:28.2609542Z getitem_76: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)] 2025-03-04T20:57:28.2609835Z getitem_77: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)] 2025-03-04T20:57:28.2610155Z heights_4: "f32[4332][1]cpu" = getitem_76 - getitem_77; getitem_76 = getitem_77 = None 2025-03-04T20:57:28.2610412Z 2025-03-04T20:57:28.2610799Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:57:28.2611274Z getitem_78: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T20:57:28.2611565Z mul_40: "f32[4332][1]cpu" = 0.5 * widths_4 2025-03-04T20:57:28.2611827Z ctr_x_4: "f32[4332][1]cpu" = getitem_78 + mul_40; getitem_78 = mul_40 = None 2025-03-04T20:57:28.2612069Z 2025-03-04T20:57:28.2612463Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:57:28.2612963Z getitem_79: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]; boxes_4 = None 2025-03-04T20:57:28.2613249Z mul_41: "f32[4332][1]cpu" = 0.5 * heights_4 2025-03-04T20:57:28.2613510Z ctr_y_4: "f32[4332][1]cpu" = getitem_79 + mul_41; getitem_79 = mul_41 = None 2025-03-04T20:57:28.2613787Z 2025-03-04T20:57:28.2614173Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:57:28.2614672Z getitem_80: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:57:28.2614989Z dx_4: "f32[4332, 1][1, 1]cpu" = getitem_80 / 1.0; getitem_80 = None 2025-03-04T20:57:28.2615212Z 2025-03-04T20:57:28.2615607Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:57:28.2616100Z getitem_81: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:57:28.2616421Z dy_4: "f32[4332, 1][1, 1]cpu" = getitem_81 / 1.0; getitem_81 = None 2025-03-04T20:57:28.2616648Z 2025-03-04T20:57:28.2617026Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:57:28.2617520Z getitem_82: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:57:28.2617825Z dw_8: "f32[4332, 1][1, 1]cpu" = getitem_82 / 1.0; getitem_82 = None 2025-03-04T20:57:28.2618049Z 2025-03-04T20:57:28.2618431Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:57:28.2618968Z getitem_83: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))]; deltas_4 = None 2025-03-04T20:57:28.2619303Z dh_8: "f32[4332, 1][1, 1]cpu" = getitem_83 / 1.0; getitem_83 = None 2025-03-04T20:57:28.2619527Z 2025-03-04T20:57:28.2619949Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:57:28.2620470Z dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356); dw_8 = None 2025-03-04T20:57:28.2620718Z 2025-03-04T20:57:28.2621131Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:57:28.2621644Z dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356); dh_8 = None 2025-03-04T20:57:28.2621891Z 2025-03-04T20:57:28.2622319Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:57:28.2622867Z getitem_84: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)] 2025-03-04T20:57:28.2623176Z mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_84; dx_4 = getitem_84 = None 2025-03-04T20:57:28.2623499Z getitem_85: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)]; ctr_x_4 = None 2025-03-04T20:57:28.2623839Z pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_85; mul_42 = getitem_85 = None 2025-03-04T20:57:28.2624108Z 2025-03-04T20:57:28.2624531Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:57:28.2625060Z getitem_86: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)] 2025-03-04T20:57:28.2625364Z mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_86; dy_4 = getitem_86 = None 2025-03-04T20:57:28.2625684Z getitem_87: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)]; ctr_y_4 = None 2025-03-04T20:57:28.2626009Z pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_87; mul_43 = getitem_87 = None 2025-03-04T20:57:28.2626276Z 2025-03-04T20:57:28.2626687Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:57:28.2627176Z exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9); dw_9 = None 2025-03-04T20:57:28.2627495Z getitem_88: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]; widths_4 = None 2025-03-04T20:57:28.2627834Z pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_88; exp_8 = getitem_88 = None 2025-03-04T20:57:28.2628094Z 2025-03-04T20:57:28.2628514Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:57:28.2629018Z exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9); dh_9 = None 2025-03-04T20:57:28.2629338Z getitem_89: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]; heights_4 = None 2025-03-04T20:57:28.2629684Z pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_89; exp_9 = getitem_89 = None 2025-03-04T20:57:28.2629934Z 2025-03-04T20:57:28.2630338Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:57:28.2630793Z mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4 2025-03-04T20:57:28.2631054Z x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46; mul_46 = None 2025-03-04T20:57:28.2631288Z 2025-03-04T20:57:28.2631680Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:57:28.2632146Z mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4 2025-03-04T20:57:28.2632404Z y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47; mul_47 = None 2025-03-04T20:57:28.2632637Z 2025-03-04T20:57:28.2633028Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:57:28.2633510Z mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4; pred_w_4 = None 2025-03-04T20:57:28.2633813Z x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48; pred_ctr_x_4 = mul_48 = None 2025-03-04T20:57:28.2634062Z 2025-03-04T20:57:28.2634456Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:57:28.2634939Z mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4; pred_h_4 = None 2025-03-04T20:57:28.2635238Z y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49; pred_ctr_y_4 = mul_49 = None 2025-03-04T20:57:28.2635487Z 2025-03-04T20:57:28.2635935Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:57:28.2636522Z pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1); x1_4 = y1_4 = x2_4 = y2_4 = None 2025-03-04T20:57:28.2636918Z 2025-03-04T20:57:28.2637348Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:57:28.2637909Z proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4)); pred_boxes_4 = None 2025-03-04T20:57:28.2638202Z 2025-03-04T20:57:28.2638696Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:57:28.2639303Z proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4); proposals_i_4 = None 2025-03-04T20:57:28.2639620Z 2025-03-04T20:57:28.2640188Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T20:57:28.2640881Z arange_10: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T20:57:28.2641131Z 2025-03-04T20:57:28.2641534Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:28.2642024Z batch_idx: "i64[4][1]cpu" = arange_10.to(device(type='cpu')); arange_10 = None 2025-03-04T20:57:28.2642299Z 2025-03-04T20:57:28.2642823Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:57:28.2643426Z topk = logits_i.topk(1000, dim = 1); logits_i = None 2025-03-04T20:57:28.2643698Z topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0] 2025-03-04T20:57:28.2643959Z topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1]; topk = None 2025-03-04T20:57:28.2644189Z 2025-03-04T20:57:28.2644760Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:57:28.2645433Z getitem_92: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T20:57:28.2645858Z topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_92, topk_idx)]; proposals_i_5 = getitem_92 = topk_idx = None 2025-03-04T20:57:28.2646200Z 2025-03-04T20:57:28.2646748Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:57:28.2647432Z full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:57:28.2647719Z 2025-03-04T20:57:28.2648096Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:28.2648564Z to_21: "i64[1000][1]cpu" = full.to(device(type='cpu')); full = None 2025-03-04T20:57:28.2648802Z 2025-03-04T20:57:28.2649307Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:57:28.2649901Z topk_1 = logits_i_1.topk(1000, dim = 1); logits_i_1 = None 2025-03-04T20:57:28.2650172Z topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0] 2025-03-04T20:57:28.2650451Z topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1]; topk_1 = None 2025-03-04T20:57:28.2650684Z 2025-03-04T20:57:28.2651239Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:57:28.2651870Z getitem_96: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T20:57:28.2652286Z topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_96, topk_idx_1)]; proposals_i_6 = getitem_96 = topk_idx_1 = None 2025-03-04T20:57:28.2652631Z 2025-03-04T20:57:28.2653157Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:57:28.2653831Z full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:57:28.2654112Z 2025-03-04T20:57:28.2654483Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:28.2654964Z to_22: "i64[1000][1]cpu" = full_1.to(device(type='cpu')); full_1 = None 2025-03-04T20:57:28.2655203Z 2025-03-04T20:57:28.2655723Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:57:28.2656309Z topk_2 = logits_i_2.topk(1000, dim = 1); logits_i_2 = None 2025-03-04T20:57:28.2656579Z topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0] 2025-03-04T20:57:28.2656849Z topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1]; topk_2 = None 2025-03-04T20:57:28.2657080Z 2025-03-04T20:57:28.2657603Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:57:28.2658234Z getitem_100: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T20:57:28.2658654Z topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_100, topk_idx_2)]; proposals_i_7 = getitem_100 = topk_idx_2 = None 2025-03-04T20:57:28.2658999Z 2025-03-04T20:57:28.2659531Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:57:28.2660186Z full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:57:28.2660463Z 2025-03-04T20:57:28.2660834Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:28.2661295Z to_23: "i64[1000][1]cpu" = full_2.to(device(type='cpu')); full_2 = None 2025-03-04T20:57:28.2661532Z 2025-03-04T20:57:28.2662041Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:57:28.2662790Z topk_3 = logits_i_3.topk(1000, dim = 1); logits_i_3 = None 2025-03-04T20:57:28.2663061Z topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0] 2025-03-04T20:57:28.2663334Z topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1]; topk_3 = None 2025-03-04T20:57:28.2663564Z 2025-03-04T20:57:28.2664094Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:57:28.2664769Z getitem_104: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T20:57:28.2665187Z topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_104, topk_idx_3)]; proposals_i_8 = getitem_104 = topk_idx_3 = None 2025-03-04T20:57:28.2665532Z 2025-03-04T20:57:28.2666075Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:57:28.2666776Z full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:57:28.2667053Z 2025-03-04T20:57:28.2667425Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:28.2667903Z to_24: "i64[1000][1]cpu" = full_3.to(device(type='cpu')); full_3 = None 2025-03-04T20:57:28.2668155Z 2025-03-04T20:57:28.2668677Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:57:28.2669277Z topk_4 = logits_i_4.topk(1000, dim = 1); logits_i_4 = None 2025-03-04T20:57:28.2669566Z topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0] 2025-03-04T20:57:28.2669842Z topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1]; topk_4 = None 2025-03-04T20:57:28.2670080Z 2025-03-04T20:57:28.2670627Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:57:28.2671306Z getitem_108: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T20:57:28.2671761Z topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_108, topk_idx_4)]; proposals_i_9 = getitem_108 = topk_idx_4 = None 2025-03-04T20:57:28.2672114Z 2025-03-04T20:57:28.2672658Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:57:28.2673331Z full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:57:28.2673616Z 2025-03-04T20:57:28.2673996Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:28.2674474Z to_25: "i64[1000][1]cpu" = full_4.to(device(type='cpu')); full_4 = None 2025-03-04T20:57:28.2674731Z 2025-03-04T20:57:28.2675119Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:57:28.2675950Z topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1); topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None 2025-03-04T20:57:28.2676510Z 2025-03-04T20:57:28.2676976Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:57:28.2677900Z topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1); topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None 2025-03-04T20:57:28.2678575Z 2025-03-04T20:57:28.2678937Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:57:28.2679473Z level_ids: "i64[5000][1]cpu" = torch.cat([to_21, to_22, to_23, to_24, to_25], 0); to_21 = to_22 = to_23 = to_24 = to_25 = level_ids = None 2025-03-04T20:57:28.2695212Z 2025-03-04T20:57:28.2695956Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T20:57:28.2696666Z getitem_110: "f32[5000, 4][4, 1]cpu" = topk_proposals[0]; topk_proposals = None 2025-03-04T20:57:28.2696945Z 2025-03-04T20:57:28.2697339Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:57:28.2697843Z tensor_5: "f32[5000, 4][4, 1]cpu" = getitem_110.to(torch.float32); getitem_110 = None 2025-03-04T20:57:28.2698109Z 2025-03-04T20:57:28.2698604Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T20:57:28.2699184Z scores_per_img: "f32[5000][1]cpu" = topk_scores[0]; topk_scores = None 2025-03-04T20:57:28.2699466Z 2025-03-04T20:57:28.2700045Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T20:57:28.2700747Z isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor_5); tensor_5 = None 2025-03-04T20:57:28.2701065Z all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:57:28.2701394Z isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T20:57:28.2701737Z valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T20:57:28.2702196Z 2025-03-04T20:57:28.2702667Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T20:57:28.2703216Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T20:57:28.2703456Z 2025-03-04T20:57:28.2703719Z 2025-03-04T20:57:28.2703811Z class GraphModule(torch.nn.Module): 2025-03-04T20:57:28.2706010Z def forward(self, L_features_p2_: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu", L_features_p3_: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu", L_features_p4_: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu", L_features_p5_: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu", L_features_p6_: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_1_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_2_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_3_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_4_: "f32[3, 4][4, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_bias_: "f32[256][1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[3, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[3][1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[12, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[12][1]cpu"): 2025-03-04T20:57:28.2708391Z l_features_p2_ = L_features_p2_ 2025-03-04T20:57:28.2708619Z l_features_p3_ = L_features_p3_ 2025-03-04T20:57:28.2708837Z l_features_p4_ = L_features_p4_ 2025-03-04T20:57:28.2709048Z l_features_p5_ = L_features_p5_ 2025-03-04T20:57:28.2709259Z l_features_p6_ = L_features_p6_ 2025-03-04T20:57:28.2709653Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T20:57:28.2710225Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_1_ 2025-03-04T20:57:28.2710819Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_2_ 2025-03-04T20:57:28.2711377Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_3_ 2025-03-04T20:57:28.2711928Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_4_ 2025-03-04T20:57:28.2712461Z l_self_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T20:57:28.2712938Z l_self_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T20:57:28.2713498Z l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T20:57:28.2714094Z l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T20:57:28.2714657Z l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T20:57:28.2715209Z l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T20:57:28.2715576Z 2025-03-04T20:57:28.2716120Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:57:28.2716838Z arange: "f32[304][1]cpu" = torch.arange(0.0, 1216, step = 4, dtype = torch.float32) 2025-03-04T20:57:28.2717133Z 2025-03-04T20:57:28.2717578Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:28.2718063Z shifts_x: "f32[304][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T20:57:28.2718312Z 2025-03-04T20:57:28.2718825Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:57:28.2719452Z arange_1: "f32[296][1]cpu" = torch.arange(0.0, 1184, step = 4, dtype = torch.float32) 2025-03-04T20:57:28.2719718Z 2025-03-04T20:57:28.2720097Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:28.2720579Z shifts_y: "f32[296][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T20:57:28.2720832Z 2025-03-04T20:57:28.2721286Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:57:28.2721875Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T20:57:28.2722224Z shift_y: "f32[296, 304][1, 0]cpu" = meshgrid[0] 2025-03-04T20:57:28.2722485Z shift_x: "f32[296, 304][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T20:57:28.2722716Z 2025-03-04T20:57:28.2723125Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:57:28.2723626Z shift_x_1: "f32[89984][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T20:57:28.2723865Z 2025-03-04T20:57:28.2724272Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:57:28.2724785Z shift_y_1: "f32[89984][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T20:57:28.2725022Z 2025-03-04T20:57:28.2725473Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:57:28.2726125Z shifts: "f32[89984, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T20:57:28.2726441Z 2025-03-04T20:57:28.2726961Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:57:28.2727550Z view: "f32[89984, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T20:57:28.2728050Z view_1: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T20:57:28.2728528Z add: "f32[89984, 3, 4][12, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T20:57:28.2728816Z x: "f32[269952, 4][4, 1]cpu" = add.reshape(-1, 4); add = None 2025-03-04T20:57:28.2729033Z 2025-03-04T20:57:28.2729551Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:57:28.2730189Z arange_2: "f32[152][1]cpu" = torch.arange(0.0, 1216, step = 8, dtype = torch.float32) 2025-03-04T20:57:28.2730461Z 2025-03-04T20:57:28.2730849Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:28.2731339Z shifts_x_1: "f32[152][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T20:57:28.2731596Z 2025-03-04T20:57:28.2732103Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:57:28.2732724Z arange_3: "f32[148][1]cpu" = torch.arange(0.0, 1184, step = 8, dtype = torch.float32) 2025-03-04T20:57:28.2732985Z 2025-03-04T20:57:28.2733359Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:28.2733839Z shifts_y_1: "f32[148][1]cpu" = arange_3.to(device(type='cpu')); arange_3 = None 2025-03-04T20:57:28.2734089Z 2025-03-04T20:57:28.2734539Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:57:28.2735146Z meshgrid_1 = torch.functional.meshgrid(shifts_y_1, shifts_x_1); shifts_y_1 = shifts_x_1 = None 2025-03-04T20:57:28.2735501Z shift_y_2: "f32[148, 152][1, 0]cpu" = meshgrid_1[0] 2025-03-04T20:57:28.2735770Z shift_x_2: "f32[148, 152][0, 1]cpu" = meshgrid_1[1]; meshgrid_1 = None 2025-03-04T20:57:28.2736002Z 2025-03-04T20:57:28.2736404Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:57:28.2736903Z shift_x_3: "f32[22496][1]cpu" = shift_x_2.reshape(-1); shift_x_2 = None 2025-03-04T20:57:28.2737140Z 2025-03-04T20:57:28.2737540Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:57:28.2738048Z shift_y_3: "f32[22496][1]cpu" = shift_y_2.reshape(-1); shift_y_2 = None 2025-03-04T20:57:28.2738283Z 2025-03-04T20:57:28.2738733Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:57:28.2739390Z shifts_1: "f32[22496, 4][4, 1]cpu" = torch.stack((shift_x_3, shift_y_3, shift_x_3, shift_y_3), dim = 1); shift_x_3 = shift_y_3 = None 2025-03-04T20:57:28.2739715Z 2025-03-04T20:57:28.2740225Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:57:28.2740805Z view_2: "f32[22496, 1, 4][4, 4, 1]cpu" = shifts_1.view(-1, 1, 4); shifts_1 = None 2025-03-04T20:57:28.2741292Z view_3: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_ = None 2025-03-04T20:57:28.2741768Z add_1: "f32[22496, 3, 4][12, 4, 1]cpu" = view_2 + view_3; view_2 = view_3 = None 2025-03-04T20:57:28.2742061Z x_1: "f32[67488, 4][4, 1]cpu" = add_1.reshape(-1, 4); add_1 = None 2025-03-04T20:57:28.2742287Z 2025-03-04T20:57:28.2742792Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:57:28.2743410Z arange_4: "f32[76][1]cpu" = torch.arange(0.0, 1216, step = 16, dtype = torch.float32) 2025-03-04T20:57:28.2743666Z 2025-03-04T20:57:28.2744028Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:28.2744502Z shifts_x_2: "f32[76][1]cpu" = arange_4.to(device(type='cpu')); arange_4 = None 2025-03-04T20:57:28.2744749Z 2025-03-04T20:57:28.2745256Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:57:28.2745874Z arange_5: "f32[74][1]cpu" = torch.arange(0.0, 1184, step = 16, dtype = torch.float32) 2025-03-04T20:57:28.2746131Z 2025-03-04T20:57:28.2746495Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:28.2746971Z shifts_y_2: "f32[74][1]cpu" = arange_5.to(device(type='cpu')); arange_5 = None 2025-03-04T20:57:28.2747223Z 2025-03-04T20:57:28.2747670Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:57:28.2748276Z meshgrid_2 = torch.functional.meshgrid(shifts_y_2, shifts_x_2); shifts_y_2 = shifts_x_2 = None 2025-03-04T20:57:28.2748637Z shift_y_4: "f32[74, 76][1, 0]cpu" = meshgrid_2[0] 2025-03-04T20:57:28.2748902Z shift_x_4: "f32[74, 76][0, 1]cpu" = meshgrid_2[1]; meshgrid_2 = None 2025-03-04T20:57:28.2749135Z 2025-03-04T20:57:28.2749546Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:57:28.2750053Z shift_x_5: "f32[5624][1]cpu" = shift_x_4.reshape(-1); shift_x_4 = None 2025-03-04T20:57:28.2750291Z 2025-03-04T20:57:28.2750694Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:57:28.2751204Z shift_y_5: "f32[5624][1]cpu" = shift_y_4.reshape(-1); shift_y_4 = None 2025-03-04T20:57:28.2751437Z 2025-03-04T20:57:28.2751892Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:57:28.2752645Z shifts_2: "f32[5624, 4][4, 1]cpu" = torch.stack((shift_x_5, shift_y_5, shift_x_5, shift_y_5), dim = 1); shift_x_5 = shift_y_5 = None 2025-03-04T20:57:28.2752970Z 2025-03-04T20:57:28.2753492Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:57:28.2754081Z view_4: "f32[5624, 1, 4][4, 4, 1]cpu" = shifts_2.view(-1, 1, 4); shifts_2 = None 2025-03-04T20:57:28.2754577Z view_5: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_ = None 2025-03-04T20:57:28.2755073Z add_2: "f32[5624, 3, 4][12, 4, 1]cpu" = view_4 + view_5; view_4 = view_5 = None 2025-03-04T20:57:28.2755369Z x_2: "f32[16872, 4][4, 1]cpu" = add_2.reshape(-1, 4); add_2 = None 2025-03-04T20:57:28.2755602Z 2025-03-04T20:57:28.2756125Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:57:28.2756847Z arange_6: "f32[38][1]cpu" = torch.arange(0.0, 1216, step = 32, dtype = torch.float32) 2025-03-04T20:57:28.2757117Z 2025-03-04T20:57:28.2757502Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:28.2758001Z shifts_x_3: "f32[38][1]cpu" = arange_6.to(device(type='cpu')); arange_6 = None 2025-03-04T20:57:28.2758252Z 2025-03-04T20:57:28.2758756Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:57:28.2759376Z arange_7: "f32[37][1]cpu" = torch.arange(0.0, 1184, step = 32, dtype = torch.float32) 2025-03-04T20:57:28.2759630Z 2025-03-04T20:57:28.2759998Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:28.2760476Z shifts_y_3: "f32[37][1]cpu" = arange_7.to(device(type='cpu')); arange_7 = None 2025-03-04T20:57:28.2760723Z 2025-03-04T20:57:28.2761092Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:57:28.2761286Z meshgrid_3 = torch.functional.meshgrid(shifts_y_3, shifts_x_3); shifts_y_3 = shifts_x_3 = None 2025-03-04T20:57:28.2761411Z shift_y_6: "f32[37, 38][1, 0]cpu" = meshgrid_3[0] 2025-03-04T20:57:28.2761525Z shift_x_6: "f32[37, 38][0, 1]cpu" = meshgrid_3[1]; meshgrid_3 = None 2025-03-04T20:57:28.2761591Z 2025-03-04T20:57:28.2761912Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:57:28.2762038Z shift_x_7: "f32[1406][1]cpu" = shift_x_6.reshape(-1); shift_x_6 = None 2025-03-04T20:57:28.2762098Z 2025-03-04T20:57:28.2762424Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:57:28.2762556Z shift_y_7: "f32[1406][1]cpu" = shift_y_6.reshape(-1); shift_y_6 = None 2025-03-04T20:57:28.2762622Z 2025-03-04T20:57:28.2762995Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:57:28.2763221Z shifts_3: "f32[1406, 4][4, 1]cpu" = torch.stack((shift_x_7, shift_y_7, shift_x_7, shift_y_7), dim = 1); shift_x_7 = shift_y_7 = None 2025-03-04T20:57:28.2763282Z 2025-03-04T20:57:28.2763707Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:57:28.2763829Z view_6: "f32[1406, 1, 4][4, 4, 1]cpu" = shifts_3.view(-1, 1, 4); shifts_3 = None 2025-03-04T20:57:28.2764136Z view_7: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_ = None 2025-03-04T20:57:28.2764253Z add_3: "f32[1406, 3, 4][12, 4, 1]cpu" = view_6 + view_7; view_6 = view_7 = None 2025-03-04T20:57:28.2764366Z x_3: "f32[4218, 4][4, 1]cpu" = add_3.reshape(-1, 4); add_3 = None 2025-03-04T20:57:28.2764427Z 2025-03-04T20:57:28.2764856Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T20:57:28.2764996Z arange_8: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32) 2025-03-04T20:57:28.2765062Z 2025-03-04T20:57:28.2765349Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:28.2765491Z shifts_x_4: "f32[19][1]cpu" = arange_8.to(device(type='cpu')); arange_8 = None 2025-03-04T20:57:28.2765549Z 2025-03-04T20:57:28.2765980Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T20:57:28.2766119Z arange_9: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32) 2025-03-04T20:57:28.2766186Z 2025-03-04T20:57:28.2766473Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:28.2766609Z shifts_y_4: "f32[19][1]cpu" = arange_9.to(device(type='cpu')); arange_9 = None 2025-03-04T20:57:28.2766668Z 2025-03-04T20:57:28.2767040Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T20:57:28.2767226Z meshgrid_4 = torch.functional.meshgrid(shifts_y_4, shifts_x_4); shifts_y_4 = shifts_x_4 = None 2025-03-04T20:57:28.2767341Z shift_y_8: "f32[19, 19][1, 0]cpu" = meshgrid_4[0] 2025-03-04T20:57:28.2767453Z shift_x_8: "f32[19, 19][0, 1]cpu" = meshgrid_4[1]; meshgrid_4 = None 2025-03-04T20:57:28.2767519Z 2025-03-04T20:57:28.2767836Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T20:57:28.2767958Z shift_x_9: "f32[361][1]cpu" = shift_x_8.reshape(-1); shift_x_8 = None 2025-03-04T20:57:28.2768016Z 2025-03-04T20:57:28.2768337Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T20:57:28.2768471Z shift_y_9: "f32[361][1]cpu" = shift_y_8.reshape(-1); shift_y_8 = None 2025-03-04T20:57:28.2768531Z 2025-03-04T20:57:28.2768903Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T20:57:28.2769129Z shifts_4: "f32[361, 4][4, 1]cpu" = torch.stack((shift_x_9, shift_y_9, shift_x_9, shift_y_9), dim = 1); shift_x_9 = shift_y_9 = None 2025-03-04T20:57:28.2769197Z 2025-03-04T20:57:28.2769615Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T20:57:28.2769742Z view_8: "f32[361, 1, 4][4, 4, 1]cpu" = shifts_4.view(-1, 1, 4); shifts_4 = None 2025-03-04T20:57:28.2770044Z view_9: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_ = None 2025-03-04T20:57:28.2770167Z add_4: "f32[361, 3, 4][12, 4, 1]cpu" = view_8 + view_9; view_8 = view_9 = None 2025-03-04T20:57:28.2770273Z x_4: "f32[1083, 4][4, 1]cpu" = add_4.reshape(-1, 4); add_4 = None 2025-03-04T20:57:28.2770339Z 2025-03-04T20:57:28.2770633Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:57:28.2770755Z tensor: "f32[269952, 4][4, 1]cpu" = x.to(torch.float32); x = None 2025-03-04T20:57:28.2770875Z tensor_1: "f32[67488, 4][4, 1]cpu" = x_1.to(torch.float32); x_1 = None 2025-03-04T20:57:28.2770997Z tensor_2: "f32[16872, 4][4, 1]cpu" = x_2.to(torch.float32); x_2 = None 2025-03-04T20:57:28.2771110Z tensor_3: "f32[4218, 4][4, 1]cpu" = x_3.to(torch.float32); x_3 = None 2025-03-04T20:57:28.2771224Z tensor_4: "f32[1083, 4][4, 1]cpu" = x_4.to(torch.float32); x_4 = None 2025-03-04T20:57:28.2771284Z 2025-03-04T20:57:28.2771546Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:28.2771968Z x_5: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(l_features_p2_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_p2_ = None 2025-03-04T20:57:28.2772037Z 2025-03-04T20:57:28.2772311Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:57:28.2772508Z x_6: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.relu(x_5, inplace = False); x_5 = None 2025-03-04T20:57:28.2772568Z 2025-03-04T20:57:28.2772951Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:57:28.2773373Z score: "f32[4, 3, 296, 304][269952, 89984, 304, 1]cpu" = torch.conv2d(x_6, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T20:57:28.2773442Z 2025-03-04T20:57:28.2773803Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:57:28.2774209Z x_15: "f32[4, 12, 296, 304][1079808, 89984, 304, 1]cpu" = torch.conv2d(x_6, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_6 = None 2025-03-04T20:57:28.2774293Z 2025-03-04T20:57:28.2774544Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:28.2774951Z x_7: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(l_features_p3_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_p3_ = None 2025-03-04T20:57:28.2775009Z 2025-03-04T20:57:28.2775298Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:57:28.2775494Z x_8: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.relu(x_7, inplace = False); x_7 = None 2025-03-04T20:57:28.2775561Z 2025-03-04T20:57:28.2775933Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:57:28.2776339Z score_1: "f32[4, 3, 148, 152][67488, 22496, 152, 1]cpu" = torch.conv2d(x_8, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T20:57:28.2776400Z 2025-03-04T20:57:28.2776762Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:57:28.2777157Z x_16: "f32[4, 12, 148, 152][269952, 22496, 152, 1]cpu" = torch.conv2d(x_8, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_8 = None 2025-03-04T20:57:28.2777222Z 2025-03-04T20:57:28.2777471Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:28.2777867Z x_9: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(l_features_p4_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_p4_ = None 2025-03-04T20:57:28.2777938Z 2025-03-04T20:57:28.2778206Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:57:28.2778383Z x_10: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.relu(x_9, inplace = False); x_9 = None 2025-03-04T20:57:28.2778443Z 2025-03-04T20:57:28.2778818Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:57:28.2779206Z score_2: "f32[4, 3, 74, 76][16872, 5624, 76, 1]cpu" = torch.conv2d(x_10, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T20:57:28.2779288Z 2025-03-04T20:57:28.2779635Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:57:28.2780026Z x_17: "f32[4, 12, 74, 76][67488, 5624, 76, 1]cpu" = torch.conv2d(x_10, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_10 = None 2025-03-04T20:57:28.2780088Z 2025-03-04T20:57:28.2780340Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:28.2780739Z x_11: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(l_features_p5_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_p5_ = None 2025-03-04T20:57:28.2780806Z 2025-03-04T20:57:28.2781068Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:57:28.2781262Z x_12: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.nn.functional.relu(x_11, inplace = False); x_11 = None 2025-03-04T20:57:28.2781321Z 2025-03-04T20:57:28.2781703Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:57:28.2782090Z score_3: "f32[4, 3, 37, 38][4218, 1406, 38, 1]cpu" = torch.conv2d(x_12, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T20:57:28.2782151Z 2025-03-04T20:57:28.2782501Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:57:28.2782886Z x_18: "f32[4, 12, 37, 38][16872, 1406, 38, 1]cpu" = torch.conv2d(x_12, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_12 = None 2025-03-04T20:57:28.2782949Z 2025-03-04T20:57:28.2783196Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T20:57:28.2783767Z x_13: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.conv2d(l_features_p6_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_p6_ = l_self_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T20:57:28.2783828Z 2025-03-04T20:57:28.2784098Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T20:57:28.2784261Z x_14: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.relu(x_13, inplace = False); x_13 = None 2025-03-04T20:57:28.2784327Z 2025-03-04T20:57:28.2784691Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T20:57:28.2785315Z score_4: "f32[4, 3, 19, 19][1083, 361, 19, 1]cpu" = torch.conv2d(x_14, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T20:57:28.2785398Z 2025-03-04T20:57:28.2785744Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T20:57:28.2786336Z x_19: "f32[4, 12, 19, 19][4332, 361, 19, 1]cpu" = torch.conv2d(x_14, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_14 = l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T20:57:28.2786395Z 2025-03-04T20:57:28.2786755Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T20:57:28.2786915Z permute: "f32[4, 296, 304, 3][269952, 304, 1, 89984]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T20:57:28.2787062Z logits_i: "f32[4, 269952][269952, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T20:57:28.2787220Z permute_1: "f32[4, 148, 152, 3][67488, 152, 1, 22496]cpu" = score_1.permute(0, 2, 3, 1); score_1 = None 2025-03-04T20:57:28.2787383Z logits_i_1: "f32[4, 67488][67488, 1]cpu" = permute_1.flatten(1); permute_1 = None 2025-03-04T20:57:28.2787533Z permute_2: "f32[4, 74, 76, 3][16872, 76, 1, 5624]cpu" = score_2.permute(0, 2, 3, 1); score_2 = None 2025-03-04T20:57:28.2787691Z logits_i_2: "f32[4, 16872][16872, 1]cpu" = permute_2.flatten(1); permute_2 = None 2025-03-04T20:57:28.2787831Z permute_3: "f32[4, 37, 38, 3][4218, 38, 1, 1406]cpu" = score_3.permute(0, 2, 3, 1); score_3 = None 2025-03-04T20:57:28.2787969Z logits_i_3: "f32[4, 4218][4218, 1]cpu" = permute_3.flatten(1); permute_3 = None 2025-03-04T20:57:28.2788109Z permute_4: "f32[4, 19, 19, 3][1083, 19, 1, 361]cpu" = score_4.permute(0, 2, 3, 1); score_4 = None 2025-03-04T20:57:28.2788244Z logits_i_4: "f32[4, 1083][1083, 1]cpu" = permute_4.flatten(1); permute_4 = None 2025-03-04T20:57:28.2788304Z 2025-03-04T20:57:28.2788725Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T20:57:28.2788905Z view_10: "f32[4, 3, 4, 296, 304][1079808, 359936, 89984, 304, 1]cpu" = x_15.view(4, -1, 4, 296, 304); x_15 = None 2025-03-04T20:57:28.2789083Z permute_5: "f32[4, 296, 304, 3, 4][1079808, 304, 1, 359936, 89984]cpu" = view_10.permute(0, 3, 4, 1, 2); view_10 = None 2025-03-04T20:57:28.2789265Z pred_anchor_deltas_i: "f32[4, 269952, 4][1079808, 4, 1]cpu" = permute_5.flatten(1, -2); permute_5 = None 2025-03-04T20:57:28.2789423Z view_11: "f32[4, 3, 4, 148, 152][269952, 89984, 22496, 152, 1]cpu" = x_16.view(4, -1, 4, 148, 152); x_16 = None 2025-03-04T20:57:28.2789603Z permute_6: "f32[4, 148, 152, 3, 4][269952, 152, 1, 89984, 22496]cpu" = view_11.permute(0, 3, 4, 1, 2); view_11 = None 2025-03-04T20:57:28.2789771Z pred_anchor_deltas_i_2: "f32[4, 67488, 4][269952, 4, 1]cpu" = permute_6.flatten(1, -2); permute_6 = None 2025-03-04T20:57:28.2789920Z view_12: "f32[4, 3, 4, 74, 76][67488, 22496, 5624, 76, 1]cpu" = x_17.view(4, -1, 4, 74, 76); x_17 = None 2025-03-04T20:57:28.2790084Z permute_7: "f32[4, 74, 76, 3, 4][67488, 76, 1, 22496, 5624]cpu" = view_12.permute(0, 3, 4, 1, 2); view_12 = None 2025-03-04T20:57:28.2790258Z pred_anchor_deltas_i_4: "f32[4, 16872, 4][67488, 4, 1]cpu" = permute_7.flatten(1, -2); permute_7 = None 2025-03-04T20:57:28.2790396Z view_13: "f32[4, 3, 4, 37, 38][16872, 5624, 1406, 38, 1]cpu" = x_18.view(4, -1, 4, 37, 38); x_18 = None 2025-03-04T20:57:28.2790559Z permute_8: "f32[4, 37, 38, 3, 4][16872, 38, 1, 5624, 1406]cpu" = view_13.permute(0, 3, 4, 1, 2); view_13 = None 2025-03-04T20:57:28.2790741Z pred_anchor_deltas_i_6: "f32[4, 4218, 4][16872, 4, 1]cpu" = permute_8.flatten(1, -2); permute_8 = None 2025-03-04T20:57:28.2790887Z view_14: "f32[4, 3, 4, 19, 19][4332, 1444, 361, 19, 1]cpu" = x_19.view(4, -1, 4, 19, 19); x_19 = None 2025-03-04T20:57:28.2791042Z permute_9: "f32[4, 19, 19, 3, 4][4332, 19, 1, 1444, 361]cpu" = view_14.permute(0, 3, 4, 1, 2); view_14 = None 2025-03-04T20:57:28.2791210Z pred_anchor_deltas_i_8: "f32[4, 1083, 4][4332, 4, 1]cpu" = permute_9.flatten(1, -2); permute_9 = None 2025-03-04T20:57:28.2791271Z 2025-03-04T20:57:28.2791690Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:57:28.2791924Z pred_anchor_deltas_i_1: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T20:57:28.2791993Z 2025-03-04T20:57:28.2792437Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:57:28.2792615Z unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T20:57:28.2792762Z expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T20:57:28.2792922Z anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T20:57:28.2792983Z 2025-03-04T20:57:28.2793373Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:57:28.2793550Z deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T20:57:28.2793613Z 2025-03-04T20:57:28.2793935Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:57:28.2794077Z boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T20:57:28.2794143Z 2025-03-04T20:57:28.2794462Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:57:28.2794602Z getitem_10: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:57:28.2794732Z getitem_11: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:57:28.2794889Z widths: "f32[1079808][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T20:57:28.2794950Z 2025-03-04T20:57:28.2795278Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:57:28.2795407Z getitem_12: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:57:28.2795536Z getitem_13: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:57:28.2795686Z heights: "f32[1079808][1]cpu" = getitem_12 - getitem_13; getitem_12 = getitem_13 = None 2025-03-04T20:57:28.2795754Z 2025-03-04T20:57:28.2796069Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:57:28.2796198Z getitem_14: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:57:28.2796281Z mul: "f32[1079808][1]cpu" = 0.5 * widths 2025-03-04T20:57:28.2796412Z ctr_x: "f32[1079808][1]cpu" = getitem_14 + mul; getitem_14 = mul = None 2025-03-04T20:57:28.2796489Z 2025-03-04T20:57:28.2796879Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:57:28.2797033Z getitem_15: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:57:28.2797129Z mul_1: "f32[1079808][1]cpu" = 0.5 * heights 2025-03-04T20:57:28.2797257Z ctr_y: "f32[1079808][1]cpu" = getitem_15 + mul_1; getitem_15 = mul_1 = None 2025-03-04T20:57:28.2797326Z 2025-03-04T20:57:28.2797651Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:57:28.2797837Z getitem_16: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:57:28.2797951Z dx: "f32[1079808, 1][1, 1]cpu" = getitem_16 / 1.0; getitem_16 = None 2025-03-04T20:57:28.2798019Z 2025-03-04T20:57:28.2798324Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:57:28.2798505Z getitem_17: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:57:28.2798617Z dy: "f32[1079808, 1][1, 1]cpu" = getitem_17 / 1.0; getitem_17 = None 2025-03-04T20:57:28.2798688Z 2025-03-04T20:57:28.2799019Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:57:28.2799178Z getitem_18: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:57:28.2799291Z dw: "f32[1079808, 1][1, 1]cpu" = getitem_18 / 1.0; getitem_18 = None 2025-03-04T20:57:28.2799356Z 2025-03-04T20:57:28.2799658Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:57:28.2799847Z getitem_19: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:57:28.2799964Z dh: "f32[1079808, 1][1, 1]cpu" = getitem_19 / 1.0; getitem_19 = None 2025-03-04T20:57:28.2800025Z 2025-03-04T20:57:28.2800373Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:57:28.2800515Z dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:57:28.2800584Z 2025-03-04T20:57:28.2800924Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:57:28.2801066Z dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:57:28.2801128Z 2025-03-04T20:57:28.2801486Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:57:28.2801623Z getitem_20: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:57:28.2801757Z mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_20; dx = getitem_20 = None 2025-03-04T20:57:28.2802061Z getitem_21: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:57:28.2802215Z pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_21; mul_2 = getitem_21 = None 2025-03-04T20:57:28.2802278Z 2025-03-04T20:57:28.2802633Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:57:28.2802815Z getitem_22: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:57:28.2802944Z mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_22; dy = getitem_22 = None 2025-03-04T20:57:28.2803094Z getitem_23: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:57:28.2803233Z pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_23; mul_3 = getitem_23 = None 2025-03-04T20:57:28.2803294Z 2025-03-04T20:57:28.2803631Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:57:28.2803772Z exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:57:28.2803938Z getitem_24: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:57:28.2804073Z pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_24; exp = getitem_24 = None 2025-03-04T20:57:28.2804143Z 2025-03-04T20:57:28.2804505Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:57:28.2804629Z exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:57:28.2804819Z getitem_25: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:57:28.2804964Z pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_25; exp_1 = getitem_25 = None 2025-03-04T20:57:28.2805027Z 2025-03-04T20:57:28.2805348Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:57:28.2805442Z mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T20:57:28.2805568Z x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:57:28.2805629Z 2025-03-04T20:57:28.2805945Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:57:28.2806037Z mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T20:57:28.2806158Z y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:57:28.2806221Z 2025-03-04T20:57:28.2806533Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:57:28.2806646Z mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:57:28.2806783Z x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:57:28.2806843Z 2025-03-04T20:57:28.2807155Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:57:28.2807263Z mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:57:28.2807396Z y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:57:28.2807456Z 2025-03-04T20:57:28.2807813Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:57:28.2807991Z pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:57:28.2808059Z 2025-03-04T20:57:28.2808393Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:57:28.2808573Z proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4)); pred_boxes = None 2025-03-04T20:57:28.2808638Z 2025-03-04T20:57:28.2809011Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:57:28.2809186Z proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T20:57:28.2809246Z 2025-03-04T20:57:28.2809637Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:57:28.2809854Z pred_anchor_deltas_i_3: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_2.reshape(-1, 4); pred_anchor_deltas_i_2 = None 2025-03-04T20:57:28.2809920Z 2025-03-04T20:57:28.2810337Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:57:28.2810507Z unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = tensor_1.unsqueeze(0); tensor_1 = None 2025-03-04T20:57:28.2810652Z expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1); unsqueeze_1 = None 2025-03-04T20:57:28.2810807Z anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4); expand_1 = None 2025-03-04T20:57:28.2810868Z 2025-03-04T20:57:28.2811236Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:57:28.2811401Z deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float(); pred_anchor_deltas_i_3 = None 2025-03-04T20:57:28.2811467Z 2025-03-04T20:57:28.2811765Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:57:28.2811911Z boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32); anchors_i_1 = None 2025-03-04T20:57:28.2811970Z 2025-03-04T20:57:28.2812281Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:57:28.2812407Z getitem_26: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)] 2025-03-04T20:57:28.2812538Z getitem_27: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T20:57:28.2812685Z widths_1: "f32[269952][1]cpu" = getitem_26 - getitem_27; getitem_26 = getitem_27 = None 2025-03-04T20:57:28.2812752Z 2025-03-04T20:57:28.2813059Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:57:28.2813185Z getitem_28: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)] 2025-03-04T20:57:28.2813301Z getitem_29: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)] 2025-03-04T20:57:28.2813453Z heights_1: "f32[269952][1]cpu" = getitem_28 - getitem_29; getitem_28 = getitem_29 = None 2025-03-04T20:57:28.2813512Z 2025-03-04T20:57:28.2813819Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:57:28.2813936Z getitem_30: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T20:57:28.2814031Z mul_10: "f32[269952][1]cpu" = 0.5 * widths_1 2025-03-04T20:57:28.2814159Z ctr_x_1: "f32[269952][1]cpu" = getitem_30 + mul_10; getitem_30 = mul_10 = None 2025-03-04T20:57:28.2814250Z 2025-03-04T20:57:28.2814551Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:57:28.2814703Z getitem_31: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]; boxes_1 = None 2025-03-04T20:57:28.2814791Z mul_11: "f32[269952][1]cpu" = 0.5 * heights_1 2025-03-04T20:57:28.2814922Z ctr_y_1: "f32[269952][1]cpu" = getitem_31 + mul_11; getitem_31 = mul_11 = None 2025-03-04T20:57:28.2814982Z 2025-03-04T20:57:28.2815283Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:57:28.2815454Z getitem_32: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:57:28.2815566Z dx_1: "f32[269952, 1][1, 1]cpu" = getitem_32 / 1.0; getitem_32 = None 2025-03-04T20:57:28.2815634Z 2025-03-04T20:57:28.2815927Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:57:28.2816097Z getitem_33: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:57:28.2816208Z dy_1: "f32[269952, 1][1, 1]cpu" = getitem_33 / 1.0; getitem_33 = None 2025-03-04T20:57:28.2816273Z 2025-03-04T20:57:28.2816578Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:57:28.2816732Z getitem_34: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:57:28.2816837Z dw_2: "f32[269952, 1][1, 1]cpu" = getitem_34 / 1.0; getitem_34 = None 2025-03-04T20:57:28.2816905Z 2025-03-04T20:57:28.2817199Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:57:28.2817385Z getitem_35: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))]; deltas_1 = None 2025-03-04T20:57:28.2817492Z dh_2: "f32[269952, 1][1, 1]cpu" = getitem_35 / 1.0; getitem_35 = None 2025-03-04T20:57:28.2817555Z 2025-03-04T20:57:28.2817887Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:57:28.2818030Z dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356); dw_2 = None 2025-03-04T20:57:28.2818092Z 2025-03-04T20:57:28.2818423Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:57:28.2818556Z dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356); dh_2 = None 2025-03-04T20:57:28.2818625Z 2025-03-04T20:57:28.2818968Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:57:28.2819113Z getitem_36: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)] 2025-03-04T20:57:28.2819235Z mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_36; dx_1 = getitem_36 = None 2025-03-04T20:57:28.2819394Z getitem_37: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)]; ctr_x_1 = None 2025-03-04T20:57:28.2819532Z pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_37; mul_12 = getitem_37 = None 2025-03-04T20:57:28.2819598Z 2025-03-04T20:57:28.2819936Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:57:28.2820091Z getitem_38: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)] 2025-03-04T20:57:28.2820210Z mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_38; dy_1 = getitem_38 = None 2025-03-04T20:57:28.2820363Z getitem_39: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)]; ctr_y_1 = None 2025-03-04T20:57:28.2820495Z pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_39; mul_13 = getitem_39 = None 2025-03-04T20:57:28.2820561Z 2025-03-04T20:57:28.2820891Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:57:28.2821016Z exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3); dw_3 = None 2025-03-04T20:57:28.2821179Z getitem_40: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]; widths_1 = None 2025-03-04T20:57:28.2821313Z pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_40; exp_2 = getitem_40 = None 2025-03-04T20:57:28.2821379Z 2025-03-04T20:57:28.2821717Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:57:28.2821832Z exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3); dh_3 = None 2025-03-04T20:57:28.2822738Z getitem_41: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]; heights_1 = None 2025-03-04T20:57:28.2822889Z pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_41; exp_3 = getitem_41 = None 2025-03-04T20:57:28.2822950Z 2025-03-04T20:57:28.2823263Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:57:28.2823361Z mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1 2025-03-04T20:57:28.2823483Z x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16; mul_16 = None 2025-03-04T20:57:28.2823543Z 2025-03-04T20:57:28.2823852Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:57:28.2823944Z mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1 2025-03-04T20:57:28.2824063Z y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17; mul_17 = None 2025-03-04T20:57:28.2824124Z 2025-03-04T20:57:28.2824430Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:57:28.2824549Z mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1; pred_w_1 = None 2025-03-04T20:57:28.2824692Z x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18; pred_ctr_x_1 = mul_18 = None 2025-03-04T20:57:28.2824754Z 2025-03-04T20:57:28.2825066Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:57:28.2825180Z mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1; pred_h_1 = None 2025-03-04T20:57:28.2825315Z y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19; pred_ctr_y_1 = mul_19 = None 2025-03-04T20:57:28.2825376Z 2025-03-04T20:57:28.2825731Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:57:28.2825928Z pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1); x1_1 = y1_1 = x2_1 = y2_1 = None 2025-03-04T20:57:28.2825995Z 2025-03-04T20:57:28.2826338Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:57:28.2826531Z proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4)); pred_boxes_1 = None 2025-03-04T20:57:28.2826594Z 2025-03-04T20:57:28.2827000Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:57:28.2827176Z proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4); proposals_i_1 = None 2025-03-04T20:57:28.2827245Z 2025-03-04T20:57:28.2827669Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:57:28.2827890Z pred_anchor_deltas_i_5: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_4.reshape(-1, 4); pred_anchor_deltas_i_4 = None 2025-03-04T20:57:28.2827954Z 2025-03-04T20:57:28.2828421Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:57:28.2828576Z unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = tensor_2.unsqueeze(0); tensor_2 = None 2025-03-04T20:57:28.2828750Z expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1); unsqueeze_2 = None 2025-03-04T20:57:28.2828897Z anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4); expand_2 = None 2025-03-04T20:57:28.2828961Z 2025-03-04T20:57:28.2829348Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:57:28.2829524Z deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_5.float(); pred_anchor_deltas_i_5 = None 2025-03-04T20:57:28.2829593Z 2025-03-04T20:57:28.2829914Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:57:28.2830065Z boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32); anchors_i_2 = None 2025-03-04T20:57:28.2830127Z 2025-03-04T20:57:28.2830457Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:57:28.2830588Z getitem_42: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)] 2025-03-04T20:57:28.2830720Z getitem_43: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T20:57:28.2830869Z widths_2: "f32[67488][1]cpu" = getitem_42 - getitem_43; getitem_42 = getitem_43 = None 2025-03-04T20:57:28.2830939Z 2025-03-04T20:57:28.2831264Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:57:28.2831396Z getitem_44: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)] 2025-03-04T20:57:28.2831519Z getitem_45: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)] 2025-03-04T20:57:28.2831675Z heights_2: "f32[67488][1]cpu" = getitem_44 - getitem_45; getitem_44 = getitem_45 = None 2025-03-04T20:57:28.2831738Z 2025-03-04T20:57:28.2832061Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:57:28.2832185Z getitem_46: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T20:57:28.2832282Z mul_20: "f32[67488][1]cpu" = 0.5 * widths_2 2025-03-04T20:57:28.2832433Z ctr_x_2: "f32[67488][1]cpu" = getitem_46 + mul_20; getitem_46 = mul_20 = None 2025-03-04T20:57:28.2832515Z 2025-03-04T20:57:28.2832835Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:57:28.2832990Z getitem_47: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]; boxes_2 = None 2025-03-04T20:57:28.2833083Z mul_21: "f32[67488][1]cpu" = 0.5 * heights_2 2025-03-04T20:57:28.2833221Z ctr_y_2: "f32[67488][1]cpu" = getitem_47 + mul_21; getitem_47 = mul_21 = None 2025-03-04T20:57:28.2833283Z 2025-03-04T20:57:28.2833641Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:57:28.2833795Z getitem_48: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:57:28.2833920Z dx_2: "f32[67488, 1][1, 1]cpu" = getitem_48 / 1.0; getitem_48 = None 2025-03-04T20:57:28.2833984Z 2025-03-04T20:57:28.2834311Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:57:28.2834466Z getitem_49: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:57:28.2834608Z dy_2: "f32[67488, 1][1, 1]cpu" = getitem_49 / 1.0; getitem_49 = None 2025-03-04T20:57:28.2834672Z 2025-03-04T20:57:28.2834984Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:57:28.2835137Z getitem_50: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:57:28.2835253Z dw_4: "f32[67488, 1][1, 1]cpu" = getitem_50 / 1.0; getitem_50 = None 2025-03-04T20:57:28.2835316Z 2025-03-04T20:57:28.2835629Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:57:28.2835823Z getitem_51: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))]; deltas_2 = None 2025-03-04T20:57:28.2835933Z dh_4: "f32[67488, 1][1, 1]cpu" = getitem_51 / 1.0; getitem_51 = None 2025-03-04T20:57:28.2836004Z 2025-03-04T20:57:28.2836352Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:57:28.2836503Z dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356); dw_4 = None 2025-03-04T20:57:28.2836636Z 2025-03-04T20:57:28.2837002Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:57:28.2837146Z dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356); dh_4 = None 2025-03-04T20:57:28.2837217Z 2025-03-04T20:57:28.2837581Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:57:28.2837739Z getitem_52: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)] 2025-03-04T20:57:28.2837867Z mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_52; dx_2 = getitem_52 = None 2025-03-04T20:57:28.2838032Z getitem_53: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)]; ctr_x_2 = None 2025-03-04T20:57:28.2838173Z pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_53; mul_22 = getitem_53 = None 2025-03-04T20:57:28.2838244Z 2025-03-04T20:57:28.2838606Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:57:28.2838778Z getitem_54: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)] 2025-03-04T20:57:28.2838907Z mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_54; dy_2 = getitem_54 = None 2025-03-04T20:57:28.2839072Z getitem_55: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)]; ctr_y_2 = None 2025-03-04T20:57:28.2839212Z pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_55; mul_23 = getitem_55 = None 2025-03-04T20:57:28.2839285Z 2025-03-04T20:57:28.2839646Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:57:28.2839773Z exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5); dw_5 = None 2025-03-04T20:57:28.2839938Z getitem_56: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]; widths_2 = None 2025-03-04T20:57:28.2840083Z pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_56; exp_4 = getitem_56 = None 2025-03-04T20:57:28.2840145Z 2025-03-04T20:57:28.2840511Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:57:28.2840639Z exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5); dh_5 = None 2025-03-04T20:57:28.2840814Z getitem_57: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]; heights_2 = None 2025-03-04T20:57:28.2840948Z pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_57; exp_5 = getitem_57 = None 2025-03-04T20:57:28.2841018Z 2025-03-04T20:57:28.2841334Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:57:28.2841441Z mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2 2025-03-04T20:57:28.2841554Z x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26; mul_26 = None 2025-03-04T20:57:28.2841626Z 2025-03-04T20:57:28.2841942Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:57:28.2842046Z mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2 2025-03-04T20:57:28.2842159Z y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27; mul_27 = None 2025-03-04T20:57:28.2842230Z 2025-03-04T20:57:28.2842540Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:57:28.2842661Z mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2; pred_w_2 = None 2025-03-04T20:57:28.2842796Z x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28; pred_ctr_x_2 = mul_28 = None 2025-03-04T20:57:28.2842862Z 2025-03-04T20:57:28.2843173Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:57:28.2843286Z mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2; pred_h_2 = None 2025-03-04T20:57:28.2843411Z y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29; pred_ctr_y_2 = mul_29 = None 2025-03-04T20:57:28.2843477Z 2025-03-04T20:57:28.2843809Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:57:28.2843997Z pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1); x1_2 = y1_2 = x2_2 = y2_2 = None 2025-03-04T20:57:28.2844063Z 2025-03-04T20:57:28.2844406Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:57:28.2844573Z proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4)); pred_boxes_2 = None 2025-03-04T20:57:28.2844632Z 2025-03-04T20:57:28.2845015Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:57:28.2845183Z proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4); proposals_i_2 = None 2025-03-04T20:57:28.2845264Z 2025-03-04T20:57:28.2845654Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:57:28.2845863Z pred_anchor_deltas_i_7: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_6.reshape(-1, 4); pred_anchor_deltas_i_6 = None 2025-03-04T20:57:28.2845923Z 2025-03-04T20:57:28.2846370Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:57:28.2846529Z unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = tensor_3.unsqueeze(0); tensor_3 = None 2025-03-04T20:57:28.2846680Z expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1); unsqueeze_3 = None 2025-03-04T20:57:28.2846812Z anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4); expand_3 = None 2025-03-04T20:57:28.2846879Z 2025-03-04T20:57:28.2847241Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:57:28.2847411Z deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_7.float(); pred_anchor_deltas_i_7 = None 2025-03-04T20:57:28.2847470Z 2025-03-04T20:57:28.2847780Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:57:28.2847917Z boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32); anchors_i_3 = None 2025-03-04T20:57:28.2847984Z 2025-03-04T20:57:28.2848291Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:57:28.2848426Z getitem_58: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)] 2025-03-04T20:57:28.2848547Z getitem_59: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T20:57:28.2848698Z widths_3: "f32[16872][1]cpu" = getitem_58 - getitem_59; getitem_58 = getitem_59 = None 2025-03-04T20:57:28.2848759Z 2025-03-04T20:57:28.2849091Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:57:28.2849209Z getitem_60: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)] 2025-03-04T20:57:28.2849331Z getitem_61: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)] 2025-03-04T20:57:28.2849473Z heights_3: "f32[16872][1]cpu" = getitem_60 - getitem_61; getitem_60 = getitem_61 = None 2025-03-04T20:57:28.2849537Z 2025-03-04T20:57:28.2849844Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:57:28.2849962Z getitem_62: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T20:57:28.2850064Z mul_30: "f32[16872][1]cpu" = 0.5 * widths_3 2025-03-04T20:57:28.2850199Z ctr_x_3: "f32[16872][1]cpu" = getitem_62 + mul_30; getitem_62 = mul_30 = None 2025-03-04T20:57:28.2850261Z 2025-03-04T20:57:28.2850580Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:57:28.2850739Z getitem_63: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]; boxes_3 = None 2025-03-04T20:57:28.2850828Z mul_31: "f32[16872][1]cpu" = 0.5 * heights_3 2025-03-04T20:57:28.2850957Z ctr_y_3: "f32[16872][1]cpu" = getitem_63 + mul_31; getitem_63 = mul_31 = None 2025-03-04T20:57:28.2851132Z 2025-03-04T20:57:28.2851435Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:57:28.2851583Z getitem_64: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:57:28.2851700Z dx_3: "f32[16872, 1][1, 1]cpu" = getitem_64 / 1.0; getitem_64 = None 2025-03-04T20:57:28.2851758Z 2025-03-04T20:57:28.2852081Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:57:28.2852227Z getitem_65: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:57:28.2852360Z dy_3: "f32[16872, 1][1, 1]cpu" = getitem_65 / 1.0; getitem_65 = None 2025-03-04T20:57:28.2852422Z 2025-03-04T20:57:28.2852717Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:57:28.2852861Z getitem_66: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:57:28.2852973Z dw_6: "f32[16872, 1][1, 1]cpu" = getitem_66 / 1.0; getitem_66 = None 2025-03-04T20:57:28.2853032Z 2025-03-04T20:57:28.2853337Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:57:28.2853518Z getitem_67: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))]; deltas_3 = None 2025-03-04T20:57:28.2853630Z dh_6: "f32[16872, 1][1, 1]cpu" = getitem_67 / 1.0; getitem_67 = None 2025-03-04T20:57:28.2853686Z 2025-03-04T20:57:28.2854029Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:57:28.2854163Z dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356); dw_6 = None 2025-03-04T20:57:28.2854232Z 2025-03-04T20:57:28.2854550Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:57:28.2854690Z dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356); dh_6 = None 2025-03-04T20:57:28.2854752Z 2025-03-04T20:57:28.2855098Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:57:28.2855230Z getitem_68: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)] 2025-03-04T20:57:28.2855359Z mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_68; dx_3 = getitem_68 = None 2025-03-04T20:57:28.2855509Z getitem_69: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)]; ctr_x_3 = None 2025-03-04T20:57:28.2855654Z pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_69; mul_32 = getitem_69 = None 2025-03-04T20:57:28.2855730Z 2025-03-04T20:57:28.2856084Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:57:28.2856225Z getitem_70: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)] 2025-03-04T20:57:28.2856345Z mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_70; dy_3 = getitem_70 = None 2025-03-04T20:57:28.2856504Z getitem_71: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)]; ctr_y_3 = None 2025-03-04T20:57:28.2856643Z pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_71; mul_33 = getitem_71 = None 2025-03-04T20:57:28.2856725Z 2025-03-04T20:57:28.2857054Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:57:28.2857176Z exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7); dw_7 = None 2025-03-04T20:57:28.2857339Z getitem_72: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]; widths_3 = None 2025-03-04T20:57:28.2857504Z pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_72; exp_6 = getitem_72 = None 2025-03-04T20:57:28.2857563Z 2025-03-04T20:57:28.2857902Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:57:28.2858007Z exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7); dh_7 = None 2025-03-04T20:57:28.2858170Z getitem_73: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]; heights_3 = None 2025-03-04T20:57:28.2858298Z pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_73; exp_7 = getitem_73 = None 2025-03-04T20:57:28.2858362Z 2025-03-04T20:57:28.2858663Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:57:28.2858762Z mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3 2025-03-04T20:57:28.2858873Z x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36; mul_36 = None 2025-03-04T20:57:28.2858941Z 2025-03-04T20:57:28.2859246Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:57:28.2859342Z mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3 2025-03-04T20:57:28.2859451Z y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37; mul_37 = None 2025-03-04T20:57:28.2859520Z 2025-03-04T20:57:28.2859819Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:57:28.2859935Z mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3; pred_w_3 = None 2025-03-04T20:57:28.2860064Z x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38; pred_ctr_x_3 = mul_38 = None 2025-03-04T20:57:28.2860132Z 2025-03-04T20:57:28.2860429Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:57:28.2860544Z mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3; pred_h_3 = None 2025-03-04T20:57:28.2860669Z y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39; pred_ctr_y_3 = mul_39 = None 2025-03-04T20:57:28.2860735Z 2025-03-04T20:57:28.2861075Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:57:28.2861263Z pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1); x1_3 = y1_3 = x2_3 = y2_3 = None 2025-03-04T20:57:28.2861341Z 2025-03-04T20:57:28.2861673Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:57:28.2861829Z proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4)); pred_boxes_3 = None 2025-03-04T20:57:28.2861898Z 2025-03-04T20:57:28.2862268Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:57:28.2862443Z proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4); proposals_i_3 = None 2025-03-04T20:57:28.2862517Z 2025-03-04T20:57:28.2862914Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:57:28.2863114Z pred_anchor_deltas_i_9: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_8.reshape(-1, 4); pred_anchor_deltas_i_8 = None 2025-03-04T20:57:28.2863180Z 2025-03-04T20:57:28.2863611Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:57:28.2863774Z unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = tensor_4.unsqueeze(0); tensor_4 = None 2025-03-04T20:57:28.2863924Z expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1); unsqueeze_4 = None 2025-03-04T20:57:28.2864054Z anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4); expand_4 = None 2025-03-04T20:57:28.2864117Z 2025-03-04T20:57:28.2864473Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:57:28.2864641Z deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_9.float(); pred_anchor_deltas_i_9 = None 2025-03-04T20:57:28.2864699Z 2025-03-04T20:57:28.2865007Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:57:28.2865146Z boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32); anchors_i_4 = None 2025-03-04T20:57:28.2865210Z 2025-03-04T20:57:28.2865515Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:57:28.2865645Z getitem_74: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)] 2025-03-04T20:57:28.2865762Z getitem_75: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T20:57:28.2865909Z widths_4: "f32[4332][1]cpu" = getitem_74 - getitem_75; getitem_74 = getitem_75 = None 2025-03-04T20:57:28.2865969Z 2025-03-04T20:57:28.2866287Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:57:28.2866405Z getitem_76: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)] 2025-03-04T20:57:28.2866527Z getitem_77: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)] 2025-03-04T20:57:28.2866667Z heights_4: "f32[4332][1]cpu" = getitem_76 - getitem_77; getitem_76 = getitem_77 = None 2025-03-04T20:57:28.2866736Z 2025-03-04T20:57:28.2867039Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:57:28.2867160Z getitem_78: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T20:57:28.2867260Z mul_40: "f32[4332][1]cpu" = 0.5 * widths_4 2025-03-04T20:57:28.2867391Z ctr_x_4: "f32[4332][1]cpu" = getitem_78 + mul_40; getitem_78 = mul_40 = None 2025-03-04T20:57:28.2867449Z 2025-03-04T20:57:28.2867759Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:57:28.2867902Z getitem_79: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]; boxes_4 = None 2025-03-04T20:57:28.2867996Z mul_41: "f32[4332][1]cpu" = 0.5 * heights_4 2025-03-04T20:57:28.2868134Z ctr_y_4: "f32[4332][1]cpu" = getitem_79 + mul_41; getitem_79 = mul_41 = None 2025-03-04T20:57:28.2868198Z 2025-03-04T20:57:28.2868492Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:57:28.2868643Z getitem_80: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:57:28.2868748Z dx_4: "f32[4332, 1][1, 1]cpu" = getitem_80 / 1.0; getitem_80 = None 2025-03-04T20:57:28.2868815Z 2025-03-04T20:57:28.2869122Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:57:28.2869292Z getitem_81: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:57:28.2869397Z dy_4: "f32[4332, 1][1, 1]cpu" = getitem_81 / 1.0; getitem_81 = None 2025-03-04T20:57:28.2869463Z 2025-03-04T20:57:28.2869753Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:57:28.2869903Z getitem_82: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:57:28.2870013Z dw_8: "f32[4332, 1][1, 1]cpu" = getitem_82 / 1.0; getitem_82 = None 2025-03-04T20:57:28.2870072Z 2025-03-04T20:57:28.2870369Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:57:28.2870545Z getitem_83: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))]; deltas_4 = None 2025-03-04T20:57:28.2870653Z dh_8: "f32[4332, 1][1, 1]cpu" = getitem_83 / 1.0; getitem_83 = None 2025-03-04T20:57:28.2870713Z 2025-03-04T20:57:28.2871051Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:57:28.2871188Z dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356); dw_8 = None 2025-03-04T20:57:28.2871255Z 2025-03-04T20:57:28.2871585Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:57:28.2871723Z dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356); dh_8 = None 2025-03-04T20:57:28.2871783Z 2025-03-04T20:57:28.2872133Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:57:28.2872267Z getitem_84: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)] 2025-03-04T20:57:28.2872429Z mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_84; dx_4 = getitem_84 = None 2025-03-04T20:57:28.2872576Z getitem_85: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)]; ctr_x_4 = None 2025-03-04T20:57:28.2872718Z pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_85; mul_42 = getitem_85 = None 2025-03-04T20:57:28.2872794Z 2025-03-04T20:57:28.2873150Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:57:28.2873283Z getitem_86: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)] 2025-03-04T20:57:28.2873409Z mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_86; dy_4 = getitem_86 = None 2025-03-04T20:57:28.2873561Z getitem_87: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)]; ctr_y_4 = None 2025-03-04T20:57:28.2873703Z pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_87; mul_43 = getitem_87 = None 2025-03-04T20:57:28.2873780Z 2025-03-04T20:57:28.2874117Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:57:28.2874231Z exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9); dw_9 = None 2025-03-04T20:57:28.2874396Z getitem_88: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]; widths_4 = None 2025-03-04T20:57:28.2874543Z pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_88; exp_8 = getitem_88 = None 2025-03-04T20:57:28.2874613Z 2025-03-04T20:57:28.2874955Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:57:28.2875073Z exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9); dh_9 = None 2025-03-04T20:57:28.2875237Z getitem_89: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]; heights_4 = None 2025-03-04T20:57:28.2875376Z pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_89; exp_9 = getitem_89 = None 2025-03-04T20:57:28.2875436Z 2025-03-04T20:57:28.2875752Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:57:28.2875846Z mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4 2025-03-04T20:57:28.2875967Z x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46; mul_46 = None 2025-03-04T20:57:28.2876030Z 2025-03-04T20:57:28.2876348Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:57:28.2876444Z mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4 2025-03-04T20:57:28.2876638Z y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47; mul_47 = None 2025-03-04T20:57:28.2876720Z 2025-03-04T20:57:28.2877063Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:57:28.2877183Z mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4; pred_w_4 = None 2025-03-04T20:57:28.2877328Z x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48; pred_ctr_x_4 = mul_48 = None 2025-03-04T20:57:28.2877394Z 2025-03-04T20:57:28.2877727Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:57:28.2877849Z mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4; pred_h_4 = None 2025-03-04T20:57:28.2877977Z y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49; pred_ctr_y_4 = mul_49 = None 2025-03-04T20:57:28.2878047Z 2025-03-04T20:57:28.2878395Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:57:28.2878588Z pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1); x1_4 = y1_4 = x2_4 = y2_4 = None 2025-03-04T20:57:28.2878676Z 2025-03-04T20:57:28.2879024Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:57:28.2879183Z proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4)); pred_boxes_4 = None 2025-03-04T20:57:28.2879252Z 2025-03-04T20:57:28.2879631Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:57:28.2879835Z proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4); proposals_i_4 = None 2025-03-04T20:57:28.2879895Z 2025-03-04T20:57:28.2880390Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T20:57:28.2880524Z arange_10: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T20:57:28.2880591Z 2025-03-04T20:57:28.2880905Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:28.2881068Z batch_idx: "i64[4][1]cpu" = arange_10.to(device(type='cpu')); arange_10 = None 2025-03-04T20:57:28.2881133Z 2025-03-04T20:57:28.2881578Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:57:28.2881691Z topk = logits_i.topk(1000, dim = 1); logits_i = None 2025-03-04T20:57:28.2881799Z topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0] 2025-03-04T20:57:28.2881912Z topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1]; topk = None 2025-03-04T20:57:28.2881978Z 2025-03-04T20:57:28.2882440Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:57:28.2882580Z getitem_92: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T20:57:28.2882806Z topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_92, topk_idx)]; proposals_i_5 = getitem_92 = topk_idx = None 2025-03-04T20:57:28.2882875Z 2025-03-04T20:57:28.2883335Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:57:28.2883507Z full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:57:28.2883569Z 2025-03-04T20:57:28.2883871Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:28.2883988Z to_21: "i64[1000][1]cpu" = full.to(device(type='cpu')); full = None 2025-03-04T20:57:28.2884056Z 2025-03-04T20:57:28.2884493Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:57:28.2884615Z topk_1 = logits_i_1.topk(1000, dim = 1); logits_i_1 = None 2025-03-04T20:57:28.2884724Z topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0] 2025-03-04T20:57:28.2884839Z topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1]; topk_1 = None 2025-03-04T20:57:28.2884927Z 2025-03-04T20:57:28.2885382Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:57:28.2885518Z getitem_96: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T20:57:28.2885749Z topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_96, topk_idx_1)]; proposals_i_6 = getitem_96 = topk_idx_1 = None 2025-03-04T20:57:28.2885817Z 2025-03-04T20:57:28.2886275Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:57:28.2886461Z full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:57:28.2886522Z 2025-03-04T20:57:28.2886822Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:28.2886962Z to_22: "i64[1000][1]cpu" = full_1.to(device(type='cpu')); full_1 = None 2025-03-04T20:57:28.2887028Z 2025-03-04T20:57:28.2887474Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:57:28.2887593Z topk_2 = logits_i_2.topk(1000, dim = 1); logits_i_2 = None 2025-03-04T20:57:28.2887695Z topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0] 2025-03-04T20:57:28.2887813Z topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1]; topk_2 = None 2025-03-04T20:57:28.2887875Z 2025-03-04T20:57:28.2888339Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:57:28.2888473Z getitem_100: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T20:57:28.2888716Z topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_100, topk_idx_2)]; proposals_i_7 = getitem_100 = topk_idx_2 = None 2025-03-04T20:57:28.2888779Z 2025-03-04T20:57:28.2889251Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:57:28.2889412Z full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:57:28.2889478Z 2025-03-04T20:57:28.2889766Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:28.2889892Z to_23: "i64[1000][1]cpu" = full_2.to(device(type='cpu')); full_2 = None 2025-03-04T20:57:28.2889952Z 2025-03-04T20:57:28.2890381Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:57:28.2890496Z topk_3 = logits_i_3.topk(1000, dim = 1); logits_i_3 = None 2025-03-04T20:57:28.2890596Z topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0] 2025-03-04T20:57:28.2890715Z topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1]; topk_3 = None 2025-03-04T20:57:28.2890777Z 2025-03-04T20:57:28.2891243Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:57:28.2891389Z getitem_104: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T20:57:28.2891626Z topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_104, topk_idx_3)]; proposals_i_8 = getitem_104 = topk_idx_3 = None 2025-03-04T20:57:28.2891687Z 2025-03-04T20:57:28.2892150Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:57:28.2892322Z full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:57:28.2892388Z 2025-03-04T20:57:28.2892675Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:28.2892800Z to_24: "i64[1000][1]cpu" = full_3.to(device(type='cpu')); full_3 = None 2025-03-04T20:57:28.2892860Z 2025-03-04T20:57:28.2893302Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:57:28.2893425Z topk_4 = logits_i_4.topk(1000, dim = 1); logits_i_4 = None 2025-03-04T20:57:28.2893533Z topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0] 2025-03-04T20:57:28.2893643Z topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1]; topk_4 = None 2025-03-04T20:57:28.2893709Z 2025-03-04T20:57:28.2894153Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:57:28.2894320Z getitem_108: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T20:57:28.2894546Z topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_108, topk_idx_4)]; proposals_i_9 = getitem_108 = topk_idx_4 = None 2025-03-04T20:57:28.2894612Z 2025-03-04T20:57:28.2895055Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:57:28.2895219Z full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:57:28.2895279Z 2025-03-04T20:57:28.2895571Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:28.2895689Z to_25: "i64[1000][1]cpu" = full_4.to(device(type='cpu')); full_4 = None 2025-03-04T20:57:28.2895755Z 2025-03-04T20:57:28.2896027Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:57:28.2896409Z topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1); topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None 2025-03-04T20:57:28.2896478Z 2025-03-04T20:57:28.2896751Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:57:28.2897216Z topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1); topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None 2025-03-04T20:57:28.2897293Z 2025-03-04T20:57:28.2897569Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:57:28.2897763Z level_ids: "i64[5000][1]cpu" = torch.cat([to_21, to_22, to_23, to_24, to_25], 0); to_21 = to_22 = to_23 = to_24 = to_25 = level_ids = None 2025-03-04T20:57:28.2897832Z 2025-03-04T20:57:28.2898211Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T20:57:28.2898369Z getitem_110: "f32[5000, 4][4, 1]cpu" = topk_proposals[0]; topk_proposals = None 2025-03-04T20:57:28.2898429Z 2025-03-04T20:57:28.2898726Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:57:28.2898867Z tensor_5: "f32[5000, 4][4, 1]cpu" = getitem_110.to(torch.float32); getitem_110 = None 2025-03-04T20:57:28.2898932Z 2025-03-04T20:57:28.2899314Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T20:57:28.2899467Z scores_per_img: "f32[5000][1]cpu" = topk_scores[0]; topk_scores = None 2025-03-04T20:57:28.2899526Z 2025-03-04T20:57:28.2900007Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T20:57:28.2900139Z isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor_5); tensor_5 = None 2025-03-04T20:57:28.2900264Z all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:57:28.2900411Z isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T20:57:28.2900547Z valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T20:57:28.2900606Z 2025-03-04T20:57:28.2900974Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T20:57:28.2901086Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T20:57:28.2901154Z 2025-03-04T20:57:29.3597539Z 2025-03-04T20:57:29.3598176Z class GraphModule(torch.nn.Module): 2025-03-04T20:57:29.3599860Z def forward(self, L_pred_anchor_deltas_0_: "f32[4, 269952, 4][1079808, 4, 1]cpu", L_anchors_0_tensor: "f32[269952, 4][4, 1]cpu", L_pred_anchor_deltas_1_: "f32[4, 67488, 4][269952, 4, 1]cpu", L_anchors_1_tensor: "f32[67488, 4][4, 1]cpu", L_pred_anchor_deltas_2_: "f32[4, 16872, 4][67488, 4, 1]cpu", L_anchors_2_tensor: "f32[16872, 4][4, 1]cpu", L_pred_anchor_deltas_3_: "f32[4, 4218, 4][16872, 4, 1]cpu", L_anchors_3_tensor: "f32[4218, 4][4, 1]cpu", L_pred_anchor_deltas_4_: "f32[4, 1083, 4][4332, 4, 1]cpu", L_anchors_4_tensor: "f32[1083, 4][4, 1]cpu", L_pred_objectness_logits_0_: "f32[4, 269952][269952, 1]cpu", L_pred_objectness_logits_1_: "f32[4, 67488][67488, 1]cpu", L_pred_objectness_logits_2_: "f32[4, 16872][16872, 1]cpu", L_pred_objectness_logits_3_: "f32[4, 4218][4218, 1]cpu", L_pred_objectness_logits_4_: "f32[4, 1083][1083, 1]cpu"): 2025-03-04T20:57:29.3601420Z l_pred_anchor_deltas_0_ = L_pred_anchor_deltas_0_ 2025-03-04T20:57:29.3601776Z l_anchors_0_tensor = L_anchors_0_tensor 2025-03-04T20:57:29.3602336Z l_pred_anchor_deltas_1_ = L_pred_anchor_deltas_1_ 2025-03-04T20:57:29.3602665Z l_anchors_1_tensor = L_anchors_1_tensor 2025-03-04T20:57:29.3603328Z l_pred_anchor_deltas_2_ = L_pred_anchor_deltas_2_ 2025-03-04T20:57:29.3603662Z l_anchors_2_tensor = L_anchors_2_tensor 2025-03-04T20:57:29.3603992Z l_pred_anchor_deltas_3_ = L_pred_anchor_deltas_3_ 2025-03-04T20:57:29.3604327Z l_anchors_3_tensor = L_anchors_3_tensor 2025-03-04T20:57:29.3604670Z l_pred_anchor_deltas_4_ = L_pred_anchor_deltas_4_ 2025-03-04T20:57:29.3604993Z l_anchors_4_tensor = L_anchors_4_tensor 2025-03-04T20:57:29.3605342Z l_pred_objectness_logits_0_ = L_pred_objectness_logits_0_ 2025-03-04T20:57:29.3605657Z l_pred_objectness_logits_1_ = L_pred_objectness_logits_1_ 2025-03-04T20:57:29.3605954Z l_pred_objectness_logits_2_ = L_pred_objectness_logits_2_ 2025-03-04T20:57:29.3606304Z l_pred_objectness_logits_3_ = L_pred_objectness_logits_3_ 2025-03-04T20:57:29.3606588Z l_pred_objectness_logits_4_ = L_pred_objectness_logits_4_ 2025-03-04T20:57:29.3606822Z 2025-03-04T20:57:29.3607368Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:57:29.3608114Z pred_anchor_deltas_i: "f32[1079808, 4][4, 1]cpu" = l_pred_anchor_deltas_0_.reshape(-1, 4); l_pred_anchor_deltas_0_ = None 2025-03-04T20:57:29.3608447Z 2025-03-04T20:57:29.3609026Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:57:29.3609724Z unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = l_anchors_0_tensor.unsqueeze(0); l_anchors_0_tensor = None 2025-03-04T20:57:29.3610127Z expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T20:57:29.3610472Z anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T20:57:29.3610736Z 2025-03-04T20:57:29.3611199Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:57:29.3611801Z deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.float(); pred_anchor_deltas_i = None 2025-03-04T20:57:29.3612074Z 2025-03-04T20:57:29.3612475Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:57:29.3613110Z boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T20:57:29.3613367Z 2025-03-04T20:57:29.3613832Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:57:29.3614325Z getitem: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:57:29.3614627Z getitem_1: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:57:29.3614946Z widths: "f32[1079808][1]cpu" = getitem - getitem_1; getitem = getitem_1 = None 2025-03-04T20:57:29.3615203Z 2025-03-04T20:57:29.3615597Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:57:29.3616082Z getitem_2: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:57:29.3616377Z getitem_3: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:57:29.3616695Z heights: "f32[1079808][1]cpu" = getitem_2 - getitem_3; getitem_2 = getitem_3 = None 2025-03-04T20:57:29.3616954Z 2025-03-04T20:57:29.3617338Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:57:29.3617844Z getitem_4: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:57:29.3618103Z mul: "f32[1079808][1]cpu" = 0.5 * widths 2025-03-04T20:57:29.3618361Z ctr_x: "f32[1079808][1]cpu" = getitem_4 + mul; getitem_4 = mul = None 2025-03-04T20:57:29.3618598Z 2025-03-04T20:57:29.3618993Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:57:29.3619511Z getitem_5: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:57:29.3619813Z mul_1: "f32[1079808][1]cpu" = 0.5 * heights 2025-03-04T20:57:29.3620071Z ctr_y: "f32[1079808][1]cpu" = getitem_5 + mul_1; getitem_5 = mul_1 = None 2025-03-04T20:57:29.3620306Z 2025-03-04T20:57:29.3620732Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:57:29.3621231Z getitem_6: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:57:29.3621571Z dx: "f32[1079808, 1][1, 1]cpu" = getitem_6 / 1.0; getitem_6 = None 2025-03-04T20:57:29.3621794Z 2025-03-04T20:57:29.3622194Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:57:29.3622678Z getitem_7: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:57:29.3622988Z dy: "f32[1079808, 1][1, 1]cpu" = getitem_7 / 1.0; getitem_7 = None 2025-03-04T20:57:29.3623207Z 2025-03-04T20:57:29.3623573Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:57:29.3624054Z getitem_8: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:57:29.3624356Z dw: "f32[1079808, 1][1, 1]cpu" = getitem_8 / 1.0; getitem_8 = None 2025-03-04T20:57:29.3624600Z 2025-03-04T20:57:29.3624976Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:57:29.3625489Z getitem_9: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:57:29.3625822Z dh: "f32[1079808, 1][1, 1]cpu" = getitem_9 / 1.0; getitem_9 = None 2025-03-04T20:57:29.3626035Z 2025-03-04T20:57:29.3626441Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:57:29.3626954Z dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:57:29.3627203Z 2025-03-04T20:57:29.3627608Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:57:29.3628109Z dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:57:29.3628353Z 2025-03-04T20:57:29.3628768Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:57:29.3629288Z getitem_10: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:57:29.3629598Z mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_10; dx = getitem_10 = None 2025-03-04T20:57:29.3629922Z getitem_11: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:57:29.3630280Z pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_11; mul_2 = getitem_11 = None 2025-03-04T20:57:29.3630530Z 2025-03-04T20:57:29.3630956Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:57:29.3631486Z getitem_12: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:57:29.3631806Z mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_12; dy = getitem_12 = None 2025-03-04T20:57:29.3632134Z getitem_13: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:57:29.3632504Z pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_13; mul_3 = getitem_13 = None 2025-03-04T20:57:29.3632770Z 2025-03-04T20:57:29.3633204Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:57:29.3633722Z exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:57:29.3634079Z getitem_14: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:57:29.3634430Z pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_14; exp = getitem_14 = None 2025-03-04T20:57:29.3634681Z 2025-03-04T20:57:29.3635115Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:57:29.3635621Z exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:57:29.3635964Z getitem_15: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:57:29.3636318Z pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_15; exp_1 = getitem_15 = None 2025-03-04T20:57:29.3636657Z 2025-03-04T20:57:29.3637082Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:57:29.3637636Z mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T20:57:29.3637899Z x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:57:29.3638141Z 2025-03-04T20:57:29.3638535Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:57:29.3639019Z mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T20:57:29.3639281Z y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:57:29.3639521Z 2025-03-04T20:57:29.3639910Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:57:29.3640386Z mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:57:29.3640683Z x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:57:29.3640929Z 2025-03-04T20:57:29.3641324Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:57:29.3641793Z mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:57:29.3642076Z y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:57:29.3642320Z 2025-03-04T20:57:29.3642759Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:57:29.3643362Z pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:57:29.3643652Z 2025-03-04T20:57:29.3644067Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:57:29.3644613Z proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4)); pred_boxes = None 2025-03-04T20:57:29.3644894Z 2025-03-04T20:57:29.3645360Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:57:29.3646005Z proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T20:57:29.3646297Z 2025-03-04T20:57:29.3646782Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:57:29.3647470Z pred_anchor_deltas_i_1: "f32[269952, 4][4, 1]cpu" = l_pred_anchor_deltas_1_.reshape(-1, 4); l_pred_anchor_deltas_1_ = None 2025-03-04T20:57:29.3647794Z 2025-03-04T20:57:29.3648425Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:57:29.3649109Z unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = l_anchors_1_tensor.unsqueeze(0); l_anchors_1_tensor = None 2025-03-04T20:57:29.3649501Z expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1); unsqueeze_1 = None 2025-03-04T20:57:29.3649846Z anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4); expand_1 = None 2025-03-04T20:57:29.3650109Z 2025-03-04T20:57:29.3650571Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:57:29.3651172Z deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T20:57:29.3651459Z 2025-03-04T20:57:29.3651857Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:57:29.3652375Z boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32); anchors_i_1 = None 2025-03-04T20:57:29.3652641Z 2025-03-04T20:57:29.3653064Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:57:29.3653553Z getitem_16: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)] 2025-03-04T20:57:29.3653861Z getitem_17: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T20:57:29.3654187Z widths_1: "f32[269952][1]cpu" = getitem_16 - getitem_17; getitem_16 = getitem_17 = None 2025-03-04T20:57:29.3654456Z 2025-03-04T20:57:29.3654853Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:57:29.3655342Z getitem_18: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)] 2025-03-04T20:57:29.3655642Z getitem_19: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)] 2025-03-04T20:57:29.3655969Z heights_1: "f32[269952][1]cpu" = getitem_18 - getitem_19; getitem_18 = getitem_19 = None 2025-03-04T20:57:29.3656236Z 2025-03-04T20:57:29.3656627Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:57:29.3657127Z getitem_20: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T20:57:29.3657397Z mul_10: "f32[269952][1]cpu" = 0.5 * widths_1 2025-03-04T20:57:29.3657669Z ctr_x_1: "f32[269952][1]cpu" = getitem_20 + mul_10; getitem_20 = mul_10 = None 2025-03-04T20:57:29.3657916Z 2025-03-04T20:57:29.3658304Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:57:29.3658804Z getitem_21: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]; boxes_1 = None 2025-03-04T20:57:29.3659118Z mul_11: "f32[269952][1]cpu" = 0.5 * heights_1 2025-03-04T20:57:29.3659385Z ctr_y_1: "f32[269952][1]cpu" = getitem_21 + mul_11; getitem_21 = mul_11 = None 2025-03-04T20:57:29.3659629Z 2025-03-04T20:57:29.3660020Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:57:29.3660520Z getitem_22: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:57:29.3660855Z dx_1: "f32[269952, 1][1, 1]cpu" = getitem_22 / 1.0; getitem_22 = None 2025-03-04T20:57:29.3661087Z 2025-03-04T20:57:29.3661491Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:57:29.3661986Z getitem_23: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:57:29.3662304Z dy_1: "f32[269952, 1][1, 1]cpu" = getitem_23 / 1.0; getitem_23 = None 2025-03-04T20:57:29.3662529Z 2025-03-04T20:57:29.3662902Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:57:29.3663394Z getitem_24: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:57:29.3663704Z dw_2: "f32[269952, 1][1, 1]cpu" = getitem_24 / 1.0; getitem_24 = None 2025-03-04T20:57:29.3663921Z 2025-03-04T20:57:29.3664298Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:57:29.3664821Z getitem_25: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))]; deltas_1 = None 2025-03-04T20:57:29.3665161Z dh_2: "f32[269952, 1][1, 1]cpu" = getitem_25 / 1.0; getitem_25 = None 2025-03-04T20:57:29.3665390Z 2025-03-04T20:57:29.3665801Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:57:29.3666318Z dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356); dw_2 = None 2025-03-04T20:57:29.3666569Z 2025-03-04T20:57:29.3666973Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:57:29.3667485Z dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356); dh_2 = None 2025-03-04T20:57:29.3667731Z 2025-03-04T20:57:29.3668151Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:57:29.3668678Z getitem_26: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)] 2025-03-04T20:57:29.3668992Z mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_26; dx_1 = getitem_26 = None 2025-03-04T20:57:29.3669324Z getitem_27: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)]; ctr_x_1 = None 2025-03-04T20:57:29.3669686Z pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_27; mul_12 = getitem_27 = None 2025-03-04T20:57:29.3669940Z 2025-03-04T20:57:29.3670369Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:57:29.3670894Z getitem_28: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)] 2025-03-04T20:57:29.3671204Z mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_28; dy_1 = getitem_28 = None 2025-03-04T20:57:29.3671554Z getitem_29: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)]; ctr_y_1 = None 2025-03-04T20:57:29.3671900Z pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_29; mul_13 = getitem_29 = None 2025-03-04T20:57:29.3672163Z 2025-03-04T20:57:29.3672578Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:57:29.3673095Z exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3); dw_3 = None 2025-03-04T20:57:29.3673427Z getitem_30: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]; widths_1 = None 2025-03-04T20:57:29.3673795Z pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_30; exp_2 = getitem_30 = None 2025-03-04T20:57:29.3674048Z 2025-03-04T20:57:29.3674469Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:57:29.3674968Z exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3); dh_3 = None 2025-03-04T20:57:29.3675299Z getitem_31: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]; heights_1 = None 2025-03-04T20:57:29.3675660Z pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_31; exp_3 = getitem_31 = None 2025-03-04T20:57:29.3675913Z 2025-03-04T20:57:29.3676317Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:57:29.3676874Z mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1 2025-03-04T20:57:29.3677147Z x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16; mul_16 = None 2025-03-04T20:57:29.3677409Z 2025-03-04T20:57:29.3677857Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:57:29.3678314Z mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1 2025-03-04T20:57:29.3678578Z y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17; mul_17 = None 2025-03-04T20:57:29.3678812Z 2025-03-04T20:57:29.3679200Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:57:29.3679683Z mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1; pred_w_1 = None 2025-03-04T20:57:29.3679989Z x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18; pred_ctr_x_1 = mul_18 = None 2025-03-04T20:57:29.3680235Z 2025-03-04T20:57:29.3680621Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:57:29.3681094Z mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1; pred_h_1 = None 2025-03-04T20:57:29.3681385Z y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19; pred_ctr_y_1 = mul_19 = None 2025-03-04T20:57:29.3681631Z 2025-03-04T20:57:29.3682062Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:57:29.3682679Z pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1); x1_1 = y1_1 = x2_1 = y2_1 = None 2025-03-04T20:57:29.3682983Z 2025-03-04T20:57:29.3683394Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:57:29.3683945Z proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4)); pred_boxes_1 = None 2025-03-04T20:57:29.3684223Z 2025-03-04T20:57:29.3684714Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:57:29.3685324Z proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4); proposals_i_1 = None 2025-03-04T20:57:29.3685617Z 2025-03-04T20:57:29.3686114Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:57:29.3686781Z pred_anchor_deltas_i_2: "f32[67488, 4][4, 1]cpu" = l_pred_anchor_deltas_2_.reshape(-1, 4); l_pred_anchor_deltas_2_ = None 2025-03-04T20:57:29.3687107Z 2025-03-04T20:57:29.3687639Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:57:29.3688314Z unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = l_anchors_2_tensor.unsqueeze(0); l_anchors_2_tensor = None 2025-03-04T20:57:29.3688704Z expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1); unsqueeze_2 = None 2025-03-04T20:57:29.3689052Z anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4); expand_2 = None 2025-03-04T20:57:29.3689310Z 2025-03-04T20:57:29.3689764Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:57:29.3690353Z deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_2.float(); pred_anchor_deltas_i_2 = None 2025-03-04T20:57:29.3690638Z 2025-03-04T20:57:29.3691044Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:57:29.3691550Z boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32); anchors_i_2 = None 2025-03-04T20:57:29.3691808Z 2025-03-04T20:57:29.3692202Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:57:29.3692700Z getitem_32: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)] 2025-03-04T20:57:29.3693009Z getitem_33: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T20:57:29.3693338Z widths_2: "f32[67488][1]cpu" = getitem_32 - getitem_33; getitem_32 = getitem_33 = None 2025-03-04T20:57:29.3693603Z 2025-03-04T20:57:29.3694009Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:57:29.3694501Z getitem_34: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)] 2025-03-04T20:57:29.3694801Z getitem_35: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)] 2025-03-04T20:57:29.3695121Z heights_2: "f32[67488][1]cpu" = getitem_34 - getitem_35; getitem_34 = getitem_35 = None 2025-03-04T20:57:29.3695520Z 2025-03-04T20:57:29.3695923Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:57:29.3696421Z getitem_36: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T20:57:29.3696696Z mul_20: "f32[67488][1]cpu" = 0.5 * widths_2 2025-03-04T20:57:29.3696989Z ctr_x_2: "f32[67488][1]cpu" = getitem_36 + mul_20; getitem_36 = mul_20 = None 2025-03-04T20:57:29.3697245Z 2025-03-04T20:57:29.3697641Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:57:29.3698159Z getitem_37: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]; boxes_2 = None 2025-03-04T20:57:29.3698447Z mul_21: "f32[67488][1]cpu" = 0.5 * heights_2 2025-03-04T20:57:29.3698705Z ctr_y_2: "f32[67488][1]cpu" = getitem_37 + mul_21; getitem_37 = mul_21 = None 2025-03-04T20:57:29.3698947Z 2025-03-04T20:57:29.3699332Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:57:29.3699856Z getitem_38: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:57:29.3700172Z dx_2: "f32[67488, 1][1, 1]cpu" = getitem_38 / 1.0; getitem_38 = None 2025-03-04T20:57:29.3700415Z 2025-03-04T20:57:29.3700787Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:57:29.3701282Z getitem_39: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:57:29.3701595Z dy_2: "f32[67488, 1][1, 1]cpu" = getitem_39 / 1.0; getitem_39 = None 2025-03-04T20:57:29.3701819Z 2025-03-04T20:57:29.3702365Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:57:29.3702857Z getitem_40: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:57:29.3703164Z dw_4: "f32[67488, 1][1, 1]cpu" = getitem_40 / 1.0; getitem_40 = None 2025-03-04T20:57:29.3703384Z 2025-03-04T20:57:29.3703761Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:57:29.3704290Z getitem_41: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))]; deltas_2 = None 2025-03-04T20:57:29.3704630Z dh_4: "f32[67488, 1][1, 1]cpu" = getitem_41 / 1.0; getitem_41 = None 2025-03-04T20:57:29.3704857Z 2025-03-04T20:57:29.3705269Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:57:29.3705790Z dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356); dw_4 = None 2025-03-04T20:57:29.3706042Z 2025-03-04T20:57:29.3706451Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:57:29.3706962Z dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356); dh_4 = None 2025-03-04T20:57:29.3707212Z 2025-03-04T20:57:29.3707630Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:57:29.3708155Z getitem_42: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)] 2025-03-04T20:57:29.3708467Z mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_42; dx_2 = getitem_42 = None 2025-03-04T20:57:29.3708851Z getitem_43: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)]; ctr_x_2 = None 2025-03-04T20:57:29.3709196Z pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_43; mul_22 = getitem_43 = None 2025-03-04T20:57:29.3709449Z 2025-03-04T20:57:29.3709874Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:57:29.3710403Z getitem_44: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)] 2025-03-04T20:57:29.3710736Z mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_44; dy_2 = getitem_44 = None 2025-03-04T20:57:29.3711057Z getitem_45: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)]; ctr_y_2 = None 2025-03-04T20:57:29.3711394Z pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_45; mul_23 = getitem_45 = None 2025-03-04T20:57:29.3711647Z 2025-03-04T20:57:29.3712077Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:57:29.3712565Z exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5); dw_5 = None 2025-03-04T20:57:29.3712905Z getitem_46: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]; widths_2 = None 2025-03-04T20:57:29.3713249Z pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_46; exp_4 = getitem_46 = None 2025-03-04T20:57:29.3713498Z 2025-03-04T20:57:29.3713916Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:57:29.3714415Z exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5); dh_5 = None 2025-03-04T20:57:29.3714742Z getitem_47: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]; heights_2 = None 2025-03-04T20:57:29.3715095Z pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_47; exp_5 = getitem_47 = None 2025-03-04T20:57:29.3715348Z 2025-03-04T20:57:29.3715746Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:57:29.3716207Z mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2 2025-03-04T20:57:29.3716469Z x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26; mul_26 = None 2025-03-04T20:57:29.3716802Z 2025-03-04T20:57:29.3717256Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:57:29.3717778Z mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2 2025-03-04T20:57:29.3718064Z y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27; mul_27 = None 2025-03-04T20:57:29.3718351Z 2025-03-04T20:57:29.3718745Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:57:29.3719225Z mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2; pred_w_2 = None 2025-03-04T20:57:29.3719532Z x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28; pred_ctr_x_2 = mul_28 = None 2025-03-04T20:57:29.3719786Z 2025-03-04T20:57:29.3720174Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:57:29.3720666Z mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2; pred_h_2 = None 2025-03-04T20:57:29.3720955Z y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29; pred_ctr_y_2 = mul_29 = None 2025-03-04T20:57:29.3721229Z 2025-03-04T20:57:29.3721664Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:57:29.3722258Z pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1); x1_2 = y1_2 = x2_2 = y2_2 = None 2025-03-04T20:57:29.3722568Z 2025-03-04T20:57:29.3722989Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:57:29.3723535Z proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4)); pred_boxes_2 = None 2025-03-04T20:57:29.3723835Z 2025-03-04T20:57:29.3724303Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:57:29.3724914Z proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4); proposals_i_2 = None 2025-03-04T20:57:29.3725207Z 2025-03-04T20:57:29.3725709Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:57:29.3726387Z pred_anchor_deltas_i_3: "f32[16872, 4][4, 1]cpu" = l_pred_anchor_deltas_3_.reshape(-1, 4); l_pred_anchor_deltas_3_ = None 2025-03-04T20:57:29.3726714Z 2025-03-04T20:57:29.3727232Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:57:29.3727906Z unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = l_anchors_3_tensor.unsqueeze(0); l_anchors_3_tensor = None 2025-03-04T20:57:29.3728293Z expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1); unsqueeze_3 = None 2025-03-04T20:57:29.3728630Z anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4); expand_3 = None 2025-03-04T20:57:29.3728885Z 2025-03-04T20:57:29.3729344Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:57:29.3729919Z deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float(); pred_anchor_deltas_i_3 = None 2025-03-04T20:57:29.3730197Z 2025-03-04T20:57:29.3730580Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:57:29.3731078Z boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32); anchors_i_3 = None 2025-03-04T20:57:29.3731330Z 2025-03-04T20:57:29.3731726Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:57:29.3732225Z getitem_48: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)] 2025-03-04T20:57:29.3732533Z getitem_49: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T20:57:29.3732860Z widths_3: "f32[16872][1]cpu" = getitem_48 - getitem_49; getitem_48 = getitem_49 = None 2025-03-04T20:57:29.3733122Z 2025-03-04T20:57:29.3733531Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:57:29.3734031Z getitem_50: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)] 2025-03-04T20:57:29.3734331Z getitem_51: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)] 2025-03-04T20:57:29.3734659Z heights_3: "f32[16872][1]cpu" = getitem_50 - getitem_51; getitem_50 = getitem_51 = None 2025-03-04T20:57:29.3734972Z 2025-03-04T20:57:29.3735367Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:57:29.3735859Z getitem_52: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T20:57:29.3736126Z mul_30: "f32[16872][1]cpu" = 0.5 * widths_3 2025-03-04T20:57:29.3736399Z ctr_x_3: "f32[16872][1]cpu" = getitem_52 + mul_30; getitem_52 = mul_30 = None 2025-03-04T20:57:29.3736651Z 2025-03-04T20:57:29.3737049Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:57:29.3737581Z getitem_53: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]; boxes_3 = None 2025-03-04T20:57:29.3737870Z mul_31: "f32[16872][1]cpu" = 0.5 * heights_3 2025-03-04T20:57:29.3738134Z ctr_y_3: "f32[16872][1]cpu" = getitem_53 + mul_31; getitem_53 = mul_31 = None 2025-03-04T20:57:29.3738374Z 2025-03-04T20:57:29.3738774Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:57:29.3739266Z getitem_54: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:57:29.3739593Z dx_3: "f32[16872, 1][1, 1]cpu" = getitem_54 / 1.0; getitem_54 = None 2025-03-04T20:57:29.3739818Z 2025-03-04T20:57:29.3740187Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:57:29.3740674Z getitem_55: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:57:29.3740981Z dy_3: "f32[16872, 1][1, 1]cpu" = getitem_55 / 1.0; getitem_55 = None 2025-03-04T20:57:29.3741205Z 2025-03-04T20:57:29.3741575Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:57:29.3742067Z getitem_56: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:57:29.3742379Z dw_6: "f32[16872, 1][1, 1]cpu" = getitem_56 / 1.0; getitem_56 = None 2025-03-04T20:57:29.3742601Z 2025-03-04T20:57:29.3742986Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:57:29.3743522Z getitem_57: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))]; deltas_3 = None 2025-03-04T20:57:29.3743860Z dh_6: "f32[16872, 1][1, 1]cpu" = getitem_57 / 1.0; getitem_57 = None 2025-03-04T20:57:29.3744089Z 2025-03-04T20:57:29.3744513Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:57:29.3745023Z dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356); dw_6 = None 2025-03-04T20:57:29.3745271Z 2025-03-04T20:57:29.3745675Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:57:29.3746194Z dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356); dh_6 = None 2025-03-04T20:57:29.3746445Z 2025-03-04T20:57:29.3746867Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:57:29.3747395Z getitem_58: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)] 2025-03-04T20:57:29.3747733Z mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_58; dx_3 = getitem_58 = None 2025-03-04T20:57:29.3748068Z getitem_59: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)]; ctr_x_3 = None 2025-03-04T20:57:29.3748416Z pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_59; mul_32 = getitem_59 = None 2025-03-04T20:57:29.3748672Z 2025-03-04T20:57:29.3749107Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:57:29.3749677Z getitem_60: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)] 2025-03-04T20:57:29.3749992Z mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_60; dy_3 = getitem_60 = None 2025-03-04T20:57:29.3750322Z getitem_61: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)]; ctr_y_3 = None 2025-03-04T20:57:29.3750671Z pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_61; mul_33 = getitem_61 = None 2025-03-04T20:57:29.3750929Z 2025-03-04T20:57:29.3751372Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:57:29.3751874Z exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7); dw_7 = None 2025-03-04T20:57:29.3752214Z getitem_62: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]; widths_3 = None 2025-03-04T20:57:29.3752567Z pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_62; exp_6 = getitem_62 = None 2025-03-04T20:57:29.3752822Z 2025-03-04T20:57:29.3753242Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:57:29.3753769Z exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7); dh_7 = None 2025-03-04T20:57:29.3754115Z getitem_63: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]; heights_3 = None 2025-03-04T20:57:29.3754487Z pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_63; exp_7 = getitem_63 = None 2025-03-04T20:57:29.3754751Z 2025-03-04T20:57:29.3755175Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:57:29.3755698Z mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3 2025-03-04T20:57:29.3755990Z x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36; mul_36 = None 2025-03-04T20:57:29.3756248Z 2025-03-04T20:57:29.3756752Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:57:29.3757285Z mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3 2025-03-04T20:57:29.3757584Z y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37; mul_37 = None 2025-03-04T20:57:29.3757849Z 2025-03-04T20:57:29.3758292Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:57:29.3758771Z mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3; pred_w_3 = None 2025-03-04T20:57:29.3759076Z x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38; pred_ctr_x_3 = mul_38 = None 2025-03-04T20:57:29.3759327Z 2025-03-04T20:57:29.3759713Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:57:29.3760188Z mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3; pred_h_3 = None 2025-03-04T20:57:29.3760485Z y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39; pred_ctr_y_3 = mul_39 = None 2025-03-04T20:57:29.3760757Z 2025-03-04T20:57:29.3761192Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:57:29.3761776Z pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1); x1_3 = y1_3 = x2_3 = y2_3 = None 2025-03-04T20:57:29.3762082Z 2025-03-04T20:57:29.3762486Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:57:29.3763063Z proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4)); pred_boxes_3 = None 2025-03-04T20:57:29.3763335Z 2025-03-04T20:57:29.3763784Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:57:29.3764375Z proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4); proposals_i_3 = None 2025-03-04T20:57:29.3764654Z 2025-03-04T20:57:29.3765137Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T20:57:29.3765792Z pred_anchor_deltas_i_4: "f32[4332, 4][4, 1]cpu" = l_pred_anchor_deltas_4_.reshape(-1, 4); l_pred_anchor_deltas_4_ = None 2025-03-04T20:57:29.3766105Z 2025-03-04T20:57:29.3766612Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T20:57:29.3767257Z unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = l_anchors_4_tensor.unsqueeze(0); l_anchors_4_tensor = None 2025-03-04T20:57:29.3767636Z expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1); unsqueeze_4 = None 2025-03-04T20:57:29.3767962Z anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4); expand_4 = None 2025-03-04T20:57:29.3768210Z 2025-03-04T20:57:29.3768653Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:57:29.3769228Z deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_4.float(); pred_anchor_deltas_i_4 = None 2025-03-04T20:57:29.3769504Z 2025-03-04T20:57:29.3769888Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:57:29.3770379Z boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32); anchors_i_4 = None 2025-03-04T20:57:29.3770635Z 2025-03-04T20:57:29.3771028Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:57:29.3771512Z getitem_64: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)] 2025-03-04T20:57:29.3771806Z getitem_65: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T20:57:29.3772125Z widths_4: "f32[4332][1]cpu" = getitem_64 - getitem_65; getitem_64 = getitem_65 = None 2025-03-04T20:57:29.3772382Z 2025-03-04T20:57:29.3772771Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:57:29.3773251Z getitem_66: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)] 2025-03-04T20:57:29.3773536Z getitem_67: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)] 2025-03-04T20:57:29.3773878Z heights_4: "f32[4332][1]cpu" = getitem_66 - getitem_67; getitem_66 = getitem_67 = None 2025-03-04T20:57:29.3774133Z 2025-03-04T20:57:29.3774515Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:57:29.3775010Z getitem_68: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T20:57:29.3775271Z mul_40: "f32[4332][1]cpu" = 0.5 * widths_4 2025-03-04T20:57:29.3775531Z ctr_x_4: "f32[4332][1]cpu" = getitem_68 + mul_40; getitem_68 = mul_40 = None 2025-03-04T20:57:29.3775790Z 2025-03-04T20:57:29.3776182Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:57:29.3776690Z getitem_69: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]; boxes_4 = None 2025-03-04T20:57:29.3776984Z mul_41: "f32[4332][1]cpu" = 0.5 * heights_4 2025-03-04T20:57:29.3777252Z ctr_y_4: "f32[4332][1]cpu" = getitem_69 + mul_41; getitem_69 = mul_41 = None 2025-03-04T20:57:29.3777496Z 2025-03-04T20:57:29.3777910Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:57:29.3778417Z getitem_70: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:57:29.3778719Z dx_4: "f32[4332, 1][1, 1]cpu" = getitem_70 / 1.0; getitem_70 = None 2025-03-04T20:57:29.3778943Z 2025-03-04T20:57:29.3779316Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:57:29.3779798Z getitem_71: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:57:29.3780103Z dy_4: "f32[4332, 1][1, 1]cpu" = getitem_71 / 1.0; getitem_71 = None 2025-03-04T20:57:29.3780317Z 2025-03-04T20:57:29.3780687Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:57:29.3781167Z getitem_72: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:57:29.3781472Z dw_8: "f32[4332, 1][1, 1]cpu" = getitem_72 / 1.0; getitem_72 = None 2025-03-04T20:57:29.3781690Z 2025-03-04T20:57:29.3782065Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:57:29.3782582Z getitem_73: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))]; deltas_4 = None 2025-03-04T20:57:29.3782909Z dh_8: "f32[4332, 1][1, 1]cpu" = getitem_73 / 1.0; getitem_73 = None 2025-03-04T20:57:29.3783130Z 2025-03-04T20:57:29.3783535Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:57:29.3784043Z dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356); dw_8 = None 2025-03-04T20:57:29.3784292Z 2025-03-04T20:57:29.3784698Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:57:29.3785199Z dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356); dh_8 = None 2025-03-04T20:57:29.3785447Z 2025-03-04T20:57:29.3785861Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:57:29.3786396Z getitem_74: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)] 2025-03-04T20:57:29.3786702Z mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_74; dx_4 = getitem_74 = None 2025-03-04T20:57:29.3787024Z getitem_75: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)]; ctr_x_4 = None 2025-03-04T20:57:29.3787357Z pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_75; mul_42 = getitem_75 = None 2025-03-04T20:57:29.3787605Z 2025-03-04T20:57:29.3788022Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:57:29.3788556Z getitem_76: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)] 2025-03-04T20:57:29.3788855Z mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_76; dy_4 = getitem_76 = None 2025-03-04T20:57:29.3789169Z getitem_77: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)]; ctr_y_4 = None 2025-03-04T20:57:29.3789497Z pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_77; mul_43 = getitem_77 = None 2025-03-04T20:57:29.3789743Z 2025-03-04T20:57:29.3790165Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:57:29.3790668Z exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9); dw_9 = None 2025-03-04T20:57:29.3790984Z getitem_78: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]; widths_4 = None 2025-03-04T20:57:29.3791320Z pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_78; exp_8 = getitem_78 = None 2025-03-04T20:57:29.3791565Z 2025-03-04T20:57:29.3791978Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:57:29.3792464Z exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9); dh_9 = None 2025-03-04T20:57:29.3792782Z getitem_79: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]; heights_4 = None 2025-03-04T20:57:29.3793123Z pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_79; exp_9 = getitem_79 = None 2025-03-04T20:57:29.3793365Z 2025-03-04T20:57:29.3793756Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:57:29.3794224Z mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4 2025-03-04T20:57:29.3794484Z x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46; mul_46 = None 2025-03-04T20:57:29.3794712Z 2025-03-04T20:57:29.3795100Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:57:29.3795567Z mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4 2025-03-04T20:57:29.3795821Z y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47; mul_47 = None 2025-03-04T20:57:29.3796046Z 2025-03-04T20:57:29.3796435Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:57:29.3796990Z mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4; pred_w_4 = None 2025-03-04T20:57:29.3797293Z x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48; pred_ctr_x_4 = mul_48 = None 2025-03-04T20:57:29.3797546Z 2025-03-04T20:57:29.3797960Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:57:29.3798459Z mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4; pred_h_4 = None 2025-03-04T20:57:29.3798804Z y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49; pred_ctr_y_4 = mul_49 = None 2025-03-04T20:57:29.3799050Z 2025-03-04T20:57:29.3799491Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:57:29.3800079Z pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1); x1_4 = y1_4 = x2_4 = y2_4 = None 2025-03-04T20:57:29.3800381Z 2025-03-04T20:57:29.3800799Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:57:29.3801370Z proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4)); pred_boxes_4 = None 2025-03-04T20:57:29.3801648Z 2025-03-04T20:57:29.3802254Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T20:57:29.3802868Z proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4); proposals_i_4 = None 2025-03-04T20:57:29.3803200Z 2025-03-04T20:57:29.3803798Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T20:57:29.3804488Z arange: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T20:57:29.3804739Z 2025-03-04T20:57:29.3805121Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:29.3805604Z batch_idx: "i64[4][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T20:57:29.3805852Z 2025-03-04T20:57:29.3806374Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:57:29.3807036Z topk = l_pred_objectness_logits_0_.topk(1000, dim = 1); l_pred_objectness_logits_0_ = None 2025-03-04T20:57:29.3807368Z topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0] 2025-03-04T20:57:29.3807639Z topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1]; topk = None 2025-03-04T20:57:29.3807866Z 2025-03-04T20:57:29.3808416Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:57:29.3809058Z getitem_82: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T20:57:29.3809479Z topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_82, topk_idx)]; proposals_i_5 = getitem_82 = topk_idx = None 2025-03-04T20:57:29.3809820Z 2025-03-04T20:57:29.3810366Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:57:29.3811040Z full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:57:29.3811321Z 2025-03-04T20:57:29.3811697Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:29.3812170Z to_6: "i64[1000][1]cpu" = full.to(device(type='cpu')); full = None 2025-03-04T20:57:29.3812401Z 2025-03-04T20:57:29.3812923Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:57:29.3813612Z topk_1 = l_pred_objectness_logits_1_.topk(1000, dim = 1); l_pred_objectness_logits_1_ = None 2025-03-04T20:57:29.3813936Z topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0] 2025-03-04T20:57:29.3814207Z topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1]; topk_1 = None 2025-03-04T20:57:29.3814434Z 2025-03-04T20:57:29.3814965Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:57:29.3815614Z getitem_86: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T20:57:29.3816025Z topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_86, topk_idx_1)]; proposals_i_6 = getitem_86 = topk_idx_1 = None 2025-03-04T20:57:29.3816364Z 2025-03-04T20:57:29.3816908Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:57:29.3817589Z full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:57:29.3817867Z 2025-03-04T20:57:29.3818239Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:29.3818704Z to_7: "i64[1000][1]cpu" = full_1.to(device(type='cpu')); full_1 = None 2025-03-04T20:57:29.3818937Z 2025-03-04T20:57:29.3819443Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:57:29.3820087Z topk_2 = l_pred_objectness_logits_2_.topk(1000, dim = 1); l_pred_objectness_logits_2_ = None 2025-03-04T20:57:29.3820413Z topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0] 2025-03-04T20:57:29.3820685Z topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1]; topk_2 = None 2025-03-04T20:57:29.3820912Z 2025-03-04T20:57:29.3821443Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:57:29.3822070Z getitem_90: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T20:57:29.3822474Z topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_90, topk_idx_2)]; proposals_i_7 = getitem_90 = topk_idx_2 = None 2025-03-04T20:57:29.3822822Z 2025-03-04T20:57:29.3823354Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:57:29.3824142Z full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:57:29.3824422Z 2025-03-04T20:57:29.3824792Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:29.3825252Z to_8: "i64[1000][1]cpu" = full_2.to(device(type='cpu')); full_2 = None 2025-03-04T20:57:29.3825485Z 2025-03-04T20:57:29.3825989Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:57:29.3826649Z topk_3 = l_pred_objectness_logits_3_.topk(1000, dim = 1); l_pred_objectness_logits_3_ = None 2025-03-04T20:57:29.3826974Z topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0] 2025-03-04T20:57:29.3827240Z topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1]; topk_3 = None 2025-03-04T20:57:29.3827465Z 2025-03-04T20:57:29.3827987Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:57:29.3828630Z getitem_94: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T20:57:29.3829034Z topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_94, topk_idx_3)]; proposals_i_8 = getitem_94 = topk_idx_3 = None 2025-03-04T20:57:29.3829371Z 2025-03-04T20:57:29.3829905Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:57:29.3830564Z full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:57:29.3830833Z 2025-03-04T20:57:29.3831212Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:29.3831688Z to_9: "i64[1000][1]cpu" = full_3.to(device(type='cpu')); full_3 = None 2025-03-04T20:57:29.3831927Z 2025-03-04T20:57:29.3832448Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T20:57:29.3833107Z topk_4 = l_pred_objectness_logits_4_.topk(1000, dim = 1); l_pred_objectness_logits_4_ = None 2025-03-04T20:57:29.3833437Z topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0] 2025-03-04T20:57:29.3833709Z topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1]; topk_4 = None 2025-03-04T20:57:29.3833941Z 2025-03-04T20:57:29.3834490Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T20:57:29.3835165Z getitem_98: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T20:57:29.3835608Z topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_98, topk_idx_4)]; proposals_i_9 = getitem_98 = topk_idx_4 = None 2025-03-04T20:57:29.3835956Z 2025-03-04T20:57:29.3836521Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T20:57:29.3837280Z full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T20:57:29.3837576Z 2025-03-04T20:57:29.3837973Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T20:57:29.3838452Z to_10: "i64[1000][1]cpu" = full_4.to(device(type='cpu')); full_4 = None 2025-03-04T20:57:29.3838700Z 2025-03-04T20:57:29.3839064Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:57:29.3839838Z topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1); topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None 2025-03-04T20:57:29.3840351Z 2025-03-04T20:57:29.3840716Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:57:29.3841510Z topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1); topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None 2025-03-04T20:57:29.3842104Z 2025-03-04T20:57:29.3842467Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:57:29.3842985Z level_ids: "i64[5000][1]cpu" = torch.cat([to_6, to_7, to_8, to_9, to_10], 0); to_6 = to_7 = to_8 = to_9 = to_10 = level_ids = None 2025-03-04T20:57:29.3843284Z 2025-03-04T20:57:29.3843770Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T20:57:29.3844342Z getitem_100: "f32[5000, 4][4, 1]cpu" = topk_proposals[0]; topk_proposals = None 2025-03-04T20:57:29.3844599Z 2025-03-04T20:57:29.3845008Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T20:57:29.3845513Z tensor: "f32[5000, 4][4, 1]cpu" = getitem_100.to(torch.float32); getitem_100 = None 2025-03-04T20:57:29.3845769Z 2025-03-04T20:57:29.3846232Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T20:57:29.3846798Z scores_per_img: "f32[5000][1]cpu" = topk_scores[0]; topk_scores = None 2025-03-04T20:57:29.3847046Z 2025-03-04T20:57:29.3847615Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T20:57:29.3848285Z isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor); tensor = None 2025-03-04T20:57:29.3848593Z all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:57:29.3848922Z isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T20:57:29.3849261Z valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T20:57:29.3849509Z 2025-03-04T20:57:29.3849966Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T20:57:29.3850581Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T20:57:29.3850815Z 2025-03-04T20:57:35.5136174Z 2025-03-04T20:57:35.5136767Z class GraphModule(torch.nn.Module): 2025-03-04T20:57:35.5139185Z def forward(self, L_stack0_: "f32[4000, 256, 7, 7][12544, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 12544][12544, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"): 2025-03-04T20:57:35.5141690Z l_stack0_ = L_stack0_ 2025-03-04T20:57:35.5142038Z l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_ 2025-03-04T20:57:35.5142573Z l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_ 2025-03-04T20:57:35.5143044Z l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_ 2025-03-04T20:57:35.5143512Z l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_ 2025-03-04T20:57:35.5144034Z l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_ 2025-03-04T20:57:35.5144648Z l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_ 2025-03-04T20:57:35.5145248Z l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ 2025-03-04T20:57:35.5145814Z l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ 2025-03-04T20:57:35.5146301Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T20:57:35.5146704Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T20:57:35.5147104Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T20:57:35.5147500Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T20:57:35.5147796Z 2025-03-04T20:57:35.5148190Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x) 2025-03-04T20:57:35.5148683Z x: "f32[4000, 12544][12544, 1]cpu" = l_stack0_.flatten(1, -1); l_stack0_ = None 2025-03-04T20:57:35.5149560Z x_1: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_); x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None 2025-03-04T20:57:35.5150267Z x_2: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False); x_1 = None 2025-03-04T20:57:35.5150968Z x_3: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_); x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None 2025-03-04T20:57:35.5151661Z x_4: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False); x_3 = None 2025-03-04T20:57:35.5151932Z 2025-03-04T20:57:35.5152326Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x) 2025-03-04T20:57:35.5153306Z scores: "f32[4000, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_); l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None 2025-03-04T20:57:35.5154038Z 2025-03-04T20:57:35.5154450Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x) 2025-03-04T20:57:35.5155485Z proposal_deltas: "f32[4000, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_); x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None 2025-03-04T20:57:35.5156253Z 2025-03-04T20:57:35.5156799Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:57:35.5157327Z size = l_proposals_0_fields_proposal_boxes_tensor.size() 2025-03-04T20:57:35.5157609Z getitem: "Sym(s0)" = size[0] 2025-03-04T20:57:35.5157830Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T20:57:35.5158097Z size_1 = l_proposals_1_fields_proposal_boxes_tensor.size() 2025-03-04T20:57:35.5158366Z getitem_2: "Sym(s1)" = size_1[0] 2025-03-04T20:57:35.5158596Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T20:57:35.5158864Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T20:57:35.5159122Z getitem_4: "Sym(s2)" = size_2[0] 2025-03-04T20:57:35.5159347Z getitem_5 = size_2[1]; size_2 = getitem_5 = None 2025-03-04T20:57:35.5159606Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T20:57:35.5159849Z getitem_6: "Sym(s3)" = size_3[0] 2025-03-04T20:57:35.5160072Z getitem_7 = size_3[1]; size_3 = getitem_7 = None 2025-03-04T20:57:35.5160281Z 2025-03-04T20:57:35.5160649Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:57:35.5161406Z proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0) 2025-03-04T20:57:35.5161943Z 2025-03-04T20:57:35.5162395Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:57:35.5162963Z deltas: "f32[4000, 320][320, 1]cpu" = proposal_deltas.float(); proposal_deltas = None 2025-03-04T20:57:35.5163231Z 2025-03-04T20:57:35.5163630Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:57:35.5164171Z boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T20:57:35.5164446Z 2025-03-04T20:57:35.5164845Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:57:35.5165340Z getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:57:35.5165651Z getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:57:35.5165972Z widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9; getitem_8 = getitem_9 = None 2025-03-04T20:57:35.5166231Z 2025-03-04T20:57:35.5166626Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:57:35.5167116Z getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:57:35.5167433Z getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:57:35.5167758Z heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T20:57:35.5168021Z 2025-03-04T20:57:35.5168405Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:57:35.5168883Z getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:57:35.5169154Z mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths 2025-03-04T20:57:35.5169433Z ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul; getitem_12 = mul = None 2025-03-04T20:57:35.5169670Z 2025-03-04T20:57:35.5170056Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:57:35.5170560Z getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:57:35.5170849Z mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights 2025-03-04T20:57:35.5171138Z ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1; getitem_13 = mul_1 = None 2025-03-04T20:57:35.5171371Z 2025-03-04T20:57:35.5171806Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:57:35.5172310Z getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:57:35.5172632Z dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0; getitem_14 = None 2025-03-04T20:57:35.5172861Z 2025-03-04T20:57:35.5173241Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:57:35.5173741Z getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:57:35.5174054Z dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0; getitem_15 = None 2025-03-04T20:57:35.5174304Z 2025-03-04T20:57:35.5174684Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:57:35.5175183Z getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:57:35.5175492Z dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0; getitem_16 = None 2025-03-04T20:57:35.5175716Z 2025-03-04T20:57:35.5176103Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:57:35.5176632Z getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:57:35.5176962Z dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0; getitem_17 = None 2025-03-04T20:57:35.5177182Z 2025-03-04T20:57:35.5177602Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:57:35.5178127Z dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:57:35.5178371Z 2025-03-04T20:57:35.5178785Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:57:35.5179294Z dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:57:35.5179533Z 2025-03-04T20:57:35.5179957Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:57:35.5180503Z getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:57:35.5180811Z mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18; dx = getitem_18 = None 2025-03-04T20:57:35.5181133Z getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:57:35.5181470Z pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19; mul_2 = getitem_19 = None 2025-03-04T20:57:35.5181727Z 2025-03-04T20:57:35.5182148Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:57:35.5182678Z getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:57:35.5182995Z mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20; dy = getitem_20 = None 2025-03-04T20:57:35.5183358Z getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:57:35.5183691Z pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21; mul_3 = getitem_21 = None 2025-03-04T20:57:35.5183931Z 2025-03-04T20:57:35.5184354Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:57:35.5184845Z exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:57:35.5185170Z getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:57:35.5185503Z pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22; exp = getitem_22 = None 2025-03-04T20:57:35.5185747Z 2025-03-04T20:57:35.5186157Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:57:35.5186645Z exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:57:35.5186968Z getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:57:35.5187308Z pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23; exp_1 = getitem_23 = None 2025-03-04T20:57:35.5187550Z 2025-03-04T20:57:35.5187935Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:57:35.5188391Z mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T20:57:35.5188644Z x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:57:35.5188871Z 2025-03-04T20:57:35.5189254Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:57:35.5189696Z mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T20:57:35.5189941Z y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:57:35.5190154Z 2025-03-04T20:57:35.5190534Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:57:35.5190993Z mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:57:35.5191278Z x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:57:35.5191514Z 2025-03-04T20:57:35.5191892Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:57:35.5192368Z mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:57:35.5192644Z y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:57:35.5192878Z 2025-03-04T20:57:35.5193302Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:57:35.5193881Z pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:57:35.5194191Z 2025-03-04T20:57:35.5194609Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:57:35.5195168Z predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320)); pred_boxes = None 2025-03-04T20:57:35.5195454Z 2025-03-04T20:57:35.5195918Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T20:57:35.5196678Z split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]); predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None 2025-03-04T20:57:35.5197129Z boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0] 2025-03-04T20:57:35.5197417Z getitem_25: "f32[s1, 320][320, 1]cpu" = split[1]; getitem_25 = None 2025-03-04T20:57:35.5197718Z getitem_26: "f32[s2, 320][320, 1]cpu" = split[2]; getitem_26 = None 2025-03-04T20:57:35.5198012Z getitem_27: "f32[s3, 320][320, 1]cpu" = split[3]; split = getitem_27 = None 2025-03-04T20:57:35.5198251Z 2025-03-04T20:57:35.5198622Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:57:35.5199184Z size_4 = l_proposals_0_fields_proposal_boxes_tensor.size(); l_proposals_0_fields_proposal_boxes_tensor = None 2025-03-04T20:57:35.5199534Z getitem_28: "Sym(s0)" = size_4[0] 2025-03-04T20:57:35.5199772Z getitem_29 = size_4[1]; size_4 = getitem_29 = None 2025-03-04T20:57:35.5200139Z size_5 = l_proposals_1_fields_proposal_boxes_tensor.size(); l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T20:57:35.5200483Z getitem_30: "Sym(s1)" = size_5[0] 2025-03-04T20:57:35.5200714Z getitem_31 = size_5[1]; size_5 = getitem_31 = None 2025-03-04T20:57:35.5201074Z size_6 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T20:57:35.5201415Z getitem_32: "Sym(s2)" = size_6[0] 2025-03-04T20:57:35.5201642Z getitem_33 = size_6[1]; size_6 = getitem_33 = None 2025-03-04T20:57:35.5202304Z size_7 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T20:57:35.5202659Z getitem_34: "Sym(s3)" = size_7[0] 2025-03-04T20:57:35.5202887Z getitem_35 = size_7[1]; size_7 = getitem_35 = None 2025-03-04T20:57:35.5203099Z 2025-03-04T20:57:35.5203505Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T20:57:35.5204059Z probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1); scores = None 2025-03-04T20:57:35.5204335Z 2025-03-04T20:57:35.5204767Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T20:57:35.5205410Z split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0); probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None 2025-03-04T20:57:35.5205862Z scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0] 2025-03-04T20:57:35.5206138Z getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1]; getitem_37 = None 2025-03-04T20:57:35.5206421Z getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2]; getitem_38 = None 2025-03-04T20:57:35.5206712Z getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_39 = None 2025-03-04T20:57:35.5206955Z 2025-03-04T20:57:35.5207493Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T20:57:35.5208197Z isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T20:57:35.5208523Z all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:57:35.5208845Z isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T20:57:35.5209166Z all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T20:57:35.5209472Z valid_mask: "b8[s0][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T20:57:35.5209705Z 2025-03-04T20:57:35.5210164Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T20:57:35.5210668Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T20:57:35.5210895Z 2025-03-04T20:57:35.5215215Z 2025-03-04T20:57:35.5215569Z class GraphModule(torch.nn.Module): 2025-03-04T20:57:35.5217653Z def forward(self, L_stack0_: "f32[4000, 256, 7, 7][12544, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 12544][12544, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"): 2025-03-04T20:57:35.5219711Z l_stack0_ = L_stack0_ 2025-03-04T20:57:35.5220056Z l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_ 2025-03-04T20:57:35.5220532Z l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_ 2025-03-04T20:57:35.5221009Z l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_ 2025-03-04T20:57:35.5221478Z l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_ 2025-03-04T20:57:35.5221992Z l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_ 2025-03-04T20:57:35.5222550Z l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_ 2025-03-04T20:57:35.5223109Z l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ 2025-03-04T20:57:35.5223791Z l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ 2025-03-04T20:57:35.5224255Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T20:57:35.5224651Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T20:57:35.5225039Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T20:57:35.5225424Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T20:57:35.5225746Z 2025-03-04T20:57:35.5226135Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x) 2025-03-04T20:57:35.5229598Z x: "f32[4000, 12544][12544, 1]cpu" = l_stack0_.flatten(1, -1); l_stack0_ = None 2025-03-04T20:57:35.5230404Z x_1: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_); x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None 2025-03-04T20:57:35.5231159Z x_2: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False); x_1 = None 2025-03-04T20:57:35.5231902Z x_3: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_); x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None 2025-03-04T20:57:35.5232625Z x_4: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False); x_3 = None 2025-03-04T20:57:35.5232895Z 2025-03-04T20:57:35.5233307Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x) 2025-03-04T20:57:35.5234278Z scores: "f32[4000, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_); l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None 2025-03-04T20:57:35.5234992Z 2025-03-04T20:57:35.5235406Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x) 2025-03-04T20:57:35.5236406Z proposal_deltas: "f32[4000, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_); x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None 2025-03-04T20:57:35.5237251Z 2025-03-04T20:57:35.5237625Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:57:35.5238082Z size = l_proposals_0_fields_proposal_boxes_tensor.size() 2025-03-04T20:57:35.5238329Z getitem: "Sym(s0)" = size[0] 2025-03-04T20:57:35.5238557Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T20:57:35.5238827Z size_1 = l_proposals_1_fields_proposal_boxes_tensor.size() 2025-03-04T20:57:35.5239071Z getitem_2: "Sym(s1)" = size_1[0] 2025-03-04T20:57:35.5239306Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T20:57:35.5239572Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T20:57:35.5239814Z getitem_4: "Sym(s2)" = size_2[0] 2025-03-04T20:57:35.5240065Z getitem_5 = size_2[1]; size_2 = getitem_5 = None 2025-03-04T20:57:35.5240325Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T20:57:35.5240563Z getitem_6: "Sym(s3)" = size_3[0] 2025-03-04T20:57:35.5240786Z getitem_7 = size_3[1]; size_3 = getitem_7 = None 2025-03-04T20:57:35.5240994Z 2025-03-04T20:57:35.5241363Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:57:35.5242127Z proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0) 2025-03-04T20:57:35.5242708Z 2025-03-04T20:57:35.5243163Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:57:35.5243738Z deltas: "f32[4000, 320][320, 1]cpu" = proposal_deltas.float(); proposal_deltas = None 2025-03-04T20:57:35.5244005Z 2025-03-04T20:57:35.5244415Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:57:35.5244956Z boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T20:57:35.5245235Z 2025-03-04T20:57:35.5245636Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:57:35.5246142Z getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:57:35.5246455Z getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:57:35.5246782Z widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9; getitem_8 = getitem_9 = None 2025-03-04T20:57:35.5247044Z 2025-03-04T20:57:35.5247450Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:57:35.5247951Z getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:57:35.5248264Z getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:57:35.5248601Z heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T20:57:35.5248872Z 2025-03-04T20:57:35.5249269Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:57:35.5249762Z getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:57:35.5250039Z mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths 2025-03-04T20:57:35.5250310Z ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul; getitem_12 = mul = None 2025-03-04T20:57:35.5250555Z 2025-03-04T20:57:35.5250951Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:57:35.5251491Z getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:57:35.5251791Z mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights 2025-03-04T20:57:35.5252070Z ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1; getitem_13 = mul_1 = None 2025-03-04T20:57:35.5252312Z 2025-03-04T20:57:35.5252737Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:57:35.5253255Z getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:57:35.5253567Z dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0; getitem_14 = None 2025-03-04T20:57:35.5253794Z 2025-03-04T20:57:35.5254160Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:57:35.5254658Z getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:57:35.5254961Z dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0; getitem_15 = None 2025-03-04T20:57:35.5255200Z 2025-03-04T20:57:35.5255573Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:57:35.5256058Z getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:57:35.5256366Z dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0; getitem_16 = None 2025-03-04T20:57:35.5256589Z 2025-03-04T20:57:35.5256982Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:57:35.5257522Z getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:57:35.5257854Z dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0; getitem_17 = None 2025-03-04T20:57:35.5258077Z 2025-03-04T20:57:35.5258486Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:57:35.5259003Z dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:57:35.5259249Z 2025-03-04T20:57:35.5259651Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:57:35.5260158Z dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:57:35.5260396Z 2025-03-04T20:57:35.5260813Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:57:35.5261339Z getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:57:35.5261651Z mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18; dx = getitem_18 = None 2025-03-04T20:57:35.5261976Z getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:57:35.5262312Z pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19; mul_2 = getitem_19 = None 2025-03-04T20:57:35.5262561Z 2025-03-04T20:57:35.5262984Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:57:35.5263511Z getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:57:35.5263823Z mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20; dy = getitem_20 = None 2025-03-04T20:57:35.5264142Z getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:57:35.5264476Z pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21; mul_3 = getitem_21 = None 2025-03-04T20:57:35.5264726Z 2025-03-04T20:57:35.5265133Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:57:35.5265637Z exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:57:35.5265954Z getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:57:35.5266288Z pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22; exp = getitem_22 = None 2025-03-04T20:57:35.5266531Z 2025-03-04T20:57:35.5266941Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:57:35.5267497Z exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:57:35.5267819Z getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:57:35.5268155Z pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23; exp_1 = getitem_23 = None 2025-03-04T20:57:35.5268395Z 2025-03-04T20:57:35.5268779Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:57:35.5269238Z mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T20:57:35.5269495Z x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:57:35.5269723Z 2025-03-04T20:57:35.5270120Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:57:35.5270578Z mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T20:57:35.5270829Z y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:57:35.5271053Z 2025-03-04T20:57:35.5271432Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:57:35.5271903Z mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:57:35.5272175Z x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:57:35.5272413Z 2025-03-04T20:57:35.5272791Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:57:35.5273257Z mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:57:35.5273535Z y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:57:35.5273771Z 2025-03-04T20:57:35.5274193Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:57:35.5274773Z pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:57:35.5275063Z 2025-03-04T20:57:35.5275488Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:57:35.5276044Z predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320)); pred_boxes = None 2025-03-04T20:57:35.5276324Z 2025-03-04T20:57:35.5276851Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T20:57:35.5277538Z split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]); predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None 2025-03-04T20:57:35.5277964Z boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0] 2025-03-04T20:57:35.5278289Z getitem_25: "f32[s1, 320][320, 1]cpu" = split[1]; getitem_25 = None 2025-03-04T20:57:35.5278572Z getitem_26: "f32[s2, 320][320, 1]cpu" = split[2]; getitem_26 = None 2025-03-04T20:57:35.5278865Z getitem_27: "f32[s3, 320][320, 1]cpu" = split[3]; split = getitem_27 = None 2025-03-04T20:57:35.5279107Z 2025-03-04T20:57:35.5279472Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:57:35.5280018Z size_4 = l_proposals_0_fields_proposal_boxes_tensor.size(); l_proposals_0_fields_proposal_boxes_tensor = None 2025-03-04T20:57:35.5280376Z getitem_28: "Sym(s0)" = size_4[0] 2025-03-04T20:57:35.5280608Z getitem_29 = size_4[1]; size_4 = getitem_29 = None 2025-03-04T20:57:35.5280961Z size_5 = l_proposals_1_fields_proposal_boxes_tensor.size(); l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T20:57:35.5281298Z getitem_30: "Sym(s1)" = size_5[0] 2025-03-04T20:57:35.5281532Z getitem_31 = size_5[1]; size_5 = getitem_31 = None 2025-03-04T20:57:35.5281878Z size_6 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T20:57:35.5282314Z getitem_32: "Sym(s2)" = size_6[0] 2025-03-04T20:57:35.5282539Z getitem_33 = size_6[1]; size_6 = getitem_33 = None 2025-03-04T20:57:35.5282905Z size_7 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T20:57:35.5283234Z getitem_34: "Sym(s3)" = size_7[0] 2025-03-04T20:57:35.5283457Z getitem_35 = size_7[1]; size_7 = getitem_35 = None 2025-03-04T20:57:35.5283667Z 2025-03-04T20:57:35.5284078Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T20:57:35.5284620Z probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1); scores = None 2025-03-04T20:57:35.5284899Z 2025-03-04T20:57:35.5285330Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T20:57:35.5285978Z split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0); probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None 2025-03-04T20:57:35.5286375Z scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0] 2025-03-04T20:57:35.5286646Z getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1]; getitem_37 = None 2025-03-04T20:57:35.5286929Z getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2]; getitem_38 = None 2025-03-04T20:57:35.5287207Z getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_39 = None 2025-03-04T20:57:35.5287445Z 2025-03-04T20:57:35.5287979Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T20:57:35.5288649Z isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T20:57:35.5288969Z all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:57:35.5289288Z isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T20:57:35.5289611Z all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T20:57:35.5289886Z valid_mask: "b8[s0][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T20:57:35.5290110Z 2025-03-04T20:57:35.5290535Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T20:57:35.5291074Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T20:57:35.5291298Z 2025-03-04T20:57:35.5291399Z 2025-03-04T20:57:35.5294598Z class GraphModule(torch.nn.Module): 2025-03-04T20:57:35.5296726Z def forward(self, L_stack0_: "f32[4000, 256, 7, 7][12544, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 12544][12544, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"): 2025-03-04T20:57:35.5298840Z l_stack0_ = L_stack0_ 2025-03-04T20:57:35.5299227Z l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_ 2025-03-04T20:57:35.5299721Z l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_ 2025-03-04T20:57:35.5300216Z l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_ 2025-03-04T20:57:35.5300705Z l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_ 2025-03-04T20:57:35.5301255Z l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_ 2025-03-04T20:57:35.5301858Z l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_ 2025-03-04T20:57:35.5303058Z l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ 2025-03-04T20:57:35.5303607Z l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ 2025-03-04T20:57:35.5304063Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T20:57:35.5304451Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T20:57:35.5304832Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T20:57:35.5305210Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T20:57:35.5305491Z 2025-03-04T20:57:35.5306068Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x) 2025-03-04T20:57:35.5306524Z x: "f32[4000, 12544][12544, 1]cpu" = l_stack0_.flatten(1, -1); l_stack0_ = None 2025-03-04T20:57:35.5307205Z x_1: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_); x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None 2025-03-04T20:57:35.5307909Z x_2: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False); x_1 = None 2025-03-04T20:57:35.5308607Z x_3: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_); x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None 2025-03-04T20:57:35.5309393Z x_4: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False); x_3 = None 2025-03-04T20:57:35.5309667Z 2025-03-04T20:57:35.5310058Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x) 2025-03-04T20:57:35.5311000Z scores: "f32[4000, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_); l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None 2025-03-04T20:57:35.5311746Z 2025-03-04T20:57:35.5312158Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x) 2025-03-04T20:57:35.5313209Z proposal_deltas: "f32[4000, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_); x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None 2025-03-04T20:57:35.5313943Z 2025-03-04T20:57:35.5314313Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:57:35.5314775Z size = l_proposals_0_fields_proposal_boxes_tensor.size() 2025-03-04T20:57:35.5315023Z getitem: "Sym(s0)" = size[0] 2025-03-04T20:57:35.5315250Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T20:57:35.5315516Z size_1 = l_proposals_1_fields_proposal_boxes_tensor.size() 2025-03-04T20:57:35.5315761Z getitem_2: "Sym(s1)" = size_1[0] 2025-03-04T20:57:35.5315992Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T20:57:35.5316259Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T20:57:35.5316565Z getitem_4: "Sym(s2)" = size_2[0] 2025-03-04T20:57:35.5316802Z getitem_5 = size_2[1]; size_2 = getitem_5 = None 2025-03-04T20:57:35.5317063Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T20:57:35.5317298Z getitem_6: "Sym(s3)" = size_3[0] 2025-03-04T20:57:35.5317534Z getitem_7 = size_3[1]; size_3 = getitem_7 = None 2025-03-04T20:57:35.5317743Z 2025-03-04T20:57:35.5318107Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:57:35.5318870Z proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0) 2025-03-04T20:57:35.5319409Z 2025-03-04T20:57:35.5319862Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:57:35.5320429Z deltas: "f32[4000, 320][320, 1]cpu" = proposal_deltas.float(); proposal_deltas = None 2025-03-04T20:57:35.5320693Z 2025-03-04T20:57:35.5321081Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:57:35.5321601Z boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T20:57:35.5321876Z 2025-03-04T20:57:35.5322291Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:57:35.5322783Z getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:57:35.5323090Z getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:57:35.5323411Z widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9; getitem_8 = getitem_9 = None 2025-03-04T20:57:35.5323666Z 2025-03-04T20:57:35.5324086Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:57:35.5324591Z getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:57:35.5324890Z getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:57:35.5325213Z heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T20:57:35.5325473Z 2025-03-04T20:57:35.5325872Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:57:35.5326350Z getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:57:35.5326635Z mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths 2025-03-04T20:57:35.5326902Z ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul; getitem_12 = mul = None 2025-03-04T20:57:35.5327141Z 2025-03-04T20:57:35.5327529Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:57:35.5328028Z getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:57:35.5328321Z mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights 2025-03-04T20:57:35.5328593Z ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1; getitem_13 = mul_1 = None 2025-03-04T20:57:35.5328834Z 2025-03-04T20:57:35.5329232Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:57:35.5329725Z getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:57:35.5330037Z dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0; getitem_14 = None 2025-03-04T20:57:35.5330264Z 2025-03-04T20:57:35.5330636Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:57:35.5331118Z getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:57:35.5331425Z dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0; getitem_15 = None 2025-03-04T20:57:35.5331641Z 2025-03-04T20:57:35.5332329Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:57:35.5333272Z getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:57:35.5333600Z dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0; getitem_16 = None 2025-03-04T20:57:35.5333830Z 2025-03-04T20:57:35.5334223Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:57:35.5334765Z getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:57:35.5335153Z dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0; getitem_17 = None 2025-03-04T20:57:35.5335372Z 2025-03-04T20:57:35.5335780Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:57:35.5336293Z dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:57:35.5336538Z 2025-03-04T20:57:35.5336989Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:57:35.5337531Z dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:57:35.5337778Z 2025-03-04T20:57:35.5338205Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:57:35.5338755Z getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:57:35.5339064Z mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18; dx = getitem_18 = None 2025-03-04T20:57:35.5339416Z getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:57:35.5339797Z pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19; mul_2 = getitem_19 = None 2025-03-04T20:57:35.5340054Z 2025-03-04T20:57:35.5340491Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:57:35.5341039Z getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:57:35.5341354Z mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20; dy = getitem_20 = None 2025-03-04T20:57:35.5341679Z getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:57:35.5342021Z pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21; mul_3 = getitem_21 = None 2025-03-04T20:57:35.5342273Z 2025-03-04T20:57:35.5342695Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:57:35.5343200Z exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:57:35.5343527Z getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:57:35.5343870Z pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22; exp = getitem_22 = None 2025-03-04T20:57:35.5344117Z 2025-03-04T20:57:35.5344543Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:57:35.5345044Z exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:57:35.5345377Z getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:57:35.5345723Z pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23; exp_1 = getitem_23 = None 2025-03-04T20:57:35.5345967Z 2025-03-04T20:57:35.5346369Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:57:35.5346828Z mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T20:57:35.5347081Z x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:57:35.5347310Z 2025-03-04T20:57:35.5347701Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:57:35.5348172Z mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T20:57:35.5348424Z y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:57:35.5348663Z 2025-03-04T20:57:35.5349043Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:57:35.5349509Z mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:57:35.5349792Z x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:57:35.5350039Z 2025-03-04T20:57:35.5350412Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:57:35.5350867Z mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:57:35.5351147Z y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:57:35.5351379Z 2025-03-04T20:57:35.5351815Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:57:35.5352403Z pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:57:35.5352685Z 2025-03-04T20:57:35.5353091Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:57:35.5353635Z predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320)); pred_boxes = None 2025-03-04T20:57:35.5353910Z 2025-03-04T20:57:35.5354339Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T20:57:35.5355004Z split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]); predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None 2025-03-04T20:57:35.5355413Z boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0] 2025-03-04T20:57:35.5355689Z getitem_25: "f32[s1, 320][320, 1]cpu" = split[1]; getitem_25 = None 2025-03-04T20:57:35.5355967Z getitem_26: "f32[s2, 320][320, 1]cpu" = split[2]; getitem_26 = None 2025-03-04T20:57:35.5356259Z getitem_27: "f32[s3, 320][320, 1]cpu" = split[3]; split = getitem_27 = None 2025-03-04T20:57:35.5356570Z 2025-03-04T20:57:35.5356951Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:57:35.5357515Z size_4 = l_proposals_0_fields_proposal_boxes_tensor.size(); l_proposals_0_fields_proposal_boxes_tensor = None 2025-03-04T20:57:35.5357857Z getitem_28: "Sym(s0)" = size_4[0] 2025-03-04T20:57:35.5358093Z getitem_29 = size_4[1]; size_4 = getitem_29 = None 2025-03-04T20:57:35.5358448Z size_5 = l_proposals_1_fields_proposal_boxes_tensor.size(); l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T20:57:35.5358782Z getitem_30: "Sym(s1)" = size_5[0] 2025-03-04T20:57:35.5359006Z getitem_31 = size_5[1]; size_5 = getitem_31 = None 2025-03-04T20:57:35.5359357Z size_6 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T20:57:35.5359684Z getitem_32: "Sym(s2)" = size_6[0] 2025-03-04T20:57:35.5359907Z getitem_33 = size_6[1]; size_6 = getitem_33 = None 2025-03-04T20:57:35.5360253Z size_7 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T20:57:35.5360601Z getitem_34: "Sym(s3)" = size_7[0] 2025-03-04T20:57:35.5360826Z getitem_35 = size_7[1]; size_7 = getitem_35 = None 2025-03-04T20:57:35.5361037Z 2025-03-04T20:57:35.5361452Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T20:57:35.5361998Z probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1); scores = None 2025-03-04T20:57:35.5362278Z 2025-03-04T20:57:35.5362712Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T20:57:35.5363382Z split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0); probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None 2025-03-04T20:57:35.5363784Z scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0] 2025-03-04T20:57:35.5364058Z getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1]; getitem_37 = None 2025-03-04T20:57:35.5364339Z getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2]; getitem_38 = None 2025-03-04T20:57:35.5364644Z getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_39 = None 2025-03-04T20:57:35.5364884Z 2025-03-04T20:57:35.5365436Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T20:57:35.5366100Z isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T20:57:35.5366422Z all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:57:35.5366737Z isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T20:57:35.5367064Z all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T20:57:35.5367339Z valid_mask: "b8[s0][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T20:57:35.5367567Z 2025-03-04T20:57:35.5367994Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T20:57:35.5368498Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T20:57:35.5368727Z 2025-03-04T20:57:35.5369001Z 2025-03-04T20:57:35.5374224Z class GraphModule(torch.nn.Module): 2025-03-04T20:57:35.5380536Z def forward(self, L_stack0_: "f32[4000, 256, 7, 7][12544, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 12544][12544, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"): 2025-03-04T20:57:35.5382577Z l_stack0_ = L_stack0_ 2025-03-04T20:57:35.5382935Z l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_ 2025-03-04T20:57:35.5383568Z l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_ 2025-03-04T20:57:35.5384074Z l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_ 2025-03-04T20:57:35.5384566Z l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_ 2025-03-04T20:57:35.5385131Z l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_ 2025-03-04T20:57:35.5385752Z l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_ 2025-03-04T20:57:35.5386413Z l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ 2025-03-04T20:57:35.5387029Z l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ 2025-03-04T20:57:35.5387555Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T20:57:35.5388005Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T20:57:35.5388419Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T20:57:35.5388852Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T20:57:35.5389168Z 2025-03-04T20:57:35.5389575Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x) 2025-03-04T20:57:35.5390069Z x: "f32[4000, 12544][12544, 1]cpu" = l_stack0_.flatten(1, -1); l_stack0_ = None 2025-03-04T20:57:35.5390796Z x_1: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_); x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None 2025-03-04T20:57:35.5391496Z x_2: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False); x_1 = None 2025-03-04T20:57:35.5392194Z x_3: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_); x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None 2025-03-04T20:57:35.5392885Z x_4: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False); x_3 = None 2025-03-04T20:57:35.5393153Z 2025-03-04T20:57:35.5393542Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x) 2025-03-04T20:57:35.5394486Z scores: "f32[4000, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_); l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None 2025-03-04T20:57:35.5395177Z 2025-03-04T20:57:35.5395581Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x) 2025-03-04T20:57:35.5396673Z proposal_deltas: "f32[4000, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_); x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None 2025-03-04T20:57:35.5397430Z 2025-03-04T20:57:35.5397796Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:57:35.5398247Z size = l_proposals_0_fields_proposal_boxes_tensor.size() 2025-03-04T20:57:35.5398497Z getitem: "Sym(s0)" = size[0] 2025-03-04T20:57:35.5398722Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T20:57:35.5398999Z size_1 = l_proposals_1_fields_proposal_boxes_tensor.size() 2025-03-04T20:57:35.5399256Z getitem_2: "Sym(s1)" = size_1[0] 2025-03-04T20:57:35.5399498Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T20:57:35.5399790Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T20:57:35.5400039Z getitem_4: "Sym(s2)" = size_2[0] 2025-03-04T20:57:35.5400272Z getitem_5 = size_2[1]; size_2 = getitem_5 = None 2025-03-04T20:57:35.5400537Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T20:57:35.5400790Z getitem_6: "Sym(s3)" = size_3[0] 2025-03-04T20:57:35.5401013Z getitem_7 = size_3[1]; size_3 = getitem_7 = None 2025-03-04T20:57:35.5401215Z 2025-03-04T20:57:35.5401619Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:57:35.5402638Z proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0) 2025-03-04T20:57:35.5403203Z 2025-03-04T20:57:35.5403655Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:57:35.5404228Z deltas: "f32[4000, 320][320, 1]cpu" = proposal_deltas.float(); proposal_deltas = None 2025-03-04T20:57:35.5404499Z 2025-03-04T20:57:35.5404897Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:57:35.5405426Z boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T20:57:35.5405698Z 2025-03-04T20:57:35.5406095Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:57:35.5406592Z getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:57:35.5406897Z getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:57:35.5407213Z widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9; getitem_8 = getitem_9 = None 2025-03-04T20:57:35.5407470Z 2025-03-04T20:57:35.5407865Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:57:35.5408365Z getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:57:35.5408668Z getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:57:35.5408990Z heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T20:57:35.5409247Z 2025-03-04T20:57:35.5409638Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:57:35.5410121Z getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:57:35.5410390Z mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths 2025-03-04T20:57:35.5410678Z ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul; getitem_12 = mul = None 2025-03-04T20:57:35.5410914Z 2025-03-04T20:57:35.5411293Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:57:35.5411791Z getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:57:35.5412083Z mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights 2025-03-04T20:57:35.5412358Z ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1; getitem_13 = mul_1 = None 2025-03-04T20:57:35.5412621Z 2025-03-04T20:57:35.5413014Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:57:35.5413509Z getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:57:35.5413826Z dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0; getitem_14 = None 2025-03-04T20:57:35.5414055Z 2025-03-04T20:57:35.5414473Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:57:35.5414974Z getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:57:35.5415311Z dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0; getitem_15 = None 2025-03-04T20:57:35.5415539Z 2025-03-04T20:57:35.5415919Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:57:35.5416415Z getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:57:35.5416725Z dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0; getitem_16 = None 2025-03-04T20:57:35.5416951Z 2025-03-04T20:57:35.5417330Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:57:35.5417858Z getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:57:35.5418189Z dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0; getitem_17 = None 2025-03-04T20:57:35.5418414Z 2025-03-04T20:57:35.5418834Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:57:35.5419352Z dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:57:35.5419601Z 2025-03-04T20:57:35.5420011Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:57:35.5420520Z dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:57:35.5420763Z 2025-03-04T20:57:35.5421184Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:57:35.5421720Z getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:57:35.5422030Z mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18; dx = getitem_18 = None 2025-03-04T20:57:35.5422358Z getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:57:35.5422693Z pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19; mul_2 = getitem_19 = None 2025-03-04T20:57:35.5422960Z 2025-03-04T20:57:35.5423383Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:57:35.5423918Z getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:57:35.5424232Z mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20; dy = getitem_20 = None 2025-03-04T20:57:35.5424557Z getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:57:35.5424896Z pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21; mul_3 = getitem_21 = None 2025-03-04T20:57:35.5425162Z 2025-03-04T20:57:35.5425573Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:57:35.5426063Z exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:57:35.5426382Z getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:57:35.5426737Z pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22; exp = getitem_22 = None 2025-03-04T20:57:35.5426979Z 2025-03-04T20:57:35.5427409Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:57:35.5427898Z exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:57:35.5428222Z getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:57:35.5428562Z pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23; exp_1 = getitem_23 = None 2025-03-04T20:57:35.5428803Z 2025-03-04T20:57:35.5429190Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:57:35.5429648Z mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T20:57:35.5429905Z x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:57:35.5430128Z 2025-03-04T20:57:35.5430521Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:57:35.5430973Z mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T20:57:35.5431222Z y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:57:35.5431449Z 2025-03-04T20:57:35.5431831Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:57:35.5432297Z mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:57:35.5432580Z x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:57:35.5432816Z 2025-03-04T20:57:35.5433198Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:57:35.5433660Z mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:57:35.5433939Z y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:57:35.5434174Z 2025-03-04T20:57:35.5434599Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:57:35.5435170Z pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:57:35.5435468Z 2025-03-04T20:57:35.5435877Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:57:35.5436419Z predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320)); pred_boxes = None 2025-03-04T20:57:35.5436775Z 2025-03-04T20:57:35.5437216Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T20:57:35.5437879Z split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]); predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None 2025-03-04T20:57:35.5438317Z boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0] 2025-03-04T20:57:35.5438594Z getitem_25: "f32[s1, 320][320, 1]cpu" = split[1]; getitem_25 = None 2025-03-04T20:57:35.5438881Z getitem_26: "f32[s2, 320][320, 1]cpu" = split[2]; getitem_26 = None 2025-03-04T20:57:35.5439178Z getitem_27: "f32[s3, 320][320, 1]cpu" = split[3]; split = getitem_27 = None 2025-03-04T20:57:35.5439412Z 2025-03-04T20:57:35.5439802Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:57:35.5440367Z size_4 = l_proposals_0_fields_proposal_boxes_tensor.size(); l_proposals_0_fields_proposal_boxes_tensor = None 2025-03-04T20:57:35.5440709Z getitem_28: "Sym(s0)" = size_4[0] 2025-03-04T20:57:35.5440939Z getitem_29 = size_4[1]; size_4 = getitem_29 = None 2025-03-04T20:57:35.5441294Z size_5 = l_proposals_1_fields_proposal_boxes_tensor.size(); l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T20:57:35.5441628Z getitem_30: "Sym(s1)" = size_5[0] 2025-03-04T20:57:35.5441853Z getitem_31 = size_5[1]; size_5 = getitem_31 = None 2025-03-04T20:57:35.5442204Z size_6 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T20:57:35.5442536Z getitem_32: "Sym(s2)" = size_6[0] 2025-03-04T20:57:35.5442759Z getitem_33 = size_6[1]; size_6 = getitem_33 = None 2025-03-04T20:57:35.5443107Z size_7 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T20:57:35.5443436Z getitem_34: "Sym(s3)" = size_7[0] 2025-03-04T20:57:35.5443656Z getitem_35 = size_7[1]; size_7 = getitem_35 = None 2025-03-04T20:57:35.5443865Z 2025-03-04T20:57:35.5444271Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T20:57:35.5444817Z probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1); scores = None 2025-03-04T20:57:35.5445095Z 2025-03-04T20:57:35.5445538Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T20:57:35.5446199Z split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0); probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None 2025-03-04T20:57:35.5446601Z scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0] 2025-03-04T20:57:35.5446875Z getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1]; getitem_37 = None 2025-03-04T20:57:35.5447157Z getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2]; getitem_38 = None 2025-03-04T20:57:35.5447450Z getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_39 = None 2025-03-04T20:57:35.5447691Z 2025-03-04T20:57:35.5448227Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T20:57:35.5448912Z isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T20:57:35.5449236Z all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:57:35.5449558Z isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T20:57:35.5449884Z all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T20:57:35.5450168Z valid_mask: "b8[s0][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T20:57:35.5450395Z 2025-03-04T20:57:35.5450856Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T20:57:35.5451366Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T20:57:35.5451596Z 2025-03-04T20:57:36.0063329Z 2025-03-04T20:57:36.0063826Z class GraphModule(torch.nn.Module): 2025-03-04T20:57:36.0065329Z def forward(self, L_predictions_0_: "f32[4000, 81][81, 1]cpu", L_predictions_1_: "f32[4000, 320][320, 1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"): 2025-03-04T20:57:36.0066298Z l_predictions_0_ = L_predictions_0_ 2025-03-04T20:57:36.0066524Z l_predictions_1_ = L_predictions_1_ 2025-03-04T20:57:36.0066839Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T20:57:36.0067238Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T20:57:36.0067640Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T20:57:36.0068034Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T20:57:36.0068326Z 2025-03-04T20:57:36.0068742Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:57:36.0069200Z size = l_proposals_0_fields_proposal_boxes_tensor.size() 2025-03-04T20:57:36.0069446Z getitem: "Sym(s0)" = size[0] 2025-03-04T20:57:36.0069670Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T20:57:36.0069941Z size_1 = l_proposals_1_fields_proposal_boxes_tensor.size() 2025-03-04T20:57:36.0070191Z getitem_2: "Sym(s1)" = size_1[0] 2025-03-04T20:57:36.0070423Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T20:57:36.0070687Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T20:57:36.0070932Z getitem_4: "Sym(s2)" = size_2[0] 2025-03-04T20:57:36.0071158Z getitem_5 = size_2[1]; size_2 = getitem_5 = None 2025-03-04T20:57:36.0071451Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T20:57:36.0071695Z getitem_6: "Sym(s3)" = size_3[0] 2025-03-04T20:57:36.0071914Z getitem_7 = size_3[1]; size_3 = getitem_7 = None 2025-03-04T20:57:36.0072126Z 2025-03-04T20:57:36.0072498Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:57:36.0073281Z proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0) 2025-03-04T20:57:36.0073830Z 2025-03-04T20:57:36.0074292Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:57:36.0074931Z deltas: "f32[4000, 320][320, 1]cpu" = l_predictions_1_.float(); l_predictions_1_ = None 2025-03-04T20:57:36.0075199Z 2025-03-04T20:57:36.0075606Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:57:36.0076143Z boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T20:57:36.0076427Z 2025-03-04T20:57:36.0077046Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:57:36.0077549Z getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:57:36.0077869Z getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:57:36.0078196Z widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9; getitem_8 = getitem_9 = None 2025-03-04T20:57:36.0078451Z 2025-03-04T20:57:36.0078883Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:57:36.0079405Z getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:57:36.0079713Z getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:57:36.0080047Z heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T20:57:36.0080313Z 2025-03-04T20:57:36.0080706Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:57:36.0081198Z getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:57:36.0081477Z mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths 2025-03-04T20:57:36.0081752Z ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul; getitem_12 = mul = None 2025-03-04T20:57:36.0081995Z 2025-03-04T20:57:36.0082394Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:57:36.0082909Z getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:57:36.0083212Z mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights 2025-03-04T20:57:36.0083494Z ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1; getitem_13 = mul_1 = None 2025-03-04T20:57:36.0083740Z 2025-03-04T20:57:36.0084166Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:57:36.0084674Z getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:57:36.0084997Z dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0; getitem_14 = None 2025-03-04T20:57:36.0085228Z 2025-03-04T20:57:36.0085610Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:57:36.0086113Z getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:57:36.0086441Z dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0; getitem_15 = None 2025-03-04T20:57:36.0086663Z 2025-03-04T20:57:36.0087034Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:57:36.0087547Z getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:57:36.0087852Z dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0; getitem_16 = None 2025-03-04T20:57:36.0088076Z 2025-03-04T20:57:36.0088453Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:57:36.0088976Z getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:57:36.0089308Z dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0; getitem_17 = None 2025-03-04T20:57:36.0089545Z 2025-03-04T20:57:36.0089954Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:57:36.0090468Z dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:57:36.0090714Z 2025-03-04T20:57:36.0091142Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:57:36.0091648Z dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:57:36.0091890Z 2025-03-04T20:57:36.0092326Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:57:36.0092862Z getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:57:36.0093170Z mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18; dx = getitem_18 = None 2025-03-04T20:57:36.0093490Z getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:57:36.0093824Z pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19; mul_2 = getitem_19 = None 2025-03-04T20:57:36.0094065Z 2025-03-04T20:57:36.0094484Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:57:36.0095011Z getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:57:36.0095317Z mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20; dy = getitem_20 = None 2025-03-04T20:57:36.0095634Z getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:57:36.0095964Z pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21; mul_3 = getitem_21 = None 2025-03-04T20:57:36.0096206Z 2025-03-04T20:57:36.0096615Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:57:36.0097105Z exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:57:36.0097423Z getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:57:36.0097786Z pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22; exp = getitem_22 = None 2025-03-04T20:57:36.0098026Z 2025-03-04T20:57:36.0098434Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:57:36.0098921Z exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:57:36.0099251Z getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:57:36.0099607Z pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23; exp_1 = getitem_23 = None 2025-03-04T20:57:36.0099850Z 2025-03-04T20:57:36.0100245Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:57:36.0100698Z mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T20:57:36.0100952Z x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:57:36.0101179Z 2025-03-04T20:57:36.0101564Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:57:36.0102234Z mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T20:57:36.0102481Z y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:57:36.0102707Z 2025-03-04T20:57:36.0103096Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:57:36.0103561Z mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:57:36.0103893Z x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:57:36.0104135Z 2025-03-04T20:57:36.0104540Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:57:36.0105003Z mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:57:36.0105284Z y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:57:36.0105522Z 2025-03-04T20:57:36.0105945Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:57:36.0106518Z pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:57:36.0106804Z 2025-03-04T20:57:36.0107215Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:57:36.0107760Z predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320)); pred_boxes = None 2025-03-04T20:57:36.0108037Z 2025-03-04T20:57:36.0108475Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T20:57:36.0109147Z split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]); predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None 2025-03-04T20:57:36.0109562Z boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0] 2025-03-04T20:57:36.0109835Z getitem_25: "f32[s1, 320][320, 1]cpu" = split[1]; getitem_25 = None 2025-03-04T20:57:36.0110120Z getitem_26: "f32[s2, 320][320, 1]cpu" = split[2]; getitem_26 = None 2025-03-04T20:57:36.0110409Z getitem_27: "f32[s3, 320][320, 1]cpu" = split[3]; split = getitem_27 = None 2025-03-04T20:57:36.0110647Z 2025-03-04T20:57:36.0111016Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:57:36.0111562Z size_4 = l_proposals_0_fields_proposal_boxes_tensor.size(); l_proposals_0_fields_proposal_boxes_tensor = None 2025-03-04T20:57:36.0111899Z getitem_28: "Sym(s0)" = size_4[0] 2025-03-04T20:57:36.0112127Z getitem_29 = size_4[1]; size_4 = getitem_29 = None 2025-03-04T20:57:36.0112480Z size_5 = l_proposals_1_fields_proposal_boxes_tensor.size(); l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T20:57:36.0112856Z getitem_30: "Sym(s1)" = size_5[0] 2025-03-04T20:57:36.0113079Z getitem_31 = size_5[1]; size_5 = getitem_31 = None 2025-03-04T20:57:36.0113431Z size_6 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T20:57:36.0113761Z getitem_32: "Sym(s2)" = size_6[0] 2025-03-04T20:57:36.0113982Z getitem_33 = size_6[1]; size_6 = getitem_33 = None 2025-03-04T20:57:36.0114334Z size_7 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T20:57:36.0114689Z getitem_34: "Sym(s3)" = size_7[0] 2025-03-04T20:57:36.0114911Z getitem_35 = size_7[1]; size_7 = getitem_35 = None 2025-03-04T20:57:36.0115121Z 2025-03-04T20:57:36.0115525Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T20:57:36.0116111Z probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1); l_predictions_0_ = None 2025-03-04T20:57:36.0116431Z 2025-03-04T20:57:36.0116995Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T20:57:36.0117722Z split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0); probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None 2025-03-04T20:57:36.0118130Z scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0] 2025-03-04T20:57:36.0118405Z getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1]; getitem_37 = None 2025-03-04T20:57:36.0118686Z getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2]; getitem_38 = None 2025-03-04T20:57:36.0118976Z getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_39 = None 2025-03-04T20:57:36.0119217Z 2025-03-04T20:57:36.0123351Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T20:57:36.0124082Z isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T20:57:36.0124422Z all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:57:36.0124757Z isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T20:57:36.0125674Z all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T20:57:36.0125980Z valid_mask: "b8[s0][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T20:57:36.0126215Z 2025-03-04T20:57:36.0126666Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T20:57:36.0127195Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T20:57:36.0127427Z 2025-03-04T20:57:36.0136301Z 2025-03-04T20:57:36.0136682Z class GraphModule(torch.nn.Module): 2025-03-04T20:57:36.0137542Z def forward(self, L_predictions_0_: "f32[4000, 81][81, 1]cpu", L_predictions_1_: "f32[4000, 320][320, 1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"): 2025-03-04T20:57:36.0138461Z l_predictions_0_ = L_predictions_0_ 2025-03-04T20:57:36.0138717Z l_predictions_1_ = L_predictions_1_ 2025-03-04T20:57:36.0139285Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T20:57:36.0139827Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T20:57:36.0140246Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T20:57:36.0141094Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T20:57:36.0141477Z 2025-03-04T20:57:36.0141916Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:57:36.0142441Z size = l_proposals_0_fields_proposal_boxes_tensor.size() 2025-03-04T20:57:36.0142691Z getitem: "Sym(s0)" = size[0] 2025-03-04T20:57:36.0142923Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T20:57:36.0143211Z size_1 = l_proposals_1_fields_proposal_boxes_tensor.size() 2025-03-04T20:57:36.0143460Z getitem_2: "Sym(s1)" = size_1[0] 2025-03-04T20:57:36.0143690Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T20:57:36.0143952Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T20:57:36.0144220Z getitem_4: "Sym(s2)" = size_2[0] 2025-03-04T20:57:36.0144442Z getitem_5 = size_2[1]; size_2 = getitem_5 = None 2025-03-04T20:57:36.0144688Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T20:57:36.0144971Z getitem_6: "Sym(s3)" = size_3[0] 2025-03-04T20:57:36.0145191Z getitem_7 = size_3[1]; size_3 = getitem_7 = None 2025-03-04T20:57:36.0145399Z 2025-03-04T20:57:36.0145773Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:57:36.0146535Z proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0) 2025-03-04T20:57:36.0147084Z 2025-03-04T20:57:36.0147552Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:57:36.0148137Z deltas: "f32[4000, 320][320, 1]cpu" = l_predictions_1_.float(); l_predictions_1_ = None 2025-03-04T20:57:36.0148409Z 2025-03-04T20:57:36.0148872Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:57:36.0149389Z boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T20:57:36.0149662Z 2025-03-04T20:57:36.0150055Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:57:36.0150547Z getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:57:36.0151188Z getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:57:36.0151533Z widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9; getitem_8 = getitem_9 = None 2025-03-04T20:57:36.0151796Z 2025-03-04T20:57:36.0152219Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:57:36.0152728Z getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:57:36.0153040Z getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:57:36.0153377Z heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T20:57:36.0153677Z 2025-03-04T20:57:36.0154081Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:57:36.0154583Z getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:57:36.0154861Z mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths 2025-03-04T20:57:36.0155135Z ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul; getitem_12 = mul = None 2025-03-04T20:57:36.0155378Z 2025-03-04T20:57:36.0155781Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:57:36.0156346Z getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:57:36.0156842Z mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights 2025-03-04T20:57:36.0157130Z ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1; getitem_13 = mul_1 = None 2025-03-04T20:57:36.0157378Z 2025-03-04T20:57:36.0157825Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:57:36.0158338Z getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:57:36.0158667Z dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0; getitem_14 = None 2025-03-04T20:57:36.0158893Z 2025-03-04T20:57:36.0159271Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:57:36.0159763Z getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:57:36.0160074Z dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0; getitem_15 = None 2025-03-04T20:57:36.0160298Z 2025-03-04T20:57:36.0161160Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:57:36.0161704Z getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:57:36.0162019Z dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0; getitem_16 = None 2025-03-04T20:57:36.0162553Z 2025-03-04T20:57:36.0162973Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:57:36.0163516Z getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:57:36.0163851Z dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0; getitem_17 = None 2025-03-04T20:57:36.0164076Z 2025-03-04T20:57:36.0164491Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:57:36.0165013Z dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:57:36.0165260Z 2025-03-04T20:57:36.0165663Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:57:36.0166174Z dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:57:36.0166415Z 2025-03-04T20:57:36.0166838Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:57:36.0167368Z getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:57:36.0168560Z mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18; dx = getitem_18 = None 2025-03-04T20:57:36.0168888Z getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:57:36.0169224Z pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19; mul_2 = getitem_19 = None 2025-03-04T20:57:36.0169473Z 2025-03-04T20:57:36.0169902Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:57:36.0170450Z getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:57:36.0171049Z mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20; dy = getitem_20 = None 2025-03-04T20:57:36.0171399Z getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:57:36.0171745Z pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21; mul_3 = getitem_21 = None 2025-03-04T20:57:36.0171997Z 2025-03-04T20:57:36.0172439Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:57:36.0172967Z exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:57:36.0186338Z getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:57:36.0186883Z pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22; exp = getitem_22 = None 2025-03-04T20:57:36.0187130Z 2025-03-04T20:57:36.0187594Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:57:36.0188107Z exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:57:36.0188445Z getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:57:36.0188795Z pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23; exp_1 = getitem_23 = None 2025-03-04T20:57:36.0189042Z 2025-03-04T20:57:36.0189458Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:57:36.0189913Z mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T20:57:36.0190173Z x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:57:36.0190400Z 2025-03-04T20:57:36.0191369Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:57:36.0191882Z mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T20:57:36.0192149Z y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:57:36.0192383Z 2025-03-04T20:57:36.0192791Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:57:36.0193266Z mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:57:36.0193557Z x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:57:36.0193805Z 2025-03-04T20:57:36.0194213Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:57:36.0194697Z mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:57:36.0194985Z y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:57:36.0195319Z 2025-03-04T20:57:36.0195775Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:57:36.0196358Z pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:57:36.0196715Z 2025-03-04T20:57:36.0197145Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:57:36.0197743Z predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320)); pred_boxes = None 2025-03-04T20:57:36.0198025Z 2025-03-04T20:57:36.0198478Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T20:57:36.0199167Z split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]); predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None 2025-03-04T20:57:36.0199621Z boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0] 2025-03-04T20:57:36.0199908Z getitem_25: "f32[s1, 320][320, 1]cpu" = split[1]; getitem_25 = None 2025-03-04T20:57:36.0200222Z getitem_26: "f32[s2, 320][320, 1]cpu" = split[2]; getitem_26 = None 2025-03-04T20:57:36.0200527Z getitem_27: "f32[s3, 320][320, 1]cpu" = split[3]; split = getitem_27 = None 2025-03-04T20:57:36.0201119Z 2025-03-04T20:57:36.0201600Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:57:36.0202430Z size_4 = l_proposals_0_fields_proposal_boxes_tensor.size(); l_proposals_0_fields_proposal_boxes_tensor = None 2025-03-04T20:57:36.0202792Z getitem_28: "Sym(s0)" = size_4[0] 2025-03-04T20:57:36.0203033Z getitem_29 = size_4[1]; size_4 = getitem_29 = None 2025-03-04T20:57:36.0203407Z size_5 = l_proposals_1_fields_proposal_boxes_tensor.size(); l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T20:57:36.0203756Z getitem_30: "Sym(s1)" = size_5[0] 2025-03-04T20:57:36.0203985Z getitem_31 = size_5[1]; size_5 = getitem_31 = None 2025-03-04T20:57:36.0204354Z size_6 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T20:57:36.0204699Z getitem_32: "Sym(s2)" = size_6[0] 2025-03-04T20:57:36.0204933Z getitem_33 = size_6[1]; size_6 = getitem_33 = None 2025-03-04T20:57:36.0205294Z size_7 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T20:57:36.0205630Z getitem_34: "Sym(s3)" = size_7[0] 2025-03-04T20:57:36.0205856Z getitem_35 = size_7[1]; size_7 = getitem_35 = None 2025-03-04T20:57:36.0206069Z 2025-03-04T20:57:36.0206504Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T20:57:36.0207115Z probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1); l_predictions_0_ = None 2025-03-04T20:57:36.0207441Z 2025-03-04T20:57:36.0207887Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T20:57:36.0208559Z split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0); probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None 2025-03-04T20:57:36.0208974Z scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0] 2025-03-04T20:57:36.0209339Z getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1]; getitem_37 = None 2025-03-04T20:57:36.0209630Z getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2]; getitem_38 = None 2025-03-04T20:57:36.0209930Z getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_39 = None 2025-03-04T20:57:36.0210183Z 2025-03-04T20:57:36.0210747Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T20:57:36.0211443Z isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T20:57:36.0211809Z all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:57:36.0212129Z isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T20:57:36.0212453Z all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T20:57:36.0212735Z valid_mask: "b8[s0][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T20:57:36.0212966Z 2025-03-04T20:57:36.0213430Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T20:57:36.0213940Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T20:57:36.0214203Z 2025-03-04T20:57:36.0215361Z 2025-03-04T20:57:36.0215611Z class GraphModule(torch.nn.Module): 2025-03-04T20:57:36.0216438Z def forward(self, L_predictions_0_: "f32[4000, 81][81, 1]cpu", L_predictions_1_: "f32[4000, 320][320, 1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"): 2025-03-04T20:57:36.0217240Z l_predictions_0_ = L_predictions_0_ 2025-03-04T20:57:36.0217458Z l_predictions_1_ = L_predictions_1_ 2025-03-04T20:57:36.0217769Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T20:57:36.0218167Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T20:57:36.0218566Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T20:57:36.0218959Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T20:57:36.0219250Z 2025-03-04T20:57:36.0219635Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:57:36.0220112Z size = l_proposals_0_fields_proposal_boxes_tensor.size() 2025-03-04T20:57:36.0220358Z getitem: "Sym(s0)" = size[0] 2025-03-04T20:57:36.0220583Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T20:57:36.0220853Z size_1 = l_proposals_1_fields_proposal_boxes_tensor.size() 2025-03-04T20:57:36.0221097Z getitem_2: "Sym(s1)" = size_1[0] 2025-03-04T20:57:36.0221325Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T20:57:36.0221590Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T20:57:36.0221836Z getitem_4: "Sym(s2)" = size_2[0] 2025-03-04T20:57:36.0222061Z getitem_5 = size_2[1]; size_2 = getitem_5 = None 2025-03-04T20:57:36.0222327Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T20:57:36.0222568Z getitem_6: "Sym(s3)" = size_3[0] 2025-03-04T20:57:36.0222790Z getitem_7 = size_3[1]; size_3 = getitem_7 = None 2025-03-04T20:57:36.0222999Z 2025-03-04T20:57:36.0223378Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T20:57:36.0224190Z proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0) 2025-03-04T20:57:36.0224725Z 2025-03-04T20:57:36.0225179Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T20:57:36.0225742Z deltas: "f32[4000, 320][320, 1]cpu" = l_predictions_1_.float(); l_predictions_1_ = None 2025-03-04T20:57:36.0226021Z 2025-03-04T20:57:36.0226409Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T20:57:36.0226931Z boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T20:57:36.0227207Z 2025-03-04T20:57:36.0227621Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T20:57:36.0228112Z getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T20:57:36.0228433Z getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:57:36.0228752Z widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9; getitem_8 = getitem_9 = None 2025-03-04T20:57:36.0229000Z 2025-03-04T20:57:36.0229396Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T20:57:36.0229884Z getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T20:57:36.0230188Z getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T20:57:36.0230515Z heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T20:57:36.0230776Z 2025-03-04T20:57:36.0231166Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T20:57:36.0231643Z getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T20:57:36.0231913Z mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths 2025-03-04T20:57:36.0232208Z ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul; getitem_12 = mul = None 2025-03-04T20:57:36.0232449Z 2025-03-04T20:57:36.0232830Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T20:57:36.0233341Z getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T20:57:36.0233628Z mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights 2025-03-04T20:57:36.0233902Z ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1; getitem_13 = mul_1 = None 2025-03-04T20:57:36.0234143Z 2025-03-04T20:57:36.0234547Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T20:57:36.0235045Z getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T20:57:36.0235364Z dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0; getitem_14 = None 2025-03-04T20:57:36.0235591Z 2025-03-04T20:57:36.0235971Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T20:57:36.0236590Z getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T20:57:36.0236918Z dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0; getitem_15 = None 2025-03-04T20:57:36.0237147Z 2025-03-04T20:57:36.0237527Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T20:57:36.0238030Z getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T20:57:36.0238391Z dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0; getitem_16 = None 2025-03-04T20:57:36.0238623Z 2025-03-04T20:57:36.0239023Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T20:57:36.0239544Z getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T20:57:36.0239874Z dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0; getitem_17 = None 2025-03-04T20:57:36.0240121Z 2025-03-04T20:57:36.0240532Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T20:57:36.0241069Z dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T20:57:36.0241317Z 2025-03-04T20:57:36.0241722Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T20:57:36.0242228Z dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T20:57:36.0242470Z 2025-03-04T20:57:36.0242898Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T20:57:36.0243445Z getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T20:57:36.0243764Z mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18; dx = getitem_18 = None 2025-03-04T20:57:36.0244097Z getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T20:57:36.0244441Z pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19; mul_2 = getitem_19 = None 2025-03-04T20:57:36.0244703Z 2025-03-04T20:57:36.0245124Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T20:57:36.0245649Z getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T20:57:36.0245968Z mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20; dy = getitem_20 = None 2025-03-04T20:57:36.0246291Z getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T20:57:36.0246630Z pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21; mul_3 = getitem_21 = None 2025-03-04T20:57:36.0246881Z 2025-03-04T20:57:36.0247298Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T20:57:36.0247799Z exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T20:57:36.0248124Z getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T20:57:36.0248460Z pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22; exp = getitem_22 = None 2025-03-04T20:57:36.0248731Z 2025-03-04T20:57:36.0249151Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T20:57:36.0249654Z exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T20:57:36.0249986Z getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T20:57:36.0250331Z pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23; exp_1 = getitem_23 = None 2025-03-04T20:57:36.0250578Z 2025-03-04T20:57:36.0250992Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T20:57:36.0251455Z mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T20:57:36.0251712Z x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T20:57:36.0251941Z 2025-03-04T20:57:36.0252337Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T20:57:36.0252808Z mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T20:57:36.0253063Z y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T20:57:36.0253289Z 2025-03-04T20:57:36.0253688Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T20:57:36.0254172Z mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T20:57:36.0254454Z x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T20:57:36.0254695Z 2025-03-04T20:57:36.0255086Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T20:57:36.0255554Z mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T20:57:36.0255842Z y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T20:57:36.0256081Z 2025-03-04T20:57:36.0256514Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T20:57:36.0257094Z pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T20:57:36.0257393Z 2025-03-04T20:57:36.0257802Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T20:57:36.0258337Z predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320)); pred_boxes = None 2025-03-04T20:57:36.0258617Z 2025-03-04T20:57:36.0259046Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T20:57:36.0259706Z split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]); predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None 2025-03-04T20:57:36.0260119Z boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0] 2025-03-04T20:57:36.0260394Z getitem_25: "f32[s1, 320][320, 1]cpu" = split[1]; getitem_25 = None 2025-03-04T20:57:36.0260678Z getitem_26: "f32[s2, 320][320, 1]cpu" = split[2]; getitem_26 = None 2025-03-04T20:57:36.0260970Z getitem_27: "f32[s3, 320][320, 1]cpu" = split[3]; split = getitem_27 = None 2025-03-04T20:57:36.0261208Z 2025-03-04T20:57:36.0261571Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T20:57:36.0262132Z size_4 = l_proposals_0_fields_proposal_boxes_tensor.size(); l_proposals_0_fields_proposal_boxes_tensor = None 2025-03-04T20:57:36.0262468Z getitem_28: "Sym(s0)" = size_4[0] 2025-03-04T20:57:36.0262698Z getitem_29 = size_4[1]; size_4 = getitem_29 = None 2025-03-04T20:57:36.0263047Z size_5 = l_proposals_1_fields_proposal_boxes_tensor.size(); l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T20:57:36.0263373Z getitem_30: "Sym(s1)" = size_5[0] 2025-03-04T20:57:36.0263601Z getitem_31 = size_5[1]; size_5 = getitem_31 = None 2025-03-04T20:57:36.0263972Z size_6 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T20:57:36.0264300Z getitem_32: "Sym(s2)" = size_6[0] 2025-03-04T20:57:36.0264526Z getitem_33 = size_6[1]; size_6 = getitem_33 = None 2025-03-04T20:57:36.0264874Z size_7 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T20:57:36.0265202Z getitem_34: "Sym(s3)" = size_7[0] 2025-03-04T20:57:36.0265440Z getitem_35 = size_7[1]; size_7 = getitem_35 = None 2025-03-04T20:57:36.0265647Z 2025-03-04T20:57:36.0266070Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T20:57:36.0266651Z probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1); l_predictions_0_ = None 2025-03-04T20:57:36.0266966Z 2025-03-04T20:57:36.0267396Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T20:57:36.0268046Z split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0); probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None 2025-03-04T20:57:36.0268449Z scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0] 2025-03-04T20:57:36.0268727Z getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1]; getitem_37 = None 2025-03-04T20:57:36.0269011Z getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2]; getitem_38 = None 2025-03-04T20:57:36.0269298Z getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_39 = None 2025-03-04T20:57:36.0269538Z 2025-03-04T20:57:36.0270075Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T20:57:36.0270748Z isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T20:57:36.0271077Z all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:57:36.0271398Z isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T20:57:36.0271725Z all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T20:57:36.0272003Z valid_mask: "b8[s0][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T20:57:36.0272233Z 2025-03-04T20:57:36.0272670Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T20:57:36.0273187Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T20:57:36.0273423Z 2025-03-04T20:57:36.4197382Z 2025-03-04T20:57:36.4203410Z class GraphModule(torch.nn.Module): 2025-03-04T20:57:36.4204106Z def forward(self, s0: "Sym(s0)", L_scores_0_: "f32[s0, 81][81, 1]cpu", s1: "Sym(s0)", L_boxes_0_: "f32[s0, 320][320, 1]cpu"): 2025-03-04T20:57:36.4206822Z l_scores_0_ = L_scores_0_ 2025-03-04T20:57:36.4207138Z l_boxes_0_ = L_boxes_0_ 2025-03-04T20:57:36.4213748Z 2025-03-04T20:57:36.4218692Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T20:57:36.4219486Z isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(l_boxes_0_); l_boxes_0_ = None 2025-03-04T20:57:36.4219826Z all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:57:36.4220386Z isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(l_scores_0_); l_scores_0_ = None 2025-03-04T20:57:36.4220689Z all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T20:57:36.4220961Z valid_mask: "b8[s0][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T20:57:36.4221198Z 2025-03-04T20:57:36.4221640Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T20:57:36.4222212Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T20:57:36.4222437Z 2025-03-04T20:57:36.4222530Z 2025-03-04T20:57:36.4222616Z class GraphModule(torch.nn.Module): 2025-03-04T20:57:36.4223023Z def forward(self, s0: "Sym(s0)", L_scores_0_: "f32[s0, 81][81, 1]cpu", s1: "Sym(s0)", L_boxes_0_: "f32[s0, 320][320, 1]cpu"): 2025-03-04T20:57:36.4223323Z l_scores_0_ = L_scores_0_ 2025-03-04T20:57:36.4223523Z l_boxes_0_ = L_boxes_0_ 2025-03-04T20:57:36.4223699Z 2025-03-04T20:57:36.4224228Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T20:57:36.4224871Z isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(l_boxes_0_); l_boxes_0_ = None 2025-03-04T20:57:36.4225170Z all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T20:57:36.4225465Z isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(l_scores_0_); l_scores_0_ = None 2025-03-04T20:57:36.4225759Z all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T20:57:36.4226033Z valid_mask: "b8[s0][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T20:57:36.4226255Z 2025-03-04T20:57:36.4226679Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T20:57:36.4227180Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T20:57:36.4227401Z 2025-03-04T20:57:50.2315572Z Compilation time (from dynamo_timed): 35.359758423 2025-03-04T20:57:50.2316032Z pass 2025-03-04T20:57:50.2316371Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T20:57:50.2317290Z TIMING: entire_frame_compile:35.35976 gc:0.03658 _recursive_pre_grad_passes:0.03463 async_compile.wait:3.4763 backend_compile:19.00682 _recursive_joint_graph_passes:0.16866 _recursive_post_grad_passes:0.075 code_gen:6.99747 inductor_compile:8.33604 total_wall_time:35.35976 2025-03-04T20:57:50.2318497Z STATS: call_* op count: 990 | FakeTensorMode.__torch_dispatch__:22930 | FakeTensor.__torch_dispatch__:1797 | ProxyTorchDispatchMode.__torch_dispatch__:5688 | attempt fast:112 | slow no contiguity match:36 | fast is_contiguous:76 2025-03-04T20:57:50.2319132Z Dynamo produced 61 graphs covering 990 ops with 46 graph breaks (6 unique) 2025-03-04T20:57:56.0555634Z 2025-03-04T20:58:00.3999193Z loading model: 0it [00:00, ?it/s] 2025-03-04T20:58:00.3999714Z loading model: 0it [00:04, ?it/s] 2025-03-04T20:58:00.4008519Z cpu eval detectron2_fcos_r_50_fpn 2025-03-04T20:58:14.7695045Z WARNING:common:fp64 golden ref were not generated for detectron2_fcos_r_50_fpn. Setting accuracy check to cosine 2025-03-04T20:58:14.7759111Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T20:58:19.1906427Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T20:58:23.7495576Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T20:59:16.5731190Z Compilation time (from dynamo_timed): 47.918759541 2025-03-04T20:59:16.5731974Z pass 2025-03-04T20:59:16.5734537Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T20:59:16.5735348Z TIMING: entire_frame_compile:47.91876 gc:0.02518 _recursive_pre_grad_passes:0.02462 async_compile.wait:14.44735 backend_compile:36.95363 _recursive_joint_graph_passes:0.27063 _recursive_post_grad_passes:0.16879 code_gen:22.59488 inductor_compile:25.71833 total_wall_time:47.91876 2025-03-04T20:59:16.5736520Z STATS: call_* op count: 944 | FakeTensorMode.__torch_dispatch__:29058 | FakeTensor.__torch_dispatch__:3334 | ProxyTorchDispatchMode.__torch_dispatch__:10968 2025-03-04T20:59:16.5737569Z Dynamo produced 29 graphs covering 944 ops with 22 graph breaks (4 unique) 2025-03-04T20:59:21.8888814Z 2025-03-04T20:59:34.5783800Z loading model: 0it [00:00, ?it/s] 2025-03-04T20:59:34.5788739Z loading model: 0it [00:12, ?it/s] 2025-03-04T20:59:34.5795876Z cpu eval detectron2_maskrcnn_r_101_c4 2025-03-04T20:59:45.8804647Z WARNING:common:fp64 golden ref were not generated for detectron2_maskrcnn_r_101_c4. Setting accuracy check to cosine 2025-03-04T20:59:45.8899784Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T21:00:08.7076552Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T21:00:29.6314672Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T21:00:40.0025389Z 2025-03-04T21:00:40.0029130Z class GraphModule(torch.nn.Module): 2025-03-04T21:00:40.0131839Z def forward(self, L_stack0_tensor: "f32[4, 3, 1156, 1199][4158132, 1386044, 1199, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", 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L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", 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1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[1024, 1024, 3, 3][9216, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[1024][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"): 2025-03-04T21:00:40.0221280Z l_stack0_tensor = L_stack0_tensor 2025-03-04T21:00:40.0221699Z l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.0222355Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0223054Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0223722Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0224369Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0225002Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.0225675Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0226408Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0227117Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0227798Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0228446Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.0229116Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0229875Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0230585Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0231261Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0231919Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.0232588Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0233341Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0234068Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0234753Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0235419Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:00:40.0236127Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0236886Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0237629Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0238384Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0239050Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.0239727Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0240452Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0241156Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0241837Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0242477Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.0243167Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0243897Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0244599Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0245297Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0245957Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.0246654Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0247374Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0248088Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0248763Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0249400Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.0250075Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0250797Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0251496Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0252172Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0252813Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.0253482Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0254203Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0254900Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0255572Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0256229Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.0256902Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0257629Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0258336Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0259026Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0259670Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.0260350Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0261087Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0261800Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0262480Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0263128Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.0263804Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0264530Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0265236Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0265912Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0266555Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.0267228Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0267953Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0268658Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0269341Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0270013Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:00:40.0270717Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0271467Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0272208Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0272906Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0273572Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.0274257Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0275000Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0275707Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0276385Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0277024Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.0277698Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0278498Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0279220Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0279893Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0280565Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.0281330Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0282147Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0282935Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0283730Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0284432Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.0285204Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0286024Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0286822Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0287562Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0288257Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.0288973Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0289723Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0290452Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0291142Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0291824Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.0292513Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0293249Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0293963Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0294658Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0295318Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.0296006Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0296752Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0297474Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0298193Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0298849Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.0299515Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0300255Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0300961Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0301659Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0302472Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.0303187Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0303935Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0304664Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0305404Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0306098Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.0306826Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0307557Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0308255Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0308930Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0309590Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.0310280Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0311003Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0311729Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0312411Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0313053Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.0313744Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0314503Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0315245Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0315937Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0316629Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:00:40.0317345Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0318160Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0318921Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0319648Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0320320Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.0321019Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0321767Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0322495Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0323192Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0323855Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.0324558Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0325313Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0326031Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0326723Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0327376Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.0328090Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0328835Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0329569Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0330279Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0330923Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.0331593Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0332318Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0333020Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0333693Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0334338Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.0335010Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0335733Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0336438Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0337114Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0337760Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.0338427Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0339167Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0339871Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0340553Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0341207Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.0341878Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0342625Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0343343Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0344019Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0344659Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.0345331Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0346059Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0346766Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0347441Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0348081Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.0348755Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0349473Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0350173Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0350844Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0351489Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.0352174Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0352902Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0353601Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0354273Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0354939Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.0355611Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0356357Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0357086Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0357826Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0358499Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.0359193Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0359963Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0360701Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0361394Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0362063Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.0362757Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0363508Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0364226Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0364919Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0365575Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.0366291Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0367042Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0367767Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0368480Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0369146Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.0369847Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0370600Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0371331Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0372014Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0372658Z l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.0373333Z l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0374064Z l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0374767Z l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0375450Z l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0376092Z l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.0376770Z l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0377499Z l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0378206Z l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0378888Z l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0379553Z l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.0380221Z l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0380961Z l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0381687Z l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0382401Z l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0383048Z l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.0383736Z l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0384489Z l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0385197Z l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0385875Z l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0386518Z l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.0387194Z l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0387917Z l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0388618Z l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0389288Z l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0389932Z l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.0390612Z l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0391357Z l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0392078Z l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0392754Z l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0393433Z l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.0394097Z l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0394813Z l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0395539Z l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0396224Z l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0396895Z l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.0397600Z l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0398491Z l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0399242Z l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0399936Z l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0400580Z l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.0401252Z l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0402097Z l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0402805Z l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0403480Z l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0404124Z l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.0404797Z l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0405521Z l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0406225Z l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0406946Z l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0407584Z l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.0408246Z l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0408991Z l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0409689Z l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0410381Z l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0411026Z l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.0411713Z l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0412438Z l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0413136Z l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0413815Z l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0414472Z l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.0415150Z l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0415881Z l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0416588Z l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0417275Z l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0417924Z l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.0418599Z l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0419327Z l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0420047Z l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0420724Z l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0421370Z l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.0422036Z l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0422776Z l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0423485Z l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0424198Z l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0424856Z l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.0425532Z l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0426260Z l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0426968Z l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0427647Z l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0428296Z l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.0428975Z l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0429706Z l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0430413Z l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0431089Z l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0431735Z l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.0432406Z l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0433133Z l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0433858Z l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0434535Z l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0435181Z l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.0435879Z l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0436626Z l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0437371Z l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0438139Z l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0438809Z l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.0439566Z l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0440316Z l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0441045Z l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0441748Z l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0442415Z l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.0443107Z l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0443856Z l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0444586Z l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0445285Z l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0445950Z l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.0446644Z l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0447406Z l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0448127Z l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0448828Z l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0449503Z l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.0450188Z l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0450949Z l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0451706Z l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0452433Z l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0453100Z l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.0453804Z l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0454554Z l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0455268Z l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0455978Z l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0456651Z l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.0457360Z l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0458098Z l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0458809Z l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0459503Z l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0460158Z l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.0460849Z l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0461573Z l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0462279Z l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0462955Z l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0463616Z l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.0464326Z l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0465316Z l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0466050Z l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0466903Z l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0467569Z l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.0468262Z l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0469005Z l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0469795Z l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0470851Z l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0471824Z l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.0472921Z l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0474075Z l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0475148Z l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0476209Z l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0477211Z l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.0478445Z l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0479580Z l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0480551Z l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0481714Z l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0482600Z l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.0483766Z l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0485012Z l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0486076Z l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0486944Z l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0487976Z l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.0488989Z l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0489908Z l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0490632Z l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0491316Z l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0491988Z l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.0492690Z l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0493443Z l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0494178Z l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0494881Z l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0495573Z l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.0496271Z l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0497020Z l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0497764Z l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0498453Z l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0499122Z l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.0499822Z l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0500565Z l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0501289Z l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0502122Z l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0502799Z l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.0503492Z l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0504237Z l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0504962Z l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0505662Z l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0506329Z l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.0507024Z l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0507776Z l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0508503Z l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0509239Z l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0509902Z l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.0510588Z l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0511361Z l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0512083Z l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0512814Z l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0513480Z l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.0514198Z l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0514948Z l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0515674Z l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0516375Z l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0517038Z l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.0517735Z l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0518580Z l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0519337Z l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0520039Z l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0520723Z l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.0521443Z l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0522219Z l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0522987Z l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0523706Z l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0524393Z l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.0525104Z l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0525894Z l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0526644Z l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0527377Z l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0528071Z l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.0528783Z l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0529553Z l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0530289Z l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0530968Z l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0531616Z l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.0532295Z l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0533016Z l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0533723Z l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0534402Z l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0535047Z l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.0535720Z l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0536445Z l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0537172Z l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0537850Z l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0538497Z l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.0539224Z l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0539961Z l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0540681Z l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0541371Z l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0542030Z l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.0542710Z l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0543447Z l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0544161Z l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0544848Z l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0545498Z l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.0546179Z l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0546917Z l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0547635Z l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0548323Z l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0548980Z l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.0549668Z l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0550421Z l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0551131Z l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0551816Z l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0552479Z l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.0553159Z l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0553909Z l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0554632Z l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0555312Z l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0555960Z l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.0556650Z l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.0557381Z l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.0558155Z l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.0558850Z l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.0559565Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T21:00:40.0560289Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T21:00:40.0560965Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T21:00:40.0561698Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T21:00:40.0562473Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T21:00:40.0563233Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T21:00:40.0563988Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T21:00:40.0564447Z 2025-03-04T21:00:40.0564831Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0565617Z x: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1); l_stack0_tensor = l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.0566222Z 2025-03-04T21:00:40.0566575Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.0569236Z x_1: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.0570838Z 2025-03-04T21:00:40.0571214Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x) 2025-03-04T21:00:40.0571694Z x_2: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.relu_(x_1); x_1 = None 2025-03-04T21:00:40.0571952Z 2025-03-04T21:00:40.0572406Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1) 2025-03-04T21:00:40.0573060Z x_3: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1); x_2 = None 2025-03-04T21:00:40.0573414Z 2025-03-04T21:00:40.0573767Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0574481Z x_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.0575001Z 2025-03-04T21:00:40.0575348Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.0577175Z x_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_4 = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.0578796Z 2025-03-04T21:00:40.0579161Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.0579627Z out: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_5); x_5 = None 2025-03-04T21:00:40.0579878Z 2025-03-04T21:00:40.0580211Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0580949Z x_6: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out = l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.0581479Z 2025-03-04T21:00:40.0581825Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.0583660Z x_7: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_6 = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.0585267Z 2025-03-04T21:00:40.0585628Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.0586093Z out_1: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_7); x_7 = None 2025-03-04T21:00:40.0586343Z 2025-03-04T21:00:40.0586674Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0587411Z x_8: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_1 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.0587949Z 2025-03-04T21:00:40.0588290Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.0590114Z x_9: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_8 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.0591732Z 2025-03-04T21:00:40.0592062Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0592793Z x_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); x_3 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:00:40.0593332Z 2025-03-04T21:00:40.0593673Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.0595581Z x_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_10 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.0597254Z 2025-03-04T21:00:40.0597615Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.0598204Z x_9 += x_11; out_2: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_9; x_9 = x_11 = None 2025-03-04T21:00:40.0598463Z 2025-03-04T21:00:40.0598853Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.0599332Z out_3: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_2); out_2 = None 2025-03-04T21:00:40.0599592Z 2025-03-04T21:00:40.0599924Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0600636Z x_12: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.0601167Z 2025-03-04T21:00:40.0601523Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.0603588Z x_13: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_12 = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.0605301Z 2025-03-04T21:00:40.0605678Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.0606212Z out_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_13); x_13 = None 2025-03-04T21:00:40.0606470Z 2025-03-04T21:00:40.0606809Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0607546Z x_14: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_4 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.0608117Z 2025-03-04T21:00:40.0608468Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.0610410Z x_15: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_14 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.0612058Z 2025-03-04T21:00:40.0612420Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.0612900Z out_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_15); x_15 = None 2025-03-04T21:00:40.0613155Z 2025-03-04T21:00:40.0613490Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0614238Z x_16: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_5 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.0614787Z 2025-03-04T21:00:40.0615140Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.0616999Z x_17: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_16 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.0618648Z 2025-03-04T21:00:40.0619030Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.0619502Z x_17 += out_3; out_6: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_17; x_17 = out_3 = None 2025-03-04T21:00:40.0619787Z 2025-03-04T21:00:40.0620141Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.0620613Z out_7: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_6); out_6 = None 2025-03-04T21:00:40.0620873Z 2025-03-04T21:00:40.0621196Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0621905Z x_18: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.0622441Z 2025-03-04T21:00:40.0622779Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.0624614Z x_19: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_18 = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.0626227Z 2025-03-04T21:00:40.0626597Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.0627074Z out_8: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_19); x_19 = None 2025-03-04T21:00:40.0627323Z 2025-03-04T21:00:40.0627656Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0628379Z x_20: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_8 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.0628912Z 2025-03-04T21:00:40.0629256Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.0631093Z x_21: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_20 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.0632701Z 2025-03-04T21:00:40.0633064Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.0633544Z out_9: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_21); x_21 = None 2025-03-04T21:00:40.0633791Z 2025-03-04T21:00:40.0634112Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0634832Z x_22: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_9 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.0635376Z 2025-03-04T21:00:40.0635719Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.0637567Z x_23: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_22 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.0639347Z 2025-03-04T21:00:40.0639727Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.0640248Z x_23 += out_7; out_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_23; x_23 = out_7 = None 2025-03-04T21:00:40.0640532Z 2025-03-04T21:00:40.0640919Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.0641446Z out_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_10); out_10 = None 2025-03-04T21:00:40.0641733Z 2025-03-04T21:00:40.0642092Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0642878Z x_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.0643452Z 2025-03-04T21:00:40.0643826Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.0645796Z x_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_24 = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.0647555Z 2025-03-04T21:00:40.0647944Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.0648444Z out_12: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_25); x_25 = None 2025-03-04T21:00:40.0648696Z 2025-03-04T21:00:40.0649030Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0649765Z x_26: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_12 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.0650342Z 2025-03-04T21:00:40.0650688Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.0652519Z x_27: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_26 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.0654129Z 2025-03-04T21:00:40.0654487Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.0654961Z out_13: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_27); x_27 = None 2025-03-04T21:00:40.0655215Z 2025-03-04T21:00:40.0655546Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0656276Z x_28: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_13 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.0656816Z 2025-03-04T21:00:40.0657160Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.0658968Z x_29: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_28 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.0660580Z 2025-03-04T21:00:40.0660920Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0661650Z x_30: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); out_11 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:00:40.0662196Z 2025-03-04T21:00:40.0662533Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.0664424Z x_31: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_30 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.0666117Z 2025-03-04T21:00:40.0666471Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.0666944Z x_29 += x_31; out_14: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_29; x_29 = x_31 = None 2025-03-04T21:00:40.0667199Z 2025-03-04T21:00:40.0667560Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.0668038Z out_15: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_14); out_14 = None 2025-03-04T21:00:40.0668299Z 2025-03-04T21:00:40.0668629Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0669350Z x_32: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.0669883Z 2025-03-04T21:00:40.0670223Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.0672034Z x_33: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_32 = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.0673641Z 2025-03-04T21:00:40.0674000Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.0674496Z out_16: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_33); x_33 = None 2025-03-04T21:00:40.0674749Z 2025-03-04T21:00:40.0675077Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0675804Z x_34: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_16 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.0676356Z 2025-03-04T21:00:40.0676706Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.0678708Z x_35: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_34 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.0680383Z 2025-03-04T21:00:40.0680754Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.0681248Z out_17: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_35); x_35 = None 2025-03-04T21:00:40.0681511Z 2025-03-04T21:00:40.0681850Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0682600Z x_36: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_17 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.0683158Z 2025-03-04T21:00:40.0683508Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.0685391Z x_37: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_36 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.0687053Z 2025-03-04T21:00:40.0687420Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.0687906Z x_37 += out_15; out_18: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_37; x_37 = out_15 = None 2025-03-04T21:00:40.0688192Z 2025-03-04T21:00:40.0688551Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.0689047Z out_19: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_18); out_18 = None 2025-03-04T21:00:40.0689313Z 2025-03-04T21:00:40.0689651Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0690390Z x_38: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.0690943Z 2025-03-04T21:00:40.0691291Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.0693202Z x_39: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_38 = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.0694799Z 2025-03-04T21:00:40.0695159Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.0695631Z out_20: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_39); x_39 = None 2025-03-04T21:00:40.0695883Z 2025-03-04T21:00:40.0696209Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0696929Z x_40: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_20 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.0697466Z 2025-03-04T21:00:40.0697805Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.0699613Z x_41: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_40 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.0701221Z 2025-03-04T21:00:40.0701580Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.0702165Z out_21: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_41); x_41 = None 2025-03-04T21:00:40.0702422Z 2025-03-04T21:00:40.0702755Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0703493Z x_42: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_21 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.0704074Z 2025-03-04T21:00:40.0704415Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.0706312Z x_43: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_42 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.0707911Z 2025-03-04T21:00:40.0708269Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.0708749Z x_43 += out_19; out_22: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_43; x_43 = out_19 = None 2025-03-04T21:00:40.0709011Z 2025-03-04T21:00:40.0709368Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.0709844Z out_23: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_22); out_22 = None 2025-03-04T21:00:40.0710103Z 2025-03-04T21:00:40.0710431Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0711147Z x_44: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.0711676Z 2025-03-04T21:00:40.0712021Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.0713828Z x_45: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_44 = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.0715452Z 2025-03-04T21:00:40.0715810Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.0716277Z out_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_45); x_45 = None 2025-03-04T21:00:40.0716527Z 2025-03-04T21:00:40.0716853Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0717573Z x_46: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_24 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.0718174Z 2025-03-04T21:00:40.0718515Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.0720363Z x_47: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_46 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.0721968Z 2025-03-04T21:00:40.0722322Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.0722792Z out_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_47); x_47 = None 2025-03-04T21:00:40.0723043Z 2025-03-04T21:00:40.0723367Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0724088Z x_48: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_25 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.0724621Z 2025-03-04T21:00:40.0724958Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.0726762Z x_49: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_48 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.0728373Z 2025-03-04T21:00:40.0728724Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.0729215Z x_49 += out_23; out_26: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_49; x_49 = out_23 = None 2025-03-04T21:00:40.0729476Z 2025-03-04T21:00:40.0729838Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.0730317Z out_27: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_26); out_26 = None 2025-03-04T21:00:40.0730579Z 2025-03-04T21:00:40.0730904Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0731633Z x_50: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.0732159Z 2025-03-04T21:00:40.0732512Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.0734332Z x_51: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_50 = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.0735946Z 2025-03-04T21:00:40.0736309Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.0736779Z out_28: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_51); x_51 = None 2025-03-04T21:00:40.0737029Z 2025-03-04T21:00:40.0737345Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0738066Z x_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_28 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.0738596Z 2025-03-04T21:00:40.0738938Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.0740740Z x_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_52 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.0742356Z 2025-03-04T21:00:40.0742718Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.0743183Z out_29: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_53); x_53 = None 2025-03-04T21:00:40.0743431Z 2025-03-04T21:00:40.0743758Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0744478Z x_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_29 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.0745023Z 2025-03-04T21:00:40.0745372Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.0747201Z x_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_54 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.0748808Z 2025-03-04T21:00:40.0749133Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0749864Z x_56: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); out_27 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:00:40.0750407Z 2025-03-04T21:00:40.0750745Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.0752683Z x_57: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_56 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.0754493Z 2025-03-04T21:00:40.0754896Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.0755429Z x_55 += x_57; out_30: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_55; x_55 = x_57 = None 2025-03-04T21:00:40.0755696Z 2025-03-04T21:00:40.0756100Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.0756609Z out_31: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_30); out_30 = None 2025-03-04T21:00:40.0756883Z 2025-03-04T21:00:40.0757247Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0758072Z x_58: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.0758660Z 2025-03-04T21:00:40.0759031Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.0761037Z x_59: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_58 = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.0762781Z 2025-03-04T21:00:40.0763176Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.0763688Z out_32: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_59); x_59 = None 2025-03-04T21:00:40.0763938Z 2025-03-04T21:00:40.0764265Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0764974Z x_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_32 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.0765498Z 2025-03-04T21:00:40.0765840Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.0767713Z x_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_60 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.0769304Z 2025-03-04T21:00:40.0769666Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.0770148Z out_33: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_61); x_61 = None 2025-03-04T21:00:40.0770398Z 2025-03-04T21:00:40.0770729Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0771448Z x_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_33 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.0771980Z 2025-03-04T21:00:40.0772352Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.0774194Z x_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_62 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.0775803Z 2025-03-04T21:00:40.0776157Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.0776628Z x_63 += out_31; out_34: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_63; x_63 = out_31 = None 2025-03-04T21:00:40.0776882Z 2025-03-04T21:00:40.0777237Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.0777703Z out_35: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_34); out_34 = None 2025-03-04T21:00:40.0777949Z 2025-03-04T21:00:40.0778274Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0778993Z x_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.0779526Z 2025-03-04T21:00:40.0779880Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.0781711Z x_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_64 = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.0783365Z 2025-03-04T21:00:40.0783748Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.0784221Z out_36: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_65); x_65 = None 2025-03-04T21:00:40.0784474Z 2025-03-04T21:00:40.0784803Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0785535Z x_66: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_36 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.0786100Z 2025-03-04T21:00:40.0786457Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.0788350Z x_67: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_66 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.0789992Z 2025-03-04T21:00:40.0790360Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.0790828Z out_37: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_67); x_67 = None 2025-03-04T21:00:40.0791084Z 2025-03-04T21:00:40.0791417Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0792145Z x_68: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_37 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.0792685Z 2025-03-04T21:00:40.0793031Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.0794858Z x_69: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_68 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.0796460Z 2025-03-04T21:00:40.0796814Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.0797323Z x_69 += out_35; out_38: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_69; x_69 = out_35 = None 2025-03-04T21:00:40.0797578Z 2025-03-04T21:00:40.0797988Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.0798497Z out_39: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_38); out_38 = None 2025-03-04T21:00:40.0798745Z 2025-03-04T21:00:40.0799087Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0799814Z x_70: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.0800339Z 2025-03-04T21:00:40.0800674Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.0802673Z x_71: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_70 = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.0804279Z 2025-03-04T21:00:40.0804641Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.0805104Z out_40: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_71); x_71 = None 2025-03-04T21:00:40.0805352Z 2025-03-04T21:00:40.0805679Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0806392Z x_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_40 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.0806921Z 2025-03-04T21:00:40.0807263Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.0809065Z x_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_72 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.0810658Z 2025-03-04T21:00:40.0811045Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.0811503Z out_41: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_73); x_73 = None 2025-03-04T21:00:40.0811746Z 2025-03-04T21:00:40.0812072Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0812780Z x_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_41 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.0813325Z 2025-03-04T21:00:40.0813669Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.0818732Z x_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_74 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.0820387Z 2025-03-04T21:00:40.0820754Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.0821236Z x_75 += out_39; out_42: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_75; x_75 = out_39 = None 2025-03-04T21:00:40.0821499Z 2025-03-04T21:00:40.0821871Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.0822354Z out_43: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_42); out_42 = None 2025-03-04T21:00:40.0822616Z 2025-03-04T21:00:40.0822950Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0823721Z x_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.0824255Z 2025-03-04T21:00:40.0824607Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.0826473Z x_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_76 = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.0828125Z 2025-03-04T21:00:40.0828496Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.0828972Z out_44: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_77); x_77 = None 2025-03-04T21:00:40.0829224Z 2025-03-04T21:00:40.0829558Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0830294Z x_78: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_44 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.0830851Z 2025-03-04T21:00:40.0831204Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.0833168Z x_79: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_78 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.0834764Z 2025-03-04T21:00:40.0835127Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.0835586Z out_45: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_79); x_79 = None 2025-03-04T21:00:40.0835831Z 2025-03-04T21:00:40.0836156Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0836874Z x_80: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_45 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.0837408Z 2025-03-04T21:00:40.0837749Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.0839671Z x_81: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_80 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.0841285Z 2025-03-04T21:00:40.0841669Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.0842148Z x_81 += out_43; out_46: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_81; x_81 = out_43 = None 2025-03-04T21:00:40.0842408Z 2025-03-04T21:00:40.0842768Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.0843238Z out_47: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_46); out_46 = None 2025-03-04T21:00:40.0843488Z 2025-03-04T21:00:40.0843829Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0844539Z x_82: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.0845056Z 2025-03-04T21:00:40.0845395Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.0847259Z x_83: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_82 = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.0848853Z 2025-03-04T21:00:40.0849209Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.0849667Z out_48: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_83); x_83 = None 2025-03-04T21:00:40.0849912Z 2025-03-04T21:00:40.0850241Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0850954Z x_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_48 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.0851478Z 2025-03-04T21:00:40.0851829Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.0853678Z x_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_84 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.0855329Z 2025-03-04T21:00:40.0855709Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.0856192Z out_49: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_85); x_85 = None 2025-03-04T21:00:40.0856439Z 2025-03-04T21:00:40.0856774Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0857514Z x_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_49 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.0858079Z 2025-03-04T21:00:40.0858425Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.0860359Z x_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_86 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.0862017Z 2025-03-04T21:00:40.0862384Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.0862864Z x_87 += out_47; out_50: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_87; x_87 = out_47 = None 2025-03-04T21:00:40.0863126Z 2025-03-04T21:00:40.0863494Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.0863975Z out_51: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_50); out_50 = None 2025-03-04T21:00:40.0864235Z 2025-03-04T21:00:40.0864571Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0865300Z x_88: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.0865835Z 2025-03-04T21:00:40.0866184Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.0868031Z x_89: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_88 = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.0869640Z 2025-03-04T21:00:40.0869999Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.0870461Z out_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_89); x_89 = None 2025-03-04T21:00:40.0870708Z 2025-03-04T21:00:40.0871065Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0871803Z x_90: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_52 = l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.0872337Z 2025-03-04T21:00:40.0872681Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.0874530Z x_91: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_90 = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.0876132Z 2025-03-04T21:00:40.0876493Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.0876967Z out_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_91); x_91 = None 2025-03-04T21:00:40.0877219Z 2025-03-04T21:00:40.0877556Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0878483Z x_92: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_53 = l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.0879404Z 2025-03-04T21:00:40.0880003Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.0882834Z x_93: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_92 = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.0885074Z 2025-03-04T21:00:40.0885615Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.0886184Z x_93 += out_51; out_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_93; x_93 = out_51 = None 2025-03-04T21:00:40.0886445Z 2025-03-04T21:00:40.0886939Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.0887670Z out_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_54); out_54 = None 2025-03-04T21:00:40.0888071Z 2025-03-04T21:00:40.0888509Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0889530Z x_94: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.0890264Z 2025-03-04T21:00:40.0890744Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.0893399Z x_95: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_94 = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.0895475Z 2025-03-04T21:00:40.0895849Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.0896327Z out_56: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_95); x_95 = None 2025-03-04T21:00:40.0896577Z 2025-03-04T21:00:40.0896920Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0897663Z x_96: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_56 = l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.0898200Z 2025-03-04T21:00:40.0898550Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.0900402Z x_97: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_96 = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.0902204Z 2025-03-04T21:00:40.0902600Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.0903103Z out_57: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_97); x_97 = None 2025-03-04T21:00:40.0903380Z 2025-03-04T21:00:40.0903758Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0904638Z x_98: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_57 = l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.0905221Z 2025-03-04T21:00:40.0905592Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.0907619Z x_99: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_98 = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.0909371Z 2025-03-04T21:00:40.0909753Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.0910255Z x_99 += out_55; out_58: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_99; x_99 = out_55 = None 2025-03-04T21:00:40.0910528Z 2025-03-04T21:00:40.0910903Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.0911410Z out_59: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_58); out_58 = None 2025-03-04T21:00:40.0911677Z 2025-03-04T21:00:40.0912030Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0912807Z x_100: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.0913369Z 2025-03-04T21:00:40.0913735Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.0915694Z x_101: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_100 = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.0917528Z 2025-03-04T21:00:40.0918021Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.0918583Z out_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_101); x_101 = None 2025-03-04T21:00:40.0918866Z 2025-03-04T21:00:40.0919243Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0920064Z x_102: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_60 = l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.0920608Z 2025-03-04T21:00:40.0920961Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.0922888Z x_103: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_102 = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.0924539Z 2025-03-04T21:00:40.0924909Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.0925390Z out_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_103); x_103 = None 2025-03-04T21:00:40.0925645Z 2025-03-04T21:00:40.0925981Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0926721Z x_104: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_61 = l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.0927267Z 2025-03-04T21:00:40.0927615Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.0929491Z x_105: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_104 = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.0931178Z 2025-03-04T21:00:40.0931543Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.0932032Z x_105 += out_59; out_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_105; x_105 = out_59 = None 2025-03-04T21:00:40.0932302Z 2025-03-04T21:00:40.0932691Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.0933180Z out_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_62); out_62 = None 2025-03-04T21:00:40.0933450Z 2025-03-04T21:00:40.0933778Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0934496Z x_106: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_63, l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.0935024Z 2025-03-04T21:00:40.0935365Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.0937244Z x_107: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_106 = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.0938888Z 2025-03-04T21:00:40.0939258Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.0939745Z out_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_107); x_107 = None 2025-03-04T21:00:40.0939994Z 2025-03-04T21:00:40.0940321Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0941039Z x_108: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_64, l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_64 = l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.0941565Z 2025-03-04T21:00:40.0941904Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.0943712Z x_109: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_108, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_108 = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.0945375Z 2025-03-04T21:00:40.0945735Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.0946218Z out_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_109); x_109 = None 2025-03-04T21:00:40.0946465Z 2025-03-04T21:00:40.0946794Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0947517Z x_110: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_65, l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_65 = l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.0948052Z 2025-03-04T21:00:40.0948394Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.0950236Z x_111: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_110, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_110 = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.0951833Z 2025-03-04T21:00:40.0952184Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.0952654Z x_111 += out_63; out_66: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_111; x_111 = out_63 = None 2025-03-04T21:00:40.0952912Z 2025-03-04T21:00:40.0953272Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.0953739Z out_67: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_66); out_66 = None 2025-03-04T21:00:40.0953990Z 2025-03-04T21:00:40.0954316Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0955028Z x_112: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_67, l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.0955549Z 2025-03-04T21:00:40.0955895Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.0957450Z x_113: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_112, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_112 = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.0957555Z 2025-03-04T21:00:40.0957926Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.0958069Z out_68: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_113); x_113 = None 2025-03-04T21:00:40.0958143Z 2025-03-04T21:00:40.0958407Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0958864Z x_114: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_68, l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_68 = l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.0958936Z 2025-03-04T21:00:40.0959220Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.0960813Z x_115: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_114, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_114 = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.0960879Z 2025-03-04T21:00:40.0961174Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.0961322Z out_69: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_115); x_115 = None 2025-03-04T21:00:40.0961383Z 2025-03-04T21:00:40.0961640Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0962071Z x_116: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_69, l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_69 = l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.0962141Z 2025-03-04T21:00:40.0962410Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.0963982Z x_117: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_116, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_116 = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.0964067Z 2025-03-04T21:00:40.0964347Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.0964517Z x_117 += out_67; out_70: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_117; x_117 = out_67 = None 2025-03-04T21:00:40.0964578Z 2025-03-04T21:00:40.0964867Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.0965006Z out_71: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_70); out_70 = None 2025-03-04T21:00:40.0965073Z 2025-03-04T21:00:40.0965324Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0965746Z x_118: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_71, l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.0965809Z 2025-03-04T21:00:40.0966120Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.0967666Z x_119: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_118, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_118 = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.0967738Z 2025-03-04T21:00:40.0968031Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.0968165Z out_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_119); x_119 = None 2025-03-04T21:00:40.0968233Z 2025-03-04T21:00:40.0968483Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0968909Z x_120: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_72, l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_72 = l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.0968967Z 2025-03-04T21:00:40.0969239Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.0970787Z x_121: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_120, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_120 = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.0970883Z 2025-03-04T21:00:40.0971188Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.0971320Z out_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_121); x_121 = None 2025-03-04T21:00:40.0971388Z 2025-03-04T21:00:40.0971638Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0972072Z x_122: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_73, l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_73 = l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.0972133Z 2025-03-04T21:00:40.0972405Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.0973983Z x_123: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_122, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_122 = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.0974053Z 2025-03-04T21:00:40.0974345Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.0974493Z x_123 += out_71; out_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_123; x_123 = out_71 = None 2025-03-04T21:00:40.0974563Z 2025-03-04T21:00:40.0974847Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.0974991Z out_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_74); out_74 = None 2025-03-04T21:00:40.0975051Z 2025-03-04T21:00:40.0975308Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0975736Z x_124: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_75, l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.0975812Z 2025-03-04T21:00:40.0976076Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.0977618Z x_125: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_124, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_124 = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.0977700Z 2025-03-04T21:00:40.0977980Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.0978120Z out_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_125); x_125 = None 2025-03-04T21:00:40.0978180Z 2025-03-04T21:00:40.0978430Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0978847Z x_126: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_76, l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_76 = l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.0978918Z 2025-03-04T21:00:40.0979205Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.0980721Z x_127: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_126, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_126 = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.0980790Z 2025-03-04T21:00:40.0981066Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.0981202Z out_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_127); x_127 = None 2025-03-04T21:00:40.0981260Z 2025-03-04T21:00:40.0981509Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0981920Z x_128: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_77, l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_77 = l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.0981992Z 2025-03-04T21:00:40.0982252Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.0983757Z x_129: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_128, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_128 = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.0983851Z 2025-03-04T21:00:40.0984123Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.0984275Z x_129 += out_75; out_78: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_129; x_129 = out_75 = None 2025-03-04T21:00:40.0984335Z 2025-03-04T21:00:40.0984621Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.0984756Z out_79: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_78); out_78 = None 2025-03-04T21:00:40.0984823Z 2025-03-04T21:00:40.0985067Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0985511Z x_130: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_79, l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.0985571Z 2025-03-04T21:00:40.0985836Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.0987362Z x_131: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_130, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_130 = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.0987425Z 2025-03-04T21:00:40.0987705Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.0987833Z out_80: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_131); x_131 = None 2025-03-04T21:00:40.0987900Z 2025-03-04T21:00:40.0988142Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0988560Z x_132: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_80, l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_80 = l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.0988620Z 2025-03-04T21:00:40.0988883Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.0990410Z x_133: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_132, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_132 = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.0990487Z 2025-03-04T21:00:40.0990776Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.0990906Z out_81: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_133); x_133 = None 2025-03-04T21:00:40.0990972Z 2025-03-04T21:00:40.0991215Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0991638Z x_134: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_81, l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_81 = l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.0991742Z 2025-03-04T21:00:40.0992006Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.0993527Z x_135: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_134, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_134 = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.0993589Z 2025-03-04T21:00:40.0993874Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.0994016Z x_135 += out_79; out_82: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_135; x_135 = out_79 = None 2025-03-04T21:00:40.0994082Z 2025-03-04T21:00:40.0994357Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.0994497Z out_83: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_82); out_82 = None 2025-03-04T21:00:40.0994556Z 2025-03-04T21:00:40.0994806Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0995225Z x_136: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_83, l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.0995301Z 2025-03-04T21:00:40.0995563Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.0997084Z x_137: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_136, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_136 = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.0997165Z 2025-03-04T21:00:40.0997452Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.0997590Z out_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_137); x_137 = None 2025-03-04T21:00:40.0997658Z 2025-03-04T21:00:40.0997980Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.0998457Z x_138: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_84, l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_84 = l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.0998521Z 2025-03-04T21:00:40.0998795Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1000346Z x_139: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_138, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_138 = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1000418Z 2025-03-04T21:00:40.1000711Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1000847Z out_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_139); x_139 = None 2025-03-04T21:00:40.1000917Z 2025-03-04T21:00:40.1001170Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1001613Z x_140: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_85, l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_85 = l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.1001694Z 2025-03-04T21:00:40.1002146Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1003710Z x_141: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_140, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_140 = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1003823Z 2025-03-04T21:00:40.1004112Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.1004259Z x_141 += out_83; out_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_141; x_141 = out_83 = None 2025-03-04T21:00:40.1004327Z 2025-03-04T21:00:40.1004607Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1004753Z out_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_86); out_86 = None 2025-03-04T21:00:40.1004817Z 2025-03-04T21:00:40.1005161Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1005578Z x_142: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_87, l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.1005649Z 2025-03-04T21:00:40.1005914Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1007476Z x_143: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_142, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_142 = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1007547Z 2025-03-04T21:00:40.1007831Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1007970Z out_88: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_143); x_143 = None 2025-03-04T21:00:40.1008031Z 2025-03-04T21:00:40.1008290Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1008716Z x_144: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_88, l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_88 = l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.1008806Z 2025-03-04T21:00:40.1009072Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1010634Z x_145: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_144, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_144 = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1010716Z 2025-03-04T21:00:40.1011004Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1011146Z out_89: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_145); x_145 = None 2025-03-04T21:00:40.1011206Z 2025-03-04T21:00:40.1011468Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1011921Z x_146: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_89, l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_89 = l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.1011992Z 2025-03-04T21:00:40.1012283Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1013816Z x_147: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_146, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_146 = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1013885Z 2025-03-04T21:00:40.1014159Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.1014307Z x_147 += out_87; out_90: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_147; x_147 = out_87 = None 2025-03-04T21:00:40.1014367Z 2025-03-04T21:00:40.1014648Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1014786Z out_91: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_90); out_90 = None 2025-03-04T21:00:40.1014852Z 2025-03-04T21:00:40.1015097Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1015523Z x_148: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_91, l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.1015583Z 2025-03-04T21:00:40.1015850Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1017356Z x_149: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_148, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_148 = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1017472Z 2025-03-04T21:00:40.1017758Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1017892Z out_92: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_149); x_149 = None 2025-03-04T21:00:40.1017959Z 2025-03-04T21:00:40.1018231Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1018658Z x_150: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_92, l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_92 = l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.1018719Z 2025-03-04T21:00:40.1018999Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1020497Z x_151: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_150, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_150 = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1020559Z 2025-03-04T21:00:40.1020846Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1020975Z out_93: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_151); x_151 = None 2025-03-04T21:00:40.1021043Z 2025-03-04T21:00:40.1021286Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1021708Z x_152: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_93, l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_93 = l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.1021783Z 2025-03-04T21:00:40.1022052Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1023568Z x_153: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_152, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_152 = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1023643Z 2025-03-04T21:00:40.1023924Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.1024065Z x_153 += out_91; out_94: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_153; x_153 = out_91 = None 2025-03-04T21:00:40.1024135Z 2025-03-04T21:00:40.1024445Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1024590Z out_95: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_94); out_94 = None 2025-03-04T21:00:40.1024652Z 2025-03-04T21:00:40.1024919Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1025328Z x_154: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_95, l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.1025394Z 2025-03-04T21:00:40.1025661Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1027171Z x_155: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_154, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_154 = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1027241Z 2025-03-04T21:00:40.1027518Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1027657Z out_96: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_155); x_155 = None 2025-03-04T21:00:40.1027717Z 2025-03-04T21:00:40.1028439Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1028860Z x_156: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_96, l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_96 = l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.1028927Z 2025-03-04T21:00:40.1029202Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1030730Z x_157: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_156, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_156 = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1030815Z 2025-03-04T21:00:40.1031090Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1031224Z out_97: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_157); x_157 = None 2025-03-04T21:00:40.1031312Z 2025-03-04T21:00:40.1031570Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1032000Z x_158: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_97, l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_97 = l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.1032062Z 2025-03-04T21:00:40.1032332Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1033873Z x_159: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_158, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_158 = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1033944Z 2025-03-04T21:00:40.1034222Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.1034377Z x_159 += out_95; out_98: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_159; x_159 = out_95 = None 2025-03-04T21:00:40.1034446Z 2025-03-04T21:00:40.1034732Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1034892Z out_99: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_98); out_98 = None 2025-03-04T21:00:40.1034953Z 2025-03-04T21:00:40.1035210Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1035629Z x_160: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_99, l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.1035697Z 2025-03-04T21:00:40.1035978Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1037540Z x_161: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_160, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_160 = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1037610Z 2025-03-04T21:00:40.1038014Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1038170Z out_100: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_161); x_161 = None 2025-03-04T21:00:40.1038234Z 2025-03-04T21:00:40.1038493Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1038925Z x_162: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_100, l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_100 = l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.1038996Z 2025-03-04T21:00:40.1039266Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1040834Z x_163: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_162, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_162 = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1040909Z 2025-03-04T21:00:40.1041200Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1041347Z out_101: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_163); x_163 = None 2025-03-04T21:00:40.1041430Z 2025-03-04T21:00:40.1041690Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1042123Z x_164: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_101, l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_101 = l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.1042191Z 2025-03-04T21:00:40.1042458Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1044031Z x_165: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_164, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_164 = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1044100Z 2025-03-04T21:00:40.1044378Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.1044564Z x_165 += out_99; out_102: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_165; x_165 = out_99 = None 2025-03-04T21:00:40.1044626Z 2025-03-04T21:00:40.1044918Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1045065Z out_103: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_102); out_102 = None 2025-03-04T21:00:40.1045135Z 2025-03-04T21:00:40.1045385Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1045816Z x_166: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_103, l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.1045878Z 2025-03-04T21:00:40.1046155Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1047724Z x_167: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_166, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_166 = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1047793Z 2025-03-04T21:00:40.1048085Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1048240Z out_104: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_167); x_167 = None 2025-03-04T21:00:40.1048308Z 2025-03-04T21:00:40.1048559Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1048998Z x_168: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_104, l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_104 = l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.1049075Z 2025-03-04T21:00:40.1049348Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1050899Z x_169: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_168, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_168 = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1050961Z 2025-03-04T21:00:40.1051274Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1051409Z out_105: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_169); x_169 = None 2025-03-04T21:00:40.1051478Z 2025-03-04T21:00:40.1051731Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1052196Z x_170: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_105, l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_105 = l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.1052260Z 2025-03-04T21:00:40.1052552Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1054223Z x_171: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_170, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_170 = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1054288Z 2025-03-04T21:00:40.1054600Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.1054754Z x_171 += out_103; out_106: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_171; x_171 = out_103 = None 2025-03-04T21:00:40.1054836Z 2025-03-04T21:00:40.1055115Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1055263Z out_107: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_106); out_106 = None 2025-03-04T21:00:40.1055324Z 2025-03-04T21:00:40.1055577Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1056004Z x_172: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_107, l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.1056088Z 2025-03-04T21:00:40.1056354Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1057945Z x_173: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_172, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_172 = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1058018Z 2025-03-04T21:00:40.1058301Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1058442Z out_108: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_173); x_173 = None 2025-03-04T21:00:40.1058504Z 2025-03-04T21:00:40.1058765Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1059193Z x_174: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_108, l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_108 = l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.1059266Z 2025-03-04T21:00:40.1059541Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1061110Z x_175: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_174, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_174 = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1061187Z 2025-03-04T21:00:40.1061494Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1061637Z out_109: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_175); x_175 = None 2025-03-04T21:00:40.1061697Z 2025-03-04T21:00:40.1061952Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1062380Z x_176: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_109, l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_109 = l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.1062466Z 2025-03-04T21:00:40.1062736Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1064293Z x_177: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_176, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_176 = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1064363Z 2025-03-04T21:00:40.1064641Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.1064803Z x_177 += out_107; out_110: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_177; x_177 = out_107 = None 2025-03-04T21:00:40.1064862Z 2025-03-04T21:00:40.1065151Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1065290Z out_111: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_110); out_110 = None 2025-03-04T21:00:40.1065357Z 2025-03-04T21:00:40.1065604Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1066026Z x_178: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_111, l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.1066094Z 2025-03-04T21:00:40.1066372Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1067916Z x_179: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_178, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_178 = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1067993Z 2025-03-04T21:00:40.1068281Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1068411Z out_112: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_179); x_179 = None 2025-03-04T21:00:40.1068477Z 2025-03-04T21:00:40.1068722Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1069161Z x_180: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_112, l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_112 = l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.1069229Z 2025-03-04T21:00:40.1069490Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1071030Z x_181: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_180, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_180 = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1071092Z 2025-03-04T21:00:40.1071389Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1071527Z out_113: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_181); x_181 = None 2025-03-04T21:00:40.1071586Z 2025-03-04T21:00:40.1071833Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1072248Z x_182: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_113, l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_113 = l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.1072316Z 2025-03-04T21:00:40.1072576Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1074103Z x_183: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_182, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_182 = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1074186Z 2025-03-04T21:00:40.1074460Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.1074616Z x_183 += out_111; out_114: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_183; x_183 = out_111 = None 2025-03-04T21:00:40.1074676Z 2025-03-04T21:00:40.1074960Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1075096Z out_115: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_114); out_114 = None 2025-03-04T21:00:40.1075178Z 2025-03-04T21:00:40.1075428Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1075841Z x_184: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_115, l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.1075903Z 2025-03-04T21:00:40.1076173Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1077744Z x_185: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_184, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_184 = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1077886Z 2025-03-04T21:00:40.1078210Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1078352Z out_116: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_185); x_185 = None 2025-03-04T21:00:40.1078423Z 2025-03-04T21:00:40.1078691Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1079132Z x_186: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_116, l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_116 = l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.1079197Z 2025-03-04T21:00:40.1079473Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1081051Z x_187: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_186, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_186 = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1081140Z 2025-03-04T21:00:40.1081435Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1081570Z out_117: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_187); x_187 = None 2025-03-04T21:00:40.1081638Z 2025-03-04T21:00:40.1081893Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1082354Z x_188: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_117, l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_117 = l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.1082415Z 2025-03-04T21:00:40.1082694Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1084282Z x_189: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_188, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_188 = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1084352Z 2025-03-04T21:00:40.1084639Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.1084790Z x_189 += out_115; out_118: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_189; x_189 = out_115 = None 2025-03-04T21:00:40.1084857Z 2025-03-04T21:00:40.1085141Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1085292Z out_119: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_118); out_118 = None 2025-03-04T21:00:40.1085356Z 2025-03-04T21:00:40.1085810Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:00:40.1085965Z arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32) 2025-03-04T21:00:40.1086034Z 2025-03-04T21:00:40.1086336Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:00:40.1086479Z shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T21:00:40.1086542Z 2025-03-04T21:00:40.1086988Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:00:40.1087142Z arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32) 2025-03-04T21:00:40.1087228Z 2025-03-04T21:00:40.1087526Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:00:40.1087667Z shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T21:00:40.1087727Z 2025-03-04T21:00:40.1088116Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:00:40.1088291Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T21:00:40.1088409Z shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0] 2025-03-04T21:00:40.1088529Z shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T21:00:40.1088596Z 2025-03-04T21:00:40.1088930Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:00:40.1089063Z shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T21:00:40.1089124Z 2025-03-04T21:00:40.1089467Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:00:40.1089582Z shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T21:00:40.1089649Z 2025-03-04T21:00:40.1090041Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:00:40.1090302Z shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T21:00:40.1090371Z 2025-03-04T21:00:40.1090801Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:00:40.1090931Z view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T21:00:40.1091371Z view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T21:00:40.1091499Z add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T21:00:40.1091626Z x_190: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4); add = None 2025-03-04T21:00:40.1091692Z 2025-03-04T21:00:40.1091992Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:00:40.1092120Z tensor: "f32[82125, 4][4, 1]cpu" = x_190.to(torch.float32); x_190 = None 2025-03-04T21:00:40.1092181Z 2025-03-04T21:00:40.1092438Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1093216Z x_191: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_119, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); out_119 = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T21:00:40.1093285Z 2025-03-04T21:00:40.1093556Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:00:40.1093759Z x_192: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_191, inplace = False); x_191 = None 2025-03-04T21:00:40.1093820Z 2025-03-04T21:00:40.1094200Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:00:40.1095048Z score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_192, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T21:00:40.1095124Z 2025-03-04T21:00:40.1095485Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:00:40.1096294Z x_193: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_192, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_192 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T21:00:40.1096364Z 2025-03-04T21:00:40.1096728Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T21:00:40.1096883Z permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T21:00:40.1097016Z logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T21:00:40.1097083Z 2025-03-04T21:00:40.1097489Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T21:00:40.1097652Z view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_193.view(4, -1, 4, 73, 75); x_193 = None 2025-03-04T21:00:40.1097827Z permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2); view_2 = None 2025-03-04T21:00:40.1097999Z pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2); permute_1 = None 2025-03-04T21:00:40.1098065Z 2025-03-04T21:00:40.1098461Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:00:40.1098663Z pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T21:00:40.1098722Z 2025-03-04T21:00:40.1099151Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:00:40.1099293Z unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T21:00:40.1099446Z expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T21:00:40.1099579Z anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T21:00:40.1099662Z 2025-03-04T21:00:40.1100034Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:00:40.1100203Z deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T21:00:40.1100262Z 2025-03-04T21:00:40.1100576Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:00:40.1100708Z boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T21:00:40.1100787Z 2025-03-04T21:00:40.1101099Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:00:40.1101229Z getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:00:40.1101347Z getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:00:40.1101493Z widths: "f32[328500][1]cpu" = getitem_2 - getitem_3; getitem_2 = getitem_3 = None 2025-03-04T21:00:40.1101552Z 2025-03-04T21:00:40.1101973Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:00:40.1102102Z getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:00:40.1102227Z getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:00:40.1102372Z heights: "f32[328500][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T21:00:40.1102505Z 2025-03-04T21:00:40.1102828Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:00:40.1102951Z getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:00:40.1103033Z mul: "f32[328500][1]cpu" = 0.5 * widths 2025-03-04T21:00:40.1103156Z ctr_x: "f32[328500][1]cpu" = getitem_6 + mul; getitem_6 = mul = None 2025-03-04T21:00:40.1103217Z 2025-03-04T21:00:40.1103531Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:00:40.1103668Z getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:00:40.1103762Z mul_1: "f32[328500][1]cpu" = 0.5 * heights 2025-03-04T21:00:40.1103883Z ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1; getitem_7 = mul_1 = None 2025-03-04T21:00:40.1103951Z 2025-03-04T21:00:40.1104291Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:00:40.1104448Z getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:00:40.1104567Z dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0; getitem_8 = None 2025-03-04T21:00:40.1104627Z 2025-03-04T21:00:40.1104929Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:00:40.1105074Z getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:00:40.1105189Z dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0; getitem_9 = None 2025-03-04T21:00:40.1105250Z 2025-03-04T21:00:40.1105551Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:00:40.1105719Z getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:00:40.1105831Z dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0; getitem_10 = None 2025-03-04T21:00:40.1105893Z 2025-03-04T21:00:40.1106195Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:00:40.1106370Z getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:00:40.1106483Z dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0; getitem_11 = None 2025-03-04T21:00:40.1106560Z 2025-03-04T21:00:40.1106904Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:00:40.1107038Z dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:00:40.1107107Z 2025-03-04T21:00:40.1107439Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:00:40.1107575Z dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:00:40.1107633Z 2025-03-04T21:00:40.1107984Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:00:40.1108115Z getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:00:40.1108279Z mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12; dx = getitem_12 = None 2025-03-04T21:00:40.1108426Z getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:00:40.1108568Z pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13; mul_2 = getitem_13 = None 2025-03-04T21:00:40.1108628Z 2025-03-04T21:00:40.1108978Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:00:40.1109109Z getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:00:40.1109232Z mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14; dy = getitem_14 = None 2025-03-04T21:00:40.1109375Z getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:00:40.1109515Z pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15; mul_3 = getitem_15 = None 2025-03-04T21:00:40.1109577Z 2025-03-04T21:00:40.1109918Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:00:40.1110029Z exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:00:40.1110192Z getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:00:40.1110314Z pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16; exp = getitem_16 = None 2025-03-04T21:00:40.1110380Z 2025-03-04T21:00:40.1110716Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:00:40.1110824Z exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:00:40.1110991Z getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:00:40.1111116Z pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17; exp_1 = getitem_17 = None 2025-03-04T21:00:40.1111197Z 2025-03-04T21:00:40.1111506Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:00:40.1111603Z mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T21:00:40.1111712Z x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:00:40.1111779Z 2025-03-04T21:00:40.1112082Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:00:40.1112178Z mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T21:00:40.1112311Z y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:00:40.1112376Z 2025-03-04T21:00:40.1112676Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:00:40.1112791Z mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:00:40.1112911Z x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:00:40.1112976Z 2025-03-04T21:00:40.1113273Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:00:40.1113385Z mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:00:40.1113501Z y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:00:40.1113567Z 2025-03-04T21:00:40.1113931Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:00:40.1114113Z pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:00:40.1114172Z 2025-03-04T21:00:40.1114506Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:00:40.1114657Z proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4)); pred_boxes = None 2025-03-04T21:00:40.1114723Z 2025-03-04T21:00:40.1115091Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:00:40.1115263Z proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T21:00:40.1115323Z 2025-03-04T21:00:40.1115801Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T21:00:40.1115928Z arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T21:00:40.1115994Z 2025-03-04T21:00:40.1116281Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:00:40.1116419Z batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T21:00:40.1116479Z 2025-03-04T21:00:40.1116911Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:00:40.1117022Z topk = logits_i.topk(6000, dim = 1); logits_i = None 2025-03-04T21:00:40.1117125Z topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0] 2025-03-04T21:00:40.1117276Z topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1]; topk = None 2025-03-04T21:00:40.1117345Z 2025-03-04T21:00:40.1117932Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:00:40.1118133Z getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T21:00:40.1118371Z topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)]; proposals_i_1 = getitem_20 = topk_idx = None 2025-03-04T21:00:40.1118463Z 2025-03-04T21:00:40.1118940Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:00:40.1119115Z full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:00:40.1119185Z 2025-03-04T21:00:40.1119475Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:00:40.1119628Z level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu')); full = level_ids = None 2025-03-04T21:00:40.1119687Z 2025-03-04T21:00:40.1120073Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T21:00:40.1120242Z getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0]; topk_proposals_i = None 2025-03-04T21:00:40.1120311Z 2025-03-04T21:00:40.1120600Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:00:40.1120744Z tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32); getitem_22 = None 2025-03-04T21:00:40.1120804Z 2025-03-04T21:00:40.1121177Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T21:00:40.1121308Z scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0]; topk_scores_i = None 2025-03-04T21:00:40.1121372Z 2025-03-04T21:00:40.1121842Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T21:00:40.1121981Z isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1); tensor_1 = None 2025-03-04T21:00:40.1122095Z all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:00:40.1122253Z isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T21:00:40.1122381Z valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T21:00:40.1122450Z 2025-03-04T21:00:40.1122811Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T21:00:40.1122930Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T21:00:40.1122992Z 2025-03-04T21:00:40.1123474Z 2025-03-04T21:00:40.1123573Z class GraphModule(torch.nn.Module): 2025-03-04T21:00:40.1214371Z def forward(self, L_stack0_tensor: "f32[4, 3, 1156, 1199][4158132, 1386044, 1199, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_: "f32[512, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_: 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L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_: "f32[1024, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 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"f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[1024, 1024, 3, 3][9216, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[1024][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"): 2025-03-04T21:00:40.1215055Z l_stack0_tensor = L_stack0_tensor 2025-03-04T21:00:40.1215387Z l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.1215796Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1216192Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1216583Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1216960Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1217325Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.1217751Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1218169Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1218607Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1219053Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1219392Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.1219795Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1220282Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1220671Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1221069Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1221417Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.1221817Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1222239Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1222635Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1223011Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1223389Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:00:40.1223840Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1224258Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1224682Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1225081Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1225420Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.1225814Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1226227Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1226614Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1226948Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1227232Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.1227580Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1228001Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1228346Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1228649Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1228935Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.1229274Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1229613Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1229933Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1230247Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1230533Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.1230873Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1231211Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1231538Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1231848Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1232132Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.1232485Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1232818Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1233139Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1233470Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1233785Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.1234198Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1234578Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1234940Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1235287Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1235617Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.1236001Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1236387Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1236753Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1237092Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1237415Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.1237864Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1238291Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1238704Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1239107Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1239465Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.1239919Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1240306Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1240663Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1241023Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1241367Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:00:40.1241807Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1242198Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1242571Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1242925Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1243243Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.1243620Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1243987Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1244345Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1244685Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1245011Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.1245380Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1245773Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1246124Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1246474Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1246808Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.1247180Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1247545Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1247857Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1248170Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1248479Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.1248827Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1249161Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1249485Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1249798Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1250081Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.1250421Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1250753Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1251076Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1251380Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1251669Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.1252018Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1252351Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1252670Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1252990Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1253274Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.1253606Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1253941Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1254254Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1254590Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1254869Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.1255210Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1255543Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1255854Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1256170Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1256448Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.1256792Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1257123Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1257442Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1257750Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1258038Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.1258395Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1258720Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1259039Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1259361Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1259650Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.1259982Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1260317Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1260627Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1260971Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1261260Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.1261592Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1261930Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1262241Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1262559Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1262856Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:00:40.1263207Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1263547Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1263886Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1264211Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1264506Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.1264844Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1265173Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1265514Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1265818Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1266108Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.1266442Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1266780Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1267129Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1267434Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1267717Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.1268048Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1268384Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1268699Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1269016Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1269290Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.1269630Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1269964Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1270281Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1270608Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1270886Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.1271223Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1271553Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1271892Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1272199Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1272483Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.1272820Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1273150Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1273500Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1273806Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1274090Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.1274419Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1274761Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1275072Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1275382Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1275669Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.1276003Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1276340Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1276653Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1276980Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1277258Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.1277596Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1278032Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1278371Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1278728Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1279035Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.1279378Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1279740Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1280066Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1280370Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1280658Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.1281015Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1281385Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1281723Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1282053Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1282369Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.1282732Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1283090Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1283438Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1283769Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1284064Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.1284424Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1284793Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1285122Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1285450Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1285741Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.1286145Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1286492Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1286833Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1287149Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1287452Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.1287814Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1288161Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1288501Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1288822Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1289119Z l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.1289469Z l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1289825Z l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1290170Z l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1290498Z l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1290798Z l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.1291165Z l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1291527Z l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1291842Z l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1292153Z l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1292433Z l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.1292806Z l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1293135Z l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1293456Z l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1293768Z l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1294045Z l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.1294392Z l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1294723Z l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1295043Z l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1295348Z l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1295633Z l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.1295968Z l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1296321Z l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1296639Z l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1296943Z l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1297246Z l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.1297581Z l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1297919Z l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1298231Z l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1298542Z l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1298853Z l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.1299192Z l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1299528Z l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1299841Z l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1300155Z l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1300436Z l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.1300778Z l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1301109Z l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1301426Z l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1301737Z l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1302117Z l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.1302464Z l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1302839Z l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1303165Z l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1303469Z l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1303782Z l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.1304113Z l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1304448Z l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1304768Z l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1305076Z l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1305404Z l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.1305743Z l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1306083Z l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1306396Z l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1306708Z l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1306990Z l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.1307333Z l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1307672Z l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1307984Z l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1308298Z l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1308585Z l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.1308945Z l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1309280Z l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1309601Z l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1309935Z l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1310223Z l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.1310566Z l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1310895Z l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1311219Z l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1311555Z l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1311847Z l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.1312187Z l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1312523Z l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1312839Z l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1313155Z l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1313443Z l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.1313779Z l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1314120Z l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1314438Z l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1314754Z l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1315050Z l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.1315392Z l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1315722Z l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1316051Z l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1316385Z l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1316671Z l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.1317024Z l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1317362Z l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1317720Z l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1318081Z l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1318381Z l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.1318724Z l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1319080Z l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1319426Z l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1319749Z l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1320043Z l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.1320396Z l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1320749Z l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1321088Z l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1321438Z l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1321737Z l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.1322100Z l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1322464Z l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1322818Z l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1323158Z l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1323450Z l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.1323814Z l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1324156Z l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1324575Z l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1324891Z l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1325186Z l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.1325532Z l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1325875Z l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1326210Z l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1326521Z l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1326814Z l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.1327167Z l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1327515Z l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1327836Z l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1328175Z l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1328468Z l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.1328821Z l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1329194Z l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1329519Z l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1329842Z l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1330130Z l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.1330493Z l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1330869Z l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1331195Z l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1331504Z l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1331789Z l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.1332130Z l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1332463Z l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1332787Z l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1333092Z l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1333379Z l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.1333714Z l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1334057Z l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1334396Z l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1334703Z l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1334989Z l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.1335345Z l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1335683Z l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1335997Z l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1336309Z l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1336591Z l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.1336959Z l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1337301Z l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1337618Z l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1337928Z l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1338209Z l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.1338554Z l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1338885Z l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1339212Z l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1339516Z l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1339804Z l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.1340152Z l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1340499Z l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1340823Z l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1341129Z l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1341419Z l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.1341771Z l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1342111Z l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1342427Z l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1342741Z l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1343035Z l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.1343397Z l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1343735Z l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1344049Z l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1344359Z l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1344642Z l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.1344982Z l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1345312Z l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1345632Z l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1345944Z l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1346231Z l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.1346571Z l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1346915Z l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1347235Z l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1347538Z l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1347845Z l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.1348180Z l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1348518Z l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1348848Z l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1349151Z l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1349462Z l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.1349796Z l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1350135Z l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1350451Z l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1350761Z l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1351045Z l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.1351388Z l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1351732Z l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1352056Z l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1352394Z l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1352695Z l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.1353083Z l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1353444Z l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1353790Z l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1354136Z l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1354451Z l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.1354832Z l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1355198Z l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1355542Z l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1355891Z l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1356188Z l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.1356532Z l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1356877Z l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1357198Z l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1357524Z l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1357876Z l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.1358226Z l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1358591Z l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1358938Z l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1359290Z l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1359615Z l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.1359995Z l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1360346Z l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1360706Z l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1361041Z l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1361332Z l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.1361696Z l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1362048Z l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1362424Z l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1362748Z l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1363044Z l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.1363396Z l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1363758Z l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1364103Z l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1364429Z l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1364723Z l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.1365076Z l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1365435Z l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1365761Z l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1366092Z l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1366377Z l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.1366724Z l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1367069Z l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1367407Z l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1367728Z l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1368016Z l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.1368377Z l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1368728Z l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1369088Z l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1369406Z l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1369715Z l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.1370061Z l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1370401Z l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1370724Z l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1371032Z l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1371325Z l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.1371660Z l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1372005Z l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1372331Z l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1372649Z l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1373002Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T21:00:40.1373317Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T21:00:40.1373644Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T21:00:40.1374008Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T21:00:40.1374362Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T21:00:40.1374706Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T21:00:40.1375047Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T21:00:40.1375139Z 2025-03-04T21:00:40.1375440Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1375922Z x: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1); l_stack0_tensor = l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.1375985Z 2025-03-04T21:00:40.1376266Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1377700Z x_1: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1377773Z 2025-03-04T21:00:40.1378053Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x) 2025-03-04T21:00:40.1378196Z x_2: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.relu_(x_1); x_1 = None 2025-03-04T21:00:40.1378258Z 2025-03-04T21:00:40.1378627Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1) 2025-03-04T21:00:40.1378865Z x_3: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1); x_2 = None 2025-03-04T21:00:40.1378940Z 2025-03-04T21:00:40.1379205Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1379614Z x_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.1379681Z 2025-03-04T21:00:40.1379957Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1381486Z x_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_4 = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1381556Z 2025-03-04T21:00:40.1381872Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1382011Z out: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_5); x_5 = None 2025-03-04T21:00:40.1382071Z 2025-03-04T21:00:40.1382326Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1382740Z x_6: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out = l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.1382807Z 2025-03-04T21:00:40.1383067Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1384561Z x_7: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_6 = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1384628Z 2025-03-04T21:00:40.1384912Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1385055Z out_1: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_7); x_7 = None 2025-03-04T21:00:40.1385114Z 2025-03-04T21:00:40.1385381Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1385805Z x_8: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_1 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.1385870Z 2025-03-04T21:00:40.1386132Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1387645Z x_9: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_8 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1387712Z 2025-03-04T21:00:40.1387959Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1388420Z x_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); x_3 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:00:40.1388481Z 2025-03-04T21:00:40.1388749Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1390311Z x_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_10 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1390382Z 2025-03-04T21:00:40.1390664Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.1390805Z x_9 += x_11; out_2: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_9; x_9 = x_11 = None 2025-03-04T21:00:40.1390869Z 2025-03-04T21:00:40.1391144Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1391297Z out_3: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_2); out_2 = None 2025-03-04T21:00:40.1391358Z 2025-03-04T21:00:40.1391608Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1392033Z x_12: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.1392098Z 2025-03-04T21:00:40.1392358Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1393876Z x_13: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_12 = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1393958Z 2025-03-04T21:00:40.1394234Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1394376Z out_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_13); x_13 = None 2025-03-04T21:00:40.1394436Z 2025-03-04T21:00:40.1394712Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1395140Z x_14: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_4 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.1395209Z 2025-03-04T21:00:40.1395473Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1397000Z x_15: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_14 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1397069Z 2025-03-04T21:00:40.1397349Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1397495Z out_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_15); x_15 = None 2025-03-04T21:00:40.1397557Z 2025-03-04T21:00:40.1397869Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1398312Z x_16: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_5 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.1398400Z 2025-03-04T21:00:40.1398663Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1400252Z x_17: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_16 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1400338Z 2025-03-04T21:00:40.1400620Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.1400785Z x_17 += out_3; out_6: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_17; x_17 = out_3 = None 2025-03-04T21:00:40.1400847Z 2025-03-04T21:00:40.1401147Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1401326Z out_7: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_6); out_6 = None 2025-03-04T21:00:40.1401398Z 2025-03-04T21:00:40.1401653Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1402269Z x_18: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.1402338Z 2025-03-04T21:00:40.1402614Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1404177Z x_19: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_18 = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1404242Z 2025-03-04T21:00:40.1404535Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1404671Z out_8: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_19); x_19 = None 2025-03-04T21:00:40.1404745Z 2025-03-04T21:00:40.1404994Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1405464Z x_20: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_8 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.1405526Z 2025-03-04T21:00:40.1405802Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1407392Z x_21: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_20 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1407484Z 2025-03-04T21:00:40.1407776Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1407912Z out_9: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_21); x_21 = None 2025-03-04T21:00:40.1407981Z 2025-03-04T21:00:40.1408267Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1408705Z x_22: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_9 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.1408767Z 2025-03-04T21:00:40.1409042Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1410616Z x_23: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_22 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1410680Z 2025-03-04T21:00:40.1410969Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.1411120Z x_23 += out_7; out_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_23; x_23 = out_7 = None 2025-03-04T21:00:40.1411188Z 2025-03-04T21:00:40.1411474Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1411632Z out_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_10); out_10 = None 2025-03-04T21:00:40.1411706Z 2025-03-04T21:00:40.1411972Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1412394Z x_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.1412463Z 2025-03-04T21:00:40.1412735Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1414297Z x_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_24 = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1414369Z 2025-03-04T21:00:40.1414653Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1414833Z out_12: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_25); x_25 = None 2025-03-04T21:00:40.1414894Z 2025-03-04T21:00:40.1415152Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1415597Z x_26: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_12 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.1415655Z 2025-03-04T21:00:40.1415918Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1417405Z x_27: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_26 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1417473Z 2025-03-04T21:00:40.1417748Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1417894Z out_13: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_27); x_27 = None 2025-03-04T21:00:40.1417952Z 2025-03-04T21:00:40.1418197Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1418637Z x_28: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_13 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.1418696Z 2025-03-04T21:00:40.1418962Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1420456Z x_29: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_28 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1420542Z 2025-03-04T21:00:40.1420784Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1421251Z x_30: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); out_11 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:00:40.1421318Z 2025-03-04T21:00:40.1421576Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1423154Z x_31: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_30 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1423222Z 2025-03-04T21:00:40.1423492Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.1423644Z x_29 += x_31; out_14: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_29; x_29 = x_31 = None 2025-03-04T21:00:40.1423704Z 2025-03-04T21:00:40.1423988Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1424133Z out_15: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_14); out_14 = None 2025-03-04T21:00:40.1424199Z 2025-03-04T21:00:40.1424456Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1424902Z x_32: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.1424964Z 2025-03-04T21:00:40.1425238Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1426777Z x_33: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_32 = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1426884Z 2025-03-04T21:00:40.1427176Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1427314Z out_16: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_33); x_33 = None 2025-03-04T21:00:40.1427381Z 2025-03-04T21:00:40.1427658Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1428084Z x_34: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_16 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.1428146Z 2025-03-04T21:00:40.1428412Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1429904Z x_35: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_34 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1429972Z 2025-03-04T21:00:40.1430260Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1430392Z out_17: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_35); x_35 = None 2025-03-04T21:00:40.1430459Z 2025-03-04T21:00:40.1430707Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1431134Z x_36: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_17 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.1431215Z 2025-03-04T21:00:40.1431479Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1433012Z x_37: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_36 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1433092Z 2025-03-04T21:00:40.1433370Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.1433520Z x_37 += out_15; out_18: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_37; x_37 = out_15 = None 2025-03-04T21:00:40.1433586Z 2025-03-04T21:00:40.1433877Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1434062Z out_19: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_18); out_18 = None 2025-03-04T21:00:40.1434124Z 2025-03-04T21:00:40.1434379Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1434800Z x_38: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.1434867Z 2025-03-04T21:00:40.1435132Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1436668Z x_39: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_38 = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1436737Z 2025-03-04T21:00:40.1437019Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1437163Z out_20: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_39); x_39 = None 2025-03-04T21:00:40.1437224Z 2025-03-04T21:00:40.1437478Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1437974Z x_40: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_20 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.1438047Z 2025-03-04T21:00:40.1438310Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1439839Z x_41: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_40 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1439928Z 2025-03-04T21:00:40.1440214Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1440358Z out_21: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_41); x_41 = None 2025-03-04T21:00:40.1440421Z 2025-03-04T21:00:40.1440703Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1441136Z x_42: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_21 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.1441205Z 2025-03-04T21:00:40.1441472Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1443035Z x_43: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_42 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1443106Z 2025-03-04T21:00:40.1443387Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.1443549Z x_43 += out_19; out_22: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_43; x_43 = out_19 = None 2025-03-04T21:00:40.1443610Z 2025-03-04T21:00:40.1443900Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1444047Z out_23: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_22); out_22 = None 2025-03-04T21:00:40.1444130Z 2025-03-04T21:00:40.1444386Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1444818Z x_44: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.1444878Z 2025-03-04T21:00:40.1445150Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1446718Z x_45: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_44 = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1446782Z 2025-03-04T21:00:40.1447073Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1447238Z out_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_45); x_45 = None 2025-03-04T21:00:40.1447309Z 2025-03-04T21:00:40.1447566Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1448021Z x_46: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_24 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.1448081Z 2025-03-04T21:00:40.1448358Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1449921Z x_47: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_46 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1449982Z 2025-03-04T21:00:40.1450272Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1450408Z out_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_47); x_47 = None 2025-03-04T21:00:40.1450476Z 2025-03-04T21:00:40.1450721Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1451157Z x_48: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_25 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.1451216Z 2025-03-04T21:00:40.1451484Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1452996Z x_49: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_48 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1453072Z 2025-03-04T21:00:40.1453350Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.1453499Z x_49 += out_23; out_26: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_49; x_49 = out_23 = None 2025-03-04T21:00:40.1453566Z 2025-03-04T21:00:40.1453873Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1454025Z out_27: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_26); out_26 = None 2025-03-04T21:00:40.1454084Z 2025-03-04T21:00:40.1454335Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1454754Z x_50: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.1454812Z 2025-03-04T21:00:40.1455081Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1456571Z x_51: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_50 = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1456641Z 2025-03-04T21:00:40.1456923Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1457062Z out_28: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_51); x_51 = None 2025-03-04T21:00:40.1457137Z 2025-03-04T21:00:40.1457391Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1457808Z x_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_28 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.1457868Z 2025-03-04T21:00:40.1458131Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1459637Z x_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_52 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1459720Z 2025-03-04T21:00:40.1460006Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1460163Z out_29: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_53); x_53 = None 2025-03-04T21:00:40.1460232Z 2025-03-04T21:00:40.1460473Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1460890Z x_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_29 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.1460950Z 2025-03-04T21:00:40.1461217Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1462716Z x_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_54 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1462778Z 2025-03-04T21:00:40.1463029Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1463453Z x_56: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); out_27 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:00:40.1463536Z 2025-03-04T21:00:40.1463792Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1465325Z x_57: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_56 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1465407Z 2025-03-04T21:00:40.1465683Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.1465824Z x_55 += x_57; out_30: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_55; x_55 = x_57 = None 2025-03-04T21:00:40.1465884Z 2025-03-04T21:00:40.1466168Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1466306Z out_31: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_30); out_30 = None 2025-03-04T21:00:40.1466403Z 2025-03-04T21:00:40.1466649Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1467059Z x_58: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.1467118Z 2025-03-04T21:00:40.1467388Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1468902Z x_59: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_58 = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1468964Z 2025-03-04T21:00:40.1469248Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1469377Z out_32: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_59); x_59 = None 2025-03-04T21:00:40.1469445Z 2025-03-04T21:00:40.1469690Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1470107Z x_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_32 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.1470181Z 2025-03-04T21:00:40.1470449Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1471939Z x_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_60 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1472015Z 2025-03-04T21:00:40.1472307Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1472436Z out_33: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_61); x_61 = None 2025-03-04T21:00:40.1472503Z 2025-03-04T21:00:40.1472749Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1473213Z x_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_33 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.1473274Z 2025-03-04T21:00:40.1473539Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1475032Z x_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_62 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1475094Z 2025-03-04T21:00:40.1475369Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.1475507Z x_63 += out_31; out_34: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_63; x_63 = out_31 = None 2025-03-04T21:00:40.1475574Z 2025-03-04T21:00:40.1475845Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1475992Z out_35: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_34); out_34 = None 2025-03-04T21:00:40.1476050Z 2025-03-04T21:00:40.1476318Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1476718Z x_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.1476785Z 2025-03-04T21:00:40.1477042Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1478604Z x_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_64 = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1478698Z 2025-03-04T21:00:40.1478977Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1479119Z out_36: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_65); x_65 = None 2025-03-04T21:00:40.1479215Z 2025-03-04T21:00:40.1479468Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1479882Z x_66: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_36 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.1479952Z 2025-03-04T21:00:40.1480219Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1481699Z x_67: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_66 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1481770Z 2025-03-04T21:00:40.1482046Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1482182Z out_37: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_67); x_67 = None 2025-03-04T21:00:40.1482243Z 2025-03-04T21:00:40.1482496Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1482902Z x_68: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_37 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.1482987Z 2025-03-04T21:00:40.1483255Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1484739Z x_69: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_68 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1484823Z 2025-03-04T21:00:40.1485096Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.1485245Z x_69 += out_35; out_38: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_69; x_69 = out_35 = None 2025-03-04T21:00:40.1485307Z 2025-03-04T21:00:40.1485621Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1485757Z out_39: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_38); out_38 = None 2025-03-04T21:00:40.1485826Z 2025-03-04T21:00:40.1486067Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1486479Z x_70: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.1486547Z 2025-03-04T21:00:40.1486804Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1488314Z x_71: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_70 = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1488376Z 2025-03-04T21:00:40.1488657Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1488795Z out_40: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_71); x_71 = None 2025-03-04T21:00:40.1488855Z 2025-03-04T21:00:40.1489103Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1489524Z x_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_40 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.1489591Z 2025-03-04T21:00:40.1489848Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1491335Z x_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_72 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1491418Z 2025-03-04T21:00:40.1491697Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1491838Z out_41: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_73); x_73 = None 2025-03-04T21:00:40.1491927Z 2025-03-04T21:00:40.1492187Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1512769Z x_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_41 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.1512960Z 2025-03-04T21:00:40.1513321Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1514944Z x_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_74 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1515019Z 2025-03-04T21:00:40.1515332Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.1515491Z x_75 += out_39; out_42: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_75; x_75 = out_39 = None 2025-03-04T21:00:40.1515567Z 2025-03-04T21:00:40.1515873Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1516180Z out_43: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_42); out_42 = None 2025-03-04T21:00:40.1516245Z 2025-03-04T21:00:40.1516527Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1516976Z x_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.1517048Z 2025-03-04T21:00:40.1517330Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1519051Z x_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_76 = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1519131Z 2025-03-04T21:00:40.1519503Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1519654Z out_44: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_77); x_77 = None 2025-03-04T21:00:40.1519718Z 2025-03-04T21:00:40.1519993Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1520435Z x_78: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_44 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.1520510Z 2025-03-04T21:00:40.1520790Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1522377Z x_79: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_78 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1522450Z 2025-03-04T21:00:40.1522749Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1522898Z out_45: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_79); x_79 = None 2025-03-04T21:00:40.1522963Z 2025-03-04T21:00:40.1523237Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1523700Z x_80: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_45 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.1523771Z 2025-03-04T21:00:40.1524048Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1525634Z x_81: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_80 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1525740Z 2025-03-04T21:00:40.1526030Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.1526183Z x_81 += out_43; out_46: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_81; x_81 = out_43 = None 2025-03-04T21:00:40.1526270Z 2025-03-04T21:00:40.1526560Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1526699Z out_47: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_46); out_46 = None 2025-03-04T21:00:40.1526769Z 2025-03-04T21:00:40.1527019Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1527433Z x_82: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.1527502Z 2025-03-04T21:00:40.1527768Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1529287Z x_83: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_82 = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1529350Z 2025-03-04T21:00:40.1529653Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1529782Z out_48: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_83); x_83 = None 2025-03-04T21:00:40.1529863Z 2025-03-04T21:00:40.1530105Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1530518Z x_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_48 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.1530586Z 2025-03-04T21:00:40.1530843Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1532374Z x_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_84 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1532437Z 2025-03-04T21:00:40.1532759Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1532885Z out_49: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_85); x_85 = None 2025-03-04T21:00:40.1532953Z 2025-03-04T21:00:40.1533199Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1533612Z x_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_49 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.1533673Z 2025-03-04T21:00:40.1533938Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1535426Z x_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_86 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1535487Z 2025-03-04T21:00:40.1535764Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.1535907Z x_87 += out_47; out_50: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_87; x_87 = out_47 = None 2025-03-04T21:00:40.1535975Z 2025-03-04T21:00:40.1536252Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1536409Z out_51: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_50); out_50 = None 2025-03-04T21:00:40.1536467Z 2025-03-04T21:00:40.1536721Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1537126Z x_88: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.1537208Z 2025-03-04T21:00:40.1537483Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1538970Z x_89: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_88 = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1539067Z 2025-03-04T21:00:40.1539342Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1539481Z out_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_89); x_89 = None 2025-03-04T21:00:40.1539541Z 2025-03-04T21:00:40.1539797Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1540226Z x_90: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_52 = l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.1540288Z 2025-03-04T21:00:40.1540561Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1542070Z x_91: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_90 = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1542140Z 2025-03-04T21:00:40.1542418Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1542551Z out_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_91); x_91 = None 2025-03-04T21:00:40.1542625Z 2025-03-04T21:00:40.1542876Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1543289Z x_92: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_53 = l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.1543350Z 2025-03-04T21:00:40.1543612Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1545103Z x_93: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_92 = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1545173Z 2025-03-04T21:00:40.1545472Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.1545618Z x_93 += out_51; out_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_93; x_93 = out_51 = None 2025-03-04T21:00:40.1545681Z 2025-03-04T21:00:40.1545961Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1546104Z out_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_54); out_54 = None 2025-03-04T21:00:40.1546163Z 2025-03-04T21:00:40.1546415Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1546823Z x_94: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.1546893Z 2025-03-04T21:00:40.1547155Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1548691Z x_95: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_94 = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1548760Z 2025-03-04T21:00:40.1549047Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1549227Z out_56: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_95); x_95 = None 2025-03-04T21:00:40.1549286Z 2025-03-04T21:00:40.1549542Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1549966Z x_96: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_56 = l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.1550045Z 2025-03-04T21:00:40.1550304Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1551790Z x_97: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_96 = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1551889Z 2025-03-04T21:00:40.1552173Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1552309Z out_57: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_97); x_97 = None 2025-03-04T21:00:40.1552369Z 2025-03-04T21:00:40.1552628Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1553053Z x_98: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_57 = l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.1553122Z 2025-03-04T21:00:40.1553390Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1554945Z x_99: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_98 = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1555013Z 2025-03-04T21:00:40.1555293Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.1555441Z x_99 += out_55; out_58: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_99; x_99 = out_55 = None 2025-03-04T21:00:40.1555517Z 2025-03-04T21:00:40.1555810Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1555947Z out_59: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_58); out_58 = None 2025-03-04T21:00:40.1556014Z 2025-03-04T21:00:40.1556267Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1556698Z x_100: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.1556774Z 2025-03-04T21:00:40.1557063Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1559007Z x_101: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_100 = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1559092Z 2025-03-04T21:00:40.1559400Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1559538Z out_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_101); x_101 = None 2025-03-04T21:00:40.1559605Z 2025-03-04T21:00:40.1559855Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1560284Z x_102: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_60 = l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.1560350Z 2025-03-04T21:00:40.1560623Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1562164Z x_103: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_102 = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1562235Z 2025-03-04T21:00:40.1562546Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1562685Z out_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_103); x_103 = None 2025-03-04T21:00:40.1562753Z 2025-03-04T21:00:40.1563004Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1563438Z x_104: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_61 = l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.1563514Z 2025-03-04T21:00:40.1563789Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1565336Z x_105: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_104 = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1565425Z 2025-03-04T21:00:40.1565712Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.1565861Z x_105 += out_59; out_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_105; x_105 = out_59 = None 2025-03-04T21:00:40.1565929Z 2025-03-04T21:00:40.1566211Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1566356Z out_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_62); out_62 = None 2025-03-04T21:00:40.1566417Z 2025-03-04T21:00:40.1566677Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1567084Z x_106: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_63, l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.1567152Z 2025-03-04T21:00:40.1567407Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1568902Z x_107: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_106 = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1568983Z 2025-03-04T21:00:40.1569261Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1569400Z out_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_107); x_107 = None 2025-03-04T21:00:40.1569459Z 2025-03-04T21:00:40.1569708Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1570122Z x_108: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_64, l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_64 = l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.1570207Z 2025-03-04T21:00:40.1570466Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1571996Z x_109: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_108, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_108 = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1572067Z 2025-03-04T21:00:40.1572351Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1572486Z out_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_109); x_109 = None 2025-03-04T21:00:40.1572544Z 2025-03-04T21:00:40.1572795Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1573209Z x_110: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_65, l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_65 = l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.1573276Z 2025-03-04T21:00:40.1573538Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1575036Z x_111: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_110, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_110 = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1575104Z 2025-03-04T21:00:40.1575389Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.1575540Z x_111 += out_63; out_66: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_111; x_111 = out_63 = None 2025-03-04T21:00:40.1575601Z 2025-03-04T21:00:40.1575882Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1576015Z out_67: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_66); out_66 = None 2025-03-04T21:00:40.1576081Z 2025-03-04T21:00:40.1576339Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1576758Z x_112: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_67, l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.1576825Z 2025-03-04T21:00:40.1577082Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1578611Z x_113: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_112, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_112 = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1578677Z 2025-03-04T21:00:40.1578961Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1579096Z out_68: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_113); x_113 = None 2025-03-04T21:00:40.1579154Z 2025-03-04T21:00:40.1579402Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1579817Z x_114: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_68, l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_68 = l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.1579883Z 2025-03-04T21:00:40.1580140Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1581650Z x_115: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_114, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_114 = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1581736Z 2025-03-04T21:00:40.1582018Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1582154Z out_69: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_115); x_115 = None 2025-03-04T21:00:40.1582212Z 2025-03-04T21:00:40.1582462Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1582894Z x_116: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_69, l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_69 = l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.1582960Z 2025-03-04T21:00:40.1583219Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1584759Z x_117: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_116, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_116 = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1584828Z 2025-03-04T21:00:40.1585100Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.1585246Z x_117 += out_67; out_70: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_117; x_117 = out_67 = None 2025-03-04T21:00:40.1585305Z 2025-03-04T21:00:40.1585586Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1585718Z out_71: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_70); out_70 = None 2025-03-04T21:00:40.1585785Z 2025-03-04T21:00:40.1586030Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1586442Z x_118: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_71, l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.1586501Z 2025-03-04T21:00:40.1586762Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1588254Z x_119: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_118, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_118 = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1588335Z 2025-03-04T21:00:40.1588619Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1588750Z out_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_119); x_119 = None 2025-03-04T21:00:40.1588826Z 2025-03-04T21:00:40.1589070Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1589492Z x_120: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_72, l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_72 = l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.1589553Z 2025-03-04T21:00:40.1589818Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1591366Z x_121: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_120, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_120 = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1591434Z 2025-03-04T21:00:40.1591717Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1591845Z out_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_121); x_121 = None 2025-03-04T21:00:40.1591912Z 2025-03-04T21:00:40.1592154Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1592576Z x_122: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_73, l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_73 = l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.1592636Z 2025-03-04T21:00:40.1592898Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1594409Z x_123: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_122, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_122 = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1594482Z 2025-03-04T21:00:40.1594758Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.1594900Z x_123 += out_71; out_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_123; x_123 = out_71 = None 2025-03-04T21:00:40.1594965Z 2025-03-04T21:00:40.1595238Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1595394Z out_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_74); out_74 = None 2025-03-04T21:00:40.1595452Z 2025-03-04T21:00:40.1595702Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1596110Z x_124: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_75, l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.1596175Z 2025-03-04T21:00:40.1596431Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1598036Z x_125: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_124, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_124 = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1598113Z 2025-03-04T21:00:40.1598397Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1598541Z out_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_125); x_125 = None 2025-03-04T21:00:40.1598603Z 2025-03-04T21:00:40.1598863Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1599297Z x_126: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_76, l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_76 = l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.1599366Z 2025-03-04T21:00:40.1599625Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1601134Z x_127: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_126, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_126 = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1601218Z 2025-03-04T21:00:40.1601494Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1601631Z out_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_127); x_127 = None 2025-03-04T21:00:40.1601707Z 2025-03-04T21:00:40.1602128Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1602549Z x_128: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_77, l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_77 = l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.1602619Z 2025-03-04T21:00:40.1602875Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1604437Z x_129: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_128, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_128 = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1604509Z 2025-03-04T21:00:40.1604784Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.1604933Z x_129 += out_75; out_78: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_129; x_129 = out_75 = None 2025-03-04T21:00:40.1604994Z 2025-03-04T21:00:40.1605281Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1605412Z out_79: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_78); out_78 = None 2025-03-04T21:00:40.1605482Z 2025-03-04T21:00:40.1605730Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1606151Z x_130: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_79, l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.1606211Z 2025-03-04T21:00:40.1606482Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1608036Z x_131: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_130, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_130 = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1608120Z 2025-03-04T21:00:40.1608412Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1608564Z out_80: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_131); x_131 = None 2025-03-04T21:00:40.1608629Z 2025-03-04T21:00:40.1608878Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1609305Z x_132: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_80, l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_80 = l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.1609370Z 2025-03-04T21:00:40.1609633Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1611220Z x_133: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_132, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_132 = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1611283Z 2025-03-04T21:00:40.1611569Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1611703Z out_81: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_133); x_133 = None 2025-03-04T21:00:40.1611770Z 2025-03-04T21:00:40.1612013Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1612449Z x_134: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_81, l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_81 = l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.1612515Z 2025-03-04T21:00:40.1612778Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1614332Z x_135: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_134, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_134 = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1614406Z 2025-03-04T21:00:40.1614695Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.1614858Z x_135 += out_79; out_82: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_135; x_135 = out_79 = None 2025-03-04T21:00:40.1614919Z 2025-03-04T21:00:40.1615209Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1615346Z out_83: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_82); out_82 = None 2025-03-04T21:00:40.1615413Z 2025-03-04T21:00:40.1615665Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1616098Z x_136: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_83, l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.1616160Z 2025-03-04T21:00:40.1616461Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1617993Z x_137: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_136, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_136 = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1618063Z 2025-03-04T21:00:40.1618359Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1618486Z out_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_137); x_137 = None 2025-03-04T21:00:40.1618554Z 2025-03-04T21:00:40.1618798Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1619214Z x_138: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_84, l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_84 = l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.1619274Z 2025-03-04T21:00:40.1619535Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1621027Z x_139: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_138, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_138 = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1621143Z 2025-03-04T21:00:40.1621430Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1621559Z out_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_139); x_139 = None 2025-03-04T21:00:40.1621627Z 2025-03-04T21:00:40.1621869Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1622294Z x_140: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_85, l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_85 = l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.1622352Z 2025-03-04T21:00:40.1622624Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1624193Z x_141: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_140, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_140 = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1624262Z 2025-03-04T21:00:40.1624539Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.1624682Z x_141 += out_83; out_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_141; x_141 = out_83 = None 2025-03-04T21:00:40.1624749Z 2025-03-04T21:00:40.1625022Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1625157Z out_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_86); out_86 = None 2025-03-04T21:00:40.1625216Z 2025-03-04T21:00:40.1625463Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1625867Z x_142: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_87, l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.1625931Z 2025-03-04T21:00:40.1626191Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1627741Z x_143: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_142, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_142 = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1627822Z 2025-03-04T21:00:40.1628099Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1628237Z out_88: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_143); x_143 = None 2025-03-04T21:00:40.1628297Z 2025-03-04T21:00:40.1628552Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1628968Z x_144: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_88, l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_88 = l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.1629039Z 2025-03-04T21:00:40.1629330Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1630863Z x_145: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_144, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_144 = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1630933Z 2025-03-04T21:00:40.1631216Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1631354Z out_89: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_145); x_145 = None 2025-03-04T21:00:40.1631415Z 2025-03-04T21:00:40.1631672Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1632093Z x_146: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_89, l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_89 = l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.1632164Z 2025-03-04T21:00:40.1632429Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1633990Z x_147: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_146, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_146 = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1634085Z 2025-03-04T21:00:40.1634363Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.1634513Z x_147 += out_87; out_90: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_147; x_147 = out_87 = None 2025-03-04T21:00:40.1634575Z 2025-03-04T21:00:40.1634863Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1634998Z out_91: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_90); out_90 = None 2025-03-04T21:00:40.1635066Z 2025-03-04T21:00:40.1635315Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1635768Z x_148: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_91, l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.1635829Z 2025-03-04T21:00:40.1636100Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1637651Z x_149: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_148, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_148 = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1637713Z 2025-03-04T21:00:40.1638071Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1638207Z out_92: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_149); x_149 = None 2025-03-04T21:00:40.1638274Z 2025-03-04T21:00:40.1638520Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1638945Z x_150: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_92, l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_92 = l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.1639010Z 2025-03-04T21:00:40.1639280Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1640840Z x_151: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_150, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_150 = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1640916Z 2025-03-04T21:00:40.1641215Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1641347Z out_93: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_151); x_151 = None 2025-03-04T21:00:40.1641415Z 2025-03-04T21:00:40.1641670Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1642107Z x_152: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_93, l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_93 = l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.1642171Z 2025-03-04T21:00:40.1642477Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1644042Z x_153: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_152, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_152 = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1644103Z 2025-03-04T21:00:40.1644386Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.1644531Z x_153 += out_91; out_94: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_153; x_153 = out_91 = None 2025-03-04T21:00:40.1644600Z 2025-03-04T21:00:40.1644881Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1645023Z out_95: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_94); out_94 = None 2025-03-04T21:00:40.1645083Z 2025-03-04T21:00:40.1645337Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1645762Z x_154: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_95, l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.1645844Z 2025-03-04T21:00:40.1646117Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1647674Z x_155: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_154, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_154 = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1647757Z 2025-03-04T21:00:40.1648043Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1648180Z out_96: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_155); x_155 = None 2025-03-04T21:00:40.1648240Z 2025-03-04T21:00:40.1648497Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1648971Z x_156: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_96, l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_96 = l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.1649032Z 2025-03-04T21:00:40.1649295Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1650797Z x_157: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_156, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_156 = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1650864Z 2025-03-04T21:00:40.1651135Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1651272Z out_97: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_157); x_157 = None 2025-03-04T21:00:40.1651336Z 2025-03-04T21:00:40.1651578Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1651993Z x_158: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_97, l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_97 = l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.1652055Z 2025-03-04T21:00:40.1652323Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1653971Z x_159: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_158, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_158 = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1654056Z 2025-03-04T21:00:40.1654366Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.1654517Z x_159 += out_95; out_98: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_159; x_159 = out_95 = None 2025-03-04T21:00:40.1654587Z 2025-03-04T21:00:40.1654891Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1655040Z out_99: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_98); out_98 = None 2025-03-04T21:00:40.1655105Z 2025-03-04T21:00:40.1655414Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1655819Z x_160: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_99, l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.1655885Z 2025-03-04T21:00:40.1656142Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1657656Z x_161: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_160, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_160 = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1657726Z 2025-03-04T21:00:40.1658009Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1658153Z out_100: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_161); x_161 = None 2025-03-04T21:00:40.1658212Z 2025-03-04T21:00:40.1658469Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1658908Z x_162: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_100, l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_100 = l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.1658999Z 2025-03-04T21:00:40.1659253Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1660765Z x_163: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_162, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_162 = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1660846Z 2025-03-04T21:00:40.1661122Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1661262Z out_101: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_163); x_163 = None 2025-03-04T21:00:40.1661321Z 2025-03-04T21:00:40.1661569Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1662013Z x_164: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_101, l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_101 = l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.1662081Z 2025-03-04T21:00:40.1662344Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1663845Z x_165: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_164, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_164 = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1663910Z 2025-03-04T21:00:40.1664177Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.1664326Z x_165 += out_99; out_102: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_165; x_165 = out_99 = None 2025-03-04T21:00:40.1664383Z 2025-03-04T21:00:40.1664661Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1664799Z out_103: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_102); out_102 = None 2025-03-04T21:00:40.1664865Z 2025-03-04T21:00:40.1665104Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1665528Z x_166: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_103, l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.1665590Z 2025-03-04T21:00:40.1665856Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1667376Z x_167: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_166, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_166 = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1667448Z 2025-03-04T21:00:40.1667729Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1667862Z out_104: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_167); x_167 = None 2025-03-04T21:00:40.1667927Z 2025-03-04T21:00:40.1668199Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1668616Z x_168: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_104, l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_104 = l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.1668675Z 2025-03-04T21:00:40.1668939Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1670452Z x_169: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_168, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_168 = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1670514Z 2025-03-04T21:00:40.1670793Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1670922Z out_105: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_169); x_169 = None 2025-03-04T21:00:40.1670988Z 2025-03-04T21:00:40.1671233Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1671665Z x_170: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_105, l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_105 = l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.1671737Z 2025-03-04T21:00:40.1672008Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1673557Z x_171: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_170, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_170 = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1673632Z 2025-03-04T21:00:40.1673916Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.1674074Z x_171 += out_103; out_106: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_171; x_171 = out_103 = None 2025-03-04T21:00:40.1674144Z 2025-03-04T21:00:40.1674428Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1674605Z out_107: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_106); out_106 = None 2025-03-04T21:00:40.1674668Z 2025-03-04T21:00:40.1674926Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1675343Z x_172: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_107, l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.1675410Z 2025-03-04T21:00:40.1675674Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1677215Z x_173: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_172, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_172 = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1677284Z 2025-03-04T21:00:40.1677564Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1677708Z out_108: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_173); x_173 = None 2025-03-04T21:00:40.1677767Z 2025-03-04T21:00:40.1678085Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1678555Z x_174: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_108, l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_108 = l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.1678628Z 2025-03-04T21:00:40.1678905Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1680468Z x_175: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_174, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_174 = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1680555Z 2025-03-04T21:00:40.1680843Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1680987Z out_109: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_175); x_175 = None 2025-03-04T21:00:40.1681074Z 2025-03-04T21:00:40.1681336Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1681765Z x_176: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_109, l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_109 = l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.1681834Z 2025-03-04T21:00:40.1682109Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1683675Z x_177: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_176, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_176 = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1683747Z 2025-03-04T21:00:40.1684028Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.1684190Z x_177 += out_107; out_110: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_177; x_177 = out_107 = None 2025-03-04T21:00:40.1684253Z 2025-03-04T21:00:40.1684546Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1684703Z out_111: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_110); out_110 = None 2025-03-04T21:00:40.1684771Z 2025-03-04T21:00:40.1685023Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1685454Z x_178: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_111, l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.1685521Z 2025-03-04T21:00:40.1685799Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1687351Z x_179: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_178, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_178 = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1687415Z 2025-03-04T21:00:40.1687738Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1687878Z out_112: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_179); x_179 = None 2025-03-04T21:00:40.1687942Z 2025-03-04T21:00:40.1688203Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1688635Z x_180: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_112, l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_112 = l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.1688705Z 2025-03-04T21:00:40.1688973Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1690599Z x_181: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_180, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_180 = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1690671Z 2025-03-04T21:00:40.1690954Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1691095Z out_113: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_181); x_181 = None 2025-03-04T21:00:40.1691165Z 2025-03-04T21:00:40.1691415Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1691826Z x_182: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_113, l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_113 = l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.1691891Z 2025-03-04T21:00:40.1692146Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1693669Z x_183: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_182, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_182 = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1693737Z 2025-03-04T21:00:40.1694007Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.1694196Z x_183 += out_111; out_114: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_183; x_183 = out_111 = None 2025-03-04T21:00:40.1694256Z 2025-03-04T21:00:40.1694540Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1694678Z out_115: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_114); out_114 = None 2025-03-04T21:00:40.1694743Z 2025-03-04T21:00:40.1694987Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1695401Z x_184: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_115, l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.1695462Z 2025-03-04T21:00:40.1695732Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1697240Z x_185: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_184, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_184 = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1697309Z 2025-03-04T21:00:40.1697596Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1697742Z out_116: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_185); x_185 = None 2025-03-04T21:00:40.1697807Z 2025-03-04T21:00:40.1698050Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1698470Z x_186: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_116, l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_116 = l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.1698544Z 2025-03-04T21:00:40.1698813Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1700320Z x_187: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_186, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_186 = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1700390Z 2025-03-04T21:00:40.1700702Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1700833Z out_117: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_187); x_187 = None 2025-03-04T21:00:40.1700900Z 2025-03-04T21:00:40.1701144Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1701570Z x_188: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_117, l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_117 = l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.1701629Z 2025-03-04T21:00:40.1701987Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1703523Z x_189: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_188, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_188 = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1703586Z 2025-03-04T21:00:40.1703874Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.1704022Z x_189 += out_115; out_118: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_189; x_189 = out_115 = None 2025-03-04T21:00:40.1704122Z 2025-03-04T21:00:40.1704400Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1704546Z out_119: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_118); out_118 = None 2025-03-04T21:00:40.1704605Z 2025-03-04T21:00:40.1705045Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:00:40.1705211Z arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32) 2025-03-04T21:00:40.1705278Z 2025-03-04T21:00:40.1705568Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:00:40.1705707Z shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T21:00:40.1705767Z 2025-03-04T21:00:40.1706201Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:00:40.1706347Z arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32) 2025-03-04T21:00:40.1706417Z 2025-03-04T21:00:40.1706701Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:00:40.1706878Z shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T21:00:40.1706937Z 2025-03-04T21:00:40.1707309Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:00:40.1707482Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T21:00:40.1707583Z shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0] 2025-03-04T21:00:40.1707700Z shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T21:00:40.1707765Z 2025-03-04T21:00:40.1708087Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:00:40.1708219Z shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T21:00:40.1708278Z 2025-03-04T21:00:40.1708607Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:00:40.1708727Z shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T21:00:40.1708785Z 2025-03-04T21:00:40.1709161Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:00:40.1709366Z shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T21:00:40.1709430Z 2025-03-04T21:00:40.1709840Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:00:40.1709970Z view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T21:00:40.1710388Z view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T21:00:40.1710527Z add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T21:00:40.1710636Z x_190: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4); add = None 2025-03-04T21:00:40.1710704Z 2025-03-04T21:00:40.1711001Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:00:40.1711140Z tensor: "f32[82125, 4][4, 1]cpu" = x_190.to(torch.float32); x_190 = None 2025-03-04T21:00:40.1711213Z 2025-03-04T21:00:40.1711472Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1712245Z x_191: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_119, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); out_119 = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T21:00:40.1712311Z 2025-03-04T21:00:40.1712580Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:00:40.1712766Z x_192: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_191, inplace = False); x_191 = None 2025-03-04T21:00:40.1712826Z 2025-03-04T21:00:40.1713234Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:00:40.1714098Z score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_192, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T21:00:40.1714158Z 2025-03-04T21:00:40.1714521Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:00:40.1715347Z x_193: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_192, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_192 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T21:00:40.1715413Z 2025-03-04T21:00:40.1715746Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T21:00:40.1715898Z permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T21:00:40.1716039Z logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T21:00:40.1716096Z 2025-03-04T21:00:40.1716526Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T21:00:40.1716698Z view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_193.view(4, -1, 4, 73, 75); x_193 = None 2025-03-04T21:00:40.1716875Z permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2); view_2 = None 2025-03-04T21:00:40.1717048Z pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2); permute_1 = None 2025-03-04T21:00:40.1717113Z 2025-03-04T21:00:40.1717519Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:00:40.1717754Z pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T21:00:40.1717861Z 2025-03-04T21:00:40.1718315Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:00:40.1718464Z unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T21:00:40.1718612Z expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T21:00:40.1718748Z anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T21:00:40.1718818Z 2025-03-04T21:00:40.1719197Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:00:40.1719415Z deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T21:00:40.1719476Z 2025-03-04T21:00:40.1719794Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:00:40.1719928Z boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T21:00:40.1719996Z 2025-03-04T21:00:40.1720304Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:00:40.1720439Z getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:00:40.1720560Z getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:00:40.1720709Z widths: "f32[328500][1]cpu" = getitem_2 - getitem_3; getitem_2 = getitem_3 = None 2025-03-04T21:00:40.1720768Z 2025-03-04T21:00:40.1721090Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:00:40.1721207Z getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:00:40.1721328Z getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:00:40.1721466Z heights: "f32[328500][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T21:00:40.1721534Z 2025-03-04T21:00:40.1721833Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:00:40.1721955Z getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:00:40.1722040Z mul: "f32[328500][1]cpu" = 0.5 * widths 2025-03-04T21:00:40.1722166Z ctr_x: "f32[328500][1]cpu" = getitem_6 + mul; getitem_6 = mul = None 2025-03-04T21:00:40.1722224Z 2025-03-04T21:00:40.1722532Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:00:40.1722682Z getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:00:40.1722774Z mul_1: "f32[328500][1]cpu" = 0.5 * heights 2025-03-04T21:00:40.1722894Z ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1; getitem_7 = mul_1 = None 2025-03-04T21:00:40.1722961Z 2025-03-04T21:00:40.1723308Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:00:40.1723453Z getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:00:40.1723584Z dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0; getitem_8 = None 2025-03-04T21:00:40.1723641Z 2025-03-04T21:00:40.1723940Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:00:40.1724086Z getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:00:40.1724198Z dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0; getitem_9 = None 2025-03-04T21:00:40.1724256Z 2025-03-04T21:00:40.1724553Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:00:40.1724698Z getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:00:40.1724812Z dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0; getitem_10 = None 2025-03-04T21:00:40.1724870Z 2025-03-04T21:00:40.1725195Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:00:40.1725373Z getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:00:40.1725483Z dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0; getitem_11 = None 2025-03-04T21:00:40.1725541Z 2025-03-04T21:00:40.1725880Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:00:40.1726014Z dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:00:40.1726079Z 2025-03-04T21:00:40.1726406Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:00:40.1726547Z dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:00:40.1726605Z 2025-03-04T21:00:40.1726955Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:00:40.1727086Z getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:00:40.1727213Z mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12; dx = getitem_12 = None 2025-03-04T21:00:40.1727356Z getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:00:40.1727499Z pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13; mul_2 = getitem_13 = None 2025-03-04T21:00:40.1727559Z 2025-03-04T21:00:40.1727908Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:00:40.1728037Z getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:00:40.1728174Z mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14; dy = getitem_14 = None 2025-03-04T21:00:40.1728316Z getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:00:40.1728452Z pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15; mul_3 = getitem_15 = None 2025-03-04T21:00:40.1728513Z 2025-03-04T21:00:40.1728839Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:00:40.1728957Z exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:00:40.1729123Z getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:00:40.1729254Z pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16; exp = getitem_16 = None 2025-03-04T21:00:40.1729315Z 2025-03-04T21:00:40.1729644Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:00:40.1729753Z exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:00:40.1729918Z getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:00:40.1730044Z pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17; exp_1 = getitem_17 = None 2025-03-04T21:00:40.1730108Z 2025-03-04T21:00:40.1730412Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:00:40.1730507Z mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T21:00:40.1730645Z x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:00:40.1730712Z 2025-03-04T21:00:40.1731012Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:00:40.1731107Z mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T21:00:40.1731214Z y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:00:40.1731278Z 2025-03-04T21:00:40.1731571Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:00:40.1731683Z mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:00:40.1731804Z x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:00:40.1731871Z 2025-03-04T21:00:40.1732166Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:00:40.1732275Z mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:00:40.1732394Z y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:00:40.1732460Z 2025-03-04T21:00:40.1732797Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:00:40.1732975Z pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:00:40.1733035Z 2025-03-04T21:00:40.1733370Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:00:40.1733523Z proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4)); pred_boxes = None 2025-03-04T21:00:40.1733605Z 2025-03-04T21:00:40.1733980Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:00:40.1734151Z proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T21:00:40.1734212Z 2025-03-04T21:00:40.1734696Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T21:00:40.1734838Z arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T21:00:40.1734905Z 2025-03-04T21:00:40.1735194Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:00:40.1735335Z batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T21:00:40.1735393Z 2025-03-04T21:00:40.1735826Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:00:40.1735936Z topk = logits_i.topk(6000, dim = 1); logits_i = None 2025-03-04T21:00:40.1736038Z topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0] 2025-03-04T21:00:40.1736145Z topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1]; topk = None 2025-03-04T21:00:40.1736214Z 2025-03-04T21:00:40.1736693Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:00:40.1736860Z getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T21:00:40.1737091Z topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)]; proposals_i_1 = getitem_20 = topk_idx = None 2025-03-04T21:00:40.1737151Z 2025-03-04T21:00:40.1737598Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:00:40.1737758Z full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:00:40.1737826Z 2025-03-04T21:00:40.1738113Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:00:40.1738261Z level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu')); full = level_ids = None 2025-03-04T21:00:40.1738321Z 2025-03-04T21:00:40.1738695Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T21:00:40.1738832Z getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0]; topk_proposals_i = None 2025-03-04T21:00:40.1738897Z 2025-03-04T21:00:40.1739184Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:00:40.1739324Z tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32); getitem_22 = None 2025-03-04T21:00:40.1739384Z 2025-03-04T21:00:40.1739755Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T21:00:40.1739909Z scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0]; topk_scores_i = None 2025-03-04T21:00:40.1739974Z 2025-03-04T21:00:40.1740440Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T21:00:40.1740576Z isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1); tensor_1 = None 2025-03-04T21:00:40.1740690Z all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:00:40.1740844Z isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T21:00:40.1740985Z valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T21:00:40.1741052Z 2025-03-04T21:00:40.1741411Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T21:00:40.1741532Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T21:00:40.1741590Z 2025-03-04T21:00:40.1741599Z 2025-03-04T21:00:40.1741694Z class GraphModule(torch.nn.Module): 2025-03-04T21:00:40.1832158Z def forward(self, L_stack0_tensor: "f32[4, 3, 1156, 1199][4158132, 1386044, 1199, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_: "f32[512, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_: 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L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", 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"f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[1024, 1024, 3, 3][9216, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[1024][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"): 2025-03-04T21:00:40.1832771Z l_stack0_tensor = L_stack0_tensor 2025-03-04T21:00:40.1833109Z l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.1833509Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1833906Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1834288Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1834648Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1835040Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.1835431Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1835765Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1836158Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1836540Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1836885Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.1837303Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1837705Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1838119Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1838489Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1838844Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.1839296Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1839663Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1839977Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1840305Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1840598Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:00:40.1840952Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1841300Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1841630Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1841982Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1842262Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.1842603Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1842933Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1843252Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1843559Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1843844Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.1844181Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1844515Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1844840Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1845149Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1845457Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.1845796Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1846140Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1846455Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1846788Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1847073Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.1847405Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1847746Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1848063Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1848404Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1848683Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.1849093Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1849482Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1849846Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1850153Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1850433Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.1850770Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1851096Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1851416Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1851719Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1852024Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.1852356Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1852693Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1853027Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1853330Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1853615Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.1853949Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1854286Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1854646Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1854996Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1855309Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.1855700Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1856035Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1856349Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1856663Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1856960Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:00:40.1857311Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1857652Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1857994Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1858323Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1858611Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.1858952Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1859320Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1859641Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1859947Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1860234Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.1860571Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1860939Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1861250Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1861566Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1861848Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.1862179Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1862518Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1862829Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1863141Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1863418Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.1863753Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1864078Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1864395Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1864723Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1864999Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.1865336Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1865680Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1865998Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1866297Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1866581Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.1866909Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1867279Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1867600Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1867903Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1868190Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.1868525Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1868864Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1869177Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1869488Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1869765Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.1870103Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1870445Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1870773Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1871084Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1871367Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.1871724Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1872052Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1872375Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1872680Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1872965Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.1873332Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1873659Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1873980Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1874282Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1874566Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.1874922Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1875302Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1875649Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1876002Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1876322Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.1876692Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1877093Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1877448Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1877875Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1878220Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:00:40.1878619Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1878993Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1879350Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1879705Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1880010Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.1880395Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1880765Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1881100Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1881418Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1881723Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.1882070Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1882427Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1882762Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1883080Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1883381Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.1883730Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1884101Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1884431Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1884760Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1885074Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.1885437Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1885797Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1886139Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1886468Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1886795Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.1887153Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1887499Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1887835Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1888151Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1888458Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.1888817Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1889148Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1889465Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1889766Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1890054Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.1890406Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1890742Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1891053Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1891361Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1891659Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.1891994Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1892329Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1892643Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1892987Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1893268Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.1893608Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1893939Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1894259Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1894575Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1894854Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.1895198Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1895527Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1895850Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1896160Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1896450Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.1896796Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1897129Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1897449Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1897767Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1898052Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.1898385Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1898720Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1899031Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1899374Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1899656Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.1899997Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1900336Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1900648Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1900966Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1901248Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.1901591Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1902009Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1902336Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1902648Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1902970Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.1903310Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1903638Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1903982Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1904280Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1904562Z l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.1904888Z l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1905219Z l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1905568Z l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1905880Z l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1906169Z l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.1906500Z l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1906835Z l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1907154Z l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1907465Z l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1907747Z l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.1908080Z l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1908405Z l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1908728Z l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1909039Z l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1909334Z l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.1909671Z l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1910001Z l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1910335Z l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1910642Z l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1910930Z l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.1911267Z l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1911602Z l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1911951Z l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1912256Z l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1912541Z l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.1912873Z l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1913206Z l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1913523Z l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1913835Z l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1914113Z l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.1914451Z l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1914798Z l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1915117Z l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1915450Z l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1915737Z l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.1916115Z l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1916499Z l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1916827Z l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1917137Z l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1917427Z l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.1917825Z l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1918233Z l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1918581Z l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1918917Z l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1919220Z l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.1919561Z l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1919919Z l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1920251Z l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1920573Z l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1920875Z l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.1921224Z l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1921573Z l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1921909Z l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1922238Z l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1922521Z l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.1922867Z l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1923223Z l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1923555Z l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1923872Z l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1924164Z l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.1924516Z l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1924886Z l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1925218Z l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1925532Z l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1925828Z l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.1926171Z l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1926526Z l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1926860Z l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1927174Z l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1927468Z l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.1927818Z l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1928168Z l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1928506Z l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1928829Z l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1929119Z l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.1929486Z l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1929837Z l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1930163Z l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1930487Z l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1930776Z l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.1931154Z l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1931496Z l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1931824Z l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1932139Z l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1932425Z l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.1932775Z l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1933115Z l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1933452Z l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1933772Z l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1934059Z l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.1934395Z l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1934744Z l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1935062Z l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1935366Z l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1935670Z l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.1936010Z l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1936351Z l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1936667Z l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1936979Z l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1937288Z l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.1937628Z l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1937968Z l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1938279Z l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1938590Z l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1938874Z l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.1939215Z l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1939546Z l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1939869Z l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1940176Z l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1940472Z l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.1940840Z l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1941181Z l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1941510Z l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1941833Z l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1942137Z l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.1942475Z l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1942809Z l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1943124Z l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1943465Z l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1943754Z l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.1944091Z l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1944430Z l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1944745Z l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1945059Z l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1945343Z l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.1945689Z l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1946023Z l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1946345Z l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1946663Z l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1946944Z l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.1947300Z l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1947632Z l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1947954Z l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1948278Z l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1948574Z l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.1948918Z l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1949264Z l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1949590Z l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1949940Z l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1950244Z l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.1950576Z l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1950921Z l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1951252Z l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1951598Z l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1951912Z l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.1952314Z l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1952701Z l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1953067Z l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1953423Z l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1953764Z l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.1954158Z l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1954535Z l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1954924Z l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1955266Z l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1955579Z l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.1955946Z l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1956304Z l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1956686Z l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1957023Z l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1957332Z l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.1957693Z l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1958128Z l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1958481Z l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1958826Z l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1959123Z l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.1959464Z l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1959809Z l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1960138Z l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1960517Z l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1960808Z l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.1961162Z l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1961518Z l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1961848Z l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1962165Z l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1962455Z l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.1962805Z l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1963184Z l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1963515Z l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1963832Z l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1964125Z l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.1964470Z l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1964821Z l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1965151Z l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1965465Z l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1965760Z l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.1966102Z l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1966450Z l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1966790Z l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1967113Z l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1967400Z l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.1967749Z l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1968111Z l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1968438Z l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1968769Z l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1969050Z l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.1969395Z l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1969754Z l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1970078Z l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1970383Z l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1970674Z l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.1971019Z l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1971356Z l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1971687Z l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1972002Z l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1972295Z l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.1972643Z l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1972991Z l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1973331Z l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1973652Z l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1973948Z l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.1974309Z l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1974653Z l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1974976Z l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1975293Z l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1975580Z l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.1975964Z l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1976313Z l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1976638Z l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1976951Z l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1977232Z l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.1977577Z l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1977910Z l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1978234Z l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1978541Z l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1978831Z l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.1979168Z l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1979524Z l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1979850Z l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1980160Z l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1980466Z l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.1980802Z l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1981142Z l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1981457Z l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1981768Z l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1982076Z l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.1982418Z l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1982759Z l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1983073Z l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1983381Z l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1983663Z l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ 2025-03-04T21:00:40.1984008Z l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1984339Z l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1984662Z l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1984965Z l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1985257Z l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ 2025-03-04T21:00:40.1985613Z l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1985947Z l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1986274Z l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1986597Z l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1986886Z l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ 2025-03-04T21:00:40.1987223Z l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:00:40.1987559Z l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:00:40.1987872Z l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:00:40.1988222Z l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:00:40.1988575Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T21:00:40.1988892Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T21:00:40.1989207Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T21:00:40.1989570Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T21:00:40.1989934Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T21:00:40.1990286Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T21:00:40.1990646Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T21:00:40.1990713Z 2025-03-04T21:00:40.1991006Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1991488Z x: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1); l_stack0_tensor = l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.1991554Z 2025-03-04T21:00:40.1991841Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1993324Z x_1: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1993412Z 2025-03-04T21:00:40.1993695Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x) 2025-03-04T21:00:40.1993841Z x_2: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.relu_(x_1); x_1 = None 2025-03-04T21:00:40.1993902Z 2025-03-04T21:00:40.1994309Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1) 2025-03-04T21:00:40.1994551Z x_3: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1); x_2 = None 2025-03-04T21:00:40.1994614Z 2025-03-04T21:00:40.1994906Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1995342Z x_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.1995415Z 2025-03-04T21:00:40.1995688Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.1997260Z x_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_4 = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.1997334Z 2025-03-04T21:00:40.1997628Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.1997768Z out: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_5); x_5 = None 2025-03-04T21:00:40.1997880Z 2025-03-04T21:00:40.1998147Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.1998622Z x_6: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out = l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.1998714Z 2025-03-04T21:00:40.1999002Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2000588Z x_7: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_6 = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2000673Z 2025-03-04T21:00:40.2000965Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2001110Z out_1: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_7); x_7 = None 2025-03-04T21:00:40.2001171Z 2025-03-04T21:00:40.2001427Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2002030Z x_8: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_1 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.2002111Z 2025-03-04T21:00:40.2002384Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2003947Z x_9: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_8 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2004018Z 2025-03-04T21:00:40.2004272Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2004731Z x_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); x_3 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:00:40.2004795Z 2025-03-04T21:00:40.2005071Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2006726Z x_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_10 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2006840Z 2025-03-04T21:00:40.2007131Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.2007275Z x_9 += x_11; out_2: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_9; x_9 = x_11 = None 2025-03-04T21:00:40.2007345Z 2025-03-04T21:00:40.2007631Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2007786Z out_3: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_2); out_2 = None 2025-03-04T21:00:40.2007846Z 2025-03-04T21:00:40.2008107Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2008562Z x_12: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.2008633Z 2025-03-04T21:00:40.2008900Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2010486Z x_13: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_12 = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2010556Z 2025-03-04T21:00:40.2010842Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2010989Z out_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_13); x_13 = None 2025-03-04T21:00:40.2011051Z 2025-03-04T21:00:40.2011308Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2011738Z x_14: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_4 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.2011808Z 2025-03-04T21:00:40.2012067Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2013585Z x_15: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_14 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2013676Z 2025-03-04T21:00:40.2013955Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2014097Z out_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_15); x_15 = None 2025-03-04T21:00:40.2014158Z 2025-03-04T21:00:40.2014408Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2014832Z x_16: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_5 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.2014901Z 2025-03-04T21:00:40.2015187Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2016690Z x_17: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_16 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2016759Z 2025-03-04T21:00:40.2017036Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.2017191Z x_17 += out_3; out_6: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_17; x_17 = out_3 = None 2025-03-04T21:00:40.2017251Z 2025-03-04T21:00:40.2017534Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2017674Z out_7: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_6); out_6 = None 2025-03-04T21:00:40.2017739Z 2025-03-04T21:00:40.2017984Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2018399Z x_18: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.2018474Z 2025-03-04T21:00:40.2018739Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2020240Z x_19: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_18 = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2020315Z 2025-03-04T21:00:40.2020597Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2020729Z out_8: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_19); x_19 = None 2025-03-04T21:00:40.2020796Z 2025-03-04T21:00:40.2021040Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2021501Z x_20: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_8 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.2021562Z 2025-03-04T21:00:40.2021835Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2023338Z x_21: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_20 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2023399Z 2025-03-04T21:00:40.2023685Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2023817Z out_9: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_21); x_21 = None 2025-03-04T21:00:40.2023884Z 2025-03-04T21:00:40.2024125Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2024552Z x_22: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_9 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.2024612Z 2025-03-04T21:00:40.2024879Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2026397Z x_23: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_22 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2026469Z 2025-03-04T21:00:40.2026749Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.2026902Z x_23 += out_7; out_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_23; x_23 = out_7 = None 2025-03-04T21:00:40.2026971Z 2025-03-04T21:00:40.2027252Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2027409Z out_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_10); out_10 = None 2025-03-04T21:00:40.2027468Z 2025-03-04T21:00:40.2027721Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2028166Z x_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.2028233Z 2025-03-04T21:00:40.2028497Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2030012Z x_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_24 = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2030081Z 2025-03-04T21:00:40.2030358Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2030505Z out_12: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_25); x_25 = None 2025-03-04T21:00:40.2030564Z 2025-03-04T21:00:40.2030816Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2031243Z x_26: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_12 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.2031319Z 2025-03-04T21:00:40.2031586Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2033123Z x_27: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_26 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2033205Z 2025-03-04T21:00:40.2033492Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2033638Z out_13: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_27); x_27 = None 2025-03-04T21:00:40.2033698Z 2025-03-04T21:00:40.2033957Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2034424Z x_28: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_13 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.2034488Z 2025-03-04T21:00:40.2034764Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2036300Z x_29: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_28 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2036374Z 2025-03-04T21:00:40.2036646Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2037121Z x_30: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); out_11 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:00:40.2037193Z 2025-03-04T21:00:40.2037481Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2039224Z x_31: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_30 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2039320Z 2025-03-04T21:00:40.2039656Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.2039813Z x_29 += x_31; out_14: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_29; x_29 = x_31 = None 2025-03-04T21:00:40.2039875Z 2025-03-04T21:00:40.2040175Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2040324Z out_15: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_14); out_14 = None 2025-03-04T21:00:40.2040395Z 2025-03-04T21:00:40.2040656Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2041114Z x_32: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.2041206Z 2025-03-04T21:00:40.2041479Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2043024Z x_33: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_32 = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2043093Z 2025-03-04T21:00:40.2043383Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2043521Z out_16: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_33); x_33 = None 2025-03-04T21:00:40.2043592Z 2025-03-04T21:00:40.2043852Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2044299Z x_34: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_16 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.2044363Z 2025-03-04T21:00:40.2044648Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2046208Z x_35: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_34 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2046312Z 2025-03-04T21:00:40.2046603Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2046741Z out_17: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_35); x_35 = None 2025-03-04T21:00:40.2046808Z 2025-03-04T21:00:40.2047054Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2047503Z x_36: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_17 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.2047566Z 2025-03-04T21:00:40.2047862Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2049438Z x_37: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_36 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2049518Z 2025-03-04T21:00:40.2049800Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.2049948Z x_37 += out_15; out_18: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_37; x_37 = out_15 = None 2025-03-04T21:00:40.2050011Z 2025-03-04T21:00:40.2050285Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2050435Z out_19: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_18); out_18 = None 2025-03-04T21:00:40.2050496Z 2025-03-04T21:00:40.2050750Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2051164Z x_38: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.2051231Z 2025-03-04T21:00:40.2051510Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2053015Z x_39: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_38 = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2053095Z 2025-03-04T21:00:40.2053372Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2053515Z out_20: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_39); x_39 = None 2025-03-04T21:00:40.2053574Z 2025-03-04T21:00:40.2053822Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2054265Z x_40: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_20 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.2054333Z 2025-03-04T21:00:40.2054589Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2056094Z x_41: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_40 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2056160Z 2025-03-04T21:00:40.2056437Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2056576Z out_21: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_41); x_41 = None 2025-03-04T21:00:40.2056634Z 2025-03-04T21:00:40.2056883Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2057303Z x_42: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_21 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.2057371Z 2025-03-04T21:00:40.2057632Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2059169Z x_43: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_42 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2059250Z 2025-03-04T21:00:40.2059522Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.2059679Z x_43 += out_19; out_22: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_43; x_43 = out_19 = None 2025-03-04T21:00:40.2059737Z 2025-03-04T21:00:40.2060017Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2060163Z out_23: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_22); out_22 = None 2025-03-04T21:00:40.2060228Z 2025-03-04T21:00:40.2060471Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2060954Z x_44: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.2061017Z 2025-03-04T21:00:40.2061283Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2062786Z x_45: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_44 = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2062849Z 2025-03-04T21:00:40.2063134Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2063266Z out_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_45); x_45 = None 2025-03-04T21:00:40.2063333Z 2025-03-04T21:00:40.2063573Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2063997Z x_46: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_24 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.2064352Z 2025-03-04T21:00:40.2064617Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2066122Z x_47: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_46 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2066199Z 2025-03-04T21:00:40.2066484Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2066616Z out_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_47); x_47 = None 2025-03-04T21:00:40.2066689Z 2025-03-04T21:00:40.2066933Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2067399Z x_48: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_25 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.2067463Z 2025-03-04T21:00:40.2067739Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2069360Z x_49: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_48 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2069425Z 2025-03-04T21:00:40.2069711Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.2069860Z x_49 += out_23; out_26: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_49; x_49 = out_23 = None 2025-03-04T21:00:40.2069930Z 2025-03-04T21:00:40.2070212Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2070376Z out_27: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_26); out_26 = None 2025-03-04T21:00:40.2070438Z 2025-03-04T21:00:40.2070699Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2071126Z x_50: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.2071203Z 2025-03-04T21:00:40.2071473Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2073040Z x_51: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_50 = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2073127Z 2025-03-04T21:00:40.2073411Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2073549Z out_28: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_51); x_51 = None 2025-03-04T21:00:40.2073609Z 2025-03-04T21:00:40.2073863Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2074326Z x_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_28 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.2074390Z 2025-03-04T21:00:40.2074661Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2076189Z x_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_52 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2076260Z 2025-03-04T21:00:40.2076542Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2076680Z out_29: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_53); x_53 = None 2025-03-04T21:00:40.2076742Z 2025-03-04T21:00:40.2076997Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2077430Z x_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_29 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.2077507Z 2025-03-04T21:00:40.2077833Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2079367Z x_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_54 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2079457Z 2025-03-04T21:00:40.2079717Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2080147Z x_56: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); out_27 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:00:40.2080213Z 2025-03-04T21:00:40.2080478Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2082120Z x_57: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_56 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2082193Z 2025-03-04T21:00:40.2082478Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.2082622Z x_55 += x_57; out_30: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_55; x_55 = x_57 = None 2025-03-04T21:00:40.2082686Z 2025-03-04T21:00:40.2082977Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2083116Z out_31: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_30); out_30 = None 2025-03-04T21:00:40.2083184Z 2025-03-04T21:00:40.2083445Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2083877Z x_58: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.2083939Z 2025-03-04T21:00:40.2084206Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2085782Z x_59: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_58 = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2085867Z 2025-03-04T21:00:40.2086163Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2086296Z out_32: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_59); x_59 = None 2025-03-04T21:00:40.2086363Z 2025-03-04T21:00:40.2086623Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2087061Z x_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_32 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.2087120Z 2025-03-04T21:00:40.2087417Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2088917Z x_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_60 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2088984Z 2025-03-04T21:00:40.2089267Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2089393Z out_33: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_61); x_61 = None 2025-03-04T21:00:40.2089458Z 2025-03-04T21:00:40.2089698Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2090114Z x_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_33 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.2090174Z 2025-03-04T21:00:40.2090437Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2091929Z x_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_62 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2092021Z 2025-03-04T21:00:40.2092303Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.2092444Z x_63 += out_31; out_34: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_63; x_63 = out_31 = None 2025-03-04T21:00:40.2092509Z 2025-03-04T21:00:40.2092783Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2092925Z out_35: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_34); out_34 = None 2025-03-04T21:00:40.2092984Z 2025-03-04T21:00:40.2093232Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2093670Z x_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.2093740Z 2025-03-04T21:00:40.2093999Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2095488Z x_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_64 = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2095556Z 2025-03-04T21:00:40.2095834Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2095966Z out_36: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_65); x_65 = None 2025-03-04T21:00:40.2096024Z 2025-03-04T21:00:40.2096271Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2096680Z x_66: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_36 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.2096746Z 2025-03-04T21:00:40.2097001Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2098505Z x_67: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_66 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2098583Z 2025-03-04T21:00:40.2098862Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2098991Z out_37: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_67); x_67 = None 2025-03-04T21:00:40.2099050Z 2025-03-04T21:00:40.2099301Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2099716Z x_68: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_37 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.2099783Z 2025-03-04T21:00:40.2100067Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2101619Z x_69: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_68 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2101691Z 2025-03-04T21:00:40.2102092Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.2102258Z x_69 += out_35; out_38: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_69; x_69 = out_35 = None 2025-03-04T21:00:40.2102322Z 2025-03-04T21:00:40.2102636Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2102788Z out_39: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_38); out_38 = None 2025-03-04T21:00:40.2102854Z 2025-03-04T21:00:40.2103102Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2103530Z x_70: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.2103625Z 2025-03-04T21:00:40.2103906Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2105557Z x_71: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_70 = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2105647Z 2025-03-04T21:00:40.2105954Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2106090Z out_40: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_71); x_71 = None 2025-03-04T21:00:40.2106158Z 2025-03-04T21:00:40.2106434Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2106943Z x_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_40 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.2107011Z 2025-03-04T21:00:40.2107311Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2109030Z x_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_72 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2109095Z 2025-03-04T21:00:40.2109400Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2109535Z out_41: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_73); x_73 = None 2025-03-04T21:00:40.2109605Z 2025-03-04T21:00:40.2109868Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2110328Z x_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_41 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.2110393Z 2025-03-04T21:00:40.2110680Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2112250Z x_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_74 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2112331Z 2025-03-04T21:00:40.2112619Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.2112762Z x_75 += out_39; out_42: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_75; x_75 = out_39 = None 2025-03-04T21:00:40.2112829Z 2025-03-04T21:00:40.2113111Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2113256Z out_43: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_42); out_42 = None 2025-03-04T21:00:40.2113316Z 2025-03-04T21:00:40.2113575Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2114018Z x_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.2114086Z 2025-03-04T21:00:40.2114356Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2115891Z x_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_76 = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2115958Z 2025-03-04T21:00:40.2116242Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2116378Z out_44: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_77); x_77 = None 2025-03-04T21:00:40.2116439Z 2025-03-04T21:00:40.2116697Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2117116Z x_78: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_44 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.2117204Z 2025-03-04T21:00:40.2117485Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2119158Z x_79: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_78 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2119255Z 2025-03-04T21:00:40.2119543Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2119675Z out_45: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_79); x_79 = None 2025-03-04T21:00:40.2119734Z 2025-03-04T21:00:40.2119986Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2120427Z x_80: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_45 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.2120489Z 2025-03-04T21:00:40.2120753Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2122283Z x_81: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_80 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2122356Z 2025-03-04T21:00:40.2122627Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.2122775Z x_81 += out_43; out_46: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_81; x_81 = out_43 = None 2025-03-04T21:00:40.2122835Z 2025-03-04T21:00:40.2123120Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2123264Z out_47: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_46); out_46 = None 2025-03-04T21:00:40.2123324Z 2025-03-04T21:00:40.2123567Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2123971Z x_82: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.2124048Z 2025-03-04T21:00:40.2124303Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2125807Z x_83: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_82 = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2125886Z 2025-03-04T21:00:40.2126188Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2126320Z out_48: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_83); x_83 = None 2025-03-04T21:00:40.2126379Z 2025-03-04T21:00:40.2126631Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2127070Z x_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_48 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.2127140Z 2025-03-04T21:00:40.2127397Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2128943Z x_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_84 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2129013Z 2025-03-04T21:00:40.2129298Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2129433Z out_49: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_85); x_85 = None 2025-03-04T21:00:40.2129493Z 2025-03-04T21:00:40.2129756Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2130173Z x_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_49 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.2130257Z 2025-03-04T21:00:40.2130519Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2132075Z x_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_86 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2132163Z 2025-03-04T21:00:40.2132459Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.2132617Z x_87 += out_47; out_50: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_87; x_87 = out_47 = None 2025-03-04T21:00:40.2132680Z 2025-03-04T21:00:40.2132994Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2133134Z out_51: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_50); out_50 = None 2025-03-04T21:00:40.2133201Z 2025-03-04T21:00:40.2133478Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2133900Z x_88: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.2133961Z 2025-03-04T21:00:40.2134231Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2135772Z x_89: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_88 = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2135844Z 2025-03-04T21:00:40.2136138Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2136266Z out_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_89); x_89 = None 2025-03-04T21:00:40.2136335Z 2025-03-04T21:00:40.2136585Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2137012Z x_90: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_52 = l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.2137092Z 2025-03-04T21:00:40.2137363Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2138881Z x_91: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_90 = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2138965Z 2025-03-04T21:00:40.2139253Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2139381Z out_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_91); x_91 = None 2025-03-04T21:00:40.2139449Z 2025-03-04T21:00:40.2139698Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2140148Z x_92: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_53 = l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.2140211Z 2025-03-04T21:00:40.2140482Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2142009Z x_93: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_92 = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2142080Z 2025-03-04T21:00:40.2142365Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.2142507Z x_93 += out_51; out_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_93; x_93 = out_51 = None 2025-03-04T21:00:40.2142573Z 2025-03-04T21:00:40.2142852Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2143000Z out_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_54); out_54 = None 2025-03-04T21:00:40.2143062Z 2025-03-04T21:00:40.2143328Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2143747Z x_94: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.2143812Z 2025-03-04T21:00:40.2144071Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2145587Z x_95: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_94 = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2145669Z 2025-03-04T21:00:40.2145947Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2146083Z out_56: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_95); x_95 = None 2025-03-04T21:00:40.2146142Z 2025-03-04T21:00:40.2146421Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2146828Z x_96: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_56 = l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.2146896Z 2025-03-04T21:00:40.2147155Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2148650Z x_97: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_96 = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2148718Z 2025-03-04T21:00:40.2148995Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2149129Z out_57: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_97); x_97 = None 2025-03-04T21:00:40.2149191Z 2025-03-04T21:00:40.2149448Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2149871Z x_98: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_57 = l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.2149958Z 2025-03-04T21:00:40.2150219Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2151759Z x_99: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_98 = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2151842Z 2025-03-04T21:00:40.2152123Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.2152270Z x_99 += out_55; out_58: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_99; x_99 = out_55 = None 2025-03-04T21:00:40.2152331Z 2025-03-04T21:00:40.2152621Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2152786Z out_59: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_58); out_58 = None 2025-03-04T21:00:40.2152857Z 2025-03-04T21:00:40.2153104Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2153530Z x_100: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.2153590Z 2025-03-04T21:00:40.2153859Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2155378Z x_101: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_100 = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2155438Z 2025-03-04T21:00:40.2155719Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2155859Z out_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_101); x_101 = None 2025-03-04T21:00:40.2155927Z 2025-03-04T21:00:40.2156174Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2156621Z x_102: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_60 = l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.2156680Z 2025-03-04T21:00:40.2156957Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2158565Z x_103: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_102 = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2158649Z 2025-03-04T21:00:40.2158950Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2159104Z out_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_103); x_103 = None 2025-03-04T21:00:40.2159179Z 2025-03-04T21:00:40.2159507Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2159943Z x_104: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_61 = l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.2160006Z 2025-03-04T21:00:40.2160311Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2162054Z x_105: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_104 = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2162125Z 2025-03-04T21:00:40.2162449Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.2162617Z x_105 += out_59; out_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_105; x_105 = out_59 = None 2025-03-04T21:00:40.2162695Z 2025-03-04T21:00:40.2163022Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2163184Z out_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_62); out_62 = None 2025-03-04T21:00:40.2163269Z 2025-03-04T21:00:40.2163562Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2164052Z x_106: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_63, l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.2164120Z 2025-03-04T21:00:40.2164430Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2166147Z x_107: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_106 = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2166224Z 2025-03-04T21:00:40.2166610Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2166769Z out_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_107); x_107 = None 2025-03-04T21:00:40.2166836Z 2025-03-04T21:00:40.2167129Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2167574Z x_108: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_64, l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_64 = l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.2167632Z 2025-03-04T21:00:40.2167897Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2169420Z x_109: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_108, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_108 = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2169488Z 2025-03-04T21:00:40.2169766Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2169907Z out_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_109); x_109 = None 2025-03-04T21:00:40.2169964Z 2025-03-04T21:00:40.2170213Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2170646Z x_110: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_65, l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_65 = l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.2170705Z 2025-03-04T21:00:40.2170971Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2172476Z x_111: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_110, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_110 = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2172554Z 2025-03-04T21:00:40.2172833Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.2172977Z x_111 += out_63; out_66: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_111; x_111 = out_63 = None 2025-03-04T21:00:40.2173069Z 2025-03-04T21:00:40.2173343Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2173487Z out_67: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_66); out_66 = None 2025-03-04T21:00:40.2173546Z 2025-03-04T21:00:40.2173800Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2174225Z x_112: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_67, l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.2174295Z 2025-03-04T21:00:40.2174562Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2176124Z x_113: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_112, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_112 = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2176194Z 2025-03-04T21:00:40.2176470Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2176622Z out_68: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_113); x_113 = None 2025-03-04T21:00:40.2176681Z 2025-03-04T21:00:40.2176931Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2177343Z x_114: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_68, l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_68 = l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.2177410Z 2025-03-04T21:00:40.2177668Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2179213Z x_115: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_114, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_114 = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2179281Z 2025-03-04T21:00:40.2179602Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2179739Z out_69: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_115); x_115 = None 2025-03-04T21:00:40.2179799Z 2025-03-04T21:00:40.2180044Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2180460Z x_116: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_69, l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_69 = l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.2180533Z 2025-03-04T21:00:40.2180788Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2182307Z x_117: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_116, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_116 = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2182373Z 2025-03-04T21:00:40.2182642Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.2182792Z x_117 += out_67; out_70: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_117; x_117 = out_67 = None 2025-03-04T21:00:40.2182869Z 2025-03-04T21:00:40.2183152Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2183285Z out_71: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_70); out_70 = None 2025-03-04T21:00:40.2183351Z 2025-03-04T21:00:40.2183596Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2184014Z x_118: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_71, l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.2184086Z 2025-03-04T21:00:40.2184355Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2185988Z x_119: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_118, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_118 = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2186050Z 2025-03-04T21:00:40.2186332Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2186463Z out_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_119); x_119 = None 2025-03-04T21:00:40.2186530Z 2025-03-04T21:00:40.2186772Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2187193Z x_120: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_72, l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_72 = l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.2187254Z 2025-03-04T21:00:40.2187523Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2189097Z x_121: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_120, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_120 = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2189162Z 2025-03-04T21:00:40.2189447Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2189590Z out_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_121); x_121 = None 2025-03-04T21:00:40.2189653Z 2025-03-04T21:00:40.2189894Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2190316Z x_122: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_73, l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_73 = l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.2190389Z 2025-03-04T21:00:40.2190652Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2192205Z x_123: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_122, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_122 = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2192268Z 2025-03-04T21:00:40.2192574Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.2192722Z x_123 += out_71; out_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_123; x_123 = out_71 = None 2025-03-04T21:00:40.2192790Z 2025-03-04T21:00:40.2193070Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2193212Z out_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_74); out_74 = None 2025-03-04T21:00:40.2193272Z 2025-03-04T21:00:40.2193525Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2193944Z x_124: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_75, l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.2194012Z 2025-03-04T21:00:40.2194280Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2195831Z x_125: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_124, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_124 = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2195914Z 2025-03-04T21:00:40.2196197Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2196335Z out_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_125); x_125 = None 2025-03-04T21:00:40.2196394Z 2025-03-04T21:00:40.2196651Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2197071Z x_126: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_76, l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_76 = l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.2197154Z 2025-03-04T21:00:40.2197422Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2199183Z x_127: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_126, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_126 = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2199260Z 2025-03-04T21:00:40.2199557Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2199699Z out_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_127); x_127 = None 2025-03-04T21:00:40.2199760Z 2025-03-04T21:00:40.2200021Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2200454Z x_128: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_77, l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_77 = l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.2200522Z 2025-03-04T21:00:40.2200804Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2202507Z x_129: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_128, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_128 = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2202581Z 2025-03-04T21:00:40.2202864Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.2203057Z x_129 += out_75; out_78: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_129; x_129 = out_75 = None 2025-03-04T21:00:40.2203119Z 2025-03-04T21:00:40.2203413Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2203551Z out_79: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_78); out_78 = None 2025-03-04T21:00:40.2203620Z 2025-03-04T21:00:40.2203869Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2204311Z x_130: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_79, l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.2204382Z 2025-03-04T21:00:40.2204648Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2206247Z x_131: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_130, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_130 = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2206311Z 2025-03-04T21:00:40.2206597Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2206735Z out_80: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_131); x_131 = None 2025-03-04T21:00:40.2206796Z 2025-03-04T21:00:40.2207050Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2207466Z x_132: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_80, l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_80 = l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.2207533Z 2025-03-04T21:00:40.2207788Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2209315Z x_133: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_132, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_132 = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2209400Z 2025-03-04T21:00:40.2209679Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2209812Z out_81: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_133); x_133 = None 2025-03-04T21:00:40.2209871Z 2025-03-04T21:00:40.2210122Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2210543Z x_134: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_81, l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_81 = l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.2210632Z 2025-03-04T21:00:40.2210894Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2212425Z x_135: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_134, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_134 = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2212495Z 2025-03-04T21:00:40.2212764Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.2212911Z x_135 += out_79; out_82: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_135; x_135 = out_79 = None 2025-03-04T21:00:40.2212969Z 2025-03-04T21:00:40.2213247Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2213381Z out_83: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_82); out_82 = None 2025-03-04T21:00:40.2213447Z 2025-03-04T21:00:40.2213689Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2214099Z x_136: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_83, l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.2214161Z 2025-03-04T21:00:40.2214424Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2215921Z x_137: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_136, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_136 = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2216004Z 2025-03-04T21:00:40.2216293Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2216422Z out_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_137); x_137 = None 2025-03-04T21:00:40.2216487Z 2025-03-04T21:00:40.2216745Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2217162Z x_138: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_84, l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_84 = l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.2217222Z 2025-03-04T21:00:40.2217489Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2219011Z x_139: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_138, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_138 = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2219080Z 2025-03-04T21:00:40.2219364Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2219491Z out_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_139); x_139 = None 2025-03-04T21:00:40.2219557Z 2025-03-04T21:00:40.2219800Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2220227Z x_140: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_85, l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_85 = l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.2220284Z 2025-03-04T21:00:40.2220552Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2222054Z x_141: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_140, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_140 = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2222129Z 2025-03-04T21:00:40.2222403Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.2222544Z x_141 += out_83; out_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_141; x_141 = out_83 = None 2025-03-04T21:00:40.2222608Z 2025-03-04T21:00:40.2222881Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2223036Z out_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_86); out_86 = None 2025-03-04T21:00:40.2223096Z 2025-03-04T21:00:40.2223346Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2223753Z x_142: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_87, l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.2223819Z 2025-03-04T21:00:40.2224077Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2225620Z x_143: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_142, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_142 = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2225689Z 2025-03-04T21:00:40.2225966Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2226098Z out_88: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_143); x_143 = None 2025-03-04T21:00:40.2226158Z 2025-03-04T21:00:40.2226417Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2226848Z x_144: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_88, l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_88 = l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.2226918Z 2025-03-04T21:00:40.2227180Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2228778Z x_145: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_144, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_144 = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2228860Z 2025-03-04T21:00:40.2229135Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2229269Z out_89: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_145); x_145 = None 2025-03-04T21:00:40.2229344Z 2025-03-04T21:00:40.2229594Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2230008Z x_146: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_89, l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_89 = l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.2230078Z 2025-03-04T21:00:40.2230334Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2231880Z x_147: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_146, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_146 = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2231950Z 2025-03-04T21:00:40.2232236Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.2232387Z x_147 += out_87; out_90: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_147; x_147 = out_87 = None 2025-03-04T21:00:40.2232448Z 2025-03-04T21:00:40.2232737Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2232874Z out_91: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_90); out_90 = None 2025-03-04T21:00:40.2232946Z 2025-03-04T21:00:40.2233197Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2233619Z x_148: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_91, l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.2233679Z 2025-03-04T21:00:40.2233961Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2235506Z x_149: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_148, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_148 = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2235591Z 2025-03-04T21:00:40.2235900Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2236048Z out_92: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_149); x_149 = None 2025-03-04T21:00:40.2236115Z 2025-03-04T21:00:40.2236372Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2236826Z x_150: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_92, l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_92 = l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.2236894Z 2025-03-04T21:00:40.2237160Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2238900Z x_151: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_150, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_150 = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2238975Z 2025-03-04T21:00:40.2239288Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2239422Z out_93: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_151); x_151 = None 2025-03-04T21:00:40.2239490Z 2025-03-04T21:00:40.2239759Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2240262Z x_152: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_93, l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_93 = l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.2240333Z 2025-03-04T21:00:40.2240614Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2242259Z x_153: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_152, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_152 = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2242335Z 2025-03-04T21:00:40.2242643Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.2242804Z x_153 += out_91; out_94: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_153; x_153 = out_91 = None 2025-03-04T21:00:40.2242886Z 2025-03-04T21:00:40.2243188Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2243335Z out_95: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_94); out_94 = None 2025-03-04T21:00:40.2243403Z 2025-03-04T21:00:40.2243662Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2244097Z x_154: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_95, l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.2244158Z 2025-03-04T21:00:40.2244438Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2246025Z x_155: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_154, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_154 = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2246096Z 2025-03-04T21:00:40.2246397Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2246527Z out_96: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_155); x_155 = None 2025-03-04T21:00:40.2246595Z 2025-03-04T21:00:40.2246854Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2247291Z x_156: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_96, l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_96 = l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.2247351Z 2025-03-04T21:00:40.2247631Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2249205Z x_157: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_156, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_156 = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2249287Z 2025-03-04T21:00:40.2249591Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2249722Z out_97: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_157); x_157 = None 2025-03-04T21:00:40.2249789Z 2025-03-04T21:00:40.2250040Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2250472Z x_158: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_97, l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_97 = l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.2250531Z 2025-03-04T21:00:40.2250794Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2252431Z x_159: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_158, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_158 = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2252508Z 2025-03-04T21:00:40.2252808Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.2252970Z x_159 += out_95; out_98: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_159; x_159 = out_95 = None 2025-03-04T21:00:40.2253038Z 2025-03-04T21:00:40.2253321Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2253467Z out_99: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_98); out_98 = None 2025-03-04T21:00:40.2253529Z 2025-03-04T21:00:40.2253789Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2254204Z x_160: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_99, l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.2254273Z 2025-03-04T21:00:40.2254551Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2256066Z x_161: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_160, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_160 = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2256161Z 2025-03-04T21:00:40.2256438Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2256581Z out_100: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_161); x_161 = None 2025-03-04T21:00:40.2256639Z 2025-03-04T21:00:40.2256890Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2257309Z x_162: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_100, l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_100 = l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.2257376Z 2025-03-04T21:00:40.2257667Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2259171Z x_163: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_162, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_162 = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2259241Z 2025-03-04T21:00:40.2259524Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2259665Z out_101: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_163); x_163 = None 2025-03-04T21:00:40.2259725Z 2025-03-04T21:00:40.2259976Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2260394Z x_164: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_101, l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_101 = l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.2260460Z 2025-03-04T21:00:40.2260716Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2262230Z x_165: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_164, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_164 = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2262327Z 2025-03-04T21:00:40.2262600Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.2262755Z x_165 += out_99; out_102: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_165; x_165 = out_99 = None 2025-03-04T21:00:40.2262816Z 2025-03-04T21:00:40.2263097Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2263238Z out_103: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_102); out_102 = None 2025-03-04T21:00:40.2263302Z 2025-03-04T21:00:40.2263546Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2263988Z x_166: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_103, l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.2264050Z 2025-03-04T21:00:40.2264316Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2265833Z x_167: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_166, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_166 = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2265894Z 2025-03-04T21:00:40.2266176Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2266307Z out_104: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_167); x_167 = None 2025-03-04T21:00:40.2266374Z 2025-03-04T21:00:40.2266616Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2267038Z x_168: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_104, l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_104 = l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.2267098Z 2025-03-04T21:00:40.2267368Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2268889Z x_169: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_168, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_168 = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2268987Z 2025-03-04T21:00:40.2269266Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2269399Z out_105: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_169); x_169 = None 2025-03-04T21:00:40.2269466Z 2025-03-04T21:00:40.2269705Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2270128Z x_170: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_105, l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_105 = l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.2270187Z 2025-03-04T21:00:40.2270476Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2272001Z x_171: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_170, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_170 = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2272067Z 2025-03-04T21:00:40.2272352Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.2272511Z x_171 += out_103; out_106: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_171; x_171 = out_103 = None 2025-03-04T21:00:40.2272581Z 2025-03-04T21:00:40.2272862Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2273011Z out_107: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_106); out_106 = None 2025-03-04T21:00:40.2273072Z 2025-03-04T21:00:40.2273329Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2273751Z x_172: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_107, l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.2273833Z 2025-03-04T21:00:40.2274102Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2275648Z x_173: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_172, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_172 = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2275731Z 2025-03-04T21:00:40.2276011Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2276149Z out_108: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_173); x_173 = None 2025-03-04T21:00:40.2276210Z 2025-03-04T21:00:40.2276460Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2276918Z x_174: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_108, l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_108 = l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.2276979Z 2025-03-04T21:00:40.2277248Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2278917Z x_175: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_174, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_174 = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2278996Z 2025-03-04T21:00:40.2279296Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2279453Z out_109: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_175); x_175 = None 2025-03-04T21:00:40.2279516Z 2025-03-04T21:00:40.2279769Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2280204Z x_176: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_109, l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_109 = l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.2280266Z 2025-03-04T21:00:40.2280536Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2282107Z x_177: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_176, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_176 = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2282191Z 2025-03-04T21:00:40.2282476Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.2282633Z x_177 += out_107; out_110: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_177; x_177 = out_107 = None 2025-03-04T21:00:40.2282700Z 2025-03-04T21:00:40.2282986Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2283134Z out_111: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_110); out_110 = None 2025-03-04T21:00:40.2283198Z 2025-03-04T21:00:40.2283489Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2283923Z x_178: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_111, l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.2283992Z 2025-03-04T21:00:40.2284256Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2285814Z x_179: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_178, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_178 = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2285886Z 2025-03-04T21:00:40.2286171Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2286311Z out_112: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_179); x_179 = None 2025-03-04T21:00:40.2286372Z 2025-03-04T21:00:40.2286630Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2287058Z x_180: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_112, l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_112 = l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.2287145Z 2025-03-04T21:00:40.2287416Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2288973Z x_181: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_180, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_180 = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2289056Z 2025-03-04T21:00:40.2289336Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2289471Z out_113: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_181); x_181 = None 2025-03-04T21:00:40.2289529Z 2025-03-04T21:00:40.2289786Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2290230Z x_182: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_113, l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_113 = l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.2290298Z 2025-03-04T21:00:40.2290559Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2292074Z x_183: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_182, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_182 = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2292142Z 2025-03-04T21:00:40.2292422Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.2292576Z x_183 += out_111; out_114: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_183; x_183 = out_111 = None 2025-03-04T21:00:40.2292635Z 2025-03-04T21:00:40.2292915Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2293053Z out_115: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_114); out_114 = None 2025-03-04T21:00:40.2293116Z 2025-03-04T21:00:40.2293361Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2293796Z x_184: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_115, l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ = None 2025-03-04T21:00:40.2293855Z 2025-03-04T21:00:40.2294125Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2295654Z x_185: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_184, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_184 = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2295730Z 2025-03-04T21:00:40.2296009Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2296141Z out_116: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_185); x_185 = None 2025-03-04T21:00:40.2296209Z 2025-03-04T21:00:40.2296477Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2296899Z x_186: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_116, l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_116 = l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ = None 2025-03-04T21:00:40.2296960Z 2025-03-04T21:00:40.2297226Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2298741Z x_187: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_186, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_186 = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2298803Z 2025-03-04T21:00:40.2299086Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2299215Z out_117: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_187); x_187 = None 2025-03-04T21:00:40.2299283Z 2025-03-04T21:00:40.2299530Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2299956Z x_188: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_117, l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_117 = l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ = None 2025-03-04T21:00:40.2300031Z 2025-03-04T21:00:40.2300297Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:00:40.2301822Z x_189: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_188, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_188 = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:00:40.2301982Z 2025-03-04T21:00:40.2302271Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:00:40.2302420Z x_189 += out_115; out_118: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_189; x_189 = out_115 = None 2025-03-04T21:00:40.2302487Z 2025-03-04T21:00:40.2302762Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:00:40.2302955Z out_119: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_118); out_118 = None 2025-03-04T21:00:40.2303015Z 2025-03-04T21:00:40.2303453Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:00:40.2303601Z arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32) 2025-03-04T21:00:40.2303669Z 2025-03-04T21:00:40.2303956Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:00:40.2304092Z shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T21:00:40.2304153Z 2025-03-04T21:00:40.2304587Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:00:40.2304734Z arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32) 2025-03-04T21:00:40.2304801Z 2025-03-04T21:00:40.2305087Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:00:40.2305226Z shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T21:00:40.2305291Z 2025-03-04T21:00:40.2305655Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:00:40.2305838Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T21:00:40.2305934Z shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0] 2025-03-04T21:00:40.2306056Z shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T21:00:40.2306138Z 2025-03-04T21:00:40.2306467Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:00:40.2306589Z shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T21:00:40.2306656Z 2025-03-04T21:00:40.2306975Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:00:40.2307094Z shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T21:00:40.2307176Z 2025-03-04T21:00:40.2307554Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:00:40.2307759Z shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T21:00:40.2307827Z 2025-03-04T21:00:40.2308236Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:00:40.2308362Z view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T21:00:40.2308779Z view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T21:00:40.2308906Z add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T21:00:40.2309043Z x_190: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4); add = None 2025-03-04T21:00:40.2309114Z 2025-03-04T21:00:40.2309413Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:00:40.2309543Z tensor: "f32[82125, 4][4, 1]cpu" = x_190.to(torch.float32); x_190 = None 2025-03-04T21:00:40.2309604Z 2025-03-04T21:00:40.2309864Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:40.2310643Z x_191: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_119, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); out_119 = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T21:00:40.2310716Z 2025-03-04T21:00:40.2310994Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:00:40.2311179Z x_192: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_191, inplace = False); x_191 = None 2025-03-04T21:00:40.2311248Z 2025-03-04T21:00:40.2311627Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:00:40.2312507Z score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_192, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T21:00:40.2312588Z 2025-03-04T21:00:40.2312964Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:00:40.2313797Z x_193: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_192, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_192 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T21:00:40.2313882Z 2025-03-04T21:00:40.2314226Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T21:00:40.2314376Z permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T21:00:40.2314521Z logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T21:00:40.2314582Z 2025-03-04T21:00:40.2315003Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T21:00:40.2315158Z view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_193.view(4, -1, 4, 73, 75); x_193 = None 2025-03-04T21:00:40.2315371Z permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2); view_2 = None 2025-03-04T21:00:40.2315546Z pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2); permute_1 = None 2025-03-04T21:00:40.2315615Z 2025-03-04T21:00:40.2316021Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:00:40.2316230Z pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T21:00:40.2316291Z 2025-03-04T21:00:40.2316732Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:00:40.2316880Z unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T21:00:40.2317034Z expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T21:00:40.2317172Z anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T21:00:40.2317242Z 2025-03-04T21:00:40.2317625Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:00:40.2317857Z deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T21:00:40.2317925Z 2025-03-04T21:00:40.2318251Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:00:40.2318400Z boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T21:00:40.2318471Z 2025-03-04T21:00:40.2318817Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:00:40.2318979Z getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:00:40.2319110Z getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:00:40.2319269Z widths: "f32[328500][1]cpu" = getitem_2 - getitem_3; getitem_2 = getitem_3 = None 2025-03-04T21:00:40.2319333Z 2025-03-04T21:00:40.2319674Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:00:40.2319793Z getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:00:40.2319931Z getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:00:40.2320084Z heights: "f32[328500][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T21:00:40.2320145Z 2025-03-04T21:00:40.2320467Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:00:40.2320584Z getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:00:40.2320676Z mul: "f32[328500][1]cpu" = 0.5 * widths 2025-03-04T21:00:40.2320796Z ctr_x: "f32[328500][1]cpu" = getitem_6 + mul; getitem_6 = mul = None 2025-03-04T21:00:40.2320863Z 2025-03-04T21:00:40.2321185Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:00:40.2321332Z getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:00:40.2321444Z mul_1: "f32[328500][1]cpu" = 0.5 * heights 2025-03-04T21:00:40.2321574Z ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1; getitem_7 = mul_1 = None 2025-03-04T21:00:40.2321637Z 2025-03-04T21:00:40.2321993Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:00:40.2322142Z getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:00:40.2322257Z dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0; getitem_8 = None 2025-03-04T21:00:40.2322318Z 2025-03-04T21:00:40.2322638Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:00:40.2322784Z getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:00:40.2322900Z dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0; getitem_9 = None 2025-03-04T21:00:40.2322960Z 2025-03-04T21:00:40.2323265Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:00:40.2323417Z getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:00:40.2323531Z dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0; getitem_10 = None 2025-03-04T21:00:40.2323592Z 2025-03-04T21:00:40.2323914Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:00:40.2324095Z getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:00:40.2324212Z dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0; getitem_11 = None 2025-03-04T21:00:40.2324274Z 2025-03-04T21:00:40.2324669Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:00:40.2324824Z dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:00:40.2324893Z 2025-03-04T21:00:40.2325287Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:00:40.2325429Z dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:00:40.2325489Z 2025-03-04T21:00:40.2325866Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:00:40.2326017Z getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:00:40.2326147Z mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12; dx = getitem_12 = None 2025-03-04T21:00:40.2326298Z getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:00:40.2326439Z pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13; mul_2 = getitem_13 = None 2025-03-04T21:00:40.2326500Z 2025-03-04T21:00:40.2326852Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:00:40.2326992Z getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:00:40.2327113Z mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14; dy = getitem_14 = None 2025-03-04T21:00:40.2327266Z getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:00:40.2327427Z pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15; mul_3 = getitem_15 = None 2025-03-04T21:00:40.2327496Z 2025-03-04T21:00:40.2327829Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:00:40.2327952Z exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:00:40.2328108Z getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:00:40.2328240Z pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16; exp = getitem_16 = None 2025-03-04T21:00:40.2328301Z 2025-03-04T21:00:40.2328640Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:00:40.2328754Z exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:00:40.2328925Z getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:00:40.2329055Z pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17; exp_1 = getitem_17 = None 2025-03-04T21:00:40.2329122Z 2025-03-04T21:00:40.2329437Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:00:40.2329545Z mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T21:00:40.2329656Z x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:00:40.2329724Z 2025-03-04T21:00:40.2330026Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:00:40.2330118Z mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T21:00:40.2330228Z y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:00:40.2330295Z 2025-03-04T21:00:40.2330591Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:00:40.2330727Z mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:00:40.2330848Z x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:00:40.2330913Z 2025-03-04T21:00:40.2331214Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:00:40.2331327Z mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:00:40.2331460Z y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:00:40.2331524Z 2025-03-04T21:00:40.2331863Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:00:40.2332044Z pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:00:40.2332103Z 2025-03-04T21:00:40.2332434Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:00:40.2332586Z proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4)); pred_boxes = None 2025-03-04T21:00:40.2332652Z 2025-03-04T21:00:40.2333022Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:00:40.2333225Z proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T21:00:40.2333285Z 2025-03-04T21:00:40.2333759Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T21:00:40.2333883Z arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T21:00:40.2333950Z 2025-03-04T21:00:40.2334235Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:00:40.2334374Z batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T21:00:40.2334435Z 2025-03-04T21:00:40.2334859Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:00:40.2334973Z topk = logits_i.topk(6000, dim = 1); logits_i = None 2025-03-04T21:00:40.2335073Z topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0] 2025-03-04T21:00:40.2335189Z topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1]; topk = None 2025-03-04T21:00:40.2335250Z 2025-03-04T21:00:40.2335703Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:00:40.2335861Z getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T21:00:40.2336098Z topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)]; proposals_i_1 = getitem_20 = topk_idx = None 2025-03-04T21:00:40.2336161Z 2025-03-04T21:00:40.2336620Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:00:40.2336800Z full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:00:40.2336867Z 2025-03-04T21:00:40.2337162Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:00:40.2337315Z level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu')); full = level_ids = None 2025-03-04T21:00:40.2337376Z 2025-03-04T21:00:40.2337761Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T21:00:40.2337920Z getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0]; topk_proposals_i = None 2025-03-04T21:00:40.2337989Z 2025-03-04T21:00:40.2338289Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:00:40.2338432Z tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32); getitem_22 = None 2025-03-04T21:00:40.2338491Z 2025-03-04T21:00:40.2338865Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T21:00:40.2338995Z scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0]; topk_scores_i = None 2025-03-04T21:00:40.2339063Z 2025-03-04T21:00:40.2339561Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T21:00:40.2339704Z isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1); tensor_1 = None 2025-03-04T21:00:40.2339818Z all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:00:40.2339975Z isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T21:00:40.2340097Z valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T21:00:40.2340164Z 2025-03-04T21:00:40.2340518Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T21:00:40.2340639Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T21:00:40.2340698Z 2025-03-04T21:00:47.8864220Z 2025-03-04T21:00:47.8868331Z class GraphModule(torch.nn.Module): 2025-03-04T21:00:47.8871480Z def forward(self, L_features_res4_: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_weight_: "f32[1024, 1024, 3, 3][9216, 9, 3, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_bias_: "f32[1024][1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"): 2025-03-04T21:00:47.8873089Z l_features_res4_ = L_features_res4_ 2025-03-04T21:00:47.8873609Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T21:00:47.8874242Z l_self_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T21:00:47.8875038Z l_self_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T21:00:47.8875594Z l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T21:00:47.8876239Z l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T21:00:47.8876862Z l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T21:00:47.8877517Z l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T21:00:47.8877929Z 2025-03-04T21:00:47.8878689Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:00:47.8879414Z arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32) 2025-03-04T21:00:47.8879691Z 2025-03-04T21:00:47.8880110Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:00:47.8880657Z shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T21:00:47.8880915Z 2025-03-04T21:00:47.8881577Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:00:47.8882293Z arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32) 2025-03-04T21:00:47.8882564Z 2025-03-04T21:00:47.8882932Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:00:47.8883411Z shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T21:00:47.8883660Z 2025-03-04T21:00:47.8884113Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:00:47.8884706Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T21:00:47.8885034Z shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0] 2025-03-04T21:00:47.8885294Z shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T21:00:47.8885522Z 2025-03-04T21:00:47.8885932Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:00:47.8886435Z shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T21:00:47.8886668Z 2025-03-04T21:00:47.8887070Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:00:47.8887556Z shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T21:00:47.8887786Z 2025-03-04T21:00:47.8888239Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:00:47.8888877Z shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T21:00:47.8889216Z 2025-03-04T21:00:47.8889710Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:00:47.8890284Z view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T21:00:47.8890768Z view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T21:00:47.8891238Z add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T21:00:47.8891536Z x: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4); add = None 2025-03-04T21:00:47.8891755Z 2025-03-04T21:00:47.8892133Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:00:47.8892593Z tensor: "f32[82125, 4][4, 1]cpu" = x.to(torch.float32); x = None 2025-03-04T21:00:47.8892823Z 2025-03-04T21:00:47.8893159Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:47.8894055Z x_1: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(l_features_res4_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_res4_ = l_self_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T21:00:47.8894754Z 2025-03-04T21:00:47.8895176Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:00:47.8895678Z x_2: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False); x_1 = None 2025-03-04T21:00:47.8895974Z 2025-03-04T21:00:47.8896428Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:00:47.8897477Z score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T21:00:47.8898216Z 2025-03-04T21:00:47.8898655Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:00:47.8899643Z x_3: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_2 = l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T21:00:47.8900342Z 2025-03-04T21:00:47.8900760Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T21:00:47.8901290Z permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T21:00:47.8901627Z logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T21:00:47.8902046Z 2025-03-04T21:00:47.8902646Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T21:00:47.8903291Z view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_3.view(4, -1, 4, 73, 75); x_3 = None 2025-03-04T21:00:47.8903656Z permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2); view_2 = None 2025-03-04T21:00:47.8904044Z pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2); permute_1 = None 2025-03-04T21:00:47.8904327Z 2025-03-04T21:00:47.8904802Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:00:47.8905469Z pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T21:00:47.8905781Z 2025-03-04T21:00:47.8906284Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:00:47.8906900Z unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T21:00:47.8907237Z expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T21:00:47.8907562Z anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T21:00:47.8907808Z 2025-03-04T21:00:47.8908256Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:00:47.8908879Z deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T21:00:47.8909159Z 2025-03-04T21:00:47.8909545Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:00:47.8910036Z boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T21:00:47.8910284Z 2025-03-04T21:00:47.8910676Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:00:47.8911155Z getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:00:47.8911454Z getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:00:47.8911770Z widths: "f32[328500][1]cpu" = getitem_2 - getitem_3; getitem_2 = getitem_3 = None 2025-03-04T21:00:47.8912024Z 2025-03-04T21:00:47.8912416Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:00:47.8912910Z getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:00:47.8913208Z getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:00:47.8913529Z heights: "f32[328500][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T21:00:47.8913793Z 2025-03-04T21:00:47.8914192Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:00:47.8914682Z getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:00:47.8914948Z mul: "f32[328500][1]cpu" = 0.5 * widths 2025-03-04T21:00:47.8915212Z ctr_x: "f32[328500][1]cpu" = getitem_6 + mul; getitem_6 = mul = None 2025-03-04T21:00:47.8915447Z 2025-03-04T21:00:47.8915865Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:00:47.8916379Z getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:00:47.8916664Z mul_1: "f32[328500][1]cpu" = 0.5 * heights 2025-03-04T21:00:47.8916930Z ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1; getitem_7 = mul_1 = None 2025-03-04T21:00:47.8917171Z 2025-03-04T21:00:47.8917614Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:00:47.8918213Z getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:00:47.8918608Z dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0; getitem_8 = None 2025-03-04T21:00:47.8918919Z 2025-03-04T21:00:47.8919340Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:00:47.8919862Z getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:00:47.8920182Z dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0; getitem_9 = None 2025-03-04T21:00:47.8920408Z 2025-03-04T21:00:47.8920797Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:00:47.8921318Z getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:00:47.8921633Z dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0; getitem_10 = None 2025-03-04T21:00:47.8921863Z 2025-03-04T21:00:47.8922287Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:00:47.8922828Z getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:00:47.8923310Z dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0; getitem_11 = None 2025-03-04T21:00:47.8923537Z 2025-03-04T21:00:47.8923965Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:00:47.8924495Z dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:00:47.8924860Z 2025-03-04T21:00:47.8925308Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:00:47.8926031Z dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:00:47.8926288Z 2025-03-04T21:00:47.8926728Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:00:47.8927530Z getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:00:47.8927852Z mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12; dx = getitem_12 = None 2025-03-04T21:00:47.8928183Z getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:00:47.8928582Z pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13; mul_2 = getitem_13 = None 2025-03-04T21:00:47.8928920Z 2025-03-04T21:00:47.8929365Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:00:47.8930020Z getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:00:47.8930391Z mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14; dy = getitem_14 = None 2025-03-04T21:00:47.8930723Z getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:00:47.8931066Z pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15; mul_3 = getitem_15 = None 2025-03-04T21:00:47.8931318Z 2025-03-04T21:00:47.8931740Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:00:47.8932282Z exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:00:47.8932688Z getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:00:47.8933038Z pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16; exp = getitem_16 = None 2025-03-04T21:00:47.8933302Z 2025-03-04T21:00:47.8933727Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:00:47.8934233Z exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:00:47.8934576Z getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:00:47.8934936Z pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17; exp_1 = getitem_17 = None 2025-03-04T21:00:47.8935193Z 2025-03-04T21:00:47.8935605Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:00:47.8936111Z mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T21:00:47.8936378Z x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:00:47.8936618Z 2025-03-04T21:00:47.8937022Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:00:47.8937502Z mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T21:00:47.8937767Z y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:00:47.8938003Z 2025-03-04T21:00:47.8938401Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:00:47.8938891Z mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:00:47.8939192Z x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:00:47.8939437Z 2025-03-04T21:00:47.8939836Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:00:47.8940325Z mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:00:47.8940616Z y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:00:47.8940864Z 2025-03-04T21:00:47.8941308Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:00:47.8941900Z pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:00:47.8942196Z 2025-03-04T21:00:47.8942630Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:00:47.8943195Z proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4)); pred_boxes = None 2025-03-04T21:00:47.8943500Z 2025-03-04T21:00:47.8943977Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:00:47.8944597Z proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T21:00:47.8944892Z 2025-03-04T21:00:47.8945480Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T21:00:47.8946188Z arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T21:00:47.8946439Z 2025-03-04T21:00:47.8946822Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:00:47.8947308Z batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T21:00:47.8947564Z 2025-03-04T21:00:47.8948085Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:00:47.8948683Z topk = logits_i.topk(6000, dim = 1); logits_i = None 2025-03-04T21:00:47.8948947Z topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0] 2025-03-04T21:00:47.8949213Z topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1]; topk = None 2025-03-04T21:00:47.8949444Z 2025-03-04T21:00:47.8950038Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:00:47.8950707Z getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T21:00:47.8951156Z topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)]; proposals_i_1 = getitem_20 = topk_idx = None 2025-03-04T21:00:47.8951499Z 2025-03-04T21:00:47.8952042Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:00:47.8952712Z full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:00:47.8952993Z 2025-03-04T21:00:47.8953374Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:00:47.8953872Z level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu')); full = level_ids = None 2025-03-04T21:00:47.8954140Z 2025-03-04T21:00:47.8954607Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T21:00:47.8955180Z getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0]; topk_proposals_i = None 2025-03-04T21:00:47.8955442Z 2025-03-04T21:00:47.8955822Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:00:47.8956313Z tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32); getitem_22 = None 2025-03-04T21:00:47.8956572Z 2025-03-04T21:00:47.8957053Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T21:00:47.8957622Z scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0]; topk_scores_i = None 2025-03-04T21:00:47.8957871Z 2025-03-04T21:00:47.8958552Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T21:00:47.8959269Z isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1); tensor_1 = None 2025-03-04T21:00:47.8959597Z all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:00:47.8959959Z isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T21:00:47.8960322Z valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T21:00:47.8960577Z 2025-03-04T21:00:47.8961038Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T21:00:47.8961583Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T21:00:47.8961820Z 2025-03-04T21:00:47.8961952Z 2025-03-04T21:00:47.8962042Z class GraphModule(torch.nn.Module): 2025-03-04T21:00:47.8963410Z def forward(self, L_features_res4_: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_weight_: "f32[1024, 1024, 3, 3][9216, 9, 3, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_bias_: "f32[1024][1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"): 2025-03-04T21:00:47.8964706Z l_features_res4_ = L_features_res4_ 2025-03-04T21:00:47.8965092Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T21:00:47.8965604Z l_self_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T21:00:47.8966070Z l_self_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T21:00:47.8966590Z l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T21:00:47.8967161Z l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T21:00:47.8967712Z l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T21:00:47.8968239Z l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T21:00:47.8968590Z 2025-03-04T21:00:47.8969112Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:00:47.8969740Z arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32) 2025-03-04T21:00:47.8970003Z 2025-03-04T21:00:47.8970391Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:00:47.8970899Z shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T21:00:47.8971149Z 2025-03-04T21:00:47.8971682Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:00:47.8972300Z arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32) 2025-03-04T21:00:47.8972559Z 2025-03-04T21:00:47.8972932Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:00:47.8973559Z shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T21:00:47.8973806Z 2025-03-04T21:00:47.8974257Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:00:47.8974847Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T21:00:47.8975170Z shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0] 2025-03-04T21:00:47.8975428Z shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T21:00:47.8975652Z 2025-03-04T21:00:47.8976058Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:00:47.8976562Z shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T21:00:47.8976804Z 2025-03-04T21:00:47.8977243Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:00:47.8977744Z shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T21:00:47.8977980Z 2025-03-04T21:00:47.8978445Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:00:47.8979092Z shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T21:00:47.8979414Z 2025-03-04T21:00:47.8979918Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:00:47.8980512Z view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T21:00:47.8981009Z view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T21:00:47.8981505Z add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T21:00:47.8981792Z x: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4); add = None 2025-03-04T21:00:47.8982020Z 2025-03-04T21:00:47.8982412Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:00:47.8982887Z tensor: "f32[82125, 4][4, 1]cpu" = x.to(torch.float32); x = None 2025-03-04T21:00:47.8983124Z 2025-03-04T21:00:47.8983467Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:00:47.8984390Z x_1: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(l_features_res4_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_res4_ = l_self_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T21:00:47.8985119Z 2025-03-04T21:00:47.8985485Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:00:47.8986001Z x_2: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False); x_1 = None 2025-03-04T21:00:47.8986300Z 2025-03-04T21:00:47.8986772Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:00:47.8987878Z score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T21:00:47.8988636Z 2025-03-04T21:00:47.8989096Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:00:47.8990157Z x_3: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_2 = l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T21:00:47.8990881Z 2025-03-04T21:00:47.8991313Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T21:00:47.8991881Z permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T21:00:47.8992220Z logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T21:00:47.8992474Z 2025-03-04T21:00:47.8992975Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T21:00:47.8993585Z view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_3.view(4, -1, 4, 73, 75); x_3 = None 2025-03-04T21:00:47.8993958Z permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2); view_2 = None 2025-03-04T21:00:47.8994351Z pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2); permute_1 = None 2025-03-04T21:00:47.8994642Z 2025-03-04T21:00:47.8995131Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:00:47.8995790Z pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T21:00:47.8996108Z 2025-03-04T21:00:47.8996628Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:00:47.8997258Z unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T21:00:47.8997606Z expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T21:00:47.8997957Z anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T21:00:47.8998323Z 2025-03-04T21:00:47.8998810Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:00:47.8999423Z deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T21:00:47.8999719Z 2025-03-04T21:00:47.9000129Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:00:47.9000660Z boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T21:00:47.9000916Z 2025-03-04T21:00:47.9001317Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:00:47.9001824Z getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:00:47.9002423Z getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:00:47.9002758Z widths: "f32[328500][1]cpu" = getitem_2 - getitem_3; getitem_2 = getitem_3 = None 2025-03-04T21:00:47.9003022Z 2025-03-04T21:00:47.9003429Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:00:47.9003915Z getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:00:47.9004211Z getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:00:47.9004603Z heights: "f32[328500][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T21:00:47.9004885Z 2025-03-04T21:00:47.9005287Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:00:47.9005767Z getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:00:47.9006026Z mul: "f32[328500][1]cpu" = 0.5 * widths 2025-03-04T21:00:47.9006279Z ctr_x: "f32[328500][1]cpu" = getitem_6 + mul; getitem_6 = mul = None 2025-03-04T21:00:47.9006513Z 2025-03-04T21:00:47.9006901Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:00:47.9007404Z getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:00:47.9007685Z mul_1: "f32[328500][1]cpu" = 0.5 * heights 2025-03-04T21:00:47.9007948Z ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1; getitem_7 = mul_1 = None 2025-03-04T21:00:47.9008188Z 2025-03-04T21:00:47.9008589Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:00:47.9009089Z getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:00:47.9009406Z dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0; getitem_8 = None 2025-03-04T21:00:47.9009633Z 2025-03-04T21:00:47.9010010Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:00:47.9010504Z getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:00:47.9010820Z dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0; getitem_9 = None 2025-03-04T21:00:47.9011047Z 2025-03-04T21:00:47.9011421Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:00:47.9011938Z getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:00:47.9012250Z dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0; getitem_10 = None 2025-03-04T21:00:47.9012478Z 2025-03-04T21:00:47.9012856Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:00:47.9013380Z getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:00:47.9013750Z dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0; getitem_11 = None 2025-03-04T21:00:47.9013970Z 2025-03-04T21:00:47.9014388Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:00:47.9014904Z dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:00:47.9015152Z 2025-03-04T21:00:47.9015557Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:00:47.9016061Z dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:00:47.9016308Z 2025-03-04T21:00:47.9016726Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:00:47.9017287Z getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:00:47.9017602Z mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12; dx = getitem_12 = None 2025-03-04T21:00:47.9017929Z getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:00:47.9018266Z pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13; mul_2 = getitem_13 = None 2025-03-04T21:00:47.9018515Z 2025-03-04T21:00:47.9018938Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:00:47.9019480Z getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:00:47.9019797Z mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14; dy = getitem_14 = None 2025-03-04T21:00:47.9020128Z getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:00:47.9020476Z pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15; mul_3 = getitem_15 = None 2025-03-04T21:00:47.9020726Z 2025-03-04T21:00:47.9021157Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:00:47.9021664Z exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:00:47.9021993Z getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:00:47.9022340Z pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16; exp = getitem_16 = None 2025-03-04T21:00:47.9022588Z 2025-03-04T21:00:47.9023020Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:00:47.9023535Z exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:00:47.9023871Z getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:00:47.9024238Z pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17; exp_1 = getitem_17 = None 2025-03-04T21:00:47.9024490Z 2025-03-04T21:00:47.9024892Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:00:47.9025356Z mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T21:00:47.9025616Z x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:00:47.9025846Z 2025-03-04T21:00:47.9026239Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:00:47.9026716Z mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T21:00:47.9026971Z y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:00:47.9027197Z 2025-03-04T21:00:47.9027589Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:00:47.9028065Z mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:00:47.9028353Z x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:00:47.9028598Z 2025-03-04T21:00:47.9028985Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:00:47.9029450Z mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:00:47.9029734Z y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:00:47.9029972Z 2025-03-04T21:00:47.9030436Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:00:47.9031018Z pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:00:47.9031307Z 2025-03-04T21:00:47.9031735Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:00:47.9032283Z proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4)); pred_boxes = None 2025-03-04T21:00:47.9032560Z 2025-03-04T21:00:47.9033024Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:00:47.9033631Z proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T21:00:47.9033913Z 2025-03-04T21:00:47.9034481Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T21:00:47.9035166Z arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T21:00:47.9035411Z 2025-03-04T21:00:47.9035789Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:00:47.9036274Z batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T21:00:47.9036521Z 2025-03-04T21:00:47.9037033Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:00:47.9037633Z topk = logits_i.topk(6000, dim = 1); logits_i = None 2025-03-04T21:00:47.9037885Z topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0] 2025-03-04T21:00:47.9038226Z topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1]; topk = None 2025-03-04T21:00:47.9038475Z 2025-03-04T21:00:47.9039015Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:00:47.9039689Z getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T21:00:47.9040158Z topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)]; proposals_i_1 = getitem_20 = topk_idx = None 2025-03-04T21:00:47.9040496Z 2025-03-04T21:00:47.9041032Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:00:47.9041696Z full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:00:47.9041969Z 2025-03-04T21:00:47.9042348Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:00:47.9042836Z level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu')); full = level_ids = None 2025-03-04T21:00:47.9043094Z 2025-03-04T21:00:47.9043557Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T21:00:47.9044165Z getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0]; topk_proposals_i = None 2025-03-04T21:00:47.9044428Z 2025-03-04T21:00:47.9044806Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:00:47.9045293Z tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32); getitem_22 = None 2025-03-04T21:00:47.9045583Z 2025-03-04T21:00:47.9046046Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T21:00:47.9046605Z scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0]; topk_scores_i = None 2025-03-04T21:00:47.9046858Z 2025-03-04T21:00:47.9047425Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T21:00:47.9048090Z isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1); tensor_1 = None 2025-03-04T21:00:47.9048396Z all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:00:47.9048717Z isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T21:00:47.9049053Z valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T21:00:47.9049305Z 2025-03-04T21:00:47.9049758Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T21:00:47.9050293Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T21:00:47.9050527Z 2025-03-04T21:00:48.4021340Z 2025-03-04T21:00:48.4026510Z class GraphModule(torch.nn.Module): 2025-03-04T21:00:48.4028436Z def forward(self, L_pred_anchor_deltas_0_: "f32[4, 82125, 4][328500, 4, 1]cpu", L_anchors_0_tensor: "f32[82125, 4][4, 1]cpu", L_pred_objectness_logits_0_: "f32[4, 82125][82125, 1]cpu"): 2025-03-04T21:00:48.4029427Z l_pred_anchor_deltas_0_ = L_pred_anchor_deltas_0_ 2025-03-04T21:00:48.4034116Z l_anchors_0_tensor = L_anchors_0_tensor 2025-03-04T21:00:48.4038698Z l_pred_objectness_logits_0_ = L_pred_objectness_logits_0_ 2025-03-04T21:00:48.4042911Z 2025-03-04T21:00:48.4047033Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:00:48.4047919Z pred_anchor_deltas_i: "f32[328500, 4][4, 1]cpu" = l_pred_anchor_deltas_0_.reshape(-1, 4); l_pred_anchor_deltas_0_ = None 2025-03-04T21:00:48.4048501Z 2025-03-04T21:00:48.4049041Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:00:48.4049709Z unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = l_anchors_0_tensor.unsqueeze(0); l_anchors_0_tensor = None 2025-03-04T21:00:48.4050087Z expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T21:00:48.4050425Z anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T21:00:48.4050685Z 2025-03-04T21:00:48.4051153Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:00:48.4051747Z deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.float(); pred_anchor_deltas_i = None 2025-03-04T21:00:48.4052119Z 2025-03-04T21:00:48.4052517Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:00:48.4053026Z boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T21:00:48.4053283Z 2025-03-04T21:00:48.4053687Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:00:48.4054200Z getitem: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:00:48.4054520Z getitem_1: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:00:48.4054851Z widths: "f32[328500][1]cpu" = getitem - getitem_1; getitem = getitem_1 = None 2025-03-04T21:00:48.4055118Z 2025-03-04T21:00:48.4055538Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:00:48.4056046Z getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:00:48.4056354Z getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:00:48.4056687Z heights: "f32[328500][1]cpu" = getitem_2 - getitem_3; getitem_2 = getitem_3 = None 2025-03-04T21:00:48.4056959Z 2025-03-04T21:00:48.4057366Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:00:48.4057864Z getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:00:48.4058129Z mul: "f32[328500][1]cpu" = 0.5 * widths 2025-03-04T21:00:48.4058392Z ctr_x: "f32[328500][1]cpu" = getitem_4 + mul; getitem_4 = mul = None 2025-03-04T21:00:48.4058635Z 2025-03-04T21:00:48.4059042Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:00:48.4059600Z getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:00:48.4059892Z mul_1: "f32[328500][1]cpu" = 0.5 * heights 2025-03-04T21:00:48.4060168Z ctr_y: "f32[328500][1]cpu" = getitem_5 + mul_1; getitem_5 = mul_1 = None 2025-03-04T21:00:48.4060415Z 2025-03-04T21:00:48.4060864Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:00:48.4061387Z getitem_6: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:00:48.4061741Z dx: "f32[328500, 1][1, 1]cpu" = getitem_6 / 1.0; getitem_6 = None 2025-03-04T21:00:48.4061982Z 2025-03-04T21:00:48.4062386Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:00:48.4062933Z getitem_7: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:00:48.4063264Z dy: "f32[328500, 1][1, 1]cpu" = getitem_7 / 1.0; getitem_7 = None 2025-03-04T21:00:48.4063499Z 2025-03-04T21:00:48.4063899Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:00:48.4064419Z getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:00:48.4064742Z dw: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0; getitem_8 = None 2025-03-04T21:00:48.4064982Z 2025-03-04T21:00:48.4065430Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:00:48.4065978Z getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:00:48.4066327Z dh: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0; getitem_9 = None 2025-03-04T21:00:48.4066558Z 2025-03-04T21:00:48.4066989Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:00:48.4067536Z dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:00:48.4067799Z 2025-03-04T21:00:48.4068226Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:00:48.4068771Z dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:00:48.4069024Z 2025-03-04T21:00:48.4069464Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:00:48.4070019Z getitem_10: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:00:48.4070349Z mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_10; dx = getitem_10 = None 2025-03-04T21:00:48.4070690Z getitem_11: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:00:48.4071043Z pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_11; mul_2 = getitem_11 = None 2025-03-04T21:00:48.4071305Z 2025-03-04T21:00:48.4071749Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:00:48.4072303Z getitem_12: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:00:48.4072629Z mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_12; dy = getitem_12 = None 2025-03-04T21:00:48.4072974Z getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:00:48.4073309Z pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_13; mul_3 = getitem_13 = None 2025-03-04T21:00:48.4073561Z 2025-03-04T21:00:48.4073978Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:00:48.4074484Z exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:00:48.4074814Z getitem_14: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:00:48.4075188Z pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_14; exp = getitem_14 = None 2025-03-04T21:00:48.4075440Z 2025-03-04T21:00:48.4075881Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:00:48.4076410Z exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:00:48.4076752Z getitem_15: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:00:48.4077116Z pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_15; exp_1 = getitem_15 = None 2025-03-04T21:00:48.4077379Z 2025-03-04T21:00:48.4077776Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:00:48.4078377Z mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T21:00:48.4078768Z x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:00:48.4079009Z 2025-03-04T21:00:48.4079409Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:00:48.4079881Z mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T21:00:48.4080137Z y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:00:48.4080362Z 2025-03-04T21:00:48.4080746Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:00:48.4081215Z mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:00:48.4081500Z x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:00:48.4081742Z 2025-03-04T21:00:48.4082128Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:00:48.4082593Z mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:00:48.4082880Z y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:00:48.4083118Z 2025-03-04T21:00:48.4083559Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:00:48.4084155Z pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:00:48.4084443Z 2025-03-04T21:00:48.4084870Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:00:48.4085434Z proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4)); pred_boxes = None 2025-03-04T21:00:48.4085720Z 2025-03-04T21:00:48.4086195Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:00:48.4086839Z proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T21:00:48.4087136Z 2025-03-04T21:00:48.4087724Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T21:00:48.4088442Z arange: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T21:00:48.4088716Z 2025-03-04T21:00:48.4089110Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:00:48.4089604Z batch_idx: "i64[4][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T21:00:48.4089860Z 2025-03-04T21:00:48.4090396Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:00:48.4091077Z topk = l_pred_objectness_logits_0_.topk(6000, dim = 1); l_pred_objectness_logits_0_ = None 2025-03-04T21:00:48.4091420Z topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0] 2025-03-04T21:00:48.4091693Z topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1]; topk = None 2025-03-04T21:00:48.4091921Z 2025-03-04T21:00:48.4092531Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:00:48.4093239Z getitem_18: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T21:00:48.4093702Z topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_18, topk_idx)]; proposals_i_1 = getitem_18 = topk_idx = None 2025-03-04T21:00:48.4094055Z 2025-03-04T21:00:48.4094622Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:00:48.4095322Z full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:00:48.4095610Z 2025-03-04T21:00:48.4096013Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:00:48.4096517Z level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu')); full = level_ids = None 2025-03-04T21:00:48.4096779Z 2025-03-04T21:00:48.4097255Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T21:00:48.4097825Z getitem_20: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0]; topk_proposals_i = None 2025-03-04T21:00:48.4098082Z 2025-03-04T21:00:48.4098460Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:00:48.4098954Z tensor: "f32[6000, 4][4, 1]cpu" = getitem_20.to(torch.float32); getitem_20 = None 2025-03-04T21:00:48.4099214Z 2025-03-04T21:00:48.4099675Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T21:00:48.4100244Z scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0]; topk_scores_i = None 2025-03-04T21:00:48.4100516Z 2025-03-04T21:00:48.4101083Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T21:00:48.4101747Z isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor); tensor = None 2025-03-04T21:00:48.4102265Z all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:00:48.4102631Z isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T21:00:48.4103060Z valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T21:00:48.4103314Z 2025-03-04T21:00:48.4103784Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T21:00:48.4104335Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T21:00:48.4104568Z 2025-03-04T21:01:01.9923274Z 2025-03-04T21:01:01.9924088Z class GraphModule(torch.nn.Module): 2025-03-04T21:01:01.9926091Z def forward(self, L_stack0_: "f32[3272, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1272 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1272 - s0, 4][4, 1]cpu"): 2025-03-04T21:01:01.9927571Z l_stack0_ = L_stack0_ 2025-03-04T21:01:01.9927986Z l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_ 2025-03-04T21:01:01.9928586Z l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_ 2025-03-04T21:01:01.9929224Z l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ 2025-03-04T21:01:01.9929884Z l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ 2025-03-04T21:01:01.9930442Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:01:01.9930890Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:01:01.9931330Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:01:01.9931763Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:01:01.9932087Z 2025-03-04T21:01:01.9932727Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/roi_heads.py:480 in torch_dynamo_resume_in_forward_at_477, code: predictions = self.box_predictor(box_features.mean(dim=[2, 3])) 2025-03-04T21:01:01.9933449Z mean: "f32[3272, 2048][2048, 1]cpu" = l_stack0_.mean(dim = [2, 3]); l_stack0_ = None 2025-03-04T21:01:01.9933739Z 2025-03-04T21:01:01.9934202Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x) 2025-03-04T21:01:01.9935343Z scores: "f32[3272, 81][81, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_); l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None 2025-03-04T21:01:01.9936268Z 2025-03-04T21:01:01.9936748Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x) 2025-03-04T21:01:01.9937924Z proposal_deltas: "f32[3272, 320][320, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_); mean = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None 2025-03-04T21:01:01.9938822Z 2025-03-04T21:01:01.9939242Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:01:01.9939759Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:01:01.9940039Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:01:01.9940290Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:01:01.9940592Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:01:01.9940879Z getitem_2: "Sym(1272 - s0)" = size_1[0] 2025-03-04T21:01:01.9941142Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:01:01.9941383Z 2025-03-04T21:01:01.9941798Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:01:01.9942953Z proposal_boxes: "f32[3272, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:01:01.9943760Z 2025-03-04T21:01:01.9944268Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:01:01.9944906Z deltas: "f32[3272, 320][320, 1]cpu" = proposal_deltas.float(); proposal_deltas = None 2025-03-04T21:01:01.9945202Z 2025-03-04T21:01:01.9945639Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:01:01.9946219Z boxes: "f32[3272, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:01:01.9946520Z 2025-03-04T21:01:01.9946963Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:01:01.9947513Z getitem_4: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:01:01.9947841Z getitem_5: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:01:01.9948190Z widths: "f32[3272][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T21:01:01.9948474Z 2025-03-04T21:01:01.9948923Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:01:01.9949468Z getitem_6: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:01:01.9949794Z getitem_7: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:01:01.9950142Z heights: "f32[3272][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T21:01:01.9950426Z 2025-03-04T21:01:01.9950894Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:01:01.9951430Z getitem_8: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:01:01.9951706Z mul: "f32[3272][1]cpu" = 0.5 * widths 2025-03-04T21:01:01.9951990Z ctr_x: "f32[3272][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T21:01:01.9952249Z 2025-03-04T21:01:01.9952699Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:01:01.9953303Z getitem_9: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:01:01.9953624Z mul_1: "f32[3272][1]cpu" = 0.5 * heights 2025-03-04T21:01:01.9953918Z ctr_y: "f32[3272][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T21:01:01.9954184Z 2025-03-04T21:01:01.9954670Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:01:01.9955254Z getitem_10: "f32[3272, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:01:01.9955623Z dx: "f32[3272, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T21:01:01.9956033Z 2025-03-04T21:01:01.9956512Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:01:01.9957104Z getitem_11: "f32[3272, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:01:01.9957499Z dy: "f32[3272, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T21:01:01.9957757Z 2025-03-04T21:01:01.9958186Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:01:01.9958744Z getitem_12: "f32[3272, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:01:01.9959097Z dw: "f32[3272, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T21:01:01.9959349Z 2025-03-04T21:01:01.9959783Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:01:01.9960376Z getitem_13: "f32[3272, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:01:01.9960760Z dh: "f32[3272, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T21:01:01.9961011Z 2025-03-04T21:01:01.9961485Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:01:01.9962063Z dw_1: "f32[3272, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:01:01.9962327Z 2025-03-04T21:01:01.9962761Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:01:01.9963306Z dh_1: "f32[3272, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:01:01.9963563Z 2025-03-04T21:01:01.9964014Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:01:01.9964582Z getitem_14: "f32[3272, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:01:01.9964909Z mul_2: "f32[3272, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T21:01:01.9965275Z getitem_15: "f32[3272, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:01:01.9965640Z pred_ctr_x: "f32[3272, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T21:01:01.9965909Z 2025-03-04T21:01:01.9966373Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:01:01.9966946Z getitem_16: "f32[3272, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:01:01.9967279Z mul_3: "f32[3272, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T21:01:01.9967635Z getitem_17: "f32[3272, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:01:01.9967987Z pred_ctr_y: "f32[3272, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T21:01:01.9968248Z 2025-03-04T21:01:01.9968694Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:01:01.9969210Z exp: "f32[3272, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:01:01.9969528Z getitem_18: "f32[3272, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:01:01.9969861Z pred_w: "f32[3272, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T21:01:01.9970104Z 2025-03-04T21:01:01.9970518Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:01:01.9971020Z exp_1: "f32[3272, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:01:01.9971377Z getitem_19: "f32[3272, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:01:01.9971720Z pred_h: "f32[3272, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T21:01:01.9971963Z 2025-03-04T21:01:01.9972357Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:01:01.9972810Z mul_6: "f32[3272, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:01:01.9973065Z x1: "f32[3272, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:01:01.9973293Z 2025-03-04T21:01:01.9973680Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:01:01.9974141Z mul_7: "f32[3272, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:01:01.9974414Z y1: "f32[3272, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:01:01.9974648Z 2025-03-04T21:01:01.9975061Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:01:01.9975555Z mul_8: "f32[3272, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:01:01.9975858Z x2: "f32[3272, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:01:01.9976107Z 2025-03-04T21:01:01.9976514Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:01:01.9977093Z mul_9: "f32[3272, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:01:01.9977489Z y2: "f32[3272, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:01:01.9977743Z 2025-03-04T21:01:01.9978334Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:01:01.9978976Z pred_boxes: "f32[3272, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:01:01.9979265Z 2025-03-04T21:01:01.9979681Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:01:01.9980296Z predict_boxes: "f32[3272, 320][320, 1]cpu" = pred_boxes.reshape((3272, 320)); pred_boxes = None 2025-03-04T21:01:01.9980608Z 2025-03-04T21:01:01.9981081Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:01:01.9981759Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T21:01:01.9982119Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T21:01:01.9982407Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T21:01:01.9982708Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T21:01:01.9983022Z getitem_23: "f32[1272 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T21:01:01.9983280Z 2025-03-04T21:01:01.9983657Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:01:01.9984214Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:01:01.9984575Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T21:01:01.9984858Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T21:01:01.9985224Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:01:01.9985602Z getitem_26: "Sym(1272 - s0)" = size_3[0] 2025-03-04T21:01:01.9985865Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T21:01:01.9986100Z 2025-03-04T21:01:01.9986553Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:01:01.9987160Z probs: "f32[3272, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1); scores = None 2025-03-04T21:01:01.9987451Z 2025-03-04T21:01:01.9987893Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:01:01.9988504Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T21:01:01.9988866Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:01:01.9989163Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T21:01:01.9989466Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T21:01:01.9989783Z getitem_31: "f32[1272 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T21:01:01.9990048Z 2025-03-04T21:01:01.9990601Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:01:01.9991312Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:01:01.9991660Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:01:01.9992007Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:01:01.9992395Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:01:01.9992686Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:01:01.9992919Z 2025-03-04T21:01:01.9993377Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:01:01.9993945Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:01:01.9994200Z 2025-03-04T21:01:01.9994343Z 2025-03-04T21:01:01.9994449Z class GraphModule(torch.nn.Module): 2025-03-04T21:01:01.9996062Z def forward(self, L_stack0_: "f32[3272, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1272 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1272 - s0, 4][4, 1]cpu"): 2025-03-04T21:01:01.9997514Z l_stack0_ = L_stack0_ 2025-03-04T21:01:01.9997918Z l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_ 2025-03-04T21:01:01.9998515Z l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_ 2025-03-04T21:01:01.9999143Z l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ 2025-03-04T21:01:01.9999740Z l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ 2025-03-04T21:01:02.0000250Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:01:02.0000683Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:01:02.0001104Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:01:02.0001525Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:01:02.0001840Z 2025-03-04T21:01:02.0002619Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/roi_heads.py:480 in torch_dynamo_resume_in_forward_at_477, code: predictions = self.box_predictor(box_features.mean(dim=[2, 3])) 2025-03-04T21:01:02.0003291Z mean: "f32[3272, 2048][2048, 1]cpu" = l_stack0_.mean(dim = [2, 3]); l_stack0_ = None 2025-03-04T21:01:02.0003563Z 2025-03-04T21:01:02.0003980Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x) 2025-03-04T21:01:02.0005005Z scores: "f32[3272, 81][81, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_); l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None 2025-03-04T21:01:02.0005761Z 2025-03-04T21:01:02.0006192Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x) 2025-03-04T21:01:02.0007263Z proposal_deltas: "f32[3272, 320][320, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_); mean = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None 2025-03-04T21:01:02.0008106Z 2025-03-04T21:01:02.0008467Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:01:02.0008914Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:01:02.0009162Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:01:02.0009391Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:01:02.0009690Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:01:02.0009950Z getitem_2: "Sym(1272 - s0)" = size_1[0] 2025-03-04T21:01:02.0010194Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:01:02.0010413Z 2025-03-04T21:01:02.0010789Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:01:02.0011711Z proposal_boxes: "f32[3272, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:01:02.0012418Z 2025-03-04T21:01:02.0012879Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:01:02.0013502Z deltas: "f32[3272, 320][320, 1]cpu" = proposal_deltas.float(); proposal_deltas = None 2025-03-04T21:01:02.0013775Z 2025-03-04T21:01:02.0014185Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:01:02.0014698Z boxes: "f32[3272, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:01:02.0014974Z 2025-03-04T21:01:02.0015380Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:01:02.0015880Z getitem_4: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:01:02.0016186Z getitem_5: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:01:02.0016510Z widths: "f32[3272][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T21:01:02.0016777Z 2025-03-04T21:01:02.0017199Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:01:02.0017696Z getitem_6: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:01:02.0017999Z getitem_7: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:01:02.0018315Z heights: "f32[3272][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T21:01:02.0018575Z 2025-03-04T21:01:02.0018991Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:01:02.0019467Z getitem_8: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:01:02.0019722Z mul: "f32[3272][1]cpu" = 0.5 * widths 2025-03-04T21:01:02.0019975Z ctr_x: "f32[3272][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T21:01:02.0020210Z 2025-03-04T21:01:02.0020605Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:01:02.0021113Z getitem_9: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:01:02.0021389Z mul_1: "f32[3272][1]cpu" = 0.5 * heights 2025-03-04T21:01:02.0021638Z ctr_y: "f32[3272][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T21:01:02.0021872Z 2025-03-04T21:01:02.0022277Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:01:02.0022776Z getitem_10: "f32[3272, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:01:02.0023106Z dx: "f32[3272, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T21:01:02.0023330Z 2025-03-04T21:01:02.0023705Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:01:02.0024195Z getitem_11: "f32[3272, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:01:02.0024506Z dy: "f32[3272, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T21:01:02.0024734Z 2025-03-04T21:01:02.0025117Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:01:02.0025613Z getitem_12: "f32[3272, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:01:02.0025928Z dw: "f32[3272, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T21:01:02.0026157Z 2025-03-04T21:01:02.0026594Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:01:02.0027120Z getitem_13: "f32[3272, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:01:02.0027455Z dh: "f32[3272, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T21:01:02.0027680Z 2025-03-04T21:01:02.0028092Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:01:02.0028604Z dw_1: "f32[3272, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:01:02.0028850Z 2025-03-04T21:01:02.0029257Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:01:02.0029766Z dh_1: "f32[3272, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:01:02.0030012Z 2025-03-04T21:01:02.0030446Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:01:02.0030977Z getitem_14: "f32[3272, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:01:02.0031284Z mul_2: "f32[3272, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T21:01:02.0031608Z getitem_15: "f32[3272, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:01:02.0032075Z pred_ctr_x: "f32[3272, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T21:01:02.0032334Z 2025-03-04T21:01:02.0032868Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:01:02.0033477Z getitem_16: "f32[3272, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:01:02.0033953Z mul_3: "f32[3272, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T21:01:02.0034287Z getitem_17: "f32[3272, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:01:02.0034766Z pred_ctr_y: "f32[3272, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T21:01:02.0035030Z 2025-03-04T21:01:02.0035470Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:01:02.0036059Z exp: "f32[3272, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:01:02.0036428Z getitem_18: "f32[3272, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:01:02.0036784Z pred_w: "f32[3272, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T21:01:02.0037044Z 2025-03-04T21:01:02.0037483Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:01:02.0037983Z exp_1: "f32[3272, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:01:02.0038309Z getitem_19: "f32[3272, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:01:02.0038651Z pred_h: "f32[3272, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T21:01:02.0038896Z 2025-03-04T21:01:02.0039297Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:01:02.0039764Z mul_6: "f32[3272, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:01:02.0040061Z x1: "f32[3272, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:01:02.0040291Z 2025-03-04T21:01:02.0040682Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:01:02.0041138Z mul_7: "f32[3272, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:01:02.0041396Z y1: "f32[3272, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:01:02.0041625Z 2025-03-04T21:01:02.0042017Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:01:02.0042491Z mul_8: "f32[3272, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:01:02.0042784Z x2: "f32[3272, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:01:02.0043150Z 2025-03-04T21:01:02.0043550Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:01:02.0044022Z mul_9: "f32[3272, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:01:02.0062438Z y2: "f32[3272, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:01:02.0062745Z 2025-03-04T21:01:02.0063233Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:01:02.0063840Z pred_boxes: "f32[3272, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:01:02.0064136Z 2025-03-04T21:01:02.0064588Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:01:02.0065174Z predict_boxes: "f32[3272, 320][320, 1]cpu" = pred_boxes.reshape((3272, 320)); pred_boxes = None 2025-03-04T21:01:02.0065564Z 2025-03-04T21:01:02.0066031Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:01:02.0066673Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T21:01:02.0067040Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T21:01:02.0067333Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T21:01:02.0067644Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T21:01:02.0068003Z getitem_23: "f32[1272 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T21:01:02.0068276Z 2025-03-04T21:01:02.0068679Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:01:02.0069270Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:01:02.0069646Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T21:01:02.0069885Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T21:01:02.0070263Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:01:02.0070631Z getitem_26: "Sym(1272 - s0)" = size_3[0] 2025-03-04T21:01:02.0070884Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T21:01:02.0071109Z 2025-03-04T21:01:02.0071564Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:01:02.0072186Z probs: "f32[3272, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1); scores = None 2025-03-04T21:01:02.0072488Z 2025-03-04T21:01:02.0072950Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:01:02.0073582Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T21:01:02.0073951Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:01:02.0074249Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T21:01:02.0074566Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T21:01:02.0074914Z getitem_31: "f32[1272 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T21:01:02.0075198Z 2025-03-04T21:01:02.0075905Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:01:02.0076642Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:01:02.0077002Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:01:02.0077358Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:01:02.0077730Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:01:02.0078029Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:01:02.0078273Z 2025-03-04T21:01:02.0078733Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:01:02.0079268Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:01:02.0079525Z 2025-03-04T21:01:02.0079665Z 2025-03-04T21:01:02.0079758Z class GraphModule(torch.nn.Module): 2025-03-04T21:01:02.0081194Z def forward(self, L_stack0_: "f32[3272, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1272 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1272 - s0, 4][4, 1]cpu"): 2025-03-04T21:01:02.0082585Z l_stack0_ = L_stack0_ 2025-03-04T21:01:02.0082977Z l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_ 2025-03-04T21:01:02.0083554Z l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_ 2025-03-04T21:01:02.0084127Z l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ 2025-03-04T21:01:02.0084692Z l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ 2025-03-04T21:01:02.0085182Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:01:02.0085584Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:01:02.0086009Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:01:02.0086404Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:01:02.0086695Z 2025-03-04T21:01:02.0087221Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/roi_heads.py:480 in torch_dynamo_resume_in_forward_at_477, code: predictions = self.box_predictor(box_features.mean(dim=[2, 3])) 2025-03-04T21:01:02.0087848Z mean: "f32[3272, 2048][2048, 1]cpu" = l_stack0_.mean(dim = [2, 3]); l_stack0_ = None 2025-03-04T21:01:02.0088107Z 2025-03-04T21:01:02.0088495Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x) 2025-03-04T21:01:02.0089461Z scores: "f32[3272, 81][81, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_); l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None 2025-03-04T21:01:02.0090237Z 2025-03-04T21:01:02.0090753Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x) 2025-03-04T21:01:02.0091943Z proposal_deltas: "f32[3272, 320][320, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_); mean = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None 2025-03-04T21:01:02.0092742Z 2025-03-04T21:01:02.0093287Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:01:02.0093800Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:01:02.0094180Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:01:02.0094416Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:01:02.0094818Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:01:02.0095092Z getitem_2: "Sym(1272 - s0)" = size_1[0] 2025-03-04T21:01:02.0095344Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:01:02.0095571Z 2025-03-04T21:01:02.0095966Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:01:02.0096969Z proposal_boxes: "f32[3272, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:01:02.0097706Z 2025-03-04T21:01:02.0098164Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:01:02.0098740Z deltas: "f32[3272, 320][320, 1]cpu" = proposal_deltas.float(); proposal_deltas = None 2025-03-04T21:01:02.0099009Z 2025-03-04T21:01:02.0099412Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:01:02.0099958Z boxes: "f32[3272, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:01:02.0100233Z 2025-03-04T21:01:02.0100664Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:01:02.0101160Z getitem_4: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:01:02.0101462Z getitem_5: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:01:02.0101780Z widths: "f32[3272][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T21:01:02.0102271Z 2025-03-04T21:01:02.0102697Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:01:02.0103208Z getitem_6: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:01:02.0103508Z getitem_7: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:01:02.0103834Z heights: "f32[3272][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T21:01:02.0104100Z 2025-03-04T21:01:02.0104508Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:01:02.0105020Z getitem_8: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:01:02.0105276Z mul: "f32[3272][1]cpu" = 0.5 * widths 2025-03-04T21:01:02.0105538Z ctr_x: "f32[3272][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T21:01:02.0105777Z 2025-03-04T21:01:02.0106188Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:01:02.0106704Z getitem_9: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:01:02.0106993Z mul_1: "f32[3272][1]cpu" = 0.5 * heights 2025-03-04T21:01:02.0107260Z ctr_y: "f32[3272][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T21:01:02.0107507Z 2025-03-04T21:01:02.0107928Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:01:02.0108531Z getitem_10: "f32[3272, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:01:02.0108867Z dx: "f32[3272, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T21:01:02.0109105Z 2025-03-04T21:01:02.0109504Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:01:02.0110023Z getitem_11: "f32[3272, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:01:02.0110346Z dy: "f32[3272, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T21:01:02.0110613Z 2025-03-04T21:01:02.0111007Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:01:02.0111518Z getitem_12: "f32[3272, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:01:02.0111841Z dw: "f32[3272, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T21:01:02.0112075Z 2025-03-04T21:01:02.0112470Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:01:02.0113015Z getitem_13: "f32[3272, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:01:02.0113357Z dh: "f32[3272, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T21:01:02.0113585Z 2025-03-04T21:01:02.0114085Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:01:02.0114640Z dw_1: "f32[3272, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:01:02.0114905Z 2025-03-04T21:01:02.0115343Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:01:02.0115947Z dh_1: "f32[3272, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:01:02.0116221Z 2025-03-04T21:01:02.0116704Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:01:02.0117265Z getitem_14: "f32[3272, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:01:02.0117589Z mul_2: "f32[3272, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T21:01:02.0117926Z getitem_15: "f32[3272, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:01:02.0118277Z pred_ctr_x: "f32[3272, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T21:01:02.0118540Z 2025-03-04T21:01:02.0118985Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:01:02.0119533Z getitem_16: "f32[3272, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:01:02.0119850Z mul_3: "f32[3272, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T21:01:02.0120175Z getitem_17: "f32[3272, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:01:02.0120523Z pred_ctr_y: "f32[3272, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T21:01:02.0120771Z 2025-03-04T21:01:02.0121190Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:01:02.0121706Z exp: "f32[3272, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:01:02.0122023Z getitem_18: "f32[3272, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:01:02.0122354Z pred_w: "f32[3272, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T21:01:02.0122598Z 2025-03-04T21:01:02.0123012Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:01:02.0123522Z exp_1: "f32[3272, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:01:02.0123861Z getitem_19: "f32[3272, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:01:02.0124210Z pred_h: "f32[3272, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T21:01:02.0124463Z 2025-03-04T21:01:02.0124879Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:01:02.0125346Z mul_6: "f32[3272, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:01:02.0125600Z x1: "f32[3272, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:01:02.0125833Z 2025-03-04T21:01:02.0126228Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:01:02.0126687Z mul_7: "f32[3272, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:01:02.0126945Z y1: "f32[3272, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:01:02.0127174Z 2025-03-04T21:01:02.0127594Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:01:02.0128070Z mul_8: "f32[3272, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:01:02.0128353Z x2: "f32[3272, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:01:02.0128594Z 2025-03-04T21:01:02.0128983Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:01:02.0129450Z mul_9: "f32[3272, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:01:02.0129731Z y2: "f32[3272, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:01:02.0129967Z 2025-03-04T21:01:02.0130398Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:01:02.0130970Z pred_boxes: "f32[3272, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:01:02.0131258Z 2025-03-04T21:01:02.0131681Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:01:02.0132258Z predict_boxes: "f32[3272, 320][320, 1]cpu" = pred_boxes.reshape((3272, 320)); pred_boxes = None 2025-03-04T21:01:02.0132537Z 2025-03-04T21:01:02.0132982Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:01:02.0133594Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T21:01:02.0133957Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T21:01:02.0134239Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T21:01:02.0134553Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T21:01:02.0134865Z getitem_23: "f32[1272 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T21:01:02.0135117Z 2025-03-04T21:01:02.0135489Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:01:02.0136040Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:01:02.0136382Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T21:01:02.0136616Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T21:01:02.0136999Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:01:02.0137357Z getitem_26: "Sym(1272 - s0)" = size_3[0] 2025-03-04T21:01:02.0137602Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T21:01:02.0137824Z 2025-03-04T21:01:02.0138250Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:01:02.0138816Z probs: "f32[3272, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1); scores = None 2025-03-04T21:01:02.0139107Z 2025-03-04T21:01:02.0139551Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:01:02.0140160Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T21:01:02.0140553Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:01:02.0140839Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T21:01:02.0141138Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T21:01:02.0141447Z getitem_31: "f32[1272 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T21:01:02.0141698Z 2025-03-04T21:01:02.0142244Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:01:02.0142935Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:01:02.0143274Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:01:02.0143613Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:01:02.0143951Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:01:02.0144244Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:01:02.0144477Z 2025-03-04T21:01:02.0144910Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:01:02.0145432Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:01:02.0145658Z 2025-03-04T21:01:02.0145785Z 2025-03-04T21:01:02.0145878Z class GraphModule(torch.nn.Module): 2025-03-04T21:01:02.0147270Z def forward(self, L_stack0_: "f32[3272, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1272 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1272 - s0, 4][4, 1]cpu"): 2025-03-04T21:01:02.0148626Z l_stack0_ = L_stack0_ 2025-03-04T21:01:02.0149009Z l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_ 2025-03-04T21:01:02.0149570Z l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_ 2025-03-04T21:01:02.0150146Z l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ 2025-03-04T21:01:02.0150693Z l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ 2025-03-04T21:01:02.0151170Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:01:02.0151568Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:01:02.0151960Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:01:02.0152349Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:01:02.0152634Z 2025-03-04T21:01:02.0153159Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/roi_heads.py:480 in torch_dynamo_resume_in_forward_at_477, code: predictions = self.box_predictor(box_features.mean(dim=[2, 3])) 2025-03-04T21:01:02.0153820Z mean: "f32[3272, 2048][2048, 1]cpu" = l_stack0_.mean(dim = [2, 3]); l_stack0_ = None 2025-03-04T21:01:02.0154078Z 2025-03-04T21:01:02.0154479Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x) 2025-03-04T21:01:02.0155485Z scores: "f32[3272, 81][81, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_); l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None 2025-03-04T21:01:02.0156310Z 2025-03-04T21:01:02.0156735Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x) 2025-03-04T21:01:02.0157814Z proposal_deltas: "f32[3272, 320][320, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_); mean = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None 2025-03-04T21:01:02.0158565Z 2025-03-04T21:01:02.0158922Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:01:02.0159369Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:01:02.0159617Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:01:02.0159840Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:01:02.0160106Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:01:02.0160368Z getitem_2: "Sym(1272 - s0)" = size_1[0] 2025-03-04T21:01:02.0160601Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:01:02.0160815Z 2025-03-04T21:01:02.0161181Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:01:02.0162152Z proposal_boxes: "f32[3272, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:01:02.0162870Z 2025-03-04T21:01:02.0163329Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:01:02.0163901Z deltas: "f32[3272, 320][320, 1]cpu" = proposal_deltas.float(); proposal_deltas = None 2025-03-04T21:01:02.0164196Z 2025-03-04T21:01:02.0164594Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:01:02.0165123Z boxes: "f32[3272, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:01:02.0165396Z 2025-03-04T21:01:02.0165796Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:01:02.0166295Z getitem_4: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:01:02.0166599Z getitem_5: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:01:02.0166912Z widths: "f32[3272][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T21:01:02.0167170Z 2025-03-04T21:01:02.0167609Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:01:02.0168096Z getitem_6: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:01:02.0168385Z getitem_7: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:01:02.0168692Z heights: "f32[3272][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T21:01:02.0168943Z 2025-03-04T21:01:02.0169337Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:01:02.0169817Z getitem_8: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:01:02.0170067Z mul: "f32[3272][1]cpu" = 0.5 * widths 2025-03-04T21:01:02.0170309Z ctr_x: "f32[3272][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T21:01:02.0170532Z 2025-03-04T21:01:02.0170922Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:01:02.0171422Z getitem_9: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:01:02.0171698Z mul_1: "f32[3272][1]cpu" = 0.5 * heights 2025-03-04T21:01:02.0171952Z ctr_y: "f32[3272][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T21:01:02.0172187Z 2025-03-04T21:01:02.0172585Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:01:02.0173093Z getitem_10: "f32[3272, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:01:02.0173408Z dx: "f32[3272, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T21:01:02.0173635Z 2025-03-04T21:01:02.0174019Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:01:02.0174524Z getitem_11: "f32[3272, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:01:02.0174858Z dy: "f32[3272, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T21:01:02.0175084Z 2025-03-04T21:01:02.0175465Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:01:02.0175963Z getitem_12: "f32[3272, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:01:02.0176278Z dw: "f32[3272, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T21:01:02.0176503Z 2025-03-04T21:01:02.0176885Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:01:02.0177435Z getitem_13: "f32[3272, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:01:02.0177775Z dh: "f32[3272, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T21:01:02.0178001Z 2025-03-04T21:01:02.0178423Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:01:02.0178952Z dw_1: "f32[3272, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:01:02.0179204Z 2025-03-04T21:01:02.0179620Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:01:02.0180141Z dh_1: "f32[3272, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:01:02.0180386Z 2025-03-04T21:01:02.0180853Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:01:02.0181388Z getitem_14: "f32[3272, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:01:02.0181699Z mul_2: "f32[3272, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T21:01:02.0182023Z getitem_15: "f32[3272, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:01:02.0182362Z pred_ctr_x: "f32[3272, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T21:01:02.0182613Z 2025-03-04T21:01:02.0183041Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:01:02.0183575Z getitem_16: "f32[3272, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:01:02.0183881Z mul_3: "f32[3272, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T21:01:02.0184196Z getitem_17: "f32[3272, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:01:02.0184524Z pred_ctr_y: "f32[3272, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T21:01:02.0184770Z 2025-03-04T21:01:02.0185178Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:01:02.0185673Z exp: "f32[3272, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:01:02.0185993Z getitem_18: "f32[3272, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:01:02.0186328Z pred_w: "f32[3272, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T21:01:02.0186569Z 2025-03-04T21:01:02.0186985Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:01:02.0187501Z exp_1: "f32[3272, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:01:02.0187825Z getitem_19: "f32[3272, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:01:02.0188167Z pred_h: "f32[3272, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T21:01:02.0188409Z 2025-03-04T21:01:02.0188803Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:01:02.0189261Z mul_6: "f32[3272, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:01:02.0189536Z x1: "f32[3272, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:01:02.0189764Z 2025-03-04T21:01:02.0190159Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:01:02.0190615Z mul_7: "f32[3272, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:01:02.0190869Z y1: "f32[3272, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:01:02.0191096Z 2025-03-04T21:01:02.0191480Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:01:02.0191949Z mul_8: "f32[3272, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:01:02.0192232Z x2: "f32[3272, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:01:02.0192471Z 2025-03-04T21:01:02.0192859Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:01:02.0193357Z mul_9: "f32[3272, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:01:02.0193651Z y2: "f32[3272, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:01:02.0193900Z 2025-03-04T21:01:02.0194347Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:01:02.0194942Z pred_boxes: "f32[3272, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:01:02.0195246Z 2025-03-04T21:01:02.0195690Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:01:02.0196343Z predict_boxes: "f32[3272, 320][320, 1]cpu" = pred_boxes.reshape((3272, 320)); pred_boxes = None 2025-03-04T21:01:02.0196648Z 2025-03-04T21:01:02.0197114Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:01:02.0197738Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T21:01:02.0198103Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T21:01:02.0198392Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T21:01:02.0198693Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T21:01:02.0199002Z getitem_23: "f32[1272 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T21:01:02.0199259Z 2025-03-04T21:01:02.0199644Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:01:02.0200208Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:01:02.0200589Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T21:01:02.0200838Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T21:01:02.0201209Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:01:02.0201572Z getitem_26: "Sym(1272 - s0)" = size_3[0] 2025-03-04T21:01:02.0201823Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T21:01:02.0202163Z 2025-03-04T21:01:02.0202585Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:01:02.0203193Z probs: "f32[3272, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1); scores = None 2025-03-04T21:01:02.0203483Z 2025-03-04T21:01:02.0203937Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:01:02.0204551Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T21:01:02.0204915Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:01:02.0205210Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T21:01:02.0205537Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T21:01:02.0205856Z getitem_31: "f32[1272 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T21:01:02.0206117Z 2025-03-04T21:01:02.0206723Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:01:02.0207424Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:01:02.0207772Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:01:02.0208115Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:01:02.0208463Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:01:02.0208762Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:01:02.0209003Z 2025-03-04T21:01:02.0209447Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:01:02.0209974Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:01:02.0210209Z 2025-03-04T21:01:03.5268625Z 2025-03-04T21:01:03.5269452Z class GraphModule(torch.nn.Module): 2025-03-04T21:01:03.5270412Z def forward(self, L_predictions_0_: "f32[3272, 81][81, 1]cpu", L_predictions_1_: "f32[3272, 320][320, 1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1272 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1272 - s0, 4][4, 1]cpu"): 2025-03-04T21:01:03.5271211Z l_predictions_0_ = L_predictions_0_ 2025-03-04T21:01:03.5271471Z l_predictions_1_ = L_predictions_1_ 2025-03-04T21:01:03.5271800Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:01:03.5272202Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:01:03.5272598Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:01:03.5272986Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:01:03.5273573Z 2025-03-04T21:01:03.5273984Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:01:03.5274450Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:01:03.5274706Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:01:03.5274938Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:01:03.5275215Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:01:03.5275482Z getitem_2: "Sym(1272 - s0)" = size_1[0] 2025-03-04T21:01:03.5275780Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:01:03.5276003Z 2025-03-04T21:01:03.5276503Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:01:03.5277516Z proposal_boxes: "f32[3272, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:01:03.5278304Z 2025-03-04T21:01:03.5278770Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:01:03.5279352Z deltas: "f32[3272, 320][320, 1]cpu" = l_predictions_1_.float(); l_predictions_1_ = None 2025-03-04T21:01:03.5279622Z 2025-03-04T21:01:03.5280100Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:01:03.5280631Z boxes: "f32[3272, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:01:03.5280909Z 2025-03-04T21:01:03.5281310Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:01:03.5281806Z getitem_4: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:01:03.5282109Z getitem_5: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:01:03.5282430Z widths: "f32[3272][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T21:01:03.5282698Z 2025-03-04T21:01:03.5283119Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:01:03.5283629Z getitem_6: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:01:03.5283928Z getitem_7: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:01:03.5284247Z heights: "f32[3272][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T21:01:03.5284507Z 2025-03-04T21:01:03.5284914Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:01:03.5285411Z getitem_8: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:01:03.5285670Z mul: "f32[3272][1]cpu" = 0.5 * widths 2025-03-04T21:01:03.5285925Z ctr_x: "f32[3272][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T21:01:03.5286170Z 2025-03-04T21:01:03.5286575Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:01:03.5287080Z getitem_9: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:01:03.5287382Z mul_1: "f32[3272][1]cpu" = 0.5 * heights 2025-03-04T21:01:03.5287641Z ctr_y: "f32[3272][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T21:01:03.5287877Z 2025-03-04T21:01:03.5288304Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:01:03.5288813Z getitem_10: "f32[3272, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:01:03.5289131Z dx: "f32[3272, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T21:01:03.5289359Z 2025-03-04T21:01:03.5289773Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:01:03.5290279Z getitem_11: "f32[3272, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:01:03.5290601Z dy: "f32[3272, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T21:01:03.5290828Z 2025-03-04T21:01:03.5291220Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:01:03.5291734Z getitem_12: "f32[3272, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:01:03.5292045Z dw: "f32[3272, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T21:01:03.5292269Z 2025-03-04T21:01:03.5292655Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:01:03.5293260Z getitem_13: "f32[3272, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:01:03.5293593Z dh: "f32[3272, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T21:01:03.5293817Z 2025-03-04T21:01:03.5294228Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:01:03.5294745Z dw_1: "f32[3272, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:01:03.5294993Z 2025-03-04T21:01:03.5295413Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:01:03.5295938Z dh_1: "f32[3272, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:01:03.5296188Z 2025-03-04T21:01:03.5296604Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:01:03.5297140Z getitem_14: "f32[3272, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:01:03.5297445Z mul_2: "f32[3272, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T21:01:03.5297782Z getitem_15: "f32[3272, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:01:03.5298115Z pred_ctr_x: "f32[3272, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T21:01:03.5298361Z 2025-03-04T21:01:03.5298785Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:01:03.5299317Z getitem_16: "f32[3272, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:01:03.5299621Z mul_3: "f32[3272, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T21:01:03.5299931Z getitem_17: "f32[3272, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:01:03.5300286Z pred_ctr_y: "f32[3272, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T21:01:03.5300532Z 2025-03-04T21:01:03.5300939Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:01:03.5301428Z exp: "f32[3272, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:01:03.5301742Z getitem_18: "f32[3272, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:01:03.5338570Z pred_w: "f32[3272, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T21:01:03.5338911Z 2025-03-04T21:01:03.5339339Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:01:03.5339828Z exp_1: "f32[3272, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:01:03.5340152Z getitem_19: "f32[3272, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:01:03.5340488Z pred_h: "f32[3272, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T21:01:03.5340730Z 2025-03-04T21:01:03.5341119Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:01:03.5341567Z mul_6: "f32[3272, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:01:03.5341821Z x1: "f32[3272, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:01:03.5342049Z 2025-03-04T21:01:03.5342500Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:01:03.5342943Z mul_7: "f32[3272, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:01:03.5343194Z y1: "f32[3272, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:01:03.5343415Z 2025-03-04T21:01:03.5343790Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:01:03.5344246Z mul_8: "f32[3272, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:01:03.5344522Z x2: "f32[3272, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:01:03.5344759Z 2025-03-04T21:01:03.5345154Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:01:03.5345616Z mul_9: "f32[3272, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:01:03.5345892Z y2: "f32[3272, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:01:03.5346127Z 2025-03-04T21:01:03.5346551Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:01:03.5347116Z pred_boxes: "f32[3272, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:01:03.5347396Z 2025-03-04T21:01:03.5347805Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:01:03.5348346Z predict_boxes: "f32[3272, 320][320, 1]cpu" = pred_boxes.reshape((3272, 320)); pred_boxes = None 2025-03-04T21:01:03.5348625Z 2025-03-04T21:01:03.5349057Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:01:03.5349696Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T21:01:03.5350048Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T21:01:03.5350328Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T21:01:03.5350620Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T21:01:03.5350927Z getitem_23: "f32[1272 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T21:01:03.5351176Z 2025-03-04T21:01:03.5351542Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:01:03.5352107Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:01:03.5352441Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T21:01:03.5352670Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T21:01:03.5353022Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:01:03.5353361Z getitem_26: "Sym(1272 - s0)" = size_3[0] 2025-03-04T21:01:03.5353596Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T21:01:03.5353797Z 2025-03-04T21:01:03.5354208Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:01:03.5354791Z probs: "f32[3272, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1); l_predictions_0_ = None 2025-03-04T21:01:03.5355115Z 2025-03-04T21:01:03.5355614Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:01:03.5356204Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T21:01:03.5356608Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:01:03.5356888Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T21:01:03.5357176Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T21:01:03.5357480Z getitem_31: "f32[1272 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T21:01:03.5357727Z 2025-03-04T21:01:03.5358276Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:01:03.5359074Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:01:03.5359416Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:01:03.5359742Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:01:03.5360071Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:01:03.5360356Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:01:03.5360588Z 2025-03-04T21:01:03.5361032Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:01:03.5361553Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:01:03.5361788Z 2025-03-04T21:01:03.5361902Z 2025-03-04T21:01:03.5361993Z class GraphModule(torch.nn.Module): 2025-03-04T21:01:03.5362800Z def forward(self, L_predictions_0_: "f32[3272, 81][81, 1]cpu", L_predictions_1_: "f32[3272, 320][320, 1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1272 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1272 - s0, 4][4, 1]cpu"): 2025-03-04T21:01:03.5363603Z l_predictions_0_ = L_predictions_0_ 2025-03-04T21:01:03.5363824Z l_predictions_1_ = L_predictions_1_ 2025-03-04T21:01:03.5364137Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:01:03.5364536Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:01:03.5364953Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:01:03.5365373Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:01:03.5365671Z 2025-03-04T21:01:03.5366058Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:01:03.5366524Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:01:03.5366774Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:01:03.5367007Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:01:03.5367280Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:01:03.5367535Z getitem_2: "Sym(1272 - s0)" = size_1[0] 2025-03-04T21:01:03.5367773Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:01:03.5367989Z 2025-03-04T21:01:03.5368394Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:01:03.5369343Z proposal_boxes: "f32[3272, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:01:03.5370067Z 2025-03-04T21:01:03.5370532Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:01:03.5371106Z deltas: "f32[3272, 320][320, 1]cpu" = l_predictions_1_.float(); l_predictions_1_ = None 2025-03-04T21:01:03.5371373Z 2025-03-04T21:01:03.5371777Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:01:03.5372316Z boxes: "f32[3272, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:01:03.5372587Z 2025-03-04T21:01:03.5372975Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:01:03.5373462Z getitem_4: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:01:03.5373754Z getitem_5: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:01:03.5374056Z widths: "f32[3272][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T21:01:03.5374313Z 2025-03-04T21:01:03.5374715Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:01:03.5375203Z getitem_6: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:01:03.5375488Z getitem_7: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:01:03.5375807Z heights: "f32[3272][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T21:01:03.5376063Z 2025-03-04T21:01:03.5376488Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:01:03.5376955Z getitem_8: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:01:03.5377196Z mul: "f32[3272][1]cpu" = 0.5 * widths 2025-03-04T21:01:03.5377440Z ctr_x: "f32[3272][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T21:01:03.5377668Z 2025-03-04T21:01:03.5378056Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:01:03.5378564Z getitem_9: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:01:03.5378837Z mul_1: "f32[3272][1]cpu" = 0.5 * heights 2025-03-04T21:01:03.5379122Z ctr_y: "f32[3272][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T21:01:03.5379353Z 2025-03-04T21:01:03.5379752Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:01:03.5380247Z getitem_10: "f32[3272, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:01:03.5380561Z dx: "f32[3272, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T21:01:03.5380778Z 2025-03-04T21:01:03.5381150Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:01:03.5381669Z getitem_11: "f32[3272, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:01:03.5381981Z dy: "f32[3272, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T21:01:03.5382202Z 2025-03-04T21:01:03.5382572Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:01:03.5383057Z getitem_12: "f32[3272, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:01:03.5383363Z dw: "f32[3272, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T21:01:03.5383582Z 2025-03-04T21:01:03.5383960Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:01:03.5384484Z getitem_13: "f32[3272, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:01:03.5384816Z dh: "f32[3272, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T21:01:03.5385039Z 2025-03-04T21:01:03.5385448Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:01:03.5385961Z dw_1: "f32[3272, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:01:03.5386204Z 2025-03-04T21:01:03.5386607Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:01:03.5387109Z dh_1: "f32[3272, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:01:03.5387353Z 2025-03-04T21:01:03.5387771Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:01:03.5388291Z getitem_14: "f32[3272, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:01:03.5388610Z mul_2: "f32[3272, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T21:01:03.5388927Z getitem_15: "f32[3272, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:01:03.5389262Z pred_ctr_x: "f32[3272, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T21:01:03.5389510Z 2025-03-04T21:01:03.5389936Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:01:03.5390462Z getitem_16: "f32[3272, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:01:03.5390778Z mul_3: "f32[3272, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T21:01:03.5391095Z getitem_17: "f32[3272, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:01:03.5391427Z pred_ctr_y: "f32[3272, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T21:01:03.5391674Z 2025-03-04T21:01:03.5392080Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:01:03.5392572Z exp: "f32[3272, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:01:03.5392886Z getitem_18: "f32[3272, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:01:03.5393217Z pred_w: "f32[3272, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T21:01:03.5393455Z 2025-03-04T21:01:03.5393894Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:01:03.5394394Z exp_1: "f32[3272, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:01:03.5394717Z getitem_19: "f32[3272, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:01:03.5395054Z pred_h: "f32[3272, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T21:01:03.5395303Z 2025-03-04T21:01:03.5395702Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:01:03.5396164Z mul_6: "f32[3272, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:01:03.5396499Z x1: "f32[3272, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:01:03.5396735Z 2025-03-04T21:01:03.5397126Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:01:03.5397587Z mul_7: "f32[3272, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:01:03.5397843Z y1: "f32[3272, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:01:03.5398064Z 2025-03-04T21:01:03.5398452Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:01:03.5398931Z mul_8: "f32[3272, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:01:03.5399218Z x2: "f32[3272, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:01:03.5399460Z 2025-03-04T21:01:03.5399849Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:01:03.5400324Z mul_9: "f32[3272, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:01:03.5400606Z y2: "f32[3272, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:01:03.5400842Z 2025-03-04T21:01:03.5401401Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:01:03.5402095Z pred_boxes: "f32[3272, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:01:03.5402394Z 2025-03-04T21:01:03.5402806Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:01:03.5403361Z predict_boxes: "f32[3272, 320][320, 1]cpu" = pred_boxes.reshape((3272, 320)); pred_boxes = None 2025-03-04T21:01:03.5403695Z 2025-03-04T21:01:03.5404142Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:01:03.5404751Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T21:01:03.5405114Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T21:01:03.5405397Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T21:01:03.5405699Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T21:01:03.5406014Z getitem_23: "f32[1272 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T21:01:03.5406273Z 2025-03-04T21:01:03.5406648Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:01:03.5407263Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:01:03.5407613Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T21:01:03.5407869Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T21:01:03.5408246Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:01:03.5408597Z getitem_26: "Sym(1272 - s0)" = size_3[0] 2025-03-04T21:01:03.5408842Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T21:01:03.5409056Z 2025-03-04T21:01:03.5409468Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:01:03.5410054Z probs: "f32[3272, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1); l_predictions_0_ = None 2025-03-04T21:01:03.5410372Z 2025-03-04T21:01:03.5410806Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:01:03.5411396Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T21:01:03.5411745Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:01:03.5412028Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T21:01:03.5412321Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T21:01:03.5412627Z getitem_31: "f32[1272 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T21:01:03.5412876Z 2025-03-04T21:01:03.5413424Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:01:03.5414120Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:01:03.5414448Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:01:03.5415734Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:01:03.5416061Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:01:03.5416344Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:01:03.5416563Z 2025-03-04T21:01:03.5416993Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:01:03.5417496Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:01:03.5417739Z 2025-03-04T21:01:03.5417859Z 2025-03-04T21:01:03.5417953Z class GraphModule(torch.nn.Module): 2025-03-04T21:01:03.5418745Z def forward(self, L_predictions_0_: "f32[3272, 81][81, 1]cpu", L_predictions_1_: "f32[3272, 320][320, 1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1272 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1272 - s0, 4][4, 1]cpu"): 2025-03-04T21:01:03.5419519Z l_predictions_0_ = L_predictions_0_ 2025-03-04T21:01:03.5419735Z l_predictions_1_ = L_predictions_1_ 2025-03-04T21:01:03.5420035Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:01:03.5420426Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:01:03.5420815Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:01:03.5421241Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:01:03.5421526Z 2025-03-04T21:01:03.5421896Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:01:03.5422350Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:01:03.5422594Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:01:03.5422813Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:01:03.5423074Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:01:03.5423322Z getitem_2: "Sym(1272 - s0)" = size_1[0] 2025-03-04T21:01:03.5423554Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:01:03.5423764Z 2025-03-04T21:01:03.5424121Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:01:03.5425059Z proposal_boxes: "f32[3272, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:01:03.5425761Z 2025-03-04T21:01:03.5426210Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:01:03.5426773Z deltas: "f32[3272, 320][320, 1]cpu" = l_predictions_1_.float(); l_predictions_1_ = None 2025-03-04T21:01:03.5427035Z 2025-03-04T21:01:03.5427421Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:01:03.5427938Z boxes: "f32[3272, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:01:03.5428206Z 2025-03-04T21:01:03.5428616Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:01:03.5429102Z getitem_4: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:01:03.5429393Z getitem_5: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:01:03.5429700Z widths: "f32[3272][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T21:01:03.5429953Z 2025-03-04T21:01:03.5430356Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:01:03.5430856Z getitem_6: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:01:03.5431141Z getitem_7: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:01:03.5431446Z heights: "f32[3272][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T21:01:03.5431698Z 2025-03-04T21:01:03.5432086Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:01:03.5432555Z getitem_8: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:01:03.5432802Z mul: "f32[3272][1]cpu" = 0.5 * widths 2025-03-04T21:01:03.5433043Z ctr_x: "f32[3272][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T21:01:03.5433265Z 2025-03-04T21:01:03.5433646Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:01:03.5434179Z getitem_9: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:01:03.5434461Z mul_1: "f32[3272][1]cpu" = 0.5 * heights 2025-03-04T21:01:03.5434719Z ctr_y: "f32[3272][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T21:01:03.5434957Z 2025-03-04T21:01:03.5435361Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:01:03.5435877Z getitem_10: "f32[3272, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:01:03.5436199Z dx: "f32[3272, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T21:01:03.5436505Z 2025-03-04T21:01:03.5436896Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:01:03.5437410Z getitem_11: "f32[3272, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:01:03.5437732Z dy: "f32[3272, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T21:01:03.5437962Z 2025-03-04T21:01:03.5438346Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:01:03.5438848Z getitem_12: "f32[3272, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:01:03.5439167Z dw: "f32[3272, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T21:01:03.5439392Z 2025-03-04T21:01:03.5439784Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:01:03.5440322Z getitem_13: "f32[3272, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:01:03.5440665Z dh: "f32[3272, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T21:01:03.5440887Z 2025-03-04T21:01:03.5441306Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:01:03.5441852Z dw_1: "f32[3272, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:01:03.5442100Z 2025-03-04T21:01:03.5442515Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:01:03.5443028Z dh_1: "f32[3272, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:01:03.5443275Z 2025-03-04T21:01:03.5443699Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:01:03.5444252Z getitem_14: "f32[3272, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:01:03.5444564Z mul_2: "f32[3272, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T21:01:03.5444890Z getitem_15: "f32[3272, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:01:03.5445231Z pred_ctr_x: "f32[3272, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T21:01:03.5445479Z 2025-03-04T21:01:03.5445913Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:01:03.5446443Z getitem_16: "f32[3272, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:01:03.5446753Z mul_3: "f32[3272, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T21:01:03.5447110Z getitem_17: "f32[3272, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:01:03.5447444Z pred_ctr_y: "f32[3272, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T21:01:03.5447694Z 2025-03-04T21:01:03.5448105Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:01:03.5448597Z exp: "f32[3272, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:01:03.5448908Z getitem_18: "f32[3272, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:01:03.5449241Z pred_w: "f32[3272, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T21:01:03.5449484Z 2025-03-04T21:01:03.5449896Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:01:03.5450399Z exp_1: "f32[3272, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:01:03.5450722Z getitem_19: "f32[3272, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:01:03.5451066Z pred_h: "f32[3272, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T21:01:03.5451312Z 2025-03-04T21:01:03.5451705Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:01:03.5452162Z mul_6: "f32[3272, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:01:03.5452419Z x1: "f32[3272, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:01:03.5452645Z 2025-03-04T21:01:03.5453036Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:01:03.5453484Z mul_7: "f32[3272, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:01:03.5453733Z y1: "f32[3272, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:01:03.5453975Z 2025-03-04T21:01:03.5454357Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:01:03.5454820Z mul_8: "f32[3272, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:01:03.5455101Z x2: "f32[3272, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:01:03.5455339Z 2025-03-04T21:01:03.5455725Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:01:03.5456229Z mul_9: "f32[3272, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:01:03.5456508Z y2: "f32[3272, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:01:03.5456742Z 2025-03-04T21:01:03.5457160Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:01:03.5457723Z pred_boxes: "f32[3272, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:01:03.5458003Z 2025-03-04T21:01:03.5458413Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:01:03.5458955Z predict_boxes: "f32[3272, 320][320, 1]cpu" = pred_boxes.reshape((3272, 320)); pred_boxes = None 2025-03-04T21:01:03.5459232Z 2025-03-04T21:01:03.5459698Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:01:03.5460291Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T21:01:03.5460642Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T21:01:03.5460922Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T21:01:03.5461206Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T21:01:03.5461510Z getitem_23: "f32[1272 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T21:01:03.5461758Z 2025-03-04T21:01:03.5462123Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:01:03.5462657Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:01:03.5462992Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T21:01:03.5463221Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T21:01:03.5463574Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:01:03.5463912Z getitem_26: "Sym(1272 - s0)" = size_3[0] 2025-03-04T21:01:03.5464143Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T21:01:03.5464351Z 2025-03-04T21:01:03.5464756Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:01:03.5465331Z probs: "f32[3272, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1); l_predictions_0_ = None 2025-03-04T21:01:03.5465644Z 2025-03-04T21:01:03.5466077Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:01:03.5466659Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T21:01:03.5467025Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:01:03.5467311Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T21:01:03.5467598Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T21:01:03.5467899Z getitem_31: "f32[1272 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T21:01:03.5468147Z 2025-03-04T21:01:03.5468682Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:01:03.5469372Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:01:03.5469701Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:01:03.5470031Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:01:03.5470360Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:01:03.5470645Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:01:03.5470877Z 2025-03-04T21:01:03.5471305Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:01:03.5471809Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:01:03.5472034Z 2025-03-04T21:01:05.7996182Z 2025-03-04T21:01:05.7996713Z class GraphModule(torch.nn.Module): 2025-03-04T21:01:05.7997539Z def forward(self, L_scores_0_: "f32[1000, 81][81, 1]cpu", L_boxes_0_: "f32[1000, 320][320, 1]cpu"): 2025-03-04T21:01:05.7997924Z l_scores_0_ = L_scores_0_ 2025-03-04T21:01:05.7998188Z l_boxes_0_ = L_boxes_0_ 2025-03-04T21:01:05.7998417Z 2025-03-04T21:01:05.7999151Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:01:05.8000061Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(l_boxes_0_); l_boxes_0_ = None 2025-03-04T21:01:05.8000472Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:01:05.8000863Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(l_scores_0_); l_scores_0_ = None 2025-03-04T21:01:05.8001206Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:01:05.8001507Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:01:05.8001752Z 2025-03-04T21:01:05.8002386Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:01:05.8002961Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:01:05.8003209Z 2025-03-04T21:01:05.8003454Z 2025-03-04T21:01:05.8007014Z class GraphModule(torch.nn.Module): 2025-03-04T21:01:05.8011297Z def forward(self, L_scores_0_: "f32[1000, 81][81, 1]cpu", L_boxes_0_: "f32[1000, 320][320, 1]cpu"): 2025-03-04T21:01:05.8015953Z l_scores_0_ = L_scores_0_ 2025-03-04T21:01:05.8020745Z l_boxes_0_ = L_boxes_0_ 2025-03-04T21:01:05.8022724Z 2025-03-04T21:01:05.8023501Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:01:05.8028005Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(l_boxes_0_); l_boxes_0_ = None 2025-03-04T21:01:05.8030324Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:01:05.8030711Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(l_scores_0_); l_scores_0_ = None 2025-03-04T21:01:05.8031042Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:01:05.8031336Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:01:05.8031576Z 2025-03-04T21:01:05.8032056Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:01:05.8032837Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:01:05.8033081Z 2025-03-04T21:02:24.0325459Z E0304 21:02:24.031546 61113 site-packages/torch/_dynamo/utils.py:2776] Accuracy failed: uint8 tensor did not match 2025-03-04T21:02:24.0327415Z E0304 21:02:24.031940 61113 site-packages/torch/_dynamo/utils.py:2739] Accuracy failed for key name pred_masks 2025-03-04T21:02:24.0328188Z E0304 21:02:24.032445 61113 site-packages/torch/_dynamo/utils.py:2739] Accuracy failed for key name instances 2025-03-04T21:02:24.0973454Z Compilation time (from dynamo_timed): 95.984158285 2025-03-04T21:02:24.0974002Z fail_accuracy 2025-03-04T21:02:24.0974879Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T21:02:24.0975824Z TIMING: entire_frame_compile:95.98416 gc:0.04214 _recursive_pre_grad_passes:0.03847 async_compile.wait:20.46566 backend_compile:43.20934 _recursive_joint_graph_passes:0.22379 inductor_compile:27.06788 _recursive_post_grad_passes:0.08194 code_gen:23.44137 total_wall_time:95.98416 2025-03-04T21:02:24.0978261Z STATS: call_* op count: 936 | FakeTensorMode.__torch_dispatch__:22038 | FakeTensor.__torch_dispatch__:1490 | ProxyTorchDispatchMode.__torch_dispatch__:4725 | attempt fast:415 | slow no contiguity match:46 | fast is_contiguous:359 | slow both tensors nontrivially broadcast:10 2025-03-04T21:02:24.0978986Z Dynamo produced 67 graphs covering 936 ops with 56 graph breaks (8 unique) 2025-03-04T21:02:32.4189350Z 2025-03-04T21:02:39.4336856Z loading model: 0it [00:00, ?it/s] 2025-03-04T21:02:39.4341480Z loading model: 0it [00:07, ?it/s] 2025-03-04T21:02:39.4349270Z cpu eval detectron2_maskrcnn_r_101_fpn 2025-03-04T21:02:55.1510757Z WARNING:common:fp64 golden ref were not generated for detectron2_maskrcnn_r_101_fpn. Setting accuracy check to cosine 2025-03-04T21:02:55.1686252Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T21:03:02.0985838Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T21:03:09.3749562Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T21:03:21.1513450Z 2025-03-04T21:03:21.1514341Z class GraphModule(torch.nn.Module): 2025-03-04T21:03:21.1658088Z def forward(self, L_stack0_tensor: "f32[4, 3, 1184, 1216][4319232, 1439744, 1216, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: 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1][2048, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_lateral_convs_0_parameters_weight_: "f32[256, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_0_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_0_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_0_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_1_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_1_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_1_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_2_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_2_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_2_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_3_parameters_weight_: "f32[256, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_3_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_3_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_3_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[3, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[3][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[12, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[12][1]cpu"): 2025-03-04T21:03:21.1780247Z l_stack0_tensor = L_stack0_tensor 2025-03-04T21:03:21.1780736Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.1781523Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.1782362Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.1783163Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.1784061Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.1784885Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.1785705Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.1786598Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.1787467Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.1788295Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.1789094Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.1789921Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.1790851Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.1791702Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.1792531Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.1793414Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.1794360Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.1795265Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.1796115Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.1796935Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.1797724Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:03:21.1798591Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.1799539Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.1800417Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:03:21.1801242Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:03:21.1802203Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.1803036Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.1803964Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.1804814Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.1805682Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.1806463Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.1807295Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.1808170Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.1809034Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.1809860Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.1810665Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.1811492Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.1812374Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.1813253Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.1814081Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.1814869Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.1815737Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.1816628Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.1817489Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.1818329Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.1819155Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.1819962Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.1820811Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.1821638Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.1822437Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.1823226Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.1824045Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.1824910Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.1825764Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.1826567Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.1827352Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.1828162Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.1829038Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.1829901Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.1830720Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.1831503Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.1832324Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.1833228Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.1834127Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.1834957Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.1835761Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.1836588Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.1837463Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.1838318Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.1839139Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.1839948Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:03:21.1840799Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.1841722Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.1842619Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:03:21.1843510Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:03:21.1844333Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.1845164Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.1846040Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.1846927Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.1847751Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.1848550Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.1849377Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.1850265Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.1851119Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.1851955Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.1852759Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.1853574Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.1854457Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.1855325Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.1856145Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.1856944Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.1857769Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.1858628Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.1859456Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.1860274Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.1861083Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.1861902Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.1862771Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.1863603Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.1864426Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.1865209Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.1866033Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.1866902Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.1867748Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.1868554Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.1869367Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.1870185Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.1871061Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.1871920Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.1872739Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.1873537Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.1874513Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.1875467Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.1876315Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.1877140Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.1877924Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.1878753Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.1879631Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.1880486Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.1881309Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.1882103Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.1882934Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.1883808Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.1884660Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.1885501Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.1886292Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.1887119Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.1887982Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.1888855Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.1889654Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.1890422Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.1891217Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.1892070Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.1892898Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.1893694Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.1894474Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:03:21.1895303Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.1896176Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.1897053Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:03:21.1897893Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:03:21.1898682Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.1899487Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.1900335Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.1901175Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.1902134Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.1903066Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.1904000Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.1904933Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.1905793Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.1906626Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.1907419Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.1908275Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.1909174Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.1910041Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.1910905Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.1911701Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.1912556Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.1913509Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.1914456Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.1915330Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.1916113Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.1916968Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.1917843Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.1918700Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.1919533Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.1920333Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.1921166Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.1922045Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.1922899Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.1923726Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.1924537Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.1925349Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.1926216Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.1927068Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.1927911Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.1928714Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.1929533Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.1930412Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.1931298Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.1932124Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.1932918Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.1933774Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.1934629Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.1935460Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.1936264Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.1937041Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.1937861Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.1938733Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.1939571Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.1940395Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.1941201Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.1942025Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.1942902Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.1943761Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.1944614Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.1945399Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.1946212Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.1947075Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.1947914Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.1948732Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.1949512Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.1950329Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.1951189Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.1952040Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.1952878Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.1953815Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.1954775Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.1955807Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.1956781Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.1957731Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.1958632Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.1959618Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.1960479Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.1961303Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.1962098Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.1962864Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.1963661Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.1964511Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.1965338Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.1966137Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.1966898Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.1967717Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.1968570Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.1969403Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.1970279Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.1971044Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.1971841Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.1972687Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.1973546Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.1974356Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.1975147Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.1975967Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.1976820Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.1977648Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.1978460Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.1979240Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.1980058Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.1980956Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.1981796Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.1982613Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.1983415Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.1984226Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.1985093Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.1985935Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.1986784Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.1987571Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.1988388Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.1989257Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.1990101Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.1990921Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.1991693Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.1992512Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.1993389Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.1994309Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.1995246Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.1996113Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.1996908Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.1997786Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.1998642Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.1999477Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.2000252Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.2001109Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.2002101Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.2002942Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.2003767Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.2004540Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.2005335Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.2006181Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.2009359Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.2010167Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.2010939Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.2011741Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.2012587Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.2013452Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.2014286Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.2015070Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.2015879Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.2016741Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.2017653Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.2018466Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.2019245Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.2020050Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.2020914Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.2021746Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.2022554Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.2023386Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.2024194Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.2025047Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.2025875Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.2026683Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.2027480Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.2028289Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.2029145Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.2029980Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.2030819Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.2031587Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.2032398Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.2033268Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.2034200Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.2035050Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.2035862Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.2036667Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.2037568Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.2038421Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.2039244Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.2040029Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.2040849Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.2041746Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.2042596Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.2043423Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.2044217Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.2045062Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.2045935Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.2046785Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.2047611Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.2048400Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.2049227Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.2050102Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.2050982Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.2051838Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.2052652Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.2053496Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.2054397Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.2055302Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.2056153Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.2056941Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.2057767Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.2058671Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.2059528Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.2060329Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.2061129Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.2061955Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.2062829Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.2063669Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.2064489Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.2065264Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.2066066Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.2066917Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.2067747Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.2068582Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.2069355Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.2070158Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.2071022Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.2071900Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.2072729Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.2073516Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.2074410Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.2075295Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.2076145Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.2076985Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.2077782Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.2078646Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.2079548Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.2080425Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.2081265Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.2082079Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.2082929Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.2083821Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.2084692Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.2085533Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.2086360Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.2087198Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.2088058Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.2088892Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.2089705Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.2090480Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.2091281Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.2092153Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.2092994Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.2093803Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.2094572Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.2095384Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.2096263Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.2097115Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.2097935Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.2098705Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.2099536Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.2100395Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.2101226Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.2102215Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.2103015Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.2103822Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.2104701Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.2105554Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.2106430Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.2107226Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.2108055Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.2108935Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.2109794Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.2110645Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.2111446Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.2112274Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.2113156Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.2114108Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.2114943Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.2115738Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.2116572Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.2117463Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.2118327Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.2119163Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.2119973Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.2120783Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.2121645Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.2122478Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.2123283Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.2124077Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.2124881Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.2125734Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.2126576Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.2127416Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.2128204Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.2129024Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.2129878Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.2130711Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.2131519Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.2132287Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.2133100Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.2134002Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.2134862Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.2135679Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.2136475Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.2137300Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.2138198Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.2139053Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.2139878Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.2140672Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.2141528Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.2142410Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.2143265Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.2144091Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.2144891Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.2145715Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.2146594Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.2147462Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.2148287Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.2149089Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.2149917Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.2150791Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.2151708Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.2152562Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.2153391Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.2154301Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.2155243Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.2156128Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.2156981Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.2157800Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.2158661Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.2159572Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.2160461Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.2161321Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.2162157Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.2163009Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.2163894Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.2164759Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.2165589Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.2166379Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.2167178Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.2168035Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.2168869Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.2169713Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.2170494Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.2171302Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.2172165Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.2173009Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.2173820Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.2174599Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.2175426Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.2176295Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.2177131Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.2177936Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.2178710Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.2179522Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.2180371Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.2181198Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.2181999Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.2182805Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.2183622Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.2184510Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.2185350Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.2186163Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.2186938Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.2187747Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.2188602Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.2189483Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.2190316Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.2191135Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:03:21.2191997Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.2192905Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.2193875Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:03:21.2194756Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:03:21.2195581Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.2196427Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.2197338Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.2198193Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.2199081Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.2199869Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.2200694Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.2201566Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.2202547Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.2203417Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.2204229Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.2205050Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.2205950Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.2206819Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.2207671Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.2208480Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.2209319Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.2210212Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.2211136Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.2211978Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.2212784Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.2213623Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.2214530Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.2215407Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.2216249Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.2217052Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.2217914Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.2218816Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.2219669Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.2220470Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.2221122Z l_self_modules_backbone_lateral_convs_0_parameters_weight_ = L_self_modules_backbone_lateral_convs_0_parameters_weight_ 2025-03-04T21:03:21.2221637Z l_self_modules_backbone_lateral_convs_0_parameters_bias_ = L_self_modules_backbone_lateral_convs_0_parameters_bias_ 2025-03-04T21:03:21.2222137Z l_self_modules_backbone_output_convs_0_parameters_weight_ = L_self_modules_backbone_output_convs_0_parameters_weight_ 2025-03-04T21:03:21.2222627Z l_self_modules_backbone_output_convs_0_parameters_bias_ = L_self_modules_backbone_output_convs_0_parameters_bias_ 2025-03-04T21:03:21.2223108Z l_self_modules_backbone_lateral_convs_1_parameters_weight_ = L_self_modules_backbone_lateral_convs_1_parameters_weight_ 2025-03-04T21:03:21.2223599Z l_self_modules_backbone_lateral_convs_1_parameters_bias_ = L_self_modules_backbone_lateral_convs_1_parameters_bias_ 2025-03-04T21:03:21.2224102Z l_self_modules_backbone_output_convs_1_parameters_weight_ = L_self_modules_backbone_output_convs_1_parameters_weight_ 2025-03-04T21:03:21.2224581Z l_self_modules_backbone_output_convs_1_parameters_bias_ = L_self_modules_backbone_output_convs_1_parameters_bias_ 2025-03-04T21:03:21.2225094Z l_self_modules_backbone_lateral_convs_2_parameters_weight_ = L_self_modules_backbone_lateral_convs_2_parameters_weight_ 2025-03-04T21:03:21.2225579Z l_self_modules_backbone_lateral_convs_2_parameters_bias_ = L_self_modules_backbone_lateral_convs_2_parameters_bias_ 2025-03-04T21:03:21.2226064Z l_self_modules_backbone_output_convs_2_parameters_weight_ = L_self_modules_backbone_output_convs_2_parameters_weight_ 2025-03-04T21:03:21.2226542Z l_self_modules_backbone_output_convs_2_parameters_bias_ = L_self_modules_backbone_output_convs_2_parameters_bias_ 2025-03-04T21:03:21.2227020Z l_self_modules_backbone_lateral_convs_3_parameters_weight_ = L_self_modules_backbone_lateral_convs_3_parameters_weight_ 2025-03-04T21:03:21.2227504Z l_self_modules_backbone_lateral_convs_3_parameters_bias_ = L_self_modules_backbone_lateral_convs_3_parameters_bias_ 2025-03-04T21:03:21.2227982Z l_self_modules_backbone_output_convs_3_parameters_weight_ = L_self_modules_backbone_output_convs_3_parameters_weight_ 2025-03-04T21:03:21.2228467Z l_self_modules_backbone_output_convs_3_parameters_bias_ = L_self_modules_backbone_output_convs_3_parameters_bias_ 2025-03-04T21:03:21.2229083Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T21:03:21.2229848Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ 2025-03-04T21:03:21.2230588Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ 2025-03-04T21:03:21.2231357Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ 2025-03-04T21:03:21.2232112Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ 2025-03-04T21:03:21.2232846Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T21:03:21.2233542Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T21:03:21.2234378Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T21:03:21.2235196Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T21:03:21.2236010Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T21:03:21.2236755Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T21:03:21.2237240Z 2025-03-04T21:03:21.2237643Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2238548Z x: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1); l_stack0_tensor = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.2239245Z 2025-03-04T21:03:21.2239632Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2241811Z x_1: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2243779Z 2025-03-04T21:03:21.2244176Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x) 2025-03-04T21:03:21.2244685Z x_2: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.relu_(x_1); x_1 = None 2025-03-04T21:03:21.2244956Z 2025-03-04T21:03:21.2245435Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1) 2025-03-04T21:03:21.2246162Z x_3: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1); x_2 = None 2025-03-04T21:03:21.2246529Z 2025-03-04T21:03:21.2246883Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2247735Z x_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.2248372Z 2025-03-04T21:03:21.2248724Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2250850Z x_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2252800Z 2025-03-04T21:03:21.2253167Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2253636Z out: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_5); x_5 = None 2025-03-04T21:03:21.2253910Z 2025-03-04T21:03:21.2254247Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2255040Z x_6: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.2255650Z 2025-03-04T21:03:21.2256004Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2258157Z x_7: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_6 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2260012Z 2025-03-04T21:03:21.2260373Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2260844Z out_1: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_7); x_7 = None 2025-03-04T21:03:21.2261094Z 2025-03-04T21:03:21.2261432Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2262240Z x_8: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_1 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.2262854Z 2025-03-04T21:03:21.2263207Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2265814Z x_9: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2267748Z 2025-03-04T21:03:21.2268136Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2268965Z x_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); x_3 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:03:21.2269688Z 2025-03-04T21:03:21.2270043Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2272279Z x_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_10 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2274619Z 2025-03-04T21:03:21.2275048Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.2275531Z x_9 += x_11; out_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_9; x_9 = x_11 = None 2025-03-04T21:03:21.2275795Z 2025-03-04T21:03:21.2276167Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2276660Z out_3: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_2); out_2 = None 2025-03-04T21:03:21.2276927Z 2025-03-04T21:03:21.2277264Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2278075Z x_12: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.2278708Z 2025-03-04T21:03:21.2279063Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2281226Z x_13: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_12 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2283136Z 2025-03-04T21:03:21.2283509Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2283992Z out_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_13); x_13 = None 2025-03-04T21:03:21.2284246Z 2025-03-04T21:03:21.2284589Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2285392Z x_14: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.2285995Z 2025-03-04T21:03:21.2286350Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2288470Z x_15: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_14 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2290363Z 2025-03-04T21:03:21.2290724Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2291202Z out_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_15); x_15 = None 2025-03-04T21:03:21.2291449Z 2025-03-04T21:03:21.2291785Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2292617Z x_16: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_5 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.2293302Z 2025-03-04T21:03:21.2293674Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2295925Z x_17: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_16 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2297840Z 2025-03-04T21:03:21.2298205Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.2298698Z x_17 += out_3; out_6: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_17; x_17 = out_3 = None 2025-03-04T21:03:21.2298968Z 2025-03-04T21:03:21.2299335Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2299827Z out_7: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_6); out_6 = None 2025-03-04T21:03:21.2300089Z 2025-03-04T21:03:21.2300424Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2301212Z x_18: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.2301801Z 2025-03-04T21:03:21.2302292Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2304476Z x_19: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_18 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2306401Z 2025-03-04T21:03:21.2306770Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2307290Z out_8: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_19); x_19 = None 2025-03-04T21:03:21.2307551Z 2025-03-04T21:03:21.2307890Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2308698Z x_20: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.2309305Z 2025-03-04T21:03:21.2309660Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2311827Z x_21: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_20 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2313896Z 2025-03-04T21:03:21.2314313Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2314833Z out_9: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_21); x_21 = None 2025-03-04T21:03:21.2315089Z 2025-03-04T21:03:21.2315425Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2316236Z x_22: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_9 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.2316864Z 2025-03-04T21:03:21.2317212Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2319347Z x_23: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_22 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2321279Z 2025-03-04T21:03:21.2321641Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.2322116Z x_23 += out_7; out_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_23; x_23 = out_7 = None 2025-03-04T21:03:21.2322378Z 2025-03-04T21:03:21.2322734Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2323215Z out_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_10); out_10 = None 2025-03-04T21:03:21.2323470Z 2025-03-04T21:03:21.2323799Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2324606Z x_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.2325194Z 2025-03-04T21:03:21.2325545Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2327618Z x_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2329477Z 2025-03-04T21:03:21.2329834Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2330300Z out_12: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_25); x_25 = None 2025-03-04T21:03:21.2330582Z 2025-03-04T21:03:21.2330907Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2331695Z x_26: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_12 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.2332289Z 2025-03-04T21:03:21.2332633Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2334716Z x_27: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_26 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2336586Z 2025-03-04T21:03:21.2336944Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2337414Z out_13: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_27); x_27 = None 2025-03-04T21:03:21.2337666Z 2025-03-04T21:03:21.2337991Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2338795Z x_28: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_13 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.2339389Z 2025-03-04T21:03:21.2339729Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2341804Z x_29: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_28 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2343706Z 2025-03-04T21:03:21.2344034Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2344845Z x_30: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:03:21.2345448Z 2025-03-04T21:03:21.2345791Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2347979Z x_31: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_30 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2350116Z 2025-03-04T21:03:21.2350477Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.2350957Z x_29 += x_31; out_14: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_29; x_29 = x_31 = None 2025-03-04T21:03:21.2351221Z 2025-03-04T21:03:21.2351587Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2352106Z out_15: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_14); out_14 = None 2025-03-04T21:03:21.2352378Z 2025-03-04T21:03:21.2352736Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2353591Z x_32: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.2354324Z 2025-03-04T21:03:21.2354721Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2356958Z x_33: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_32 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2358878Z 2025-03-04T21:03:21.2359248Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2359733Z out_16: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_33); x_33 = None 2025-03-04T21:03:21.2359994Z 2025-03-04T21:03:21.2360334Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2361141Z x_34: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_16 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.2361757Z 2025-03-04T21:03:21.2362143Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2364343Z x_35: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_34 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2366297Z 2025-03-04T21:03:21.2366700Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2367178Z out_17: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_35); x_35 = None 2025-03-04T21:03:21.2367436Z 2025-03-04T21:03:21.2367764Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2368578Z x_36: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_17 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.2369195Z 2025-03-04T21:03:21.2369550Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2371679Z x_37: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_36 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2373611Z 2025-03-04T21:03:21.2373969Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.2374447Z x_37 += out_15; out_18: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_37; x_37 = out_15 = None 2025-03-04T21:03:21.2374714Z 2025-03-04T21:03:21.2375076Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2375556Z out_19: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_18); out_18 = None 2025-03-04T21:03:21.2375819Z 2025-03-04T21:03:21.2376145Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2376964Z x_38: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.2377568Z 2025-03-04T21:03:21.2377912Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2380114Z x_39: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_38 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2382032Z 2025-03-04T21:03:21.2382401Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2382908Z out_20: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_39); x_39 = None 2025-03-04T21:03:21.2383182Z 2025-03-04T21:03:21.2383518Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2384377Z x_40: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_20 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.2384990Z 2025-03-04T21:03:21.2385342Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2387478Z x_41: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_40 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2389436Z 2025-03-04T21:03:21.2389805Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2390304Z out_21: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_41); x_41 = None 2025-03-04T21:03:21.2390602Z 2025-03-04T21:03:21.2390937Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2391755Z x_42: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_21 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.2392412Z 2025-03-04T21:03:21.2392778Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2395121Z x_43: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_42 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2397166Z 2025-03-04T21:03:21.2397555Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.2398080Z x_43 += out_19; out_22: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_43; x_43 = out_19 = None 2025-03-04T21:03:21.2398367Z 2025-03-04T21:03:21.2398756Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2399280Z out_23: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_22); out_22 = None 2025-03-04T21:03:21.2399562Z 2025-03-04T21:03:21.2399918Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2400773Z x_44: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.2401430Z 2025-03-04T21:03:21.2401807Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2404204Z x_45: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_44 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2406314Z 2025-03-04T21:03:21.2406684Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2407161Z out_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_45); x_45 = None 2025-03-04T21:03:21.2407417Z 2025-03-04T21:03:21.2407751Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2408615Z x_46: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.2409225Z 2025-03-04T21:03:21.2409617Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2411761Z x_47: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_46 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2413676Z 2025-03-04T21:03:21.2414046Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2414521Z out_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_47); x_47 = None 2025-03-04T21:03:21.2414781Z 2025-03-04T21:03:21.2415114Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2415946Z x_48: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_25 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.2416559Z 2025-03-04T21:03:21.2416907Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2419026Z x_49: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_48 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2420968Z 2025-03-04T21:03:21.2421329Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.2421808Z x_49 += out_23; out_26: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_49; x_49 = out_23 = None 2025-03-04T21:03:21.2422075Z 2025-03-04T21:03:21.2422444Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2423030Z out_27: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_26); out_26 = None 2025-03-04T21:03:21.2423300Z 2025-03-04T21:03:21.2423717Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2424575Z x_50: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.2425237Z 2025-03-04T21:03:21.2425618Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2427759Z x_51: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_50 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2429788Z 2025-03-04T21:03:21.2430177Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2430687Z out_28: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_51); x_51 = None 2025-03-04T21:03:21.2430958Z 2025-03-04T21:03:21.2431308Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2432143Z x_52: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_28 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.2432780Z 2025-03-04T21:03:21.2433174Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2435606Z x_53: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_52 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2437641Z 2025-03-04T21:03:21.2438083Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2438582Z out_29: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_53); x_53 = None 2025-03-04T21:03:21.2438844Z 2025-03-04T21:03:21.2439197Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2440041Z x_54: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_29 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.2440675Z 2025-03-04T21:03:21.2441045Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2443281Z x_55: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_54 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2445198Z 2025-03-04T21:03:21.2445534Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2446335Z x_56: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:03:21.2446949Z 2025-03-04T21:03:21.2447296Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2449482Z x_57: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_56 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2451617Z 2025-03-04T21:03:21.2451986Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.2452494Z x_55 += x_57; out_30: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_55; x_55 = x_57 = None 2025-03-04T21:03:21.2452749Z 2025-03-04T21:03:21.2453121Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2453623Z out_31: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_30); out_30 = None 2025-03-04T21:03:21.2453896Z 2025-03-04T21:03:21.2454253Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2455084Z x_58: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.2455681Z 2025-03-04T21:03:21.2456032Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2458154Z x_59: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_58 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2460082Z 2025-03-04T21:03:21.2460454Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2460932Z out_32: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_59); x_59 = None 2025-03-04T21:03:21.2461181Z 2025-03-04T21:03:21.2461513Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2462316Z x_60: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_32 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.2462942Z 2025-03-04T21:03:21.2463302Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2465581Z x_61: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_60 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2467598Z 2025-03-04T21:03:21.2468063Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2468574Z out_33: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_61); x_61 = None 2025-03-04T21:03:21.2468840Z 2025-03-04T21:03:21.2469196Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2470054Z x_62: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_33 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.2470705Z 2025-03-04T21:03:21.2471075Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2473425Z x_63: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_62 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2475567Z 2025-03-04T21:03:21.2475951Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.2476450Z x_63 += out_31; out_34: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_63; x_63 = out_31 = None 2025-03-04T21:03:21.2476726Z 2025-03-04T21:03:21.2477110Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2477640Z out_35: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_34); out_34 = None 2025-03-04T21:03:21.2477908Z 2025-03-04T21:03:21.2478262Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2479097Z x_64: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.2479721Z 2025-03-04T21:03:21.2480087Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2482265Z x_65: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_64 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2484169Z 2025-03-04T21:03:21.2484540Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2485019Z out_36: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_65); x_65 = None 2025-03-04T21:03:21.2485273Z 2025-03-04T21:03:21.2485602Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2486398Z x_66: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_36 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.2486995Z 2025-03-04T21:03:21.2487366Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2489560Z x_67: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_66 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2491503Z 2025-03-04T21:03:21.2491880Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2492357Z out_37: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_67); x_67 = None 2025-03-04T21:03:21.2492605Z 2025-03-04T21:03:21.2492937Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2493738Z x_68: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_37 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.2494344Z 2025-03-04T21:03:21.2494696Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2496941Z x_69: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_68 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2498839Z 2025-03-04T21:03:21.2499232Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.2499792Z x_69 += out_35; out_38: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_69; x_69 = out_35 = None 2025-03-04T21:03:21.2500051Z 2025-03-04T21:03:21.2500416Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2500897Z out_39: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_38); out_38 = None 2025-03-04T21:03:21.2501162Z 2025-03-04T21:03:21.2501542Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2502532Z x_70: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.2503232Z 2025-03-04T21:03:21.2503633Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2505968Z x_71: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_70 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2507979Z 2025-03-04T21:03:21.2508347Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2508859Z out_40: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_71); x_71 = None 2025-03-04T21:03:21.2509129Z 2025-03-04T21:03:21.2509481Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2510327Z x_72: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_40 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.2510960Z 2025-03-04T21:03:21.2511328Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2513758Z x_73: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_72 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2515909Z 2025-03-04T21:03:21.2516319Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2516882Z out_41: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_73); x_73 = None 2025-03-04T21:03:21.2517152Z 2025-03-04T21:03:21.2517519Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2518422Z x_74: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_41 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.2519093Z 2025-03-04T21:03:21.2519479Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2521863Z x_75: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_74 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2523724Z 2025-03-04T21:03:21.2524077Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.2524557Z x_75 += out_39; out_42: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_75; x_75 = out_39 = None 2025-03-04T21:03:21.2524815Z 2025-03-04T21:03:21.2525211Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2525693Z out_43: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_42); out_42 = None 2025-03-04T21:03:21.2525951Z 2025-03-04T21:03:21.2526290Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2527063Z x_76: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.2527649Z 2025-03-04T21:03:21.2527987Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2530055Z x_77: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_76 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2531974Z 2025-03-04T21:03:21.2532350Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2532834Z out_44: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_77); x_77 = None 2025-03-04T21:03:21.2533080Z 2025-03-04T21:03:21.2533404Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2534209Z x_78: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_44 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.2534831Z 2025-03-04T21:03:21.2535182Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2537493Z x_79: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_78 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2539417Z 2025-03-04T21:03:21.2539791Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2540289Z out_45: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_79); x_79 = None 2025-03-04T21:03:21.2540568Z 2025-03-04T21:03:21.2540937Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2541833Z x_80: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_45 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.2542517Z 2025-03-04T21:03:21.2542910Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2545307Z x_81: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_80 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2547515Z 2025-03-04T21:03:21.2547879Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.2548365Z x_81 += out_43; out_46: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_81; x_81 = out_43 = None 2025-03-04T21:03:21.2548625Z 2025-03-04T21:03:21.2548993Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2549483Z out_47: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_46); out_46 = None 2025-03-04T21:03:21.2549737Z 2025-03-04T21:03:21.2550094Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2550896Z x_82: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.2551497Z 2025-03-04T21:03:21.2551866Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2554313Z x_83: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_82 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2556278Z 2025-03-04T21:03:21.2556650Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2557126Z out_48: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_83); x_83 = None 2025-03-04T21:03:21.2557382Z 2025-03-04T21:03:21.2557718Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2558512Z x_84: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_48 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.2559115Z 2025-03-04T21:03:21.2559465Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2561635Z x_85: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_84 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2563568Z 2025-03-04T21:03:21.2563929Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2564409Z out_49: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_85); x_85 = None 2025-03-04T21:03:21.2564656Z 2025-03-04T21:03:21.2564987Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2565790Z x_86: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_49 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.2566393Z 2025-03-04T21:03:21.2566743Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2568904Z x_87: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_86 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2570804Z 2025-03-04T21:03:21.2571177Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.2571656Z x_87 += out_47; out_50: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_87; x_87 = out_47 = None 2025-03-04T21:03:21.2571914Z 2025-03-04T21:03:21.2572279Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2572758Z out_51: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_50); out_50 = None 2025-03-04T21:03:21.2573013Z 2025-03-04T21:03:21.2573344Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2574151Z x_88: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.2574753Z 2025-03-04T21:03:21.2575105Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2577226Z x_89: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_88 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2579135Z 2025-03-04T21:03:21.2579493Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2579959Z out_52: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_89); x_89 = None 2025-03-04T21:03:21.2580205Z 2025-03-04T21:03:21.2580536Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2581361Z x_90: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_52 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.2582000Z 2025-03-04T21:03:21.2582371Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2584524Z x_91: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_90 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2586377Z 2025-03-04T21:03:21.2586732Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2587202Z out_53: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_91); x_91 = None 2025-03-04T21:03:21.2587457Z 2025-03-04T21:03:21.2587803Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2588610Z x_92: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_53 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.2589225Z 2025-03-04T21:03:21.2589569Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2591697Z x_93: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_92 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2593639Z 2025-03-04T21:03:21.2594103Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.2594619Z x_93 += out_51; out_54: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_93; x_93 = out_51 = None 2025-03-04T21:03:21.2594907Z 2025-03-04T21:03:21.2595285Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2595826Z out_55: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_54); out_54 = None 2025-03-04T21:03:21.2596090Z 2025-03-04T21:03:21.2596426Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2597219Z x_94: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.2597815Z 2025-03-04T21:03:21.2598169Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2600311Z x_95: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_94 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2602389Z 2025-03-04T21:03:21.2602769Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2603248Z out_56: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_95); x_95 = None 2025-03-04T21:03:21.2603506Z 2025-03-04T21:03:21.2603842Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2604644Z x_96: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_56 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.2605256Z 2025-03-04T21:03:21.2605606Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2607775Z x_97: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_96 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2609705Z 2025-03-04T21:03:21.2610144Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2610608Z out_57: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_97); x_97 = None 2025-03-04T21:03:21.2610851Z 2025-03-04T21:03:21.2611173Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2611948Z x_98: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_57 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.2612540Z 2025-03-04T21:03:21.2612884Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2615001Z x_99: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_98 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2616861Z 2025-03-04T21:03:21.2617144Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.2617284Z x_99 += out_55; out_58: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_99; x_99 = out_55 = None 2025-03-04T21:03:21.2617350Z 2025-03-04T21:03:21.2617626Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2617775Z out_59: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_58); out_58 = None 2025-03-04T21:03:21.2617836Z 2025-03-04T21:03:21.2618097Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2618610Z x_100: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.2618671Z 2025-03-04T21:03:21.2618948Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2620787Z x_101: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_100 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2620862Z 2025-03-04T21:03:21.2621143Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2621279Z out_60: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_101); x_101 = None 2025-03-04T21:03:21.2621347Z 2025-03-04T21:03:21.2621587Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2622080Z x_102: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_60 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.2622139Z 2025-03-04T21:03:21.2622406Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2624238Z x_103: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_102 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2624329Z 2025-03-04T21:03:21.2624621Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2624760Z out_61: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_103); x_103 = None 2025-03-04T21:03:21.2624844Z 2025-03-04T21:03:21.2625100Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2625605Z x_104: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_61 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.2625666Z 2025-03-04T21:03:21.2625940Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2627797Z x_105: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_104 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2627864Z 2025-03-04T21:03:21.2628148Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.2628298Z x_105 += out_59; out_62: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_105; x_105 = out_59 = None 2025-03-04T21:03:21.2628368Z 2025-03-04T21:03:21.2628653Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2628804Z out_63: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_62); out_62 = None 2025-03-04T21:03:21.2628863Z 2025-03-04T21:03:21.2629120Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2629634Z x_106: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_63, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.2629723Z 2025-03-04T21:03:21.2630004Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2631928Z x_107: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_106 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2632018Z 2025-03-04T21:03:21.2632319Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2632467Z out_64: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_107); x_107 = None 2025-03-04T21:03:21.2632530Z 2025-03-04T21:03:21.2632798Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2633318Z x_108: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_64, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_64 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.2633385Z 2025-03-04T21:03:21.2633834Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2635775Z x_109: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_108, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_108 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2635851Z 2025-03-04T21:03:21.2636147Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2636283Z out_65: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_109); x_109 = None 2025-03-04T21:03:21.2636352Z 2025-03-04T21:03:21.2636601Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2637113Z x_110: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_65, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_65 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.2637179Z 2025-03-04T21:03:21.2637452Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2639256Z x_111: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_110, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_110 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2639344Z 2025-03-04T21:03:21.2639631Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.2639778Z x_111 += out_63; out_66: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_111; x_111 = out_63 = None 2025-03-04T21:03:21.2639848Z 2025-03-04T21:03:21.2640126Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2640273Z out_67: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_66); out_66 = None 2025-03-04T21:03:21.2640336Z 2025-03-04T21:03:21.2640624Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2641153Z x_112: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_67, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.2641223Z 2025-03-04T21:03:21.2641490Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2643336Z x_113: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_112, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_112 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2643425Z 2025-03-04T21:03:21.2643723Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2643872Z out_68: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_113); x_113 = None 2025-03-04T21:03:21.2643938Z 2025-03-04T21:03:21.2644205Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2644723Z x_114: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_68, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_68 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.2644793Z 2025-03-04T21:03:21.2645059Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2646884Z x_115: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_114, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_114 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2647040Z 2025-03-04T21:03:21.2647350Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2647569Z out_69: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_115); x_115 = None 2025-03-04T21:03:21.2647774Z 2025-03-04T21:03:21.2648082Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2648593Z x_116: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_69, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_69 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.2648711Z 2025-03-04T21:03:21.2649000Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2651000Z x_117: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_116, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_116 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2651155Z 2025-03-04T21:03:21.2651493Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.2651717Z x_117 += out_67; out_70: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_117; x_117 = out_67 = None 2025-03-04T21:03:21.2651809Z 2025-03-04T21:03:21.2652206Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2652395Z out_71: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_70); out_70 = None 2025-03-04T21:03:21.2652514Z 2025-03-04T21:03:21.2652824Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2653400Z x_118: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_71, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.2653538Z 2025-03-04T21:03:21.2653840Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2655795Z x_119: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_118, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_118 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2655912Z 2025-03-04T21:03:21.2656236Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2656430Z out_72: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_119); x_119 = None 2025-03-04T21:03:21.2656531Z 2025-03-04T21:03:21.2656841Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2657387Z x_120: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_72, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_72 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.2657503Z 2025-03-04T21:03:21.2657794Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2659782Z x_121: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_120, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_120 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2659924Z 2025-03-04T21:03:21.2660268Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2660457Z out_73: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_121); x_121 = None 2025-03-04T21:03:21.2660530Z 2025-03-04T21:03:21.2660892Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2661454Z x_122: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_73, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_73 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.2661568Z 2025-03-04T21:03:21.2661867Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2663779Z x_123: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_122, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_122 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2663918Z 2025-03-04T21:03:21.2664218Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.2664414Z x_123 += out_71; out_74: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_123; x_123 = out_71 = None 2025-03-04T21:03:21.2664503Z 2025-03-04T21:03:21.2664819Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2665029Z out_75: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_74); out_74 = None 2025-03-04T21:03:21.2665109Z 2025-03-04T21:03:21.2665375Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2665910Z x_124: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_75, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.2666025Z 2025-03-04T21:03:21.2666327Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2668248Z x_125: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_124, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_124 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2668392Z 2025-03-04T21:03:21.2668702Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2668879Z out_76: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_125); x_125 = None 2025-03-04T21:03:21.2668977Z 2025-03-04T21:03:21.2669299Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2669839Z x_126: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_76, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_76 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.2669956Z 2025-03-04T21:03:21.2670282Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2672247Z x_127: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_126, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_126 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2672382Z 2025-03-04T21:03:21.2672701Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2672892Z out_77: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_127); x_127 = None 2025-03-04T21:03:21.2672977Z 2025-03-04T21:03:21.2673302Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2673984Z x_128: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_77, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_77 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.2674107Z 2025-03-04T21:03:21.2674414Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2676366Z x_129: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_128, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_128 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2676540Z 2025-03-04T21:03:21.2676849Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.2677042Z x_129 += out_75; out_78: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_129; x_129 = out_75 = None 2025-03-04T21:03:21.2677151Z 2025-03-04T21:03:21.2677459Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2677659Z out_79: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_78); out_78 = None 2025-03-04T21:03:21.2677753Z 2025-03-04T21:03:21.2678082Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2678576Z x_130: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_79, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.2678688Z 2025-03-04T21:03:21.2678961Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2680848Z x_131: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_130, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_130 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2680969Z 2025-03-04T21:03:21.2681279Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2681458Z out_80: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_131); x_131 = None 2025-03-04T21:03:21.2681528Z 2025-03-04T21:03:21.2681874Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2682388Z x_132: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_80, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_80 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.2682498Z 2025-03-04T21:03:21.2682800Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2684616Z x_133: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_132, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_132 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2684782Z 2025-03-04T21:03:21.2685094Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2685313Z out_81: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_133); x_133 = None 2025-03-04T21:03:21.2685397Z 2025-03-04T21:03:21.2685679Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2686227Z x_134: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_81, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_81 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.2686308Z 2025-03-04T21:03:21.2686610Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2688396Z x_135: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_134, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_134 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2688507Z 2025-03-04T21:03:21.2688856Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.2689018Z x_135 += out_79; out_82: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_135; x_135 = out_79 = None 2025-03-04T21:03:21.2689123Z 2025-03-04T21:03:21.2689416Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2689588Z out_83: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_82); out_82 = None 2025-03-04T21:03:21.2689684Z 2025-03-04T21:03:21.2690006Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2690510Z x_136: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_83, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.2690637Z 2025-03-04T21:03:21.2690928Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2692818Z x_137: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_136, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_136 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2692939Z 2025-03-04T21:03:21.2693253Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2693441Z out_84: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_137); x_137 = None 2025-03-04T21:03:21.2693521Z 2025-03-04T21:03:21.2693811Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2694321Z x_138: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_84, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_84 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.2694426Z 2025-03-04T21:03:21.2694712Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2696616Z x_139: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_138, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_138 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2696754Z 2025-03-04T21:03:21.2697068Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2697245Z out_85: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_139); x_139 = None 2025-03-04T21:03:21.2697434Z 2025-03-04T21:03:21.2697708Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2698295Z x_140: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_85, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_85 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.2698377Z 2025-03-04T21:03:21.2698738Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2700616Z x_141: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_140, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_140 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2700744Z 2025-03-04T21:03:21.2701096Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.2701354Z x_141 += out_83; out_86: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_141; x_141 = out_83 = None 2025-03-04T21:03:21.2701503Z 2025-03-04T21:03:21.2701813Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2702170Z out_87: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_86); out_86 = None 2025-03-04T21:03:21.2702258Z 2025-03-04T21:03:21.2702577Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2703084Z x_142: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_87, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.2703239Z 2025-03-04T21:03:21.2703526Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2705402Z x_143: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_142, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_142 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2705536Z 2025-03-04T21:03:21.2705875Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2706048Z out_88: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_143); x_143 = None 2025-03-04T21:03:21.2706242Z 2025-03-04T21:03:21.2706511Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2707050Z x_144: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_88, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_88 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.2707134Z 2025-03-04T21:03:21.2707475Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2709348Z x_145: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_144, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_144 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2709472Z 2025-03-04T21:03:21.2709807Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2709960Z out_89: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_145); x_145 = None 2025-03-04T21:03:21.2710061Z 2025-03-04T21:03:21.2710344Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2710896Z x_146: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_89, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_89 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.2710992Z 2025-03-04T21:03:21.2711306Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2713167Z x_147: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_146, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_146 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2713280Z 2025-03-04T21:03:21.2713622Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.2713854Z x_147 += out_87; out_90: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_147; x_147 = out_87 = None 2025-03-04T21:03:21.2713998Z 2025-03-04T21:03:21.2714346Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2714571Z out_91: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_90); out_90 = None 2025-03-04T21:03:21.2714677Z 2025-03-04T21:03:21.2715056Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2715674Z x_148: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_91, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.2715758Z 2025-03-04T21:03:21.2716074Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2718118Z x_149: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_148, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_148 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2718899Z 2025-03-04T21:03:21.2719278Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2719457Z out_92: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_149); x_149 = None 2025-03-04T21:03:21.2719587Z 2025-03-04T21:03:21.2719905Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2720528Z x_150: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_92, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_92 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.2720622Z 2025-03-04T21:03:21.2720973Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2723010Z x_151: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_150, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_150 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2723128Z 2025-03-04T21:03:21.2723485Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2723662Z out_93: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_151); x_151 = None 2025-03-04T21:03:21.2723765Z 2025-03-04T21:03:21.2724014Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2724590Z x_152: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_93, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_93 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.2724671Z 2025-03-04T21:03:21.2724982Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2726850Z x_153: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_152, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_152 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2726941Z 2025-03-04T21:03:21.2727294Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.2727457Z x_153 += out_91; out_94: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_153; x_153 = out_91 = None 2025-03-04T21:03:21.2727563Z 2025-03-04T21:03:21.2727859Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2728036Z out_95: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_94); out_94 = None 2025-03-04T21:03:21.2728170Z 2025-03-04T21:03:21.2728432Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2728952Z x_154: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_95, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.2729052Z 2025-03-04T21:03:21.2729341Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2731206Z x_155: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_154, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_154 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2731325Z 2025-03-04T21:03:21.2731639Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2759952Z out_96: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_155); x_155 = None 2025-03-04T21:03:21.2760081Z 2025-03-04T21:03:21.2760395Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2760936Z x_156: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_96, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_96 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.2761002Z 2025-03-04T21:03:21.2761286Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2763138Z x_157: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_156, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_156 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2763296Z 2025-03-04T21:03:21.2763612Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2763761Z out_97: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_157); x_157 = None 2025-03-04T21:03:21.2763834Z 2025-03-04T21:03:21.2764099Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2764655Z x_158: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_97, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_97 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.2764717Z 2025-03-04T21:03:21.2764998Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2766926Z x_159: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_158, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_158 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2766998Z 2025-03-04T21:03:21.2767299Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.2767460Z x_159 += out_95; out_98: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_159; x_159 = out_95 = None 2025-03-04T21:03:21.2767527Z 2025-03-04T21:03:21.2767823Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2767976Z out_99: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_98); out_98 = None 2025-03-04T21:03:21.2768037Z 2025-03-04T21:03:21.2768303Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2768813Z x_160: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_99, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.2768897Z 2025-03-04T21:03:21.2769157Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2770938Z x_161: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_160, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_160 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2771020Z 2025-03-04T21:03:21.2771302Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2771447Z out_100: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_161); x_161 = None 2025-03-04T21:03:21.2771506Z 2025-03-04T21:03:21.2771760Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2772252Z x_162: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_100, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_100 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.2772322Z 2025-03-04T21:03:21.2772611Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2774378Z x_163: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_162, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_162 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2774447Z 2025-03-04T21:03:21.2774729Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2774870Z out_101: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_163); x_163 = None 2025-03-04T21:03:21.2774930Z 2025-03-04T21:03:21.2775180Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2775679Z x_164: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_101, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_101 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.2775756Z 2025-03-04T21:03:21.2776024Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2777796Z x_165: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_164, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_164 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2777894Z 2025-03-04T21:03:21.2778174Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.2778328Z x_165 += out_99; out_102: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_165; x_165 = out_99 = None 2025-03-04T21:03:21.2778396Z 2025-03-04T21:03:21.2778679Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2778843Z out_103: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_102); out_102 = None 2025-03-04T21:03:21.2778904Z 2025-03-04T21:03:21.2779186Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2779662Z x_166: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_103, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.2779728Z 2025-03-04T21:03:21.2779989Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2781765Z x_167: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_166, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_166 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2781851Z 2025-03-04T21:03:21.2782131Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2782274Z out_104: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_167); x_167 = None 2025-03-04T21:03:21.2782334Z 2025-03-04T21:03:21.2782587Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2783068Z x_168: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_104, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_104 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.2783154Z 2025-03-04T21:03:21.2783526Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2785306Z x_169: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_168, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_168 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2785392Z 2025-03-04T21:03:21.2785673Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2785861Z out_105: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_169); x_169 = None 2025-03-04T21:03:21.2785924Z 2025-03-04T21:03:21.2786177Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2786661Z x_170: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_105, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_105 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.2786730Z 2025-03-04T21:03:21.2787000Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2788828Z x_171: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_170, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_170 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2788912Z 2025-03-04T21:03:21.2789196Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.2789362Z x_171 += out_103; out_106: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_171; x_171 = out_103 = None 2025-03-04T21:03:21.2789423Z 2025-03-04T21:03:21.2789713Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2789873Z out_107: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_106); out_106 = None 2025-03-04T21:03:21.2789933Z 2025-03-04T21:03:21.2790183Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2790672Z x_172: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_107, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.2790755Z 2025-03-04T21:03:21.2791017Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2792864Z x_173: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_172, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_172 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2792937Z 2025-03-04T21:03:21.2793224Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2793371Z out_108: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_173); x_173 = None 2025-03-04T21:03:21.2793436Z 2025-03-04T21:03:21.2793767Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2794305Z x_174: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_108, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_108 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.2794380Z 2025-03-04T21:03:21.2794660Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2796525Z x_175: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_174, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_174 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2796614Z 2025-03-04T21:03:21.2796903Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2797049Z out_109: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_175); x_175 = None 2025-03-04T21:03:21.2797114Z 2025-03-04T21:03:21.2797373Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2797883Z x_176: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_109, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_109 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.2797955Z 2025-03-04T21:03:21.2798220Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2800067Z x_177: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_176, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_176 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2800141Z 2025-03-04T21:03:21.2800425Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.2800595Z x_177 += out_107; out_110: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_177; x_177 = out_107 = None 2025-03-04T21:03:21.2800657Z 2025-03-04T21:03:21.2800978Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2801132Z out_111: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_110); out_110 = None 2025-03-04T21:03:21.2801206Z 2025-03-04T21:03:21.2801471Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2802147Z x_178: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_111, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.2802311Z 2025-03-04T21:03:21.2802596Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2804651Z x_179: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_178, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_178 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2804755Z 2025-03-04T21:03:21.2805065Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2805219Z out_112: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_179); x_179 = None 2025-03-04T21:03:21.2805284Z 2025-03-04T21:03:21.2805556Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2806056Z x_180: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_112, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_112 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.2806127Z 2025-03-04T21:03:21.2806442Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2808279Z x_181: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_180, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_180 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2808349Z 2025-03-04T21:03:21.2808633Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2808773Z out_113: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_181); x_181 = None 2025-03-04T21:03:21.2808833Z 2025-03-04T21:03:21.2809091Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2809592Z x_182: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_113, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_113 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.2809680Z 2025-03-04T21:03:21.2809950Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2811757Z x_183: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_182, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_182 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2811846Z 2025-03-04T21:03:21.2812117Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.2812273Z x_183 += out_111; out_114: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_183; x_183 = out_111 = None 2025-03-04T21:03:21.2812333Z 2025-03-04T21:03:21.2812612Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2812750Z out_115: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_114); out_114 = None 2025-03-04T21:03:21.2812820Z 2025-03-04T21:03:21.2813090Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2813573Z x_184: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_115, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.2813634Z 2025-03-04T21:03:21.2813902Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2815728Z x_185: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_184, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_184 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2815807Z 2025-03-04T21:03:21.2816101Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2816235Z out_116: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_185); x_185 = None 2025-03-04T21:03:21.2816301Z 2025-03-04T21:03:21.2816553Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2817047Z x_186: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_116, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_116 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.2817115Z 2025-03-04T21:03:21.2817378Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2819183Z x_187: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_186, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_186 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2819267Z 2025-03-04T21:03:21.2819549Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2819716Z out_117: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_187); x_187 = None 2025-03-04T21:03:21.2819777Z 2025-03-04T21:03:21.2820037Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2820515Z x_188: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_117, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_117 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.2820583Z 2025-03-04T21:03:21.2820840Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2822594Z x_189: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_188, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_188 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2822679Z 2025-03-04T21:03:21.2822954Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.2823109Z x_189 += out_115; out_118: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_189; x_189 = out_115 = None 2025-03-04T21:03:21.2823168Z 2025-03-04T21:03:21.2823453Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2823589Z out_119: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_118); out_118 = None 2025-03-04T21:03:21.2823657Z 2025-03-04T21:03:21.2823898Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2824383Z x_190: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.2824459Z 2025-03-04T21:03:21.2824726Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2826511Z x_191: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_190, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_190 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2826574Z 2025-03-04T21:03:21.2826860Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2826992Z out_120: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_191); x_191 = None 2025-03-04T21:03:21.2827063Z 2025-03-04T21:03:21.2827305Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2827792Z x_192: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_120, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_120 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.2827855Z 2025-03-04T21:03:21.2828123Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2829963Z x_193: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_192, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_192 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2830043Z 2025-03-04T21:03:21.2830338Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2830475Z out_121: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_193); x_193 = None 2025-03-04T21:03:21.2830557Z 2025-03-04T21:03:21.2830800Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2831304Z x_194: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_121, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_121 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.2831371Z 2025-03-04T21:03:21.2831632Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2833498Z x_195: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_194, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_194 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2833570Z 2025-03-04T21:03:21.2833871Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2834392Z x_196: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:03:21.2834458Z 2025-03-04T21:03:21.2834758Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2836617Z x_197: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_196, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_196 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2836714Z 2025-03-04T21:03:21.2837001Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.2837147Z x_195 += x_197; out_122: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_195; x_195 = x_197 = None 2025-03-04T21:03:21.2837216Z 2025-03-04T21:03:21.2837501Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2837675Z out_123: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_122); out_122 = None 2025-03-04T21:03:21.2837736Z 2025-03-04T21:03:21.2837998Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2838485Z x_198: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_123, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.2838557Z 2025-03-04T21:03:21.2838821Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2840681Z x_199: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_198, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_198 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2840753Z 2025-03-04T21:03:21.2841047Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2841193Z out_124: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_199); x_199 = None 2025-03-04T21:03:21.2841254Z 2025-03-04T21:03:21.2841513Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2841999Z x_200: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_124, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_124 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.2842068Z 2025-03-04T21:03:21.2842352Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2844158Z x_201: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_200, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_200 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2844231Z 2025-03-04T21:03:21.2844529Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2844672Z out_125: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_201); x_201 = None 2025-03-04T21:03:21.2844734Z 2025-03-04T21:03:21.2844988Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2845483Z x_202: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_125, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_125 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.2845553Z 2025-03-04T21:03:21.2845820Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2847665Z x_203: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_202, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_202 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2847738Z 2025-03-04T21:03:21.2848019Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.2848184Z x_203 += out_123; out_126: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_203; x_203 = out_123 = None 2025-03-04T21:03:21.2848244Z 2025-03-04T21:03:21.2848536Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2848677Z out_127: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_126); out_126 = None 2025-03-04T21:03:21.2848744Z 2025-03-04T21:03:21.2849005Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2849493Z x_204: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_127, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.2849562Z 2025-03-04T21:03:21.2849827Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2851636Z x_205: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_204, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_204 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2851722Z 2025-03-04T21:03:21.2852016Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2852156Z out_128: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_205); x_205 = None 2025-03-04T21:03:21.2852218Z 2025-03-04T21:03:21.2852481Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2853008Z x_206: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_128, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_128 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.2853075Z 2025-03-04T21:03:21.2853340Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2855147Z x_207: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_206, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_206 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2855218Z 2025-03-04T21:03:21.2855501Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2855657Z out_129: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_207); x_207 = None 2025-03-04T21:03:21.2855718Z 2025-03-04T21:03:21.2855973Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2856470Z x_208: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_129, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_129 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.2856536Z 2025-03-04T21:03:21.2856802Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.2858611Z x_209: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_208, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_208 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.2858697Z 2025-03-04T21:03:21.2858973Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.2859129Z x_209 += out_127; out_130: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_209; x_209 = out_127 = None 2025-03-04T21:03:21.2859189Z 2025-03-04T21:03:21.2859518Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.2859659Z out_131: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_130); out_130 = None 2025-03-04T21:03:21.2859724Z 2025-03-04T21:03:21.2859970Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2860553Z x_210: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(out_131, l_self_modules_backbone_lateral_convs_0_parameters_weight_, l_self_modules_backbone_lateral_convs_0_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_131 = l_self_modules_backbone_lateral_convs_0_parameters_weight_ = l_self_modules_backbone_lateral_convs_0_parameters_bias_ = None 2025-03-04T21:03:21.2860624Z 2025-03-04T21:03:21.2860876Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2861430Z x_211: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_210, l_self_modules_backbone_output_convs_0_parameters_weight_, l_self_modules_backbone_output_convs_0_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_self_modules_backbone_output_convs_0_parameters_weight_ = l_self_modules_backbone_output_convs_0_parameters_bias_ = None 2025-03-04T21:03:21.2861490Z 2025-03-04T21:03:21.2861897Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest") 2025-03-04T21:03:21.2862175Z top_down_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.interpolate(x_210, scale_factor = 2.0, mode = 'nearest'); x_210 = None 2025-03-04T21:03:21.2862242Z 2025-03-04T21:03:21.2862490Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2863058Z x_212: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_lateral_convs_1_parameters_weight_, l_self_modules_backbone_lateral_convs_1_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_119 = l_self_modules_backbone_lateral_convs_1_parameters_weight_ = l_self_modules_backbone_lateral_convs_1_parameters_bias_ = None 2025-03-04T21:03:21.2863116Z 2025-03-04T21:03:21.2863462Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features 2025-03-04T21:03:21.2863647Z prev_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = x_212 + top_down_features; x_212 = top_down_features = None 2025-03-04T21:03:21.2863729Z 2025-03-04T21:03:21.2863976Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2864559Z x_213: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(prev_features, l_self_modules_backbone_output_convs_1_parameters_weight_, l_self_modules_backbone_output_convs_1_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_self_modules_backbone_output_convs_1_parameters_weight_ = l_self_modules_backbone_output_convs_1_parameters_bias_ = None 2025-03-04T21:03:21.2864628Z 2025-03-04T21:03:21.2865049Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest") 2025-03-04T21:03:21.2865377Z top_down_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.interpolate(prev_features, scale_factor = 2.0, mode = 'nearest'); prev_features = None 2025-03-04T21:03:21.2865437Z 2025-03-04T21:03:21.2865719Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2866284Z x_214: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_lateral_convs_2_parameters_weight_, l_self_modules_backbone_lateral_convs_2_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_27 = l_self_modules_backbone_lateral_convs_2_parameters_weight_ = l_self_modules_backbone_lateral_convs_2_parameters_bias_ = None 2025-03-04T21:03:21.2866352Z 2025-03-04T21:03:21.2866688Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features 2025-03-04T21:03:21.2866900Z prev_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = x_214 + top_down_features_1; x_214 = top_down_features_1 = None 2025-03-04T21:03:21.2866960Z 2025-03-04T21:03:21.2867210Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2867777Z x_215: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(prev_features_1, l_self_modules_backbone_output_convs_2_parameters_weight_, l_self_modules_backbone_output_convs_2_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_self_modules_backbone_output_convs_2_parameters_weight_ = l_self_modules_backbone_output_convs_2_parameters_bias_ = None 2025-03-04T21:03:21.2867846Z 2025-03-04T21:03:21.2868241Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest") 2025-03-04T21:03:21.2868574Z top_down_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.interpolate(prev_features_1, scale_factor = 2.0, mode = 'nearest'); prev_features_1 = None 2025-03-04T21:03:21.2868640Z 2025-03-04T21:03:21.2868885Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2869463Z x_216: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_lateral_convs_3_parameters_weight_, l_self_modules_backbone_lateral_convs_3_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_11 = l_self_modules_backbone_lateral_convs_3_parameters_weight_ = l_self_modules_backbone_lateral_convs_3_parameters_bias_ = None 2025-03-04T21:03:21.2869521Z 2025-03-04T21:03:21.2869863Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features 2025-03-04T21:03:21.2870066Z prev_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_216 + top_down_features_2; x_216 = top_down_features_2 = None 2025-03-04T21:03:21.2870148Z 2025-03-04T21:03:21.2870394Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2870998Z x_217: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(prev_features_2, l_self_modules_backbone_output_convs_3_parameters_weight_, l_self_modules_backbone_output_convs_3_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); prev_features_2 = l_self_modules_backbone_output_convs_3_parameters_weight_ = l_self_modules_backbone_output_convs_3_parameters_bias_ = None 2025-03-04T21:03:21.2871064Z 2025-03-04T21:03:21.2871418Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:200 in forward, code: return [F.max_pool2d(x, kernel_size=1, stride=2, padding=0)] 2025-03-04T21:03:21.2871632Z res: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.max_pool2d(x_211, kernel_size = 1, stride = 2, padding = 0) 2025-03-04T21:03:21.2871693Z 2025-03-04T21:03:21.2872151Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:03:21.2872300Z arange: "f32[304][1]cpu" = torch.arange(0.0, 1216, step = 4, dtype = torch.float32) 2025-03-04T21:03:21.2872368Z 2025-03-04T21:03:21.2872655Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:21.2872799Z shifts_x: "f32[304][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T21:03:21.2872860Z 2025-03-04T21:03:21.2873293Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:03:21.2873443Z arange_1: "f32[296][1]cpu" = torch.arange(0.0, 1184, step = 4, dtype = torch.float32) 2025-03-04T21:03:21.2873509Z 2025-03-04T21:03:21.2873855Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:21.2874011Z shifts_y: "f32[296][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T21:03:21.2874083Z 2025-03-04T21:03:21.2874489Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:03:21.2874701Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T21:03:21.2874813Z shift_y: "f32[296, 304][1, 0]cpu" = meshgrid[0] 2025-03-04T21:03:21.2874943Z shift_x: "f32[296, 304][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T21:03:21.2875021Z 2025-03-04T21:03:21.2875373Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:03:21.2875516Z shift_x_1: "f32[89984][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T21:03:21.2875581Z 2025-03-04T21:03:21.2875934Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:03:21.2876056Z shift_y_1: "f32[89984][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T21:03:21.2876130Z 2025-03-04T21:03:21.2876535Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:03:21.2876786Z shifts: "f32[89984, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T21:03:21.2876855Z 2025-03-04T21:03:21.2877300Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:03:21.2877435Z view: "f32[89984, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T21:03:21.2877886Z view_1: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T21:03:21.2878023Z add_3: "f32[89984, 3, 4][12, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T21:03:21.2878147Z x_218: "f32[269952, 4][4, 1]cpu" = add_3.reshape(-1, 4); add_3 = None 2025-03-04T21:03:21.2878218Z 2025-03-04T21:03:21.2878703Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:03:21.2878865Z arange_2: "f32[152][1]cpu" = torch.arange(0.0, 1216, step = 8, dtype = torch.float32) 2025-03-04T21:03:21.2878928Z 2025-03-04T21:03:21.2879242Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:21.2879386Z shifts_x_1: "f32[152][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T21:03:21.2879457Z 2025-03-04T21:03:21.2879906Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:03:21.2880068Z arange_3: "f32[148][1]cpu" = torch.arange(0.0, 1184, step = 8, dtype = torch.float32) 2025-03-04T21:03:21.2880132Z 2025-03-04T21:03:21.2880444Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:21.2880585Z shifts_y_1: "f32[148][1]cpu" = arange_3.to(device(type='cpu')); arange_3 = None 2025-03-04T21:03:21.2880655Z 2025-03-04T21:03:21.2881047Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:03:21.2881277Z meshgrid_1 = torch.functional.meshgrid(shifts_y_1, shifts_x_1); shifts_y_1 = shifts_x_1 = None 2025-03-04T21:03:21.2881383Z shift_y_2: "f32[148, 152][1, 0]cpu" = meshgrid_1[0] 2025-03-04T21:03:21.2881520Z shift_x_2: "f32[148, 152][0, 1]cpu" = meshgrid_1[1]; meshgrid_1 = None 2025-03-04T21:03:21.2881582Z 2025-03-04T21:03:21.2881937Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:03:21.2882068Z shift_x_3: "f32[22496][1]cpu" = shift_x_2.reshape(-1); shift_x_2 = None 2025-03-04T21:03:21.2882140Z 2025-03-04T21:03:21.2882491Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:03:21.2882622Z shift_y_3: "f32[22496][1]cpu" = shift_y_2.reshape(-1); shift_y_2 = None 2025-03-04T21:03:21.2882685Z 2025-03-04T21:03:21.2883096Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:03:21.2883340Z shifts_1: "f32[22496, 4][4, 1]cpu" = torch.stack((shift_x_3, shift_y_3, shift_x_3, shift_y_3), dim = 1); shift_x_3 = shift_y_3 = None 2025-03-04T21:03:21.2883402Z 2025-03-04T21:03:21.2883873Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:03:21.2883997Z view_2: "f32[22496, 1, 4][4, 4, 1]cpu" = shifts_1.view(-1, 1, 4); shifts_1 = None 2025-03-04T21:03:21.2884427Z view_3: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = None 2025-03-04T21:03:21.2884555Z add_4: "f32[22496, 3, 4][12, 4, 1]cpu" = view_2 + view_3; view_2 = view_3 = None 2025-03-04T21:03:21.2884710Z x_219: "f32[67488, 4][4, 1]cpu" = add_4.reshape(-1, 4); add_4 = None 2025-03-04T21:03:21.2884771Z 2025-03-04T21:03:21.2885206Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:03:21.2885351Z arange_4: "f32[76][1]cpu" = torch.arange(0.0, 1216, step = 16, dtype = torch.float32) 2025-03-04T21:03:21.2885418Z 2025-03-04T21:03:21.2885711Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:21.2885854Z shifts_x_2: "f32[76][1]cpu" = arange_4.to(device(type='cpu')); arange_4 = None 2025-03-04T21:03:21.2885914Z 2025-03-04T21:03:21.2886361Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:03:21.2886498Z arange_5: "f32[74][1]cpu" = torch.arange(0.0, 1184, step = 16, dtype = torch.float32) 2025-03-04T21:03:21.2886565Z 2025-03-04T21:03:21.2886860Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:21.2886996Z shifts_y_2: "f32[74][1]cpu" = arange_5.to(device(type='cpu')); arange_5 = None 2025-03-04T21:03:21.2887056Z 2025-03-04T21:03:21.2887438Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:03:21.2887641Z meshgrid_2 = torch.functional.meshgrid(shifts_y_2, shifts_x_2); shifts_y_2 = shifts_x_2 = None 2025-03-04T21:03:21.2887747Z shift_y_4: "f32[74, 76][1, 0]cpu" = meshgrid_2[0] 2025-03-04T21:03:21.2887862Z shift_x_4: "f32[74, 76][0, 1]cpu" = meshgrid_2[1]; meshgrid_2 = None 2025-03-04T21:03:21.2887931Z 2025-03-04T21:03:21.2888263Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:03:21.2888390Z shift_x_5: "f32[5624][1]cpu" = shift_x_4.reshape(-1); shift_x_4 = None 2025-03-04T21:03:21.2888450Z 2025-03-04T21:03:21.2888787Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:03:21.2888903Z shift_y_5: "f32[5624][1]cpu" = shift_y_4.reshape(-1); shift_y_4 = None 2025-03-04T21:03:21.2888970Z 2025-03-04T21:03:21.2889372Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:03:21.2889586Z shifts_2: "f32[5624, 4][4, 1]cpu" = torch.stack((shift_x_5, shift_y_5, shift_x_5, shift_y_5), dim = 1); shift_x_5 = shift_y_5 = None 2025-03-04T21:03:21.2889654Z 2025-03-04T21:03:21.2890077Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:03:21.2890210Z view_4: "f32[5624, 1, 4][4, 4, 1]cpu" = shifts_2.view(-1, 1, 4); shifts_2 = None 2025-03-04T21:03:21.2890644Z view_5: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = None 2025-03-04T21:03:21.2890776Z add_5: "f32[5624, 3, 4][12, 4, 1]cpu" = view_4 + view_5; view_4 = view_5 = None 2025-03-04T21:03:21.2890923Z x_220: "f32[16872, 4][4, 1]cpu" = add_5.reshape(-1, 4); add_5 = None 2025-03-04T21:03:21.2890990Z 2025-03-04T21:03:21.2891422Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:03:21.2891565Z arange_6: "f32[38][1]cpu" = torch.arange(0.0, 1216, step = 32, dtype = torch.float32) 2025-03-04T21:03:21.2891625Z 2025-03-04T21:03:21.2891928Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:21.2892058Z shifts_x_3: "f32[38][1]cpu" = arange_6.to(device(type='cpu')); arange_6 = None 2025-03-04T21:03:21.2892124Z 2025-03-04T21:03:21.2892542Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:03:21.2892687Z arange_7: "f32[37][1]cpu" = torch.arange(0.0, 1184, step = 32, dtype = torch.float32) 2025-03-04T21:03:21.2892747Z 2025-03-04T21:03:21.2893042Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:21.2893168Z shifts_y_3: "f32[37][1]cpu" = arange_7.to(device(type='cpu')); arange_7 = None 2025-03-04T21:03:21.2893234Z 2025-03-04T21:03:21.2893614Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:03:21.2893810Z meshgrid_3 = torch.functional.meshgrid(shifts_y_3, shifts_x_3); shifts_y_3 = shifts_x_3 = None 2025-03-04T21:03:21.2893909Z shift_y_6: "f32[37, 38][1, 0]cpu" = meshgrid_3[0] 2025-03-04T21:03:21.2894031Z shift_x_6: "f32[37, 38][0, 1]cpu" = meshgrid_3[1]; meshgrid_3 = None 2025-03-04T21:03:21.2894093Z 2025-03-04T21:03:21.2894420Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:03:21.2894537Z shift_x_7: "f32[1406][1]cpu" = shift_x_6.reshape(-1); shift_x_6 = None 2025-03-04T21:03:21.2894604Z 2025-03-04T21:03:21.2894924Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:03:21.2895051Z shift_y_7: "f32[1406][1]cpu" = shift_y_6.reshape(-1); shift_y_6 = None 2025-03-04T21:03:21.2895128Z 2025-03-04T21:03:21.2895532Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:03:21.2895747Z shifts_3: "f32[1406, 4][4, 1]cpu" = torch.stack((shift_x_7, shift_y_7, shift_x_7, shift_y_7), dim = 1); shift_x_7 = shift_y_7 = None 2025-03-04T21:03:21.2895808Z 2025-03-04T21:03:21.2896234Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:03:21.2896358Z view_6: "f32[1406, 1, 4][4, 4, 1]cpu" = shifts_3.view(-1, 1, 4); shifts_3 = None 2025-03-04T21:03:21.2896801Z view_7: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = None 2025-03-04T21:03:21.2896948Z add_6: "f32[1406, 3, 4][12, 4, 1]cpu" = view_6 + view_7; view_6 = view_7 = None 2025-03-04T21:03:21.2897064Z x_221: "f32[4218, 4][4, 1]cpu" = add_6.reshape(-1, 4); add_6 = None 2025-03-04T21:03:21.2897123Z 2025-03-04T21:03:21.2897557Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:03:21.2897693Z arange_8: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32) 2025-03-04T21:03:21.2897759Z 2025-03-04T21:03:21.2898043Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:21.2898181Z shifts_x_4: "f32[19][1]cpu" = arange_8.to(device(type='cpu')); arange_8 = None 2025-03-04T21:03:21.2898240Z 2025-03-04T21:03:21.2898666Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:03:21.2898799Z arange_9: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32) 2025-03-04T21:03:21.2898865Z 2025-03-04T21:03:21.2899149Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:21.2899286Z shifts_y_4: "f32[19][1]cpu" = arange_9.to(device(type='cpu')); arange_9 = None 2025-03-04T21:03:21.2899364Z 2025-03-04T21:03:21.2899730Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:03:21.2899915Z meshgrid_4 = torch.functional.meshgrid(shifts_y_4, shifts_x_4); shifts_y_4 = shifts_x_4 = None 2025-03-04T21:03:21.2900021Z shift_y_8: "f32[19, 19][1, 0]cpu" = meshgrid_4[0] 2025-03-04T21:03:21.2900131Z shift_x_8: "f32[19, 19][0, 1]cpu" = meshgrid_4[1]; meshgrid_4 = None 2025-03-04T21:03:21.2900199Z 2025-03-04T21:03:21.2900513Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:03:21.2900639Z shift_x_9: "f32[361][1]cpu" = shift_x_8.reshape(-1); shift_x_8 = None 2025-03-04T21:03:21.2900699Z 2025-03-04T21:03:21.2901022Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:03:21.2901138Z shift_y_9: "f32[361][1]cpu" = shift_y_8.reshape(-1); shift_y_8 = None 2025-03-04T21:03:21.2901222Z 2025-03-04T21:03:21.2901591Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:03:21.2901802Z shifts_4: "f32[361, 4][4, 1]cpu" = torch.stack((shift_x_9, shift_y_9, shift_x_9, shift_y_9), dim = 1); shift_x_9 = shift_y_9 = None 2025-03-04T21:03:21.2902009Z 2025-03-04T21:03:21.2902437Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:03:21.2902564Z view_8: "f32[361, 1, 4][4, 4, 1]cpu" = shifts_4.view(-1, 1, 4); shifts_4 = None 2025-03-04T21:03:21.2902975Z view_9: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = None 2025-03-04T21:03:21.2903156Z add_7: "f32[361, 3, 4][12, 4, 1]cpu" = view_8 + view_9; view_8 = view_9 = None 2025-03-04T21:03:21.2903264Z x_222: "f32[1083, 4][4, 1]cpu" = add_7.reshape(-1, 4); add_7 = None 2025-03-04T21:03:21.2903333Z 2025-03-04T21:03:21.2903637Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:03:21.2903769Z tensor: "f32[269952, 4][4, 1]cpu" = x_218.to(torch.float32); x_218 = None 2025-03-04T21:03:21.2903889Z tensor_1: "f32[67488, 4][4, 1]cpu" = x_219.to(torch.float32); x_219 = None 2025-03-04T21:03:21.2904017Z tensor_2: "f32[16872, 4][4, 1]cpu" = x_220.to(torch.float32); x_220 = None 2025-03-04T21:03:21.2904132Z tensor_3: "f32[4218, 4][4, 1]cpu" = x_221.to(torch.float32); x_221 = None 2025-03-04T21:03:21.2904257Z tensor_4: "f32[1083, 4][4, 1]cpu" = x_222.to(torch.float32); x_222 = None 2025-03-04T21:03:21.2904317Z 2025-03-04T21:03:21.2904592Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2905103Z x_223: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_217, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_217 = None 2025-03-04T21:03:21.2905175Z 2025-03-04T21:03:21.2905458Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:03:21.2905691Z x_224: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.relu(x_223, inplace = False); x_223 = None 2025-03-04T21:03:21.2905752Z 2025-03-04T21:03:21.2906137Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:03:21.2906666Z score: "f32[4, 3, 296, 304][269952, 89984, 304, 1]cpu" = torch.conv2d(x_224, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:03:21.2906733Z 2025-03-04T21:03:21.2907094Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:03:21.2907625Z x_233: "f32[4, 12, 296, 304][1079808, 89984, 304, 1]cpu" = torch.conv2d(x_224, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_224 = None 2025-03-04T21:03:21.2907726Z 2025-03-04T21:03:21.2907978Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2908474Z x_225: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(x_215, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_215 = None 2025-03-04T21:03:21.2908533Z 2025-03-04T21:03:21.2908820Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:03:21.2909009Z x_226: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.relu(x_225, inplace = False); x_225 = None 2025-03-04T21:03:21.2909077Z 2025-03-04T21:03:21.2909485Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:03:21.2910022Z score_1: "f32[4, 3, 148, 152][67488, 22496, 152, 1]cpu" = torch.conv2d(x_226, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:03:21.2910082Z 2025-03-04T21:03:21.2910435Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:03:21.2910964Z x_234: "f32[4, 12, 148, 152][269952, 22496, 152, 1]cpu" = torch.conv2d(x_226, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_226 = None 2025-03-04T21:03:21.2911025Z 2025-03-04T21:03:21.2911289Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2911771Z x_227: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(x_213, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_213 = None 2025-03-04T21:03:21.2911838Z 2025-03-04T21:03:21.2912115Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:03:21.2912319Z x_228: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.relu(x_227, inplace = False); x_227 = None 2025-03-04T21:03:21.2912381Z 2025-03-04T21:03:21.2912770Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:03:21.2913265Z score_2: "f32[4, 3, 74, 76][16872, 5624, 76, 1]cpu" = torch.conv2d(x_228, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:03:21.2913335Z 2025-03-04T21:03:21.2913746Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:03:21.2914272Z x_235: "f32[4, 12, 74, 76][67488, 5624, 76, 1]cpu" = torch.conv2d(x_228, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_228 = None 2025-03-04T21:03:21.2914361Z 2025-03-04T21:03:21.2914615Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2915092Z x_229: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_211, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_211 = None 2025-03-04T21:03:21.2915153Z 2025-03-04T21:03:21.2915437Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:03:21.2915613Z x_230: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.nn.functional.relu(x_229, inplace = False); x_229 = None 2025-03-04T21:03:21.2915683Z 2025-03-04T21:03:21.2916102Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:03:21.2916600Z score_3: "f32[4, 3, 37, 38][4218, 1406, 38, 1]cpu" = torch.conv2d(x_230, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:03:21.2916660Z 2025-03-04T21:03:21.2917016Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:03:21.2917508Z x_236: "f32[4, 12, 37, 38][16872, 1406, 38, 1]cpu" = torch.conv2d(x_230, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_230 = None 2025-03-04T21:03:21.2917578Z 2025-03-04T21:03:21.2917824Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.2918590Z x_231: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.conv2d(res, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); res = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T21:03:21.2918673Z 2025-03-04T21:03:21.2918936Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:03:21.2919111Z x_232: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.relu(x_231, inplace = False); x_231 = None 2025-03-04T21:03:21.2919170Z 2025-03-04T21:03:21.2919540Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:03:21.2920401Z score_4: "f32[4, 3, 19, 19][1083, 361, 19, 1]cpu" = torch.conv2d(x_232, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T21:03:21.2920482Z 2025-03-04T21:03:21.2920830Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:03:21.2921654Z x_237: "f32[4, 12, 19, 19][4332, 361, 19, 1]cpu" = torch.conv2d(x_232, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_232 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T21:03:21.2921721Z 2025-03-04T21:03:21.2922055Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T21:03:21.2922220Z permute: "f32[4, 296, 304, 3][269952, 304, 1, 89984]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T21:03:21.2922383Z logits_i: "f32[4, 269952][269952, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T21:03:21.2922546Z permute_1: "f32[4, 148, 152, 3][67488, 152, 1, 22496]cpu" = score_1.permute(0, 2, 3, 1); score_1 = None 2025-03-04T21:03:21.2922682Z logits_i_1: "f32[4, 67488][67488, 1]cpu" = permute_1.flatten(1); permute_1 = None 2025-03-04T21:03:21.2922832Z permute_2: "f32[4, 74, 76, 3][16872, 76, 1, 5624]cpu" = score_2.permute(0, 2, 3, 1); score_2 = None 2025-03-04T21:03:21.2922964Z logits_i_2: "f32[4, 16872][16872, 1]cpu" = permute_2.flatten(1); permute_2 = None 2025-03-04T21:03:21.2923111Z permute_3: "f32[4, 37, 38, 3][4218, 38, 1, 1406]cpu" = score_3.permute(0, 2, 3, 1); score_3 = None 2025-03-04T21:03:21.2923238Z logits_i_3: "f32[4, 4218][4218, 1]cpu" = permute_3.flatten(1); permute_3 = None 2025-03-04T21:03:21.2923384Z permute_4: "f32[4, 19, 19, 3][1083, 19, 1, 361]cpu" = score_4.permute(0, 2, 3, 1); score_4 = None 2025-03-04T21:03:21.2923512Z logits_i_4: "f32[4, 1083][1083, 1]cpu" = permute_4.flatten(1); permute_4 = None 2025-03-04T21:03:21.2923580Z 2025-03-04T21:03:21.2924005Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T21:03:21.2924185Z view_10: "f32[4, 3, 4, 296, 304][1079808, 359936, 89984, 304, 1]cpu" = x_233.view(4, -1, 4, 296, 304); x_233 = None 2025-03-04T21:03:21.2924360Z permute_5: "f32[4, 296, 304, 3, 4][1079808, 304, 1, 359936, 89984]cpu" = view_10.permute(0, 3, 4, 1, 2); view_10 = None 2025-03-04T21:03:21.2924555Z pred_anchor_deltas_i: "f32[4, 269952, 4][1079808, 4, 1]cpu" = permute_5.flatten(1, -2); permute_5 = None 2025-03-04T21:03:21.2924714Z view_11: "f32[4, 3, 4, 148, 152][269952, 89984, 22496, 152, 1]cpu" = x_234.view(4, -1, 4, 148, 152); x_234 = None 2025-03-04T21:03:21.2924890Z permute_6: "f32[4, 148, 152, 3, 4][269952, 152, 1, 89984, 22496]cpu" = view_11.permute(0, 3, 4, 1, 2); view_11 = None 2025-03-04T21:03:21.2925063Z pred_anchor_deltas_i_2: "f32[4, 67488, 4][269952, 4, 1]cpu" = permute_6.flatten(1, -2); permute_6 = None 2025-03-04T21:03:21.2925207Z view_12: "f32[4, 3, 4, 74, 76][67488, 22496, 5624, 76, 1]cpu" = x_235.view(4, -1, 4, 74, 76); x_235 = None 2025-03-04T21:03:21.2925374Z permute_7: "f32[4, 74, 76, 3, 4][67488, 76, 1, 22496, 5624]cpu" = view_12.permute(0, 3, 4, 1, 2); view_12 = None 2025-03-04T21:03:21.2925537Z pred_anchor_deltas_i_4: "f32[4, 16872, 4][67488, 4, 1]cpu" = permute_7.flatten(1, -2); permute_7 = None 2025-03-04T21:03:21.2925681Z view_13: "f32[4, 3, 4, 37, 38][16872, 5624, 1406, 38, 1]cpu" = x_236.view(4, -1, 4, 37, 38); x_236 = None 2025-03-04T21:03:21.2925849Z permute_8: "f32[4, 37, 38, 3, 4][16872, 38, 1, 5624, 1406]cpu" = view_13.permute(0, 3, 4, 1, 2); view_13 = None 2025-03-04T21:03:21.2926016Z pred_anchor_deltas_i_6: "f32[4, 4218, 4][16872, 4, 1]cpu" = permute_8.flatten(1, -2); permute_8 = None 2025-03-04T21:03:21.2926148Z view_14: "f32[4, 3, 4, 19, 19][4332, 1444, 361, 19, 1]cpu" = x_237.view(4, -1, 4, 19, 19); x_237 = None 2025-03-04T21:03:21.2926309Z permute_9: "f32[4, 19, 19, 3, 4][4332, 19, 1, 1444, 361]cpu" = view_14.permute(0, 3, 4, 1, 2); view_14 = None 2025-03-04T21:03:21.2926469Z pred_anchor_deltas_i_8: "f32[4, 1083, 4][4332, 4, 1]cpu" = permute_9.flatten(1, -2); permute_9 = None 2025-03-04T21:03:21.2926534Z 2025-03-04T21:03:21.2926945Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:03:21.2927150Z pred_anchor_deltas_i_1: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T21:03:21.2927210Z 2025-03-04T21:03:21.2927686Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:03:21.2927836Z unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T21:03:21.2927986Z expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T21:03:21.2928119Z anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T21:03:21.2928189Z 2025-03-04T21:03:21.2928567Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:03:21.2928740Z deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T21:03:21.2928800Z 2025-03-04T21:03:21.2929114Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:03:21.2929247Z boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T21:03:21.2929312Z 2025-03-04T21:03:21.2929634Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:03:21.2929764Z getitem_10: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:03:21.2929915Z getitem_11: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:03:21.2930062Z widths: "f32[1079808][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T21:03:21.2930130Z 2025-03-04T21:03:21.2930458Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:03:21.2930585Z getitem_12: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:03:21.2930699Z getitem_13: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:03:21.2930851Z heights: "f32[1079808][1]cpu" = getitem_12 - getitem_13; getitem_12 = getitem_13 = None 2025-03-04T21:03:21.2930910Z 2025-03-04T21:03:21.2931233Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:03:21.2931353Z getitem_14: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:03:21.2931443Z mul: "f32[1079808][1]cpu" = 0.5 * widths 2025-03-04T21:03:21.2931578Z ctr_x: "f32[1079808][1]cpu" = getitem_14 + mul; getitem_14 = mul = None 2025-03-04T21:03:21.2931646Z 2025-03-04T21:03:21.2931964Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:03:21.2932112Z getitem_15: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:03:21.2932197Z mul_1: "f32[1079808][1]cpu" = 0.5 * heights 2025-03-04T21:03:21.2932328Z ctr_y: "f32[1079808][1]cpu" = getitem_15 + mul_1; getitem_15 = mul_1 = None 2025-03-04T21:03:21.2932388Z 2025-03-04T21:03:21.2932746Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:03:21.2932899Z getitem_16: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:03:21.2933016Z dx: "f32[1079808, 1][1, 1]cpu" = getitem_16 / 1.0; getitem_16 = None 2025-03-04T21:03:21.2933075Z 2025-03-04T21:03:21.2933401Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:03:21.2933550Z getitem_17: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:03:21.2933661Z dy: "f32[1079808, 1][1, 1]cpu" = getitem_17 / 1.0; getitem_17 = None 2025-03-04T21:03:21.2933721Z 2025-03-04T21:03:21.2934018Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:03:21.2934162Z getitem_18: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:03:21.2934272Z dw: "f32[1079808, 1][1, 1]cpu" = getitem_18 / 1.0; getitem_18 = None 2025-03-04T21:03:21.2934331Z 2025-03-04T21:03:21.2934632Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:03:21.2934809Z getitem_19: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:03:21.2934922Z dh: "f32[1079808, 1][1, 1]cpu" = getitem_19 / 1.0; getitem_19 = None 2025-03-04T21:03:21.2934982Z 2025-03-04T21:03:21.2935318Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:03:21.2935454Z dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:03:21.2935536Z 2025-03-04T21:03:21.2935861Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:03:21.2936001Z dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:03:21.2936060Z 2025-03-04T21:03:21.2936402Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:03:21.2936543Z getitem_20: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:03:21.2936661Z mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_20; dx = getitem_20 = None 2025-03-04T21:03:21.2936816Z getitem_21: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:03:21.2936952Z pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_21; mul_2 = getitem_21 = None 2025-03-04T21:03:21.2937017Z 2025-03-04T21:03:21.2937371Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:03:21.2937511Z getitem_22: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:03:21.2937627Z mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_22; dy = getitem_22 = None 2025-03-04T21:03:21.2937778Z getitem_23: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:03:21.2937907Z pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_23; mul_3 = getitem_23 = None 2025-03-04T21:03:21.2937972Z 2025-03-04T21:03:21.2938292Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:03:21.2938412Z exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:03:21.2938566Z getitem_24: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:03:21.2938726Z pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_24; exp = getitem_24 = None 2025-03-04T21:03:21.2938785Z 2025-03-04T21:03:21.2939116Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:03:21.2939228Z exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:03:21.2939396Z getitem_25: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:03:21.2939525Z pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_25; exp_1 = getitem_25 = None 2025-03-04T21:03:21.2939592Z 2025-03-04T21:03:21.2939896Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:03:21.2939996Z mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T21:03:21.2940110Z x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:03:21.2940174Z 2025-03-04T21:03:21.2940476Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:03:21.2940568Z mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T21:03:21.2940674Z y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:03:21.2940739Z 2025-03-04T21:03:21.2941035Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:03:21.2941167Z mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:03:21.2941291Z x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:03:21.2941358Z 2025-03-04T21:03:21.2941655Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:03:21.2941775Z mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:03:21.2941895Z y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:03:21.2941962Z 2025-03-04T21:03:21.2942299Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:03:21.2942484Z pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:03:21.2942541Z 2025-03-04T21:03:21.2942875Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:03:21.2943045Z proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4)); pred_boxes = None 2025-03-04T21:03:21.2943107Z 2025-03-04T21:03:21.2943476Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:03:21.2943647Z proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T21:03:21.2943703Z 2025-03-04T21:03:21.2944094Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:03:21.2944301Z pred_anchor_deltas_i_3: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_2.reshape(-1, 4); pred_anchor_deltas_i_2 = None 2025-03-04T21:03:21.2944363Z 2025-03-04T21:03:21.2944814Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:03:21.2944966Z unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = tensor_1.unsqueeze(0); tensor_1 = None 2025-03-04T21:03:21.2945116Z expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1); unsqueeze_1 = None 2025-03-04T21:03:21.2945250Z anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4); expand_1 = None 2025-03-04T21:03:21.2945317Z 2025-03-04T21:03:21.2945684Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:03:21.2945858Z deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float(); pred_anchor_deltas_i_3 = None 2025-03-04T21:03:21.2945918Z 2025-03-04T21:03:21.2946232Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:03:21.2946373Z boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32); anchors_i_1 = None 2025-03-04T21:03:21.2946438Z 2025-03-04T21:03:21.2946743Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:03:21.2946877Z getitem_26: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)] 2025-03-04T21:03:21.2947013Z getitem_27: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T21:03:21.2947166Z widths_1: "f32[269952][1]cpu" = getitem_26 - getitem_27; getitem_26 = getitem_27 = None 2025-03-04T21:03:21.2947227Z 2025-03-04T21:03:21.2947544Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:03:21.2947660Z getitem_28: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)] 2025-03-04T21:03:21.2947776Z getitem_29: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)] 2025-03-04T21:03:21.2947918Z heights_1: "f32[269952][1]cpu" = getitem_28 - getitem_29; getitem_28 = getitem_29 = None 2025-03-04T21:03:21.2947980Z 2025-03-04T21:03:21.2948282Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:03:21.2948403Z getitem_30: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T21:03:21.2948490Z mul_10: "f32[269952][1]cpu" = 0.5 * widths_1 2025-03-04T21:03:21.2948639Z ctr_x_1: "f32[269952][1]cpu" = getitem_30 + mul_10; getitem_30 = mul_10 = None 2025-03-04T21:03:21.2948697Z 2025-03-04T21:03:21.2949002Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:03:21.2949141Z getitem_31: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]; boxes_1 = None 2025-03-04T21:03:21.2949234Z mul_11: "f32[269952][1]cpu" = 0.5 * heights_1 2025-03-04T21:03:21.2949360Z ctr_y_1: "f32[269952][1]cpu" = getitem_31 + mul_11; getitem_31 = mul_11 = None 2025-03-04T21:03:21.2949430Z 2025-03-04T21:03:21.2949731Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:03:21.2949898Z getitem_32: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:03:21.2950014Z dx_1: "f32[269952, 1][1, 1]cpu" = getitem_32 / 1.0; getitem_32 = None 2025-03-04T21:03:21.2950119Z 2025-03-04T21:03:21.2950419Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:03:21.2950579Z getitem_33: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:03:21.2950694Z dy_1: "f32[269952, 1][1, 1]cpu" = getitem_33 / 1.0; getitem_33 = None 2025-03-04T21:03:21.2950758Z 2025-03-04T21:03:21.2951060Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:03:21.2951209Z getitem_34: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:03:21.2951325Z dw_2: "f32[269952, 1][1, 1]cpu" = getitem_34 / 1.0; getitem_34 = None 2025-03-04T21:03:21.2951388Z 2025-03-04T21:03:21.2951700Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:03:21.2951884Z getitem_35: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))]; deltas_1 = None 2025-03-04T21:03:21.2951995Z dh_2: "f32[269952, 1][1, 1]cpu" = getitem_35 / 1.0; getitem_35 = None 2025-03-04T21:03:21.2952055Z 2025-03-04T21:03:21.2952397Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:03:21.2952550Z dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356); dw_2 = None 2025-03-04T21:03:21.2952620Z 2025-03-04T21:03:21.2952960Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:03:21.2953109Z dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356); dh_2 = None 2025-03-04T21:03:21.2953172Z 2025-03-04T21:03:21.2953532Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:03:21.2953734Z getitem_36: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)] 2025-03-04T21:03:21.2953884Z mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_36; dx_1 = getitem_36 = None 2025-03-04T21:03:21.2954049Z getitem_37: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)]; ctr_x_1 = None 2025-03-04T21:03:21.2954199Z pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_37; mul_12 = getitem_37 = None 2025-03-04T21:03:21.2954280Z 2025-03-04T21:03:21.2954643Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:03:21.2954783Z getitem_38: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)] 2025-03-04T21:03:21.2954917Z mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_38; dy_1 = getitem_38 = None 2025-03-04T21:03:21.2955070Z getitem_39: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)]; ctr_y_1 = None 2025-03-04T21:03:21.2955219Z pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_39; mul_13 = getitem_39 = None 2025-03-04T21:03:21.2955279Z 2025-03-04T21:03:21.2955623Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:03:21.2955740Z exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3); dw_3 = None 2025-03-04T21:03:21.2955946Z getitem_40: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]; widths_1 = None 2025-03-04T21:03:21.2956082Z pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_40; exp_2 = getitem_40 = None 2025-03-04T21:03:21.2956153Z 2025-03-04T21:03:21.2956485Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:03:21.2956603Z exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3); dh_3 = None 2025-03-04T21:03:21.2956774Z getitem_41: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]; heights_1 = None 2025-03-04T21:03:21.2956907Z pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_41; exp_3 = getitem_41 = None 2025-03-04T21:03:21.2956974Z 2025-03-04T21:03:21.2957286Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:03:21.2957392Z mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1 2025-03-04T21:03:21.2957508Z x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16; mul_16 = None 2025-03-04T21:03:21.2957575Z 2025-03-04T21:03:21.2957881Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:03:21.2957980Z mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1 2025-03-04T21:03:21.2958093Z y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17; mul_17 = None 2025-03-04T21:03:21.2958159Z 2025-03-04T21:03:21.2958478Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:03:21.2958599Z mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1; pred_w_1 = None 2025-03-04T21:03:21.2958736Z x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18; pred_ctr_x_1 = mul_18 = None 2025-03-04T21:03:21.2958803Z 2025-03-04T21:03:21.2959106Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:03:21.2959224Z mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1; pred_h_1 = None 2025-03-04T21:03:21.2959354Z y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19; pred_ctr_y_1 = mul_19 = None 2025-03-04T21:03:21.2959421Z 2025-03-04T21:03:21.2959767Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:03:21.2959965Z pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1); x1_1 = y1_1 = x2_1 = y2_1 = None 2025-03-04T21:03:21.2960041Z 2025-03-04T21:03:21.2960382Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:03:21.2960544Z proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4)); pred_boxes_1 = None 2025-03-04T21:03:21.2960611Z 2025-03-04T21:03:21.2960996Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:03:21.2961176Z proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4); proposals_i_1 = None 2025-03-04T21:03:21.2961237Z 2025-03-04T21:03:21.2961642Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:03:21.2961878Z pred_anchor_deltas_i_5: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_4.reshape(-1, 4); pred_anchor_deltas_i_4 = None 2025-03-04T21:03:21.2961949Z 2025-03-04T21:03:21.2962398Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:03:21.2962556Z unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = tensor_2.unsqueeze(0); tensor_2 = None 2025-03-04T21:03:21.2962705Z expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1); unsqueeze_2 = None 2025-03-04T21:03:21.2962849Z anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4); expand_2 = None 2025-03-04T21:03:21.2962910Z 2025-03-04T21:03:21.2963284Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:03:21.2963459Z deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_5.float(); pred_anchor_deltas_i_5 = None 2025-03-04T21:03:21.2963529Z 2025-03-04T21:03:21.2963837Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:03:21.2963982Z boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32); anchors_i_2 = None 2025-03-04T21:03:21.2964042Z 2025-03-04T21:03:21.2964361Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:03:21.2964510Z getitem_42: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)] 2025-03-04T21:03:21.2964631Z getitem_43: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T21:03:21.2964784Z widths_2: "f32[67488][1]cpu" = getitem_42 - getitem_43; getitem_42 = getitem_43 = None 2025-03-04T21:03:21.2964848Z 2025-03-04T21:03:21.2965175Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:03:21.2965299Z getitem_44: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)] 2025-03-04T21:03:21.2965428Z getitem_45: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)] 2025-03-04T21:03:21.2965583Z heights_2: "f32[67488][1]cpu" = getitem_44 - getitem_45; getitem_44 = getitem_45 = None 2025-03-04T21:03:21.2965654Z 2025-03-04T21:03:21.2965984Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:03:21.2966113Z getitem_46: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T21:03:21.2966217Z mul_20: "f32[67488][1]cpu" = 0.5 * widths_2 2025-03-04T21:03:21.2966354Z ctr_x_2: "f32[67488][1]cpu" = getitem_46 + mul_20; getitem_46 = mul_20 = None 2025-03-04T21:03:21.2966416Z 2025-03-04T21:03:21.2966731Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:03:21.2966878Z getitem_47: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]; boxes_2 = None 2025-03-04T21:03:21.2966977Z mul_21: "f32[67488][1]cpu" = 0.5 * heights_2 2025-03-04T21:03:21.2967104Z ctr_y_2: "f32[67488][1]cpu" = getitem_47 + mul_21; getitem_47 = mul_21 = None 2025-03-04T21:03:21.2967177Z 2025-03-04T21:03:21.2967483Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:03:21.2967645Z getitem_48: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:03:21.2967787Z dx_2: "f32[67488, 1][1, 1]cpu" = getitem_48 / 1.0; getitem_48 = None 2025-03-04T21:03:21.2967858Z 2025-03-04T21:03:21.2968165Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:03:21.2968320Z getitem_49: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:03:21.2968429Z dy_2: "f32[67488, 1][1, 1]cpu" = getitem_49 / 1.0; getitem_49 = None 2025-03-04T21:03:21.2968499Z 2025-03-04T21:03:21.2968799Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:03:21.2968955Z getitem_50: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:03:21.2969063Z dw_4: "f32[67488, 1][1, 1]cpu" = getitem_50 / 1.0; getitem_50 = None 2025-03-04T21:03:21.2969133Z 2025-03-04T21:03:21.2969437Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:03:21.2969626Z getitem_51: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))]; deltas_2 = None 2025-03-04T21:03:21.2969731Z dh_4: "f32[67488, 1][1, 1]cpu" = getitem_51 / 1.0; getitem_51 = None 2025-03-04T21:03:21.2969799Z 2025-03-04T21:03:21.2970138Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:03:21.2970294Z dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356); dw_4 = None 2025-03-04T21:03:21.2970355Z 2025-03-04T21:03:21.2970689Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:03:21.2970824Z dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356); dh_4 = None 2025-03-04T21:03:21.2970883Z 2025-03-04T21:03:21.2971222Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:03:21.2971348Z getitem_52: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)] 2025-03-04T21:03:21.2971472Z mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_52; dx_2 = getitem_52 = None 2025-03-04T21:03:21.2971621Z getitem_53: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)]; ctr_x_2 = None 2025-03-04T21:03:21.2971759Z pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_53; mul_22 = getitem_53 = None 2025-03-04T21:03:21.2971832Z 2025-03-04T21:03:21.2972185Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:03:21.2972314Z getitem_54: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)] 2025-03-04T21:03:21.2972438Z mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_54; dy_2 = getitem_54 = None 2025-03-04T21:03:21.2972582Z getitem_55: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)]; ctr_y_2 = None 2025-03-04T21:03:21.2972719Z pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_55; mul_23 = getitem_55 = None 2025-03-04T21:03:21.2972781Z 2025-03-04T21:03:21.2973113Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:03:21.2973224Z exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5); dw_5 = None 2025-03-04T21:03:21.2973420Z getitem_56: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]; widths_2 = None 2025-03-04T21:03:21.2973548Z pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_56; exp_4 = getitem_56 = None 2025-03-04T21:03:21.2973616Z 2025-03-04T21:03:21.2973941Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:03:21.2974057Z exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5); dh_5 = None 2025-03-04T21:03:21.2974223Z getitem_57: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]; heights_2 = None 2025-03-04T21:03:21.2974359Z pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_57; exp_5 = getitem_57 = None 2025-03-04T21:03:21.2974422Z 2025-03-04T21:03:21.2974753Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:03:21.2974847Z mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2 2025-03-04T21:03:21.2974964Z x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26; mul_26 = None 2025-03-04T21:03:21.2975023Z 2025-03-04T21:03:21.2975336Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:03:21.2975427Z mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2 2025-03-04T21:03:21.2975544Z y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27; mul_27 = None 2025-03-04T21:03:21.2975623Z 2025-03-04T21:03:21.2975936Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:03:21.2976048Z mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2; pred_w_2 = None 2025-03-04T21:03:21.2976183Z x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28; pred_ctr_x_2 = mul_28 = None 2025-03-04T21:03:21.2976242Z 2025-03-04T21:03:21.2976541Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:03:21.2976647Z mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2; pred_h_2 = None 2025-03-04T21:03:21.2976777Z y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29; pred_ctr_y_2 = mul_29 = None 2025-03-04T21:03:21.2976836Z 2025-03-04T21:03:21.2977178Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:03:21.2977359Z pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1); x1_2 = y1_2 = x2_2 = y2_2 = None 2025-03-04T21:03:21.2977438Z 2025-03-04T21:03:21.2977767Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:03:21.2977930Z proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4)); pred_boxes_2 = None 2025-03-04T21:03:21.2977990Z 2025-03-04T21:03:21.2978370Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:03:21.2978544Z proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4); proposals_i_2 = None 2025-03-04T21:03:21.2978603Z 2025-03-04T21:03:21.2979010Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:03:21.2979235Z pred_anchor_deltas_i_7: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_6.reshape(-1, 4); pred_anchor_deltas_i_6 = None 2025-03-04T21:03:21.2979302Z 2025-03-04T21:03:21.2979737Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:03:21.2979884Z unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = tensor_3.unsqueeze(0); tensor_3 = None 2025-03-04T21:03:21.2980027Z expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1); unsqueeze_3 = None 2025-03-04T21:03:21.2980164Z anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4); expand_3 = None 2025-03-04T21:03:21.2980224Z 2025-03-04T21:03:21.2980593Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:03:21.2980756Z deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_7.float(); pred_anchor_deltas_i_7 = None 2025-03-04T21:03:21.2980821Z 2025-03-04T21:03:21.2981122Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:03:21.2981266Z boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32); anchors_i_3 = None 2025-03-04T21:03:21.2981325Z 2025-03-04T21:03:21.2981637Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:03:21.2981771Z getitem_58: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)] 2025-03-04T21:03:21.2981899Z getitem_59: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T21:03:21.2982043Z widths_3: "f32[16872][1]cpu" = getitem_58 - getitem_59; getitem_58 = getitem_59 = None 2025-03-04T21:03:21.2982109Z 2025-03-04T21:03:21.2982415Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:03:21.2982540Z getitem_60: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)] 2025-03-04T21:03:21.2982655Z getitem_61: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)] 2025-03-04T21:03:21.2982804Z heights_3: "f32[16872][1]cpu" = getitem_60 - getitem_61; getitem_60 = getitem_61 = None 2025-03-04T21:03:21.2982865Z 2025-03-04T21:03:21.2983171Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:03:21.2983300Z getitem_62: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T21:03:21.2983393Z mul_30: "f32[16872][1]cpu" = 0.5 * widths_3 2025-03-04T21:03:21.2983518Z ctr_x_3: "f32[16872][1]cpu" = getitem_62 + mul_30; getitem_62 = mul_30 = None 2025-03-04T21:03:21.2983587Z 2025-03-04T21:03:21.2983886Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:03:21.2984037Z getitem_63: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]; boxes_3 = None 2025-03-04T21:03:21.2984128Z mul_31: "f32[16872][1]cpu" = 0.5 * heights_3 2025-03-04T21:03:21.2984263Z ctr_y_3: "f32[16872][1]cpu" = getitem_63 + mul_31; getitem_63 = mul_31 = None 2025-03-04T21:03:21.2984324Z 2025-03-04T21:03:21.2984628Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:03:21.2984807Z getitem_64: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:03:21.2984924Z dx_3: "f32[16872, 1][1, 1]cpu" = getitem_64 / 1.0; getitem_64 = None 2025-03-04T21:03:21.2984984Z 2025-03-04T21:03:21.2985295Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:03:21.2985459Z getitem_65: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:03:21.2985564Z dy_3: "f32[16872, 1][1, 1]cpu" = getitem_65 / 1.0; getitem_65 = None 2025-03-04T21:03:21.2985630Z 2025-03-04T21:03:21.2985916Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:03:21.2986066Z getitem_66: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:03:21.2986171Z dw_6: "f32[16872, 1][1, 1]cpu" = getitem_66 / 1.0; getitem_66 = None 2025-03-04T21:03:21.2986238Z 2025-03-04T21:03:21.2986530Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:03:21.2986710Z getitem_67: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))]; deltas_3 = None 2025-03-04T21:03:21.2986813Z dh_6: "f32[16872, 1][1, 1]cpu" = getitem_67 / 1.0; getitem_67 = None 2025-03-04T21:03:21.2986881Z 2025-03-04T21:03:21.2987206Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:03:21.2987363Z dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356); dw_6 = None 2025-03-04T21:03:21.2987423Z 2025-03-04T21:03:21.2987750Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:03:21.2987879Z dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356); dh_6 = None 2025-03-04T21:03:21.2987946Z 2025-03-04T21:03:21.2988277Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:03:21.2988412Z getitem_68: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)] 2025-03-04T21:03:21.2988532Z mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_68; dx_3 = getitem_68 = None 2025-03-04T21:03:21.2988686Z getitem_69: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)]; ctr_x_3 = None 2025-03-04T21:03:21.2988835Z pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_69; mul_32 = getitem_69 = None 2025-03-04T21:03:21.2988902Z 2025-03-04T21:03:21.2989242Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:03:21.2989378Z getitem_70: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)] 2025-03-04T21:03:21.2989495Z mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_70; dy_3 = getitem_70 = None 2025-03-04T21:03:21.2989652Z getitem_71: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)]; ctr_y_3 = None 2025-03-04T21:03:21.2989787Z pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_71; mul_33 = getitem_71 = None 2025-03-04T21:03:21.2989854Z 2025-03-04T21:03:21.2990183Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:03:21.2990632Z exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7); dw_7 = None 2025-03-04T21:03:21.2990800Z getitem_72: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]; widths_3 = None 2025-03-04T21:03:21.2990931Z pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_72; exp_6 = getitem_72 = None 2025-03-04T21:03:21.2990999Z 2025-03-04T21:03:21.2991334Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:03:21.2991448Z exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7); dh_7 = None 2025-03-04T21:03:21.2991609Z getitem_73: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]; heights_3 = None 2025-03-04T21:03:21.2991752Z pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_73; exp_7 = getitem_73 = None 2025-03-04T21:03:21.2991814Z 2025-03-04T21:03:21.2992120Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:03:21.2992211Z mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3 2025-03-04T21:03:21.2992329Z x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36; mul_36 = None 2025-03-04T21:03:21.2992390Z 2025-03-04T21:03:21.2992700Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:03:21.2992790Z mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3 2025-03-04T21:03:21.2992926Z y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37; mul_37 = None 2025-03-04T21:03:21.2992987Z 2025-03-04T21:03:21.2993305Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:03:21.2993421Z mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3; pred_w_3 = None 2025-03-04T21:03:21.2993554Z x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38; pred_ctr_x_3 = mul_38 = None 2025-03-04T21:03:21.2993615Z 2025-03-04T21:03:21.2993990Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:03:21.2994104Z mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3; pred_h_3 = None 2025-03-04T21:03:21.2994238Z y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39; pred_ctr_y_3 = mul_39 = None 2025-03-04T21:03:21.2994301Z 2025-03-04T21:03:21.2994655Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:03:21.2994878Z pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1); x1_3 = y1_3 = x2_3 = y2_3 = None 2025-03-04T21:03:21.2994952Z 2025-03-04T21:03:21.2995307Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:03:21.2995484Z proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4)); pred_boxes_3 = None 2025-03-04T21:03:21.2995560Z 2025-03-04T21:03:21.2995948Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:03:21.2996118Z proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4); proposals_i_3 = None 2025-03-04T21:03:21.2996186Z 2025-03-04T21:03:21.2996620Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:03:21.2996832Z pred_anchor_deltas_i_9: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_8.reshape(-1, 4); pred_anchor_deltas_i_8 = None 2025-03-04T21:03:21.2996892Z 2025-03-04T21:03:21.2997327Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:03:21.2997470Z unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = tensor_4.unsqueeze(0); tensor_4 = None 2025-03-04T21:03:21.2997627Z expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1); unsqueeze_4 = None 2025-03-04T21:03:21.2997760Z anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4); expand_4 = None 2025-03-04T21:03:21.2997829Z 2025-03-04T21:03:21.2998197Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:03:21.2998366Z deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_9.float(); pred_anchor_deltas_i_9 = None 2025-03-04T21:03:21.2998434Z 2025-03-04T21:03:21.2998741Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:03:21.2998884Z boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32); anchors_i_4 = None 2025-03-04T21:03:21.2998944Z 2025-03-04T21:03:21.2999287Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:03:21.2999416Z getitem_74: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)] 2025-03-04T21:03:21.2999545Z getitem_75: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T21:03:21.2999691Z widths_4: "f32[4332][1]cpu" = getitem_74 - getitem_75; getitem_74 = getitem_75 = None 2025-03-04T21:03:21.2999759Z 2025-03-04T21:03:21.3000075Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:03:21.3000202Z getitem_76: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)] 2025-03-04T21:03:21.3000316Z getitem_77: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)] 2025-03-04T21:03:21.3000466Z heights_4: "f32[4332][1]cpu" = getitem_76 - getitem_77; getitem_76 = getitem_77 = None 2025-03-04T21:03:21.3000528Z 2025-03-04T21:03:21.3000842Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:03:21.3000982Z getitem_78: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T21:03:21.3001074Z mul_40: "f32[4332][1]cpu" = 0.5 * widths_4 2025-03-04T21:03:21.3001199Z ctr_x_4: "f32[4332][1]cpu" = getitem_78 + mul_40; getitem_78 = mul_40 = None 2025-03-04T21:03:21.3001266Z 2025-03-04T21:03:21.3001577Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:03:21.3001731Z getitem_79: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]; boxes_4 = None 2025-03-04T21:03:21.3001817Z mul_41: "f32[4332][1]cpu" = 0.5 * heights_4 2025-03-04T21:03:21.3002098Z ctr_y_4: "f32[4332][1]cpu" = getitem_79 + mul_41; getitem_79 = mul_41 = None 2025-03-04T21:03:21.3002165Z 2025-03-04T21:03:21.3002480Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:03:21.3002687Z getitem_80: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:03:21.3002804Z dx_4: "f32[4332, 1][1, 1]cpu" = getitem_80 / 1.0; getitem_80 = None 2025-03-04T21:03:21.3002865Z 2025-03-04T21:03:21.3003170Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:03:21.3003318Z getitem_81: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:03:21.3003432Z dy_4: "f32[4332, 1][1, 1]cpu" = getitem_81 / 1.0; getitem_81 = None 2025-03-04T21:03:21.3003497Z 2025-03-04T21:03:21.3003801Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:03:21.3003951Z getitem_82: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:03:21.3004065Z dw_8: "f32[4332, 1][1, 1]cpu" = getitem_82 / 1.0; getitem_82 = None 2025-03-04T21:03:21.3004126Z 2025-03-04T21:03:21.3004435Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:03:21.3004617Z getitem_83: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))]; deltas_4 = None 2025-03-04T21:03:21.3004730Z dh_8: "f32[4332, 1][1, 1]cpu" = getitem_83 / 1.0; getitem_83 = None 2025-03-04T21:03:21.3004793Z 2025-03-04T21:03:21.3005154Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:03:21.3005294Z dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356); dw_8 = None 2025-03-04T21:03:21.3005354Z 2025-03-04T21:03:21.3005695Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:03:21.3005823Z dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356); dh_8 = None 2025-03-04T21:03:21.3005890Z 2025-03-04T21:03:21.3006231Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:03:21.3006368Z getitem_84: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)] 2025-03-04T21:03:21.3006489Z mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_84; dx_4 = getitem_84 = None 2025-03-04T21:03:21.3006641Z getitem_85: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)]; ctr_x_4 = None 2025-03-04T21:03:21.3006795Z pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_85; mul_42 = getitem_85 = None 2025-03-04T21:03:21.3006863Z 2025-03-04T21:03:21.3007206Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:03:21.3007342Z getitem_86: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)] 2025-03-04T21:03:21.3007456Z mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_86; dy_4 = getitem_86 = None 2025-03-04T21:03:21.3007607Z getitem_87: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)]; ctr_y_4 = None 2025-03-04T21:03:21.3007746Z pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_87; mul_43 = getitem_87 = None 2025-03-04T21:03:21.3007813Z 2025-03-04T21:03:21.3008134Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:03:21.3008277Z exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9); dw_9 = None 2025-03-04T21:03:21.3008429Z getitem_88: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]; widths_4 = None 2025-03-04T21:03:21.3008561Z pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_88; exp_8 = getitem_88 = None 2025-03-04T21:03:21.3008621Z 2025-03-04T21:03:21.3008949Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:03:21.3009055Z exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9); dh_9 = None 2025-03-04T21:03:21.3009223Z getitem_89: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]; heights_4 = None 2025-03-04T21:03:21.3009347Z pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_89; exp_9 = getitem_89 = None 2025-03-04T21:03:21.3009414Z 2025-03-04T21:03:21.3009718Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:03:21.3009816Z mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4 2025-03-04T21:03:21.3009923Z x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46; mul_46 = None 2025-03-04T21:03:21.3009991Z 2025-03-04T21:03:21.3010289Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:03:21.3010384Z mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4 2025-03-04T21:03:21.3010505Z y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47; mul_47 = None 2025-03-04T21:03:21.3010572Z 2025-03-04T21:03:21.3010869Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:03:21.3010989Z mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4; pred_w_4 = None 2025-03-04T21:03:21.3011114Z x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48; pred_ctr_x_4 = mul_48 = None 2025-03-04T21:03:21.3011180Z 2025-03-04T21:03:21.3011476Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:03:21.3011589Z mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4; pred_h_4 = None 2025-03-04T21:03:21.3011709Z y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49; pred_ctr_y_4 = mul_49 = None 2025-03-04T21:03:21.3011777Z 2025-03-04T21:03:21.3012115Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:03:21.3012315Z pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1); x1_4 = y1_4 = x2_4 = y2_4 = None 2025-03-04T21:03:21.3012376Z 2025-03-04T21:03:21.3012700Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:03:21.3012856Z proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4)); pred_boxes_4 = None 2025-03-04T21:03:21.3012917Z 2025-03-04T21:03:21.3013290Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:03:21.3013457Z proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4); proposals_i_4 = None 2025-03-04T21:03:21.3013525Z 2025-03-04T21:03:21.3014024Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T21:03:21.3014161Z arange_10: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T21:03:21.3014219Z 2025-03-04T21:03:21.3014514Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:21.3014648Z batch_idx: "i64[4][1]cpu" = arange_10.to(device(type='cpu')); arange_10 = None 2025-03-04T21:03:21.3014713Z 2025-03-04T21:03:21.3015142Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:03:21.3015259Z topk = logits_i.topk(1000, dim = 1); logits_i = None 2025-03-04T21:03:21.3015357Z topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0] 2025-03-04T21:03:21.3015472Z topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1]; topk = None 2025-03-04T21:03:21.3015532Z 2025-03-04T21:03:21.3015994Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:03:21.3016119Z getitem_92: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:03:21.3016352Z topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_92, topk_idx)]; proposals_i_5 = getitem_92 = topk_idx = None 2025-03-04T21:03:21.3016435Z 2025-03-04T21:03:21.3016888Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:03:21.3017054Z full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:03:21.3017124Z 2025-03-04T21:03:21.3017413Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:21.3017537Z to_21: "i64[1000][1]cpu" = full.to(device(type='cpu')); full = None 2025-03-04T21:03:21.3017596Z 2025-03-04T21:03:21.3018030Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:03:21.3018141Z topk_1 = logits_i_1.topk(1000, dim = 1); logits_i_1 = None 2025-03-04T21:03:21.3018264Z topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0] 2025-03-04T21:03:21.3018375Z topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1]; topk_1 = None 2025-03-04T21:03:21.3018446Z 2025-03-04T21:03:21.3018890Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:03:21.3019021Z getitem_96: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:03:21.3019250Z topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_96, topk_idx_1)]; proposals_i_6 = getitem_96 = topk_idx_1 = None 2025-03-04T21:03:21.3019309Z 2025-03-04T21:03:21.3019754Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:03:21.3019937Z full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:03:21.3020004Z 2025-03-04T21:03:21.3020287Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:21.3020410Z to_22: "i64[1000][1]cpu" = full_1.to(device(type='cpu')); full_1 = None 2025-03-04T21:03:21.3020470Z 2025-03-04T21:03:21.3020896Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:03:21.3021004Z topk_2 = logits_i_2.topk(1000, dim = 1); logits_i_2 = None 2025-03-04T21:03:21.3021110Z topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0] 2025-03-04T21:03:21.3021222Z topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1]; topk_2 = None 2025-03-04T21:03:21.3021287Z 2025-03-04T21:03:21.3021728Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:03:21.3021861Z getitem_100: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:03:21.3022089Z topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_100, topk_idx_2)]; proposals_i_7 = getitem_100 = topk_idx_2 = None 2025-03-04T21:03:21.3022153Z 2025-03-04T21:03:21.3022599Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:03:21.3022780Z full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:03:21.3022839Z 2025-03-04T21:03:21.3023135Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:21.3023251Z to_23: "i64[1000][1]cpu" = full_2.to(device(type='cpu')); full_2 = None 2025-03-04T21:03:21.3023317Z 2025-03-04T21:03:21.3023730Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:03:21.3023846Z topk_3 = logits_i_3.topk(1000, dim = 1); logits_i_3 = None 2025-03-04T21:03:21.3023946Z topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0] 2025-03-04T21:03:21.3024063Z topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1]; topk_3 = None 2025-03-04T21:03:21.3024139Z 2025-03-04T21:03:21.3024604Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:03:21.3024730Z getitem_104: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:03:21.3024969Z topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_104, topk_idx_3)]; proposals_i_8 = getitem_104 = topk_idx_3 = None 2025-03-04T21:03:21.3025034Z 2025-03-04T21:03:21.3025488Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:03:21.3025650Z full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:03:21.3025712Z 2025-03-04T21:03:21.3026042Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:21.3026160Z to_24: "i64[1000][1]cpu" = full_3.to(device(type='cpu')); full_3 = None 2025-03-04T21:03:21.3026226Z 2025-03-04T21:03:21.3026647Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:03:21.3026758Z topk_4 = logits_i_4.topk(1000, dim = 1); logits_i_4 = None 2025-03-04T21:03:21.3026856Z topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0] 2025-03-04T21:03:21.3026971Z topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1]; topk_4 = None 2025-03-04T21:03:21.3027029Z 2025-03-04T21:03:21.3027480Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:03:21.3027638Z getitem_108: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T21:03:21.3027870Z topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_108, topk_idx_4)]; proposals_i_9 = getitem_108 = topk_idx_4 = None 2025-03-04T21:03:21.3027926Z 2025-03-04T21:03:21.3028374Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:03:21.3028543Z full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:03:21.3028608Z 2025-03-04T21:03:21.3028900Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:21.3029023Z to_25: "i64[1000][1]cpu" = full_4.to(device(type='cpu')); full_4 = None 2025-03-04T21:03:21.3029083Z 2025-03-04T21:03:21.3029362Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:03:21.3029754Z topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1); topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None 2025-03-04T21:03:21.3029820Z 2025-03-04T21:03:21.3030089Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:03:21.3030563Z topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1); topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None 2025-03-04T21:03:21.3030628Z 2025-03-04T21:03:21.3030896Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:03:21.3031096Z level_ids: "i64[5000][1]cpu" = torch.cat([to_21, to_22, to_23, to_24, to_25], 0); to_21 = to_22 = to_23 = to_24 = to_25 = level_ids = None 2025-03-04T21:03:21.3031159Z 2025-03-04T21:03:21.3031550Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T21:03:21.3031690Z getitem_110: "f32[5000, 4][4, 1]cpu" = topk_proposals[0]; topk_proposals = None 2025-03-04T21:03:21.3031759Z 2025-03-04T21:03:21.3032079Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:03:21.3032229Z tensor_5: "f32[5000, 4][4, 1]cpu" = getitem_110.to(torch.float32); getitem_110 = None 2025-03-04T21:03:21.3032290Z 2025-03-04T21:03:21.3032669Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T21:03:21.3032797Z scores_per_img: "f32[5000][1]cpu" = topk_scores[0]; topk_scores = None 2025-03-04T21:03:21.3032863Z 2025-03-04T21:03:21.3033345Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T21:03:21.3033481Z isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor_5); tensor_5 = None 2025-03-04T21:03:21.3033600Z all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:03:21.3033821Z isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T21:03:21.3033955Z valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T21:03:21.3034025Z 2025-03-04T21:03:21.3034395Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T21:03:21.3034518Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T21:03:21.3034597Z 2025-03-04T21:03:21.3035022Z 2025-03-04T21:03:21.3035121Z class GraphModule(torch.nn.Module): 2025-03-04T21:03:21.3156454Z def forward(self, L_stack0_tensor: "f32[4, 3, 1184, 1216][4319232, 1439744, 1216, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", 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1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_: "f32[512, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_: "f32[2048, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_lateral_convs_0_parameters_weight_: "f32[256, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_0_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_0_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_0_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_1_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_1_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_1_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_2_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_2_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_2_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_3_parameters_weight_: "f32[256, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_3_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_3_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_3_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[3, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[3][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[12, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[12][1]cpu"): 2025-03-04T21:03:21.3157357Z l_stack0_tensor = L_stack0_tensor 2025-03-04T21:03:21.3157832Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.3158359Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3158825Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3159277Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3159747Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3160195Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.3160689Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3161159Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3161630Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3162097Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3162532Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.3163005Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3163475Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3163941Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3164379Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3164813Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.3165308Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3165817Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3166290Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3166727Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3167158Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:03:21.3167676Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3168186Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3168663Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3169114Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3169530Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.3170041Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3170534Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3170991Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3171426Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3171869Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.3172339Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3172795Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3173196Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3173618Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3173985Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.3174428Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3174850Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3175249Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3175674Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3176025Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.3176453Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3176861Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3177296Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3177686Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3178046Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.3178474Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3178885Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3179286Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3179676Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3180043Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.3180484Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3180920Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3181334Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3181744Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3182124Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.3182545Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3182952Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3183348Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3183780Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3184163Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.3184598Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3185035Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3185459Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3185840Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3186201Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.3186615Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3187042Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3187453Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3187861Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3188236Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:03:21.3188687Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3189157Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3189585Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3190010Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3190381Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.3190850Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3191283Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3191687Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3192096Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3192466Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.3192905Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3193330Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3193803Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3194240Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3194630Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.3195097Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3195518Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3195939Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3196373Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3196752Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.3197191Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3197624Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3198075Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3198474Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3198854Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.3199287Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3199730Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3200142Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3200540Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3200939Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.3201385Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3201823Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3202461Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3202875Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3203301Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.3203741Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3204181Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3204596Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3205066Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3205420Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.3205830Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3206237Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3206629Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3207015Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3207362Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.3207779Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3208209Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3208597Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3208987Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3209336Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.3209750Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3210171Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3210573Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3210948Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3211289Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.3211720Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3212118Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3212504Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3212873Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3213228Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.3213634Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3214026Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3214424Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3214798Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3215166Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:03:21.3215581Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3215998Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3216417Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3216800Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3217147Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.3217546Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3217980Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3218359Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3218739Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3219092Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.3219497Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3219894Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3220269Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3220644Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3221024Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.3221439Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3221839Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3222217Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3222596Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3222955Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.3223360Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3223751Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3224132Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3224539Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3224880Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.3225288Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3225683Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3226067Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3226433Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3226782Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.3227188Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3227601Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3227982Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3228351Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3228699Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.3229101Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3229513Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3229894Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3230264Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3230615Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.3231043Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3231443Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3231824Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3232219Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3232576Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.3232976Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3233389Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3233870Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3234267Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3234638Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.3235070Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3235485Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3235894Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3236295Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3236659Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.3237092Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3237550Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3237953Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3238348Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3238711Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.3239144Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3239558Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3239961Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3240355Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3240722Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.3241155Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3241571Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3241978Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3242369Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3242745Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.3243175Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3243582Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3243977Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3244395Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3244753Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.3245185Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3245607Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3246003Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3246390Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3246752Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.3247178Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3247597Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3247985Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3248382Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3248751Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.3249168Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3249578Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3249973Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3250388Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3250802Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.3251279Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3251726Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3252131Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3252540Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3252909Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.3253334Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3253724Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3254124Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3254506Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3254856Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.3255270Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3255678Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3256088Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3256460Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3256823Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.3257243Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3258215Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3258617Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3259005Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3259357Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.3259760Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3260159Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3260542Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3260919Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3261296Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.3261704Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3262115Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3262510Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3262885Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3263250Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.3263648Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3264049Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3264425Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3264829Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3265170Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.3265572Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3265971Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3266359Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3266749Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3267089Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.3267510Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3267919Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3268307Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3268695Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3269042Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.3269473Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3269875Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3270266Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3270653Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3271035Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.3271457Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3271869Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3272265Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3272651Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3273015Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.3273432Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3273906Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3274341Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3274747Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3275128Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.3275569Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3275993Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3276397Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3276768Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3277120Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.3277521Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3277963Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3278350Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3278731Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3279083Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.3279486Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3279886Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3280264Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3280660Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3281009Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.3281417Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3281829Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3282229Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3282627Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3282979Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.3283398Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3283803Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3284231Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3284623Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3284976Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.3285390Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3285805Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3286201Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3286581Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3286940Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.3287385Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3287798Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3288197Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3288579Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3288944Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.3289372Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3289784Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3290179Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3290562Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3290951Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.3291361Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3291780Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3292174Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3292561Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3292924Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.3293335Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3293764Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3294153Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3294540Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3294900Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.3295314Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3295746Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3296138Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3296528Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3296880Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.3297324Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3297741Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3298130Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3298519Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3298875Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.3299295Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3299701Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3300094Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3300504Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3300858Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.3301278Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3301683Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3302240Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3302665Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3303029Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.3303450Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3303845Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3304283Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3304666Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3305042Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.3305447Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3305863Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3306250Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3306629Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3307502Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.3308352Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3309264Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3310127Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3310983Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3311794Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.3312624Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3313502Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3314446Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3315417Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3316197Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.3317004Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3317874Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3318746Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3319560Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3320347Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.3321168Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3322062Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3322913Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3323733Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3324519Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.3325340Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3326233Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3327083Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3327914Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3328707Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.3329559Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3330445Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3331306Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3332133Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3332930Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.3333752Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3334631Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3335503Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3336331Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3337100Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.3337904Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3338778Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3339654Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3340460Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3341233Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.3342040Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3342942Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3343794Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3344619Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3345431Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.3346252Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3347126Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3347979Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3348826Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3349670Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.3350516Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3351399Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3352270Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3353134Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3354009Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.3354875Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3355785Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3356693Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3357555Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3358379Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.3359231Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3360142Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3361031Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3361888Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3362708Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.3363571Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3364483Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3365353Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3366189Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3366986Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.3367833Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3368702Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3369534Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3370340Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3371150Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.3371951Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3372801Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3373637Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3374465Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3375254Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.3376071Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3376958Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3377813Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3378641Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3379429Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.3380252Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3381152Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3382043Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3382869Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3383658Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.3384512Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3385392Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3386248Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3387072Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3387867Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.3388700Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3389575Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3390423Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3391267Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3392060Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.3392879Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3393793Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3394682Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3395552Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3396340Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.3397152Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3398029Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3398921Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3399755Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3400567Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.3401411Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3402553Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3403445Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3404303Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3405122Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.3406012Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3406905Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3407771Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3408618Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3409469Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:03:21.3410366Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3411299Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3412207Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3413118Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3413937Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.3414737Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3415591Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3416438Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3417267Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3418062Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.3418854Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3419721Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3420554Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3421359Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3422121Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.3422911Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3423784Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3424618Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3425420Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3426187Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.3427029Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3427901Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3428748Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3429576Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3430367Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.3431183Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3432055Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3432902Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3433796Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3434614Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.3435460Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.3436333Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.3437211Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.3438074Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.3438767Z l_self_modules_backbone_lateral_convs_0_parameters_weight_ = L_self_modules_backbone_lateral_convs_0_parameters_weight_ 2025-03-04T21:03:21.3439276Z l_self_modules_backbone_lateral_convs_0_parameters_bias_ = L_self_modules_backbone_lateral_convs_0_parameters_bias_ 2025-03-04T21:03:21.3439777Z l_self_modules_backbone_output_convs_0_parameters_weight_ = L_self_modules_backbone_output_convs_0_parameters_weight_ 2025-03-04T21:03:21.3440269Z l_self_modules_backbone_output_convs_0_parameters_bias_ = L_self_modules_backbone_output_convs_0_parameters_bias_ 2025-03-04T21:03:21.3440793Z l_self_modules_backbone_lateral_convs_1_parameters_weight_ = L_self_modules_backbone_lateral_convs_1_parameters_weight_ 2025-03-04T21:03:21.3441288Z l_self_modules_backbone_lateral_convs_1_parameters_bias_ = L_self_modules_backbone_lateral_convs_1_parameters_bias_ 2025-03-04T21:03:21.3441787Z l_self_modules_backbone_output_convs_1_parameters_weight_ = L_self_modules_backbone_output_convs_1_parameters_weight_ 2025-03-04T21:03:21.3442275Z l_self_modules_backbone_output_convs_1_parameters_bias_ = L_self_modules_backbone_output_convs_1_parameters_bias_ 2025-03-04T21:03:21.3442769Z l_self_modules_backbone_lateral_convs_2_parameters_weight_ = L_self_modules_backbone_lateral_convs_2_parameters_weight_ 2025-03-04T21:03:21.3443268Z l_self_modules_backbone_lateral_convs_2_parameters_bias_ = L_self_modules_backbone_lateral_convs_2_parameters_bias_ 2025-03-04T21:03:21.3443760Z l_self_modules_backbone_output_convs_2_parameters_weight_ = L_self_modules_backbone_output_convs_2_parameters_weight_ 2025-03-04T21:03:21.3444259Z l_self_modules_backbone_output_convs_2_parameters_bias_ = L_self_modules_backbone_output_convs_2_parameters_bias_ 2025-03-04T21:03:21.3444749Z l_self_modules_backbone_lateral_convs_3_parameters_weight_ = L_self_modules_backbone_lateral_convs_3_parameters_weight_ 2025-03-04T21:03:21.3445240Z l_self_modules_backbone_lateral_convs_3_parameters_bias_ = L_self_modules_backbone_lateral_convs_3_parameters_bias_ 2025-03-04T21:03:21.3445729Z l_self_modules_backbone_output_convs_3_parameters_weight_ = L_self_modules_backbone_output_convs_3_parameters_weight_ 2025-03-04T21:03:21.3446215Z l_self_modules_backbone_output_convs_3_parameters_bias_ = L_self_modules_backbone_output_convs_3_parameters_bias_ 2025-03-04T21:03:21.3446878Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T21:03:21.3447644Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ 2025-03-04T21:03:21.3448394Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ 2025-03-04T21:03:21.3449153Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ 2025-03-04T21:03:21.3449882Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ 2025-03-04T21:03:21.3450596Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T21:03:21.3451289Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T21:03:21.3452027Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T21:03:21.3452826Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T21:03:21.3453607Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T21:03:21.3454411Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T21:03:21.3454883Z 2025-03-04T21:03:21.3455268Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3456138Z x: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1); l_stack0_tensor = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.3456791Z 2025-03-04T21:03:21.3457154Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3459195Z x_1: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3461066Z 2025-03-04T21:03:21.3461442Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x) 2025-03-04T21:03:21.3461928Z x_2: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.relu_(x_1); x_1 = None 2025-03-04T21:03:21.3462186Z 2025-03-04T21:03:21.3462635Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1) 2025-03-04T21:03:21.3463288Z x_3: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1); x_2 = None 2025-03-04T21:03:21.3463624Z 2025-03-04T21:03:21.3463963Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3464746Z x_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.3465342Z 2025-03-04T21:03:21.3465685Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3467774Z x_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3469639Z 2025-03-04T21:03:21.3470004Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3470472Z out: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_5); x_5 = None 2025-03-04T21:03:21.3470722Z 2025-03-04T21:03:21.3471061Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3471867Z x_6: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.3472472Z 2025-03-04T21:03:21.3472829Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3475075Z x_7: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_6 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3477018Z 2025-03-04T21:03:21.3477379Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3477852Z out_1: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_7); x_7 = None 2025-03-04T21:03:21.3478105Z 2025-03-04T21:03:21.3478434Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3479252Z x_8: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_1 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.3479870Z 2025-03-04T21:03:21.3480221Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3482330Z x_9: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3484202Z 2025-03-04T21:03:21.3484538Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3485368Z x_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); x_3 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:03:21.3485990Z 2025-03-04T21:03:21.3486333Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3488522Z x_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_10 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3490665Z 2025-03-04T21:03:21.3491030Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.3491516Z x_9 += x_11; out_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_9; x_9 = x_11 = None 2025-03-04T21:03:21.3491772Z 2025-03-04T21:03:21.3492140Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3492630Z out_3: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_2); out_2 = None 2025-03-04T21:03:21.3492911Z 2025-03-04T21:03:21.3493251Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3494050Z x_12: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.3494647Z 2025-03-04T21:03:21.3495000Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3497233Z x_13: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_12 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3499141Z 2025-03-04T21:03:21.3499508Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3499993Z out_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_13); x_13 = None 2025-03-04T21:03:21.3500247Z 2025-03-04T21:03:21.3500575Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3501378Z x_14: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.3502124Z 2025-03-04T21:03:21.3502481Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3504638Z x_15: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_14 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3506496Z 2025-03-04T21:03:21.3506857Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3507359Z out_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_15); x_15 = None 2025-03-04T21:03:21.3507615Z 2025-03-04T21:03:21.3507946Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3508739Z x_16: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_5 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.3509344Z 2025-03-04T21:03:21.3509690Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3511825Z x_17: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_16 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3513782Z 2025-03-04T21:03:21.3514159Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.3514673Z x_17 += out_3; out_6: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_17; x_17 = out_3 = None 2025-03-04T21:03:21.3514951Z 2025-03-04T21:03:21.3515312Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3515794Z out_7: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_6); out_6 = None 2025-03-04T21:03:21.3516053Z 2025-03-04T21:03:21.3516382Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3517181Z x_18: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.3517770Z 2025-03-04T21:03:21.3518116Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3520218Z x_19: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_18 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3522124Z 2025-03-04T21:03:21.3522494Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3522973Z out_8: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_19); x_19 = None 2025-03-04T21:03:21.3523229Z 2025-03-04T21:03:21.3523568Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3524396Z x_20: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.3525003Z 2025-03-04T21:03:21.3525361Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3527480Z x_21: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_20 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3529399Z 2025-03-04T21:03:21.3529764Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3530238Z out_9: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_21); x_21 = None 2025-03-04T21:03:21.3530513Z 2025-03-04T21:03:21.3530842Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3531643Z x_22: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_9 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.3532248Z 2025-03-04T21:03:21.3532597Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3534712Z x_23: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_22 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3536614Z 2025-03-04T21:03:21.3536965Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.3537439Z x_23 += out_7; out_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_23; x_23 = out_7 = None 2025-03-04T21:03:21.3537699Z 2025-03-04T21:03:21.3538091Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3538570Z out_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_10); out_10 = None 2025-03-04T21:03:21.3538831Z 2025-03-04T21:03:21.3539160Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3539937Z x_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.3540524Z 2025-03-04T21:03:21.3540867Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3542939Z x_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3544816Z 2025-03-04T21:03:21.3545174Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3545647Z out_12: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_25); x_25 = None 2025-03-04T21:03:21.3545898Z 2025-03-04T21:03:21.3546222Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3547010Z x_26: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_12 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.3547605Z 2025-03-04T21:03:21.3547973Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3550068Z x_27: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_26 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3552118Z 2025-03-04T21:03:21.3552512Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3553022Z out_13: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_27); x_27 = None 2025-03-04T21:03:21.3553294Z 2025-03-04T21:03:21.3553640Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3554577Z x_28: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_13 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.3555235Z 2025-03-04T21:03:21.3555609Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3557885Z x_29: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_28 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3559952Z 2025-03-04T21:03:21.3560313Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3561177Z x_30: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:03:21.3561838Z 2025-03-04T21:03:21.3562204Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3564486Z x_31: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_30 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3566615Z 2025-03-04T21:03:21.3567012Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.3567495Z x_29 += x_31; out_14: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_29; x_29 = x_31 = None 2025-03-04T21:03:21.3567754Z 2025-03-04T21:03:21.3568119Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3568609Z out_15: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_14); out_14 = None 2025-03-04T21:03:21.3568874Z 2025-03-04T21:03:21.3569210Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3570015Z x_32: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.3570626Z 2025-03-04T21:03:21.3570975Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3573133Z x_33: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_32 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3575062Z 2025-03-04T21:03:21.3575434Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3575918Z out_16: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_33); x_33 = None 2025-03-04T21:03:21.3576180Z 2025-03-04T21:03:21.3576521Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3577335Z x_34: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_16 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.3577935Z 2025-03-04T21:03:21.3578279Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3580382Z x_35: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_34 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3582245Z 2025-03-04T21:03:21.3582608Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3583092Z out_17: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_35); x_35 = None 2025-03-04T21:03:21.3583341Z 2025-03-04T21:03:21.3583666Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3584449Z x_36: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_17 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.3585046Z 2025-03-04T21:03:21.3585387Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3587453Z x_37: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_36 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3589363Z 2025-03-04T21:03:21.3589718Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.3590205Z x_37 += out_15; out_18: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_37; x_37 = out_15 = None 2025-03-04T21:03:21.3590493Z 2025-03-04T21:03:21.3590903Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3591384Z out_19: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_18); out_18 = None 2025-03-04T21:03:21.3591641Z 2025-03-04T21:03:21.3591967Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3592750Z x_38: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.3593339Z 2025-03-04T21:03:21.3593722Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3595847Z x_39: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_38 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3597709Z 2025-03-04T21:03:21.3598063Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3598526Z out_20: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_39); x_39 = None 2025-03-04T21:03:21.3598776Z 2025-03-04T21:03:21.3599107Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3599923Z x_40: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_20 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.3600530Z 2025-03-04T21:03:21.3600876Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3603098Z x_41: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_40 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3605067Z 2025-03-04T21:03:21.3605433Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3605910Z out_21: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_41); x_41 = None 2025-03-04T21:03:21.3606167Z 2025-03-04T21:03:21.3606503Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3607307Z x_42: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_21 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.3607974Z 2025-03-04T21:03:21.3608330Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3610477Z x_43: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_42 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3612414Z 2025-03-04T21:03:21.3612776Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.3613265Z x_43 += out_19; out_22: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_43; x_43 = out_19 = None 2025-03-04T21:03:21.3613534Z 2025-03-04T21:03:21.3613914Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3614420Z out_23: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_22); out_22 = None 2025-03-04T21:03:21.3614682Z 2025-03-04T21:03:21.3615011Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3615802Z x_44: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.3616397Z 2025-03-04T21:03:21.3616742Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3618832Z x_45: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_44 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3620728Z 2025-03-04T21:03:21.3621093Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3621574Z out_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_45); x_45 = None 2025-03-04T21:03:21.3621850Z 2025-03-04T21:03:21.3622183Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3622974Z x_46: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.3623572Z 2025-03-04T21:03:21.3623912Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3626023Z x_47: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_46 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3627927Z 2025-03-04T21:03:21.3628286Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3628757Z out_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_47); x_47 = None 2025-03-04T21:03:21.3629005Z 2025-03-04T21:03:21.3629329Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3630115Z x_48: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_25 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.3630708Z 2025-03-04T21:03:21.3631045Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3633174Z x_49: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_48 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3635132Z 2025-03-04T21:03:21.3635527Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.3636011Z x_49 += out_23; out_26: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_49; x_49 = out_23 = None 2025-03-04T21:03:21.3636276Z 2025-03-04T21:03:21.3636644Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3637131Z out_27: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_26); out_26 = None 2025-03-04T21:03:21.3637393Z 2025-03-04T21:03:21.3637725Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3638516Z x_50: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.3639120Z 2025-03-04T21:03:21.3639468Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3641585Z x_51: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_50 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3643500Z 2025-03-04T21:03:21.3643869Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3644344Z out_28: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_51); x_51 = None 2025-03-04T21:03:21.3644596Z 2025-03-04T21:03:21.3644928Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3645741Z x_52: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_28 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.3646345Z 2025-03-04T21:03:21.3646695Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3648848Z x_53: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_52 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3650729Z 2025-03-04T21:03:21.3651086Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3651544Z out_29: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_53); x_53 = None 2025-03-04T21:03:21.3651787Z 2025-03-04T21:03:21.3652111Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3652887Z x_54: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_29 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.3653471Z 2025-03-04T21:03:21.3653808Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3655861Z x_55: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_54 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3657729Z 2025-03-04T21:03:21.3658052Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3658828Z x_56: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:03:21.3659442Z 2025-03-04T21:03:21.3659793Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3661959Z x_57: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_56 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3664035Z 2025-03-04T21:03:21.3664401Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.3664856Z x_55 += x_57; out_30: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_55; x_55 = x_57 = None 2025-03-04T21:03:21.3665099Z 2025-03-04T21:03:21.3665456Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3665920Z out_31: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_30); out_30 = None 2025-03-04T21:03:21.3666169Z 2025-03-04T21:03:21.3666494Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3667258Z x_58: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.3667839Z 2025-03-04T21:03:21.3668178Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3670270Z x_59: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_58 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3672115Z 2025-03-04T21:03:21.3672472Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3672952Z out_32: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_59); x_59 = None 2025-03-04T21:03:21.3673198Z 2025-03-04T21:03:21.3673527Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3674381Z x_60: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_32 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.3674981Z 2025-03-04T21:03:21.3675318Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3677404Z x_61: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_60 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3679292Z 2025-03-04T21:03:21.3679674Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3680154Z out_33: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_61); x_61 = None 2025-03-04T21:03:21.3680405Z 2025-03-04T21:03:21.3680738Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3681533Z x_62: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_33 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.3682151Z 2025-03-04T21:03:21.3682503Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3684634Z x_63: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_62 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3686543Z 2025-03-04T21:03:21.3686905Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.3687381Z x_63 += out_31; out_34: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_63; x_63 = out_31 = None 2025-03-04T21:03:21.3687640Z 2025-03-04T21:03:21.3688005Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3688483Z out_35: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_34); out_34 = None 2025-03-04T21:03:21.3688735Z 2025-03-04T21:03:21.3689064Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3689877Z x_64: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.3690475Z 2025-03-04T21:03:21.3690823Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3692912Z x_65: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_64 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3694752Z 2025-03-04T21:03:21.3695115Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3695586Z out_36: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_65); x_65 = None 2025-03-04T21:03:21.3695852Z 2025-03-04T21:03:21.3696185Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3696986Z x_66: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_36 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.3697568Z 2025-03-04T21:03:21.3697908Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3700002Z x_67: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_66 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3702243Z 2025-03-04T21:03:21.3702638Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3703140Z out_37: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_67); x_67 = None 2025-03-04T21:03:21.3703425Z 2025-03-04T21:03:21.3703683Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3704247Z x_68: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_37 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.3704319Z 2025-03-04T21:03:21.3704587Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3706352Z x_69: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_68 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3706426Z 2025-03-04T21:03:21.3706721Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.3706902Z x_69 += out_35; out_38: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_69; x_69 = out_35 = None 2025-03-04T21:03:21.3706969Z 2025-03-04T21:03:21.3707278Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3707424Z out_39: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_38); out_38 = None 2025-03-04T21:03:21.3707498Z 2025-03-04T21:03:21.3707772Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3708258Z x_70: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.3708322Z 2025-03-04T21:03:21.3708595Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3710411Z x_71: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_70 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3710482Z 2025-03-04T21:03:21.3710805Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3710937Z out_40: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_71); x_71 = None 2025-03-04T21:03:21.3711004Z 2025-03-04T21:03:21.3711258Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3711747Z x_72: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_40 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.3711811Z 2025-03-04T21:03:21.3712089Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3713928Z x_73: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_72 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3714022Z 2025-03-04T21:03:21.3714319Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3714455Z out_41: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_73); x_73 = None 2025-03-04T21:03:21.3714528Z 2025-03-04T21:03:21.3714793Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3715300Z x_74: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_41 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.3715382Z 2025-03-04T21:03:21.3715663Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3717506Z x_75: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_74 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3717572Z 2025-03-04T21:03:21.3717861Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.3718005Z x_75 += out_39; out_42: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_75; x_75 = out_39 = None 2025-03-04T21:03:21.3718075Z 2025-03-04T21:03:21.3718360Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3718507Z out_43: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_42); out_42 = None 2025-03-04T21:03:21.3718566Z 2025-03-04T21:03:21.3718824Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3719315Z x_76: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.3719375Z 2025-03-04T21:03:21.3719647Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3721453Z x_77: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_76 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3721539Z 2025-03-04T21:03:21.3721826Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3721957Z out_44: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_77); x_77 = None 2025-03-04T21:03:21.3722046Z 2025-03-04T21:03:21.3722297Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3722786Z x_78: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_44 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.3722846Z 2025-03-04T21:03:21.3723114Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3724930Z x_79: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_78 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3725002Z 2025-03-04T21:03:21.3725289Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3725416Z out_45: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_79); x_79 = None 2025-03-04T21:03:21.3725482Z 2025-03-04T21:03:21.3725732Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3726235Z x_80: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_45 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.3726296Z 2025-03-04T21:03:21.3726568Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3728342Z x_81: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_80 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3728404Z 2025-03-04T21:03:21.3728683Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.3728842Z x_81 += out_43; out_46: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_81; x_81 = out_43 = None 2025-03-04T21:03:21.3728909Z 2025-03-04T21:03:21.3729184Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3729326Z out_47: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_46); out_46 = None 2025-03-04T21:03:21.3729388Z 2025-03-04T21:03:21.3729640Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3730108Z x_82: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.3730179Z 2025-03-04T21:03:21.3730468Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3732243Z x_83: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_82 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3732312Z 2025-03-04T21:03:21.3732586Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3732720Z out_48: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_83); x_83 = None 2025-03-04T21:03:21.3732778Z 2025-03-04T21:03:21.3733030Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3733530Z x_84: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_48 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.3733594Z 2025-03-04T21:03:21.3733858Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3735631Z x_85: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_84 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3735714Z 2025-03-04T21:03:21.3735996Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3736122Z out_49: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_85); x_85 = None 2025-03-04T21:03:21.3736189Z 2025-03-04T21:03:21.3736468Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3736990Z x_86: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_49 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.3737053Z 2025-03-04T21:03:21.3737325Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3739091Z x_87: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_86 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3739161Z 2025-03-04T21:03:21.3739440Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.3739576Z x_87 += out_47; out_50: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_87; x_87 = out_47 = None 2025-03-04T21:03:21.3739659Z 2025-03-04T21:03:21.3739934Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3740077Z out_51: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_50); out_50 = None 2025-03-04T21:03:21.3740138Z 2025-03-04T21:03:21.3740387Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3740858Z x_88: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.3740925Z 2025-03-04T21:03:21.3741184Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3742962Z x_89: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_88 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3743043Z 2025-03-04T21:03:21.3743318Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3743479Z out_52: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_89); x_89 = None 2025-03-04T21:03:21.3743539Z 2025-03-04T21:03:21.3743789Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3744258Z x_90: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_52 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.3744326Z 2025-03-04T21:03:21.3744584Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3746337Z x_91: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_90 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3746420Z 2025-03-04T21:03:21.3746695Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3746828Z out_53: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_91); x_91 = None 2025-03-04T21:03:21.3746888Z 2025-03-04T21:03:21.3747136Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3747609Z x_92: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_53 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.3747678Z 2025-03-04T21:03:21.3747940Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3749701Z x_93: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_92 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3749771Z 2025-03-04T21:03:21.3750075Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.3750216Z x_93 += out_51; out_54: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_93; x_93 = out_51 = None 2025-03-04T21:03:21.3750283Z 2025-03-04T21:03:21.3750562Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3750707Z out_55: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_54); out_54 = None 2025-03-04T21:03:21.3750769Z 2025-03-04T21:03:21.3751023Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3751500Z x_94: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.3751570Z 2025-03-04T21:03:21.3751835Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3753645Z x_95: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_94 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3753783Z 2025-03-04T21:03:21.3754070Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3754208Z out_56: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_95); x_95 = None 2025-03-04T21:03:21.3754270Z 2025-03-04T21:03:21.3754525Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3755026Z x_96: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_56 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.3755094Z 2025-03-04T21:03:21.3755360Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3757213Z x_97: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_96 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3757287Z 2025-03-04T21:03:21.3757571Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3757707Z out_57: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_97); x_97 = None 2025-03-04T21:03:21.3757767Z 2025-03-04T21:03:21.3758024Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3758515Z x_98: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_57 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.3758580Z 2025-03-04T21:03:21.3758847Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3760667Z x_99: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_98 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3760758Z 2025-03-04T21:03:21.3761036Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.3761188Z x_99 += out_55; out_58: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_99; x_99 = out_55 = None 2025-03-04T21:03:21.3761262Z 2025-03-04T21:03:21.3761559Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3761698Z out_59: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_58); out_58 = None 2025-03-04T21:03:21.3761767Z 2025-03-04T21:03:21.3762019Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3762522Z x_100: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.3762590Z 2025-03-04T21:03:21.3762860Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3764728Z x_101: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_100 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3764804Z 2025-03-04T21:03:21.3765102Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3765244Z out_60: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_101); x_101 = None 2025-03-04T21:03:21.3765305Z 2025-03-04T21:03:21.3765561Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3766046Z x_102: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_60 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.3766134Z 2025-03-04T21:03:21.3766406Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3768222Z x_103: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_102 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3768311Z 2025-03-04T21:03:21.3768599Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3768745Z out_61: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_103); x_103 = None 2025-03-04T21:03:21.3768804Z 2025-03-04T21:03:21.3769067Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3769570Z x_104: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_61 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.3769640Z 2025-03-04T21:03:21.3769925Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3771701Z x_105: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_104 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3771770Z 2025-03-04T21:03:21.3772046Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.3772197Z x_105 += out_59; out_62: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_105; x_105 = out_59 = None 2025-03-04T21:03:21.3772256Z 2025-03-04T21:03:21.3772539Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3772690Z out_63: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_62); out_62 = None 2025-03-04T21:03:21.3772758Z 2025-03-04T21:03:21.3773005Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3773488Z x_106: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_63, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.3773548Z 2025-03-04T21:03:21.3773815Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3775586Z x_107: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_106 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3775663Z 2025-03-04T21:03:21.3775949Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3776082Z out_64: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_107); x_107 = None 2025-03-04T21:03:21.3776152Z 2025-03-04T21:03:21.3776426Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3776916Z x_108: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_64, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_64 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.3776982Z 2025-03-04T21:03:21.3777249Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3779129Z x_109: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_108, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_108 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3779211Z 2025-03-04T21:03:21.3779489Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3779631Z out_65: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_109); x_109 = None 2025-03-04T21:03:21.3779690Z 2025-03-04T21:03:21.3779940Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3780413Z x_110: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_65, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_65 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.3780479Z 2025-03-04T21:03:21.3780738Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3782506Z x_111: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_110, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_110 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3782589Z 2025-03-04T21:03:21.3782859Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.3783045Z x_111 += out_63; out_66: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_111; x_111 = out_63 = None 2025-03-04T21:03:21.3783105Z 2025-03-04T21:03:21.3783387Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3783522Z out_67: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_66); out_66 = None 2025-03-04T21:03:21.3783586Z 2025-03-04T21:03:21.3783828Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3784314Z x_112: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_67, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.3784377Z 2025-03-04T21:03:21.3784641Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3786423Z x_113: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_112, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_112 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3786502Z 2025-03-04T21:03:21.3786790Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3786920Z out_68: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_113); x_113 = None 2025-03-04T21:03:21.3786985Z 2025-03-04T21:03:21.3787233Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3787732Z x_114: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_68, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_68 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.3787805Z 2025-03-04T21:03:21.3788069Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3789880Z x_115: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_114, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_114 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3789945Z 2025-03-04T21:03:21.3790245Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3790375Z out_69: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_115); x_115 = None 2025-03-04T21:03:21.3790443Z 2025-03-04T21:03:21.3790685Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3791176Z x_116: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_69, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_69 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.3791244Z 2025-03-04T21:03:21.3791502Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3793318Z x_117: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_116, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_116 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3793402Z 2025-03-04T21:03:21.3793732Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.3793899Z x_117 += out_67; out_70: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_117; x_117 = out_67 = None 2025-03-04T21:03:21.3793960Z 2025-03-04T21:03:21.3794276Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3794413Z out_71: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_70); out_70 = None 2025-03-04T21:03:21.3794480Z 2025-03-04T21:03:21.3794722Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3795208Z x_118: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_71, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.3795270Z 2025-03-04T21:03:21.3795540Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3797385Z x_119: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_118, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_118 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3797460Z 2025-03-04T21:03:21.3797758Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3797890Z out_72: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_119); x_119 = None 2025-03-04T21:03:21.3797958Z 2025-03-04T21:03:21.3798210Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3798712Z x_120: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_72, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_72 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.3798788Z 2025-03-04T21:03:21.3799062Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3800888Z x_121: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_120, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_120 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3800969Z 2025-03-04T21:03:21.3801266Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3801399Z out_73: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_121); x_121 = None 2025-03-04T21:03:21.3801466Z 2025-03-04T21:03:21.3801726Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3802361Z x_122: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_73, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_73 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.3802430Z 2025-03-04T21:03:21.3802760Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3804598Z x_123: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_122, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_122 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3804660Z 2025-03-04T21:03:21.3804949Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.3805095Z x_123 += out_71; out_74: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_123; x_123 = out_71 = None 2025-03-04T21:03:21.3805163Z 2025-03-04T21:03:21.3805449Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3805625Z out_75: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_74); out_74 = None 2025-03-04T21:03:21.3805694Z 2025-03-04T21:03:21.3805947Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3806439Z x_124: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_75, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.3806501Z 2025-03-04T21:03:21.3806772Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3808559Z x_125: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_124, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_124 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3808658Z 2025-03-04T21:03:21.3808943Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3809076Z out_76: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_125); x_125 = None 2025-03-04T21:03:21.3809142Z 2025-03-04T21:03:21.3809417Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3809904Z x_126: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_76, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_76 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.3809964Z 2025-03-04T21:03:21.3810228Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3812032Z x_127: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_126, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_126 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3812111Z 2025-03-04T21:03:21.3812395Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3812524Z out_77: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_127); x_127 = None 2025-03-04T21:03:21.3812591Z 2025-03-04T21:03:21.3812833Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3813319Z x_128: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_77, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_77 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.3813378Z 2025-03-04T21:03:21.3813642Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3815432Z x_129: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_128, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_128 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3815510Z 2025-03-04T21:03:21.3815794Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.3815969Z x_129 += out_75; out_78: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_129; x_129 = out_75 = None 2025-03-04T21:03:21.3816039Z 2025-03-04T21:03:21.3816322Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3816468Z out_79: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_78); out_78 = None 2025-03-04T21:03:21.3816530Z 2025-03-04T21:03:21.3816794Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3817276Z x_130: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_79, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.3817346Z 2025-03-04T21:03:21.3817615Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3819389Z x_131: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_130, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_130 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3819473Z 2025-03-04T21:03:21.3819760Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3819891Z out_80: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_131); x_131 = None 2025-03-04T21:03:21.3819957Z 2025-03-04T21:03:21.3820202Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3820709Z x_132: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_80, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_80 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.3820784Z 2025-03-04T21:03:21.3821065Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3822866Z x_133: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_132, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_132 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3822938Z 2025-03-04T21:03:21.3823220Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3823349Z out_81: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_133); x_133 = None 2025-03-04T21:03:21.3823419Z 2025-03-04T21:03:21.3823663Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3824155Z x_134: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_81, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_81 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.3824216Z 2025-03-04T21:03:21.3824480Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3826244Z x_135: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_134, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_134 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3826323Z 2025-03-04T21:03:21.3826610Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.3826756Z x_135 += out_79; out_82: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_135; x_135 = out_79 = None 2025-03-04T21:03:21.3826822Z 2025-03-04T21:03:21.3827097Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3827270Z out_83: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_82); out_82 = None 2025-03-04T21:03:21.3827329Z 2025-03-04T21:03:21.3827575Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3828041Z x_136: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_83, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.3828109Z 2025-03-04T21:03:21.3828363Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3830191Z x_137: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_136, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_136 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3830265Z 2025-03-04T21:03:21.3830556Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3830694Z out_84: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_137); x_137 = None 2025-03-04T21:03:21.3830751Z 2025-03-04T21:03:21.3831000Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3831499Z x_138: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_84, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_84 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.3831583Z 2025-03-04T21:03:21.3831858Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3833757Z x_139: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_138, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_138 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3833854Z 2025-03-04T21:03:21.3834155Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3834290Z out_85: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_139); x_139 = None 2025-03-04T21:03:21.3834357Z 2025-03-04T21:03:21.3834607Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3835113Z x_140: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_85, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_85 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.3835176Z 2025-03-04T21:03:21.3835483Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3837294Z x_141: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_140, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_140 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3837367Z 2025-03-04T21:03:21.3837653Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.3837798Z x_141 += out_83; out_86: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_141; x_141 = out_83 = None 2025-03-04T21:03:21.3837868Z 2025-03-04T21:03:21.3838148Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3838314Z out_87: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_86); out_86 = None 2025-03-04T21:03:21.3838375Z 2025-03-04T21:03:21.3838631Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3839122Z x_142: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_87, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.3839192Z 2025-03-04T21:03:21.3839458Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3841292Z x_143: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_142, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_142 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3841373Z 2025-03-04T21:03:21.3841656Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3841797Z out_88: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_143); x_143 = None 2025-03-04T21:03:21.3841858Z 2025-03-04T21:03:21.3842144Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3842627Z x_144: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_88, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_88 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.3842693Z 2025-03-04T21:03:21.3842955Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3844767Z x_145: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_144, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_144 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3844854Z 2025-03-04T21:03:21.3845141Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3845280Z out_89: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_145); x_145 = None 2025-03-04T21:03:21.3845339Z 2025-03-04T21:03:21.3845600Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3846099Z x_146: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_89, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_89 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.3846162Z 2025-03-04T21:03:21.3846434Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3848265Z x_147: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_146, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_146 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3848348Z 2025-03-04T21:03:21.3848637Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.3848812Z x_147 += out_87; out_90: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_147; x_147 = out_87 = None 2025-03-04T21:03:21.3848883Z 2025-03-04T21:03:21.3849173Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3849317Z out_91: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_90); out_90 = None 2025-03-04T21:03:21.3849377Z 2025-03-04T21:03:21.3849637Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3850124Z x_148: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_91, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.3850196Z 2025-03-04T21:03:21.3850480Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3852330Z x_149: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_148, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_148 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3852420Z 2025-03-04T21:03:21.3852718Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3852863Z out_92: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_149); x_149 = None 2025-03-04T21:03:21.3852926Z 2025-03-04T21:03:21.3853194Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3853716Z x_150: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_92, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_92 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.3853804Z 2025-03-04T21:03:21.3854086Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3856042Z x_151: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_150, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_150 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3856120Z 2025-03-04T21:03:21.3856422Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3856568Z out_93: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_151); x_151 = None 2025-03-04T21:03:21.3856633Z 2025-03-04T21:03:21.3856903Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3857428Z x_152: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_93, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_93 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.3857501Z 2025-03-04T21:03:21.3857780Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3859740Z x_153: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_152, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_152 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3859827Z 2025-03-04T21:03:21.3860124Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.3860285Z x_153 += out_91; out_94: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_153; x_153 = out_91 = None 2025-03-04T21:03:21.3860350Z 2025-03-04T21:03:21.3860652Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3860823Z out_95: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_94); out_94 = None 2025-03-04T21:03:21.3860887Z 2025-03-04T21:03:21.3861162Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3861650Z x_154: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_95, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.3861721Z 2025-03-04T21:03:21.3861989Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3863853Z x_155: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_154, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_154 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3863925Z 2025-03-04T21:03:21.3864211Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3864350Z out_96: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_155); x_155 = None 2025-03-04T21:03:21.3864409Z 2025-03-04T21:03:21.3864665Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3865150Z x_156: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_96, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_96 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.3865232Z 2025-03-04T21:03:21.3865498Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3867324Z x_157: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_156, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_156 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3867407Z 2025-03-04T21:03:21.3867704Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3867845Z out_97: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_157); x_157 = None 2025-03-04T21:03:21.3867905Z 2025-03-04T21:03:21.3868168Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3868665Z x_158: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_97, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_97 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.3868736Z 2025-03-04T21:03:21.3869038Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3870892Z x_159: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_158, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_158 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3870963Z 2025-03-04T21:03:21.3871240Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.3871392Z x_159 += out_95; out_98: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_159; x_159 = out_95 = None 2025-03-04T21:03:21.3871452Z 2025-03-04T21:03:21.3871744Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3871899Z out_99: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_98); out_98 = None 2025-03-04T21:03:21.3871968Z 2025-03-04T21:03:21.3872214Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3872735Z x_160: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_99, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.3872806Z 2025-03-04T21:03:21.3873083Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3875062Z x_161: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_160, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_160 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3875149Z 2025-03-04T21:03:21.3875449Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3875599Z out_100: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_161); x_161 = None 2025-03-04T21:03:21.3875663Z 2025-03-04T21:03:21.3875960Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3876456Z x_162: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_100, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_100 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.3876524Z 2025-03-04T21:03:21.3876792Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3878659Z x_163: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_162, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_162 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3878752Z 2025-03-04T21:03:21.3879086Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3879231Z out_101: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_163); x_163 = None 2025-03-04T21:03:21.3879291Z 2025-03-04T21:03:21.3879544Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3880037Z x_164: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_101, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_101 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.3880106Z 2025-03-04T21:03:21.3880367Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3882181Z x_165: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_164, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_164 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3882267Z 2025-03-04T21:03:21.3882538Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.3882732Z x_165 += out_99; out_102: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_165; x_165 = out_99 = None 2025-03-04T21:03:21.3882794Z 2025-03-04T21:03:21.3883075Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3883216Z out_103: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_102); out_102 = None 2025-03-04T21:03:21.3883283Z 2025-03-04T21:03:21.3883528Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3884008Z x_166: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_103, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.3884069Z 2025-03-04T21:03:21.3884334Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3886100Z x_167: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_166, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_166 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3886178Z 2025-03-04T21:03:21.3886460Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3886593Z out_104: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_167); x_167 = None 2025-03-04T21:03:21.3886659Z 2025-03-04T21:03:21.3886900Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3887391Z x_168: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_104, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_104 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.3887474Z 2025-03-04T21:03:21.3887734Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3889559Z x_169: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_168, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_168 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3889630Z 2025-03-04T21:03:21.3889905Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3890042Z out_105: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_169); x_169 = None 2025-03-04T21:03:21.3890103Z 2025-03-04T21:03:21.3890350Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3890832Z x_170: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_105, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_105 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.3890898Z 2025-03-04T21:03:21.3891155Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3892932Z x_171: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_170, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_170 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3893018Z 2025-03-04T21:03:21.3893296Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.3893457Z x_171 += out_103; out_106: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_171; x_171 = out_103 = None 2025-03-04T21:03:21.3893518Z 2025-03-04T21:03:21.3893807Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3893965Z out_107: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_106); out_106 = None 2025-03-04T21:03:21.3894032Z 2025-03-04T21:03:21.3894274Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3894753Z x_172: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_107, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.3894812Z 2025-03-04T21:03:21.3895080Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3896905Z x_173: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_172, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_172 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3896966Z 2025-03-04T21:03:21.3897251Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3897381Z out_108: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_173); x_173 = None 2025-03-04T21:03:21.3897447Z 2025-03-04T21:03:21.3897689Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3898170Z x_174: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_108, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_108 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.3898247Z 2025-03-04T21:03:21.3898512Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3900286Z x_175: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_174, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_174 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3900364Z 2025-03-04T21:03:21.3900654Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3900785Z out_109: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_175); x_175 = None 2025-03-04T21:03:21.3900850Z 2025-03-04T21:03:21.3901095Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3901589Z x_176: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_109, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_109 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.3901658Z 2025-03-04T21:03:21.3902121Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3903903Z x_177: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_176, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_176 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3903973Z 2025-03-04T21:03:21.3904247Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.3904407Z x_177 += out_107; out_110: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_177; x_177 = out_107 = None 2025-03-04T21:03:21.3904466Z 2025-03-04T21:03:21.3904753Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3904917Z out_111: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_110); out_110 = None 2025-03-04T21:03:21.3904984Z 2025-03-04T21:03:21.3905230Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3905722Z x_178: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_111, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.3905781Z 2025-03-04T21:03:21.3906047Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3907874Z x_179: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_178, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_178 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3907973Z 2025-03-04T21:03:21.3908268Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3908403Z out_112: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_179); x_179 = None 2025-03-04T21:03:21.3908474Z 2025-03-04T21:03:21.3908754Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3909254Z x_180: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_112, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_112 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.3909317Z 2025-03-04T21:03:21.3909589Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3911406Z x_181: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_180, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_180 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3911482Z 2025-03-04T21:03:21.3911774Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3911908Z out_113: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_181); x_181 = None 2025-03-04T21:03:21.3911977Z 2025-03-04T21:03:21.3912230Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3912742Z x_182: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_113, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_113 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.3912803Z 2025-03-04T21:03:21.3913078Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3914994Z x_183: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_182, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_182 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3915082Z 2025-03-04T21:03:21.3915368Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.3915554Z x_183 += out_111; out_114: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_183; x_183 = out_111 = None 2025-03-04T21:03:21.3915620Z 2025-03-04T21:03:21.3915902Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3916048Z out_115: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_114); out_114 = None 2025-03-04T21:03:21.3916114Z 2025-03-04T21:03:21.3916360Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3916851Z x_184: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_115, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.3916915Z 2025-03-04T21:03:21.3917193Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3919002Z x_185: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_184, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_184 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3919087Z 2025-03-04T21:03:21.3919375Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3919508Z out_116: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_185); x_185 = None 2025-03-04T21:03:21.3919576Z 2025-03-04T21:03:21.3919822Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3920322Z x_186: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_116, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_116 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.3920397Z 2025-03-04T21:03:21.3920669Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3922522Z x_187: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_186, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_186 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3922587Z 2025-03-04T21:03:21.3922879Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3923012Z out_117: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_187); x_187 = None 2025-03-04T21:03:21.3923080Z 2025-03-04T21:03:21.3923327Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3923835Z x_188: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_117, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_117 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.3923898Z 2025-03-04T21:03:21.3924172Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3925970Z x_189: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_188, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_188 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3926043Z 2025-03-04T21:03:21.3926321Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.3926470Z x_189 += out_115; out_118: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_189; x_189 = out_115 = None 2025-03-04T21:03:21.3926536Z 2025-03-04T21:03:21.3926813Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3926976Z out_119: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_118); out_118 = None 2025-03-04T21:03:21.3927035Z 2025-03-04T21:03:21.3927287Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3927764Z x_190: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.3927825Z 2025-03-04T21:03:21.3928091Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3929867Z x_191: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_190, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_190 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3929936Z 2025-03-04T21:03:21.3930222Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3930351Z out_120: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_191); x_191 = None 2025-03-04T21:03:21.3930416Z 2025-03-04T21:03:21.3930659Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3931141Z x_192: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_120, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_120 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.3931221Z 2025-03-04T21:03:21.3931483Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3933235Z x_193: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_192, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_192 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3933318Z 2025-03-04T21:03:21.3933606Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3933738Z out_121: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_193); x_193 = None 2025-03-04T21:03:21.3933805Z 2025-03-04T21:03:21.3934054Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3934552Z x_194: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_121, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_121 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.3934615Z 2025-03-04T21:03:21.3934918Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3936711Z x_195: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_194, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_194 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3936775Z 2025-03-04T21:03:21.3937030Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3937533Z x_196: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:03:21.3937599Z 2025-03-04T21:03:21.3937867Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3939673Z x_197: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_196, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_196 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3939760Z 2025-03-04T21:03:21.3940033Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.3940185Z x_195 += x_197; out_122: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_195; x_195 = x_197 = None 2025-03-04T21:03:21.3940244Z 2025-03-04T21:03:21.3940531Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3940669Z out_123: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_122); out_122 = None 2025-03-04T21:03:21.3940735Z 2025-03-04T21:03:21.3940979Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3941489Z x_198: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_123, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.3941558Z 2025-03-04T21:03:21.3941815Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3943566Z x_199: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_198, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_198 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3943637Z 2025-03-04T21:03:21.3943913Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3944047Z out_124: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_199); x_199 = None 2025-03-04T21:03:21.3944106Z 2025-03-04T21:03:21.3944369Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3944843Z x_200: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_124, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_124 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.3944911Z 2025-03-04T21:03:21.3945165Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3946912Z x_201: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_200, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_200 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3946994Z 2025-03-04T21:03:21.3947273Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3947409Z out_125: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_201); x_201 = None 2025-03-04T21:03:21.3947469Z 2025-03-04T21:03:21.3947723Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3948246Z x_202: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_125, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_125 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.3948315Z 2025-03-04T21:03:21.3948586Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3950359Z x_203: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_202, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_202 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3950430Z 2025-03-04T21:03:21.3950705Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.3950881Z x_203 += out_123; out_126: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_203; x_203 = out_123 = None 2025-03-04T21:03:21.3950944Z 2025-03-04T21:03:21.3951239Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3951378Z out_127: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_126); out_126 = None 2025-03-04T21:03:21.3951446Z 2025-03-04T21:03:21.3951694Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3952180Z x_204: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_127, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.3952242Z 2025-03-04T21:03:21.3952515Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3954395Z x_205: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_204, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_204 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3954464Z 2025-03-04T21:03:21.3954790Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3954925Z out_128: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_205); x_205 = None 2025-03-04T21:03:21.3954992Z 2025-03-04T21:03:21.3955243Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3955740Z x_206: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_128, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_128 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.3955809Z 2025-03-04T21:03:21.3956078Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3957884Z x_207: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_206, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_206 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3957973Z 2025-03-04T21:03:21.3958259Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3958399Z out_129: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_207); x_207 = None 2025-03-04T21:03:21.3958459Z 2025-03-04T21:03:21.3958715Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3959215Z x_208: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_129, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_129 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.3959298Z 2025-03-04T21:03:21.3959564Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.3961399Z x_209: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_208, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_208 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.3961469Z 2025-03-04T21:03:21.3961748Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.3961906Z x_209 += out_127; out_130: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_209; x_209 = out_127 = None 2025-03-04T21:03:21.3961968Z 2025-03-04T21:03:21.3962263Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.3962405Z out_131: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_130); out_130 = None 2025-03-04T21:03:21.3962471Z 2025-03-04T21:03:21.3962722Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3963316Z x_210: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(out_131, l_self_modules_backbone_lateral_convs_0_parameters_weight_, l_self_modules_backbone_lateral_convs_0_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_131 = l_self_modules_backbone_lateral_convs_0_parameters_weight_ = l_self_modules_backbone_lateral_convs_0_parameters_bias_ = None 2025-03-04T21:03:21.3963377Z 2025-03-04T21:03:21.3963636Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3964192Z x_211: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_210, l_self_modules_backbone_output_convs_0_parameters_weight_, l_self_modules_backbone_output_convs_0_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_self_modules_backbone_output_convs_0_parameters_weight_ = l_self_modules_backbone_output_convs_0_parameters_bias_ = None 2025-03-04T21:03:21.3964275Z 2025-03-04T21:03:21.3964693Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest") 2025-03-04T21:03:21.3964962Z top_down_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.interpolate(x_210, scale_factor = 2.0, mode = 'nearest'); x_210 = None 2025-03-04T21:03:21.3965029Z 2025-03-04T21:03:21.3965280Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3965865Z x_212: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_lateral_convs_1_parameters_weight_, l_self_modules_backbone_lateral_convs_1_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_119 = l_self_modules_backbone_lateral_convs_1_parameters_weight_ = l_self_modules_backbone_lateral_convs_1_parameters_bias_ = None 2025-03-04T21:03:21.3965945Z 2025-03-04T21:03:21.3966298Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features 2025-03-04T21:03:21.3966489Z prev_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = x_212 + top_down_features; x_212 = top_down_features = None 2025-03-04T21:03:21.3966558Z 2025-03-04T21:03:21.3966806Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3967389Z x_213: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(prev_features, l_self_modules_backbone_output_convs_1_parameters_weight_, l_self_modules_backbone_output_convs_1_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_self_modules_backbone_output_convs_1_parameters_weight_ = l_self_modules_backbone_output_convs_1_parameters_bias_ = None 2025-03-04T21:03:21.3967451Z 2025-03-04T21:03:21.3967888Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest") 2025-03-04T21:03:21.3968213Z top_down_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.interpolate(prev_features, scale_factor = 2.0, mode = 'nearest'); prev_features = None 2025-03-04T21:03:21.3968273Z 2025-03-04T21:03:21.3968524Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3969102Z x_214: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_lateral_convs_2_parameters_weight_, l_self_modules_backbone_lateral_convs_2_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_27 = l_self_modules_backbone_lateral_convs_2_parameters_weight_ = l_self_modules_backbone_lateral_convs_2_parameters_bias_ = None 2025-03-04T21:03:21.3969172Z 2025-03-04T21:03:21.3969519Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features 2025-03-04T21:03:21.3969745Z prev_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = x_214 + top_down_features_1; x_214 = top_down_features_1 = None 2025-03-04T21:03:21.3969803Z 2025-03-04T21:03:21.3970055Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3970643Z x_215: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(prev_features_1, l_self_modules_backbone_output_convs_2_parameters_weight_, l_self_modules_backbone_output_convs_2_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_self_modules_backbone_output_convs_2_parameters_weight_ = l_self_modules_backbone_output_convs_2_parameters_bias_ = None 2025-03-04T21:03:21.3970712Z 2025-03-04T21:03:21.3971108Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest") 2025-03-04T21:03:21.3971432Z top_down_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.interpolate(prev_features_1, scale_factor = 2.0, mode = 'nearest'); prev_features_1 = None 2025-03-04T21:03:21.3971498Z 2025-03-04T21:03:21.3971743Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3972322Z x_216: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_lateral_convs_3_parameters_weight_, l_self_modules_backbone_lateral_convs_3_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_11 = l_self_modules_backbone_lateral_convs_3_parameters_weight_ = l_self_modules_backbone_lateral_convs_3_parameters_bias_ = None 2025-03-04T21:03:21.3972400Z 2025-03-04T21:03:21.3972746Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features 2025-03-04T21:03:21.3972948Z prev_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_216 + top_down_features_2; x_216 = top_down_features_2 = None 2025-03-04T21:03:21.3973017Z 2025-03-04T21:03:21.3973261Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.3973910Z x_217: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(prev_features_2, l_self_modules_backbone_output_convs_3_parameters_weight_, l_self_modules_backbone_output_convs_3_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); prev_features_2 = l_self_modules_backbone_output_convs_3_parameters_weight_ = l_self_modules_backbone_output_convs_3_parameters_bias_ = None 2025-03-04T21:03:21.3973974Z 2025-03-04T21:03:21.3974350Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:200 in forward, code: return [F.max_pool2d(x, kernel_size=1, stride=2, padding=0)] 2025-03-04T21:03:21.3974558Z res: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.max_pool2d(x_211, kernel_size = 1, stride = 2, padding = 0) 2025-03-04T21:03:21.3974627Z 2025-03-04T21:03:21.3975066Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:03:21.3975226Z arange: "f32[304][1]cpu" = torch.arange(0.0, 1216, step = 4, dtype = torch.float32) 2025-03-04T21:03:21.3975288Z 2025-03-04T21:03:21.3975593Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:21.3975740Z shifts_x: "f32[304][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T21:03:21.3975801Z 2025-03-04T21:03:21.3976250Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:03:21.3978448Z arange_1: "f32[296][1]cpu" = torch.arange(0.0, 1184, step = 4, dtype = torch.float32) 2025-03-04T21:03:21.3978539Z 2025-03-04T21:03:21.3978848Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:21.3978997Z shifts_y: "f32[296][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T21:03:21.3979061Z 2025-03-04T21:03:21.3979445Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:03:21.3979624Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T21:03:21.3979728Z shift_y: "f32[296, 304][1, 0]cpu" = meshgrid[0] 2025-03-04T21:03:21.3979857Z shift_x: "f32[296, 304][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T21:03:21.3979925Z 2025-03-04T21:03:21.3980279Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:03:21.3980402Z shift_x_1: "f32[89984][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T21:03:21.3980497Z 2025-03-04T21:03:21.3980814Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:03:21.3980936Z shift_y_1: "f32[89984][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T21:03:21.3980995Z 2025-03-04T21:03:21.3981371Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:03:21.3981579Z shifts: "f32[89984, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T21:03:21.3981649Z 2025-03-04T21:03:21.3982070Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:03:21.3982212Z view: "f32[89984, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T21:03:21.3982643Z view_1: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T21:03:21.3982766Z add_3: "f32[89984, 3, 4][12, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T21:03:21.3982896Z x_218: "f32[269952, 4][4, 1]cpu" = add_3.reshape(-1, 4); add_3 = None 2025-03-04T21:03:21.3982954Z 2025-03-04T21:03:21.3983384Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:03:21.3983535Z arange_2: "f32[152][1]cpu" = torch.arange(0.0, 1216, step = 8, dtype = torch.float32) 2025-03-04T21:03:21.3983600Z 2025-03-04T21:03:21.3983893Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:21.3984038Z shifts_x_1: "f32[152][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T21:03:21.3984097Z 2025-03-04T21:03:21.3984532Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:03:21.3984742Z arange_3: "f32[148][1]cpu" = torch.arange(0.0, 1184, step = 8, dtype = torch.float32) 2025-03-04T21:03:21.3984825Z 2025-03-04T21:03:21.3985116Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:21.3985262Z shifts_y_1: "f32[148][1]cpu" = arange_3.to(device(type='cpu')); arange_3 = None 2025-03-04T21:03:21.3985322Z 2025-03-04T21:03:21.3985698Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:03:21.3985891Z meshgrid_1 = torch.functional.meshgrid(shifts_y_1, shifts_x_1); shifts_y_1 = shifts_x_1 = None 2025-03-04T21:03:21.3985997Z shift_y_2: "f32[148, 152][1, 0]cpu" = meshgrid_1[0] 2025-03-04T21:03:21.3986120Z shift_x_2: "f32[148, 152][0, 1]cpu" = meshgrid_1[1]; meshgrid_1 = None 2025-03-04T21:03:21.3986191Z 2025-03-04T21:03:21.3986520Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:03:21.3986667Z shift_x_3: "f32[22496][1]cpu" = shift_x_2.reshape(-1); shift_x_2 = None 2025-03-04T21:03:21.3986729Z 2025-03-04T21:03:21.3987064Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:03:21.3987186Z shift_y_3: "f32[22496][1]cpu" = shift_y_2.reshape(-1); shift_y_2 = None 2025-03-04T21:03:21.3987255Z 2025-03-04T21:03:21.3987632Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:03:21.3987854Z shifts_1: "f32[22496, 4][4, 1]cpu" = torch.stack((shift_x_3, shift_y_3, shift_x_3, shift_y_3), dim = 1); shift_x_3 = shift_y_3 = None 2025-03-04T21:03:21.3987916Z 2025-03-04T21:03:21.3988337Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:03:21.3988485Z view_2: "f32[22496, 1, 4][4, 4, 1]cpu" = shifts_1.view(-1, 1, 4); shifts_1 = None 2025-03-04T21:03:21.3988908Z view_3: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = None 2025-03-04T21:03:21.3989038Z add_4: "f32[22496, 3, 4][12, 4, 1]cpu" = view_2 + view_3; view_2 = view_3 = None 2025-03-04T21:03:21.3989149Z x_219: "f32[67488, 4][4, 1]cpu" = add_4.reshape(-1, 4); add_4 = None 2025-03-04T21:03:21.3989217Z 2025-03-04T21:03:21.3989651Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:03:21.3989804Z arange_4: "f32[76][1]cpu" = torch.arange(0.0, 1216, step = 16, dtype = torch.float32) 2025-03-04T21:03:21.3989867Z 2025-03-04T21:03:21.3990163Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:21.3990297Z shifts_x_2: "f32[76][1]cpu" = arange_4.to(device(type='cpu')); arange_4 = None 2025-03-04T21:03:21.3990365Z 2025-03-04T21:03:21.3990796Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:03:21.3990982Z arange_5: "f32[74][1]cpu" = torch.arange(0.0, 1184, step = 16, dtype = torch.float32) 2025-03-04T21:03:21.3991042Z 2025-03-04T21:03:21.3991338Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:21.3991472Z shifts_y_2: "f32[74][1]cpu" = arange_5.to(device(type='cpu')); arange_5 = None 2025-03-04T21:03:21.3991539Z 2025-03-04T21:03:21.3991914Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:03:21.3992111Z meshgrid_2 = torch.functional.meshgrid(shifts_y_2, shifts_x_2); shifts_y_2 = shifts_x_2 = None 2025-03-04T21:03:21.3992209Z shift_y_4: "f32[74, 76][1, 0]cpu" = meshgrid_2[0] 2025-03-04T21:03:21.3992334Z shift_x_4: "f32[74, 76][0, 1]cpu" = meshgrid_2[1]; meshgrid_2 = None 2025-03-04T21:03:21.3992396Z 2025-03-04T21:03:21.3992731Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:03:21.3992869Z shift_x_5: "f32[5624][1]cpu" = shift_x_4.reshape(-1); shift_x_4 = None 2025-03-04T21:03:21.3992941Z 2025-03-04T21:03:21.3993263Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:03:21.3993388Z shift_y_5: "f32[5624][1]cpu" = shift_y_4.reshape(-1); shift_y_4 = None 2025-03-04T21:03:21.3993450Z 2025-03-04T21:03:21.3993918Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:03:21.3994149Z shifts_2: "f32[5624, 4][4, 1]cpu" = torch.stack((shift_x_5, shift_y_5, shift_x_5, shift_y_5), dim = 1); shift_x_5 = shift_y_5 = None 2025-03-04T21:03:21.3994224Z 2025-03-04T21:03:21.3994685Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:03:21.3994820Z view_4: "f32[5624, 1, 4][4, 4, 1]cpu" = shifts_2.view(-1, 1, 4); shifts_2 = None 2025-03-04T21:03:21.3995283Z view_5: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = None 2025-03-04T21:03:21.3995403Z add_5: "f32[5624, 3, 4][12, 4, 1]cpu" = view_4 + view_5; view_4 = view_5 = None 2025-03-04T21:03:21.3995524Z x_220: "f32[16872, 4][4, 1]cpu" = add_5.reshape(-1, 4); add_5 = None 2025-03-04T21:03:21.3995586Z 2025-03-04T21:03:21.3996021Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:03:21.3996164Z arange_6: "f32[38][1]cpu" = torch.arange(0.0, 1216, step = 32, dtype = torch.float32) 2025-03-04T21:03:21.3996233Z 2025-03-04T21:03:21.3996524Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:21.3996664Z shifts_x_3: "f32[38][1]cpu" = arange_6.to(device(type='cpu')); arange_6 = None 2025-03-04T21:03:21.3996725Z 2025-03-04T21:03:21.3997182Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:03:21.3997337Z arange_7: "f32[37][1]cpu" = torch.arange(0.0, 1184, step = 32, dtype = torch.float32) 2025-03-04T21:03:21.3997406Z 2025-03-04T21:03:21.3997698Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:21.3997836Z shifts_y_3: "f32[37][1]cpu" = arange_7.to(device(type='cpu')); arange_7 = None 2025-03-04T21:03:21.3997896Z 2025-03-04T21:03:21.3998274Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:03:21.3998462Z meshgrid_3 = torch.functional.meshgrid(shifts_y_3, shifts_x_3); shifts_y_3 = shifts_x_3 = None 2025-03-04T21:03:21.3998564Z shift_y_6: "f32[37, 38][1, 0]cpu" = meshgrid_3[0] 2025-03-04T21:03:21.3998680Z shift_x_6: "f32[37, 38][0, 1]cpu" = meshgrid_3[1]; meshgrid_3 = None 2025-03-04T21:03:21.3998748Z 2025-03-04T21:03:21.3999071Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:03:21.3999214Z shift_x_7: "f32[1406][1]cpu" = shift_x_6.reshape(-1); shift_x_6 = None 2025-03-04T21:03:21.3999273Z 2025-03-04T21:03:21.3999599Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:03:21.3999715Z shift_y_7: "f32[1406][1]cpu" = shift_y_6.reshape(-1); shift_y_6 = None 2025-03-04T21:03:21.3999784Z 2025-03-04T21:03:21.4000159Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:03:21.4000378Z shifts_3: "f32[1406, 4][4, 1]cpu" = torch.stack((shift_x_7, shift_y_7, shift_x_7, shift_y_7), dim = 1); shift_x_7 = shift_y_7 = None 2025-03-04T21:03:21.4000441Z 2025-03-04T21:03:21.4000871Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:03:21.4001003Z view_6: "f32[1406, 1, 4][4, 4, 1]cpu" = shifts_3.view(-1, 1, 4); shifts_3 = None 2025-03-04T21:03:21.4001426Z view_7: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = None 2025-03-04T21:03:21.4001550Z add_6: "f32[1406, 3, 4][12, 4, 1]cpu" = view_6 + view_7; view_6 = view_7 = None 2025-03-04T21:03:21.4001664Z x_221: "f32[4218, 4][4, 1]cpu" = add_6.reshape(-1, 4); add_6 = None 2025-03-04T21:03:21.4001733Z 2025-03-04T21:03:21.4002381Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:03:21.4002543Z arange_8: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32) 2025-03-04T21:03:21.4002607Z 2025-03-04T21:03:21.4002910Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:21.4003043Z shifts_x_4: "f32[19][1]cpu" = arange_8.to(device(type='cpu')); arange_8 = None 2025-03-04T21:03:21.4003113Z 2025-03-04T21:03:21.4003588Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:03:21.4003780Z arange_9: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32) 2025-03-04T21:03:21.4003845Z 2025-03-04T21:03:21.4004155Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:21.4004287Z shifts_y_4: "f32[19][1]cpu" = arange_9.to(device(type='cpu')); arange_9 = None 2025-03-04T21:03:21.4004357Z 2025-03-04T21:03:21.4004737Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:03:21.4004942Z meshgrid_4 = torch.functional.meshgrid(shifts_y_4, shifts_x_4); shifts_y_4 = shifts_x_4 = None 2025-03-04T21:03:21.4005042Z shift_y_8: "f32[19, 19][1, 0]cpu" = meshgrid_4[0] 2025-03-04T21:03:21.4005171Z shift_x_8: "f32[19, 19][0, 1]cpu" = meshgrid_4[1]; meshgrid_4 = None 2025-03-04T21:03:21.4005233Z 2025-03-04T21:03:21.4005609Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:03:21.4005731Z shift_x_9: "f32[361][1]cpu" = shift_x_8.reshape(-1); shift_x_8 = None 2025-03-04T21:03:21.4005803Z 2025-03-04T21:03:21.4006138Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:03:21.4006263Z shift_y_9: "f32[361][1]cpu" = shift_y_8.reshape(-1); shift_y_8 = None 2025-03-04T21:03:21.4006324Z 2025-03-04T21:03:21.4006716Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:03:21.4006934Z shifts_4: "f32[361, 4][4, 1]cpu" = torch.stack((shift_x_9, shift_y_9, shift_x_9, shift_y_9), dim = 1); shift_x_9 = shift_y_9 = None 2025-03-04T21:03:21.4007001Z 2025-03-04T21:03:21.4007432Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:03:21.4007551Z view_8: "f32[361, 1, 4][4, 4, 1]cpu" = shifts_4.view(-1, 1, 4); shifts_4 = None 2025-03-04T21:03:21.4007968Z view_9: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = None 2025-03-04T21:03:21.4008084Z add_7: "f32[361, 3, 4][12, 4, 1]cpu" = view_8 + view_9; view_8 = view_9 = None 2025-03-04T21:03:21.4008200Z x_222: "f32[1083, 4][4, 1]cpu" = add_7.reshape(-1, 4); add_7 = None 2025-03-04T21:03:21.4008258Z 2025-03-04T21:03:21.4008559Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:03:21.4008689Z tensor: "f32[269952, 4][4, 1]cpu" = x_218.to(torch.float32); x_218 = None 2025-03-04T21:03:21.4008830Z tensor_1: "f32[67488, 4][4, 1]cpu" = x_219.to(torch.float32); x_219 = None 2025-03-04T21:03:21.4008950Z tensor_2: "f32[16872, 4][4, 1]cpu" = x_220.to(torch.float32); x_220 = None 2025-03-04T21:03:21.4009074Z tensor_3: "f32[4218, 4][4, 1]cpu" = x_221.to(torch.float32); x_221 = None 2025-03-04T21:03:21.4009187Z tensor_4: "f32[1083, 4][4, 1]cpu" = x_222.to(torch.float32); x_222 = None 2025-03-04T21:03:21.4009254Z 2025-03-04T21:03:21.4009532Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4010072Z x_223: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_217, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_217 = None 2025-03-04T21:03:21.4010135Z 2025-03-04T21:03:21.4010416Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:03:21.4010611Z x_224: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.relu(x_223, inplace = False); x_223 = None 2025-03-04T21:03:21.4010679Z 2025-03-04T21:03:21.4011068Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:03:21.4011583Z score: "f32[4, 3, 296, 304][269952, 89984, 304, 1]cpu" = torch.conv2d(x_224, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:03:21.4011668Z 2025-03-04T21:03:21.4012019Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:03:21.4012534Z x_233: "f32[4, 12, 296, 304][1079808, 89984, 304, 1]cpu" = torch.conv2d(x_224, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_224 = None 2025-03-04T21:03:21.4012595Z 2025-03-04T21:03:21.4012851Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4013340Z x_225: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(x_215, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_215 = None 2025-03-04T21:03:21.4013411Z 2025-03-04T21:03:21.4013675Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:03:21.4013866Z x_226: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.relu(x_225, inplace = False); x_225 = None 2025-03-04T21:03:21.4013927Z 2025-03-04T21:03:21.4014301Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:03:21.4014811Z score_1: "f32[4, 3, 148, 152][67488, 22496, 152, 1]cpu" = torch.conv2d(x_226, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:03:21.4014879Z 2025-03-04T21:03:21.4015239Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:03:21.4015751Z x_234: "f32[4, 12, 148, 152][269952, 22496, 152, 1]cpu" = torch.conv2d(x_226, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_226 = None 2025-03-04T21:03:21.4015837Z 2025-03-04T21:03:21.4016104Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4016587Z x_227: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(x_213, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_213 = None 2025-03-04T21:03:21.4016649Z 2025-03-04T21:03:21.4016931Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:03:21.4017107Z x_228: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.relu(x_227, inplace = False); x_227 = None 2025-03-04T21:03:21.4017172Z 2025-03-04T21:03:21.4017539Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:03:21.4018037Z score_2: "f32[4, 3, 74, 76][16872, 5624, 76, 1]cpu" = torch.conv2d(x_228, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:03:21.4018111Z 2025-03-04T21:03:21.4018469Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:03:21.4018962Z x_235: "f32[4, 12, 74, 76][67488, 5624, 76, 1]cpu" = torch.conv2d(x_228, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_228 = None 2025-03-04T21:03:21.4019024Z 2025-03-04T21:03:21.4019275Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4019775Z x_229: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_211, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_211 = None 2025-03-04T21:03:21.4019844Z 2025-03-04T21:03:21.4020115Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:03:21.4020299Z x_230: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.nn.functional.relu(x_229, inplace = False); x_229 = None 2025-03-04T21:03:21.4020358Z 2025-03-04T21:03:21.4020742Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:03:21.4021250Z score_3: "f32[4, 3, 37, 38][4218, 1406, 38, 1]cpu" = torch.conv2d(x_230, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:03:21.4021319Z 2025-03-04T21:03:21.4021677Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:03:21.4022191Z x_236: "f32[4, 12, 37, 38][16872, 1406, 38, 1]cpu" = torch.conv2d(x_230, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_230 = None 2025-03-04T21:03:21.4022288Z 2025-03-04T21:03:21.4022541Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4023320Z x_231: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.conv2d(res, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); res = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T21:03:21.4023383Z 2025-03-04T21:03:21.4023662Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:03:21.4023833Z x_232: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.relu(x_231, inplace = False); x_231 = None 2025-03-04T21:03:21.4023903Z 2025-03-04T21:03:21.4024273Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:03:21.4025150Z score_4: "f32[4, 3, 19, 19][1083, 361, 19, 1]cpu" = torch.conv2d(x_232, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T21:03:21.4025212Z 2025-03-04T21:03:21.4025575Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:03:21.4026413Z x_237: "f32[4, 12, 19, 19][4332, 361, 19, 1]cpu" = torch.conv2d(x_232, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_232 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T21:03:21.4026476Z 2025-03-04T21:03:21.4026822Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T21:03:21.4026987Z permute: "f32[4, 296, 304, 3][269952, 304, 1, 89984]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T21:03:21.4027136Z logits_i: "f32[4, 269952][269952, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T21:03:21.4027298Z permute_1: "f32[4, 148, 152, 3][67488, 152, 1, 22496]cpu" = score_1.permute(0, 2, 3, 1); score_1 = None 2025-03-04T21:03:21.4027448Z logits_i_1: "f32[4, 67488][67488, 1]cpu" = permute_1.flatten(1); permute_1 = None 2025-03-04T21:03:21.4027602Z permute_2: "f32[4, 74, 76, 3][16872, 76, 1, 5624]cpu" = score_2.permute(0, 2, 3, 1); score_2 = None 2025-03-04T21:03:21.4027746Z logits_i_2: "f32[4, 16872][16872, 1]cpu" = permute_2.flatten(1); permute_2 = None 2025-03-04T21:03:21.4027891Z permute_3: "f32[4, 37, 38, 3][4218, 38, 1, 1406]cpu" = score_3.permute(0, 2, 3, 1); score_3 = None 2025-03-04T21:03:21.4028030Z logits_i_3: "f32[4, 4218][4218, 1]cpu" = permute_3.flatten(1); permute_3 = None 2025-03-04T21:03:21.4028171Z permute_4: "f32[4, 19, 19, 3][1083, 19, 1, 361]cpu" = score_4.permute(0, 2, 3, 1); score_4 = None 2025-03-04T21:03:21.4028331Z logits_i_4: "f32[4, 1083][1083, 1]cpu" = permute_4.flatten(1); permute_4 = None 2025-03-04T21:03:21.4028409Z 2025-03-04T21:03:21.4028856Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T21:03:21.4029034Z view_10: "f32[4, 3, 4, 296, 304][1079808, 359936, 89984, 304, 1]cpu" = x_233.view(4, -1, 4, 296, 304); x_233 = None 2025-03-04T21:03:21.4029224Z permute_5: "f32[4, 296, 304, 3, 4][1079808, 304, 1, 359936, 89984]cpu" = view_10.permute(0, 3, 4, 1, 2); view_10 = None 2025-03-04T21:03:21.4029402Z pred_anchor_deltas_i: "f32[4, 269952, 4][1079808, 4, 1]cpu" = permute_5.flatten(1, -2); permute_5 = None 2025-03-04T21:03:21.4029572Z view_11: "f32[4, 3, 4, 148, 152][269952, 89984, 22496, 152, 1]cpu" = x_234.view(4, -1, 4, 148, 152); x_234 = None 2025-03-04T21:03:21.4029754Z permute_6: "f32[4, 148, 152, 3, 4][269952, 152, 1, 89984, 22496]cpu" = view_11.permute(0, 3, 4, 1, 2); view_11 = None 2025-03-04T21:03:21.4029928Z pred_anchor_deltas_i_2: "f32[4, 67488, 4][269952, 4, 1]cpu" = permute_6.flatten(1, -2); permute_6 = None 2025-03-04T21:03:21.4030097Z view_12: "f32[4, 3, 4, 74, 76][67488, 22496, 5624, 76, 1]cpu" = x_235.view(4, -1, 4, 74, 76); x_235 = None 2025-03-04T21:03:21.4030265Z permute_7: "f32[4, 74, 76, 3, 4][67488, 76, 1, 22496, 5624]cpu" = view_12.permute(0, 3, 4, 1, 2); view_12 = None 2025-03-04T21:03:21.4030438Z pred_anchor_deltas_i_4: "f32[4, 16872, 4][67488, 4, 1]cpu" = permute_7.flatten(1, -2); permute_7 = None 2025-03-04T21:03:21.4030582Z view_13: "f32[4, 3, 4, 37, 38][16872, 5624, 1406, 38, 1]cpu" = x_236.view(4, -1, 4, 37, 38); x_236 = None 2025-03-04T21:03:21.4030751Z permute_8: "f32[4, 37, 38, 3, 4][16872, 38, 1, 5624, 1406]cpu" = view_13.permute(0, 3, 4, 1, 2); view_13 = None 2025-03-04T21:03:21.4030919Z pred_anchor_deltas_i_6: "f32[4, 4218, 4][16872, 4, 1]cpu" = permute_8.flatten(1, -2); permute_8 = None 2025-03-04T21:03:21.4031066Z view_14: "f32[4, 3, 4, 19, 19][4332, 1444, 361, 19, 1]cpu" = x_237.view(4, -1, 4, 19, 19); x_237 = None 2025-03-04T21:03:21.4031226Z permute_9: "f32[4, 19, 19, 3, 4][4332, 19, 1, 1444, 361]cpu" = view_14.permute(0, 3, 4, 1, 2); view_14 = None 2025-03-04T21:03:21.4031413Z pred_anchor_deltas_i_8: "f32[4, 1083, 4][4332, 4, 1]cpu" = permute_9.flatten(1, -2); permute_9 = None 2025-03-04T21:03:21.4031476Z 2025-03-04T21:03:21.4031889Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:03:21.4032091Z pred_anchor_deltas_i_1: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T21:03:21.4032162Z 2025-03-04T21:03:21.4032598Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:03:21.4032762Z unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T21:03:21.4032907Z expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T21:03:21.4033051Z anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T21:03:21.4033113Z 2025-03-04T21:03:21.4033498Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:03:21.4033713Z deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T21:03:21.4033790Z 2025-03-04T21:03:21.4034135Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:03:21.4034300Z boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T21:03:21.4034364Z 2025-03-04T21:03:21.4034696Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:03:21.4034837Z getitem_10: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:03:21.4034966Z getitem_11: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:03:21.4035126Z widths: "f32[1079808][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T21:03:21.4035189Z 2025-03-04T21:03:21.4035525Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:03:21.4035654Z getitem_12: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:03:21.4035795Z getitem_13: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:03:21.4035966Z heights: "f32[1079808][1]cpu" = getitem_12 - getitem_13; getitem_12 = getitem_13 = None 2025-03-04T21:03:21.4036035Z 2025-03-04T21:03:21.4036347Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:03:21.4036475Z getitem_14: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:03:21.4036563Z mul: "f32[1079808][1]cpu" = 0.5 * widths 2025-03-04T21:03:21.4036695Z ctr_x: "f32[1079808][1]cpu" = getitem_14 + mul; getitem_14 = mul = None 2025-03-04T21:03:21.4036756Z 2025-03-04T21:03:21.4037075Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:03:21.4037221Z getitem_15: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:03:21.4037316Z mul_1: "f32[1079808][1]cpu" = 0.5 * heights 2025-03-04T21:03:21.4037456Z ctr_y: "f32[1079808][1]cpu" = getitem_15 + mul_1; getitem_15 = mul_1 = None 2025-03-04T21:03:21.4037525Z 2025-03-04T21:03:21.4037869Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:03:21.4038026Z getitem_16: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:03:21.4038137Z dx: "f32[1079808, 1][1, 1]cpu" = getitem_16 / 1.0; getitem_16 = None 2025-03-04T21:03:21.4038204Z 2025-03-04T21:03:21.4038499Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:03:21.4038654Z getitem_17: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:03:21.4038764Z dy: "f32[1079808, 1][1, 1]cpu" = getitem_17 / 1.0; getitem_17 = None 2025-03-04T21:03:21.4038831Z 2025-03-04T21:03:21.4039126Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:03:21.4039282Z getitem_18: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:03:21.4039390Z dw: "f32[1079808, 1][1, 1]cpu" = getitem_18 / 1.0; getitem_18 = None 2025-03-04T21:03:21.4039459Z 2025-03-04T21:03:21.4039761Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:03:21.4039987Z getitem_19: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:03:21.4040096Z dh: "f32[1079808, 1][1, 1]cpu" = getitem_19 / 1.0; getitem_19 = None 2025-03-04T21:03:21.4040167Z 2025-03-04T21:03:21.4040507Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:03:21.4040653Z dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:03:21.4040714Z 2025-03-04T21:03:21.4041055Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:03:21.4041196Z dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:03:21.4041258Z 2025-03-04T21:03:21.4041610Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:03:21.4041767Z getitem_20: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:03:21.4041900Z mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_20; dx = getitem_20 = None 2025-03-04T21:03:21.4042052Z getitem_21: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:03:21.4042196Z pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_21; mul_2 = getitem_21 = None 2025-03-04T21:03:21.4042257Z 2025-03-04T21:03:21.4042614Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:03:21.4042755Z getitem_22: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:03:21.4042887Z mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_22; dy = getitem_22 = None 2025-03-04T21:03:21.4043035Z getitem_23: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:03:21.4043190Z pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_23; mul_3 = getitem_23 = None 2025-03-04T21:03:21.4043251Z 2025-03-04T21:03:21.4043587Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:03:21.4043702Z exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:03:21.4043867Z getitem_24: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:03:21.4043994Z pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_24; exp = getitem_24 = None 2025-03-04T21:03:21.4044063Z 2025-03-04T21:03:21.4044394Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:03:21.4044514Z exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:03:21.4044678Z getitem_25: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:03:21.4044818Z pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_25; exp_1 = getitem_25 = None 2025-03-04T21:03:21.4044878Z 2025-03-04T21:03:21.4045197Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:03:21.4045289Z mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T21:03:21.4045409Z x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:03:21.4045501Z 2025-03-04T21:03:21.4045820Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:03:21.4045913Z mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T21:03:21.4046036Z y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:03:21.4046097Z 2025-03-04T21:03:21.4046406Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:03:21.4046517Z mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:03:21.4046653Z x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:03:21.4046713Z 2025-03-04T21:03:21.4047029Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:03:21.4047141Z mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:03:21.4047287Z y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:03:21.4047348Z 2025-03-04T21:03:21.4047699Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:03:21.4047878Z pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:03:21.4047949Z 2025-03-04T21:03:21.4048291Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:03:21.4048457Z proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4)); pred_boxes = None 2025-03-04T21:03:21.4048519Z 2025-03-04T21:03:21.4048908Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:03:21.4049111Z proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T21:03:21.4049179Z 2025-03-04T21:03:21.4049589Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:03:21.4049795Z pred_anchor_deltas_i_3: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_2.reshape(-1, 4); pred_anchor_deltas_i_2 = None 2025-03-04T21:03:21.4049862Z 2025-03-04T21:03:21.4050294Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:03:21.4050453Z unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = tensor_1.unsqueeze(0); tensor_1 = None 2025-03-04T21:03:21.4050601Z expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1); unsqueeze_1 = None 2025-03-04T21:03:21.4050742Z anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4); expand_1 = None 2025-03-04T21:03:21.4050802Z 2025-03-04T21:03:21.4051175Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:03:21.4051345Z deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float(); pred_anchor_deltas_i_3 = None 2025-03-04T21:03:21.4051412Z 2025-03-04T21:03:21.4051737Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:03:21.4051901Z boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32); anchors_i_1 = None 2025-03-04T21:03:21.4051962Z 2025-03-04T21:03:21.4052283Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:03:21.4052414Z getitem_26: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)] 2025-03-04T21:03:21.4052545Z getitem_27: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T21:03:21.4052693Z widths_1: "f32[269952][1]cpu" = getitem_26 - getitem_27; getitem_26 = getitem_27 = None 2025-03-04T21:03:21.4052759Z 2025-03-04T21:03:21.4053078Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:03:21.4053209Z getitem_28: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)] 2025-03-04T21:03:21.4053328Z getitem_29: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)] 2025-03-04T21:03:21.4053505Z heights_1: "f32[269952][1]cpu" = getitem_28 - getitem_29; getitem_28 = getitem_29 = None 2025-03-04T21:03:21.4053564Z 2025-03-04T21:03:21.4053879Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:03:21.4053996Z getitem_30: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T21:03:21.4054093Z mul_10: "f32[269952][1]cpu" = 0.5 * widths_1 2025-03-04T21:03:21.4054225Z ctr_x_1: "f32[269952][1]cpu" = getitem_30 + mul_10; getitem_30 = mul_10 = None 2025-03-04T21:03:21.4054292Z 2025-03-04T21:03:21.4054605Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:03:21.4054762Z getitem_31: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]; boxes_1 = None 2025-03-04T21:03:21.4054852Z mul_11: "f32[269952][1]cpu" = 0.5 * heights_1 2025-03-04T21:03:21.4055002Z ctr_y_1: "f32[269952][1]cpu" = getitem_31 + mul_11; getitem_31 = mul_11 = None 2025-03-04T21:03:21.4055064Z 2025-03-04T21:03:21.4055374Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:03:21.4055527Z getitem_32: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:03:21.4055644Z dx_1: "f32[269952, 1][1, 1]cpu" = getitem_32 / 1.0; getitem_32 = None 2025-03-04T21:03:21.4055704Z 2025-03-04T21:03:21.4056015Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:03:21.4056172Z getitem_33: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:03:21.4056286Z dy_1: "f32[269952, 1][1, 1]cpu" = getitem_33 / 1.0; getitem_33 = None 2025-03-04T21:03:21.4056355Z 2025-03-04T21:03:21.4056653Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:03:21.4056808Z getitem_34: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:03:21.4056915Z dw_2: "f32[269952, 1][1, 1]cpu" = getitem_34 / 1.0; getitem_34 = None 2025-03-04T21:03:21.4056982Z 2025-03-04T21:03:21.4057299Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:03:21.4057505Z getitem_35: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))]; deltas_1 = None 2025-03-04T21:03:21.4057615Z dh_2: "f32[269952, 1][1, 1]cpu" = getitem_35 / 1.0; getitem_35 = None 2025-03-04T21:03:21.4057685Z 2025-03-04T21:03:21.4058022Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:03:21.4058167Z dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356); dw_2 = None 2025-03-04T21:03:21.4058227Z 2025-03-04T21:03:21.4058565Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:03:21.4058703Z dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356); dh_2 = None 2025-03-04T21:03:21.4058770Z 2025-03-04T21:03:21.4059116Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:03:21.4059276Z getitem_36: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)] 2025-03-04T21:03:21.4059399Z mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_36; dx_1 = getitem_36 = None 2025-03-04T21:03:21.4059560Z getitem_37: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)]; ctr_x_1 = None 2025-03-04T21:03:21.4059700Z pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_37; mul_12 = getitem_37 = None 2025-03-04T21:03:21.4059767Z 2025-03-04T21:03:21.4060114Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:03:21.4060260Z getitem_38: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)] 2025-03-04T21:03:21.4060379Z mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_38; dy_1 = getitem_38 = None 2025-03-04T21:03:21.4060543Z getitem_39: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)]; ctr_y_1 = None 2025-03-04T21:03:21.4060697Z pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_39; mul_13 = getitem_39 = None 2025-03-04T21:03:21.4060767Z 2025-03-04T21:03:21.4061100Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:03:21.4061221Z exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3); dw_3 = None 2025-03-04T21:03:21.4061380Z getitem_40: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]; widths_1 = None 2025-03-04T21:03:21.4061521Z pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_40; exp_2 = getitem_40 = None 2025-03-04T21:03:21.4061583Z 2025-03-04T21:03:21.4061924Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:03:21.4062043Z exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3); dh_3 = None 2025-03-04T21:03:21.4062209Z getitem_41: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]; heights_1 = None 2025-03-04T21:03:21.4062347Z pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_41; exp_3 = getitem_41 = None 2025-03-04T21:03:21.4062406Z 2025-03-04T21:03:21.4062724Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:03:21.4062819Z mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1 2025-03-04T21:03:21.4062974Z x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16; mul_16 = None 2025-03-04T21:03:21.4063034Z 2025-03-04T21:03:21.4063348Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:03:21.4063440Z mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1 2025-03-04T21:03:21.4063560Z y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17; mul_17 = None 2025-03-04T21:03:21.4063620Z 2025-03-04T21:03:21.4063928Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:03:21.4064041Z mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1; pred_w_1 = None 2025-03-04T21:03:21.4064178Z x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18; pred_ctr_x_1 = mul_18 = None 2025-03-04T21:03:21.4064239Z 2025-03-04T21:03:21.4064548Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:03:21.4064671Z mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1; pred_h_1 = None 2025-03-04T21:03:21.4064803Z y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19; pred_ctr_y_1 = mul_19 = None 2025-03-04T21:03:21.4064864Z 2025-03-04T21:03:21.4065214Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:03:21.4065410Z pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1); x1_1 = y1_1 = x2_1 = y2_1 = None 2025-03-04T21:03:21.4065475Z 2025-03-04T21:03:21.4065797Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:03:21.4065963Z proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4)); pred_boxes_1 = None 2025-03-04T21:03:21.4066025Z 2025-03-04T21:03:21.4066421Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:03:21.4066593Z proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4); proposals_i_1 = None 2025-03-04T21:03:21.4066660Z 2025-03-04T21:03:21.4067057Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:03:21.4067269Z pred_anchor_deltas_i_5: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_4.reshape(-1, 4); pred_anchor_deltas_i_4 = None 2025-03-04T21:03:21.4067333Z 2025-03-04T21:03:21.4067770Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:03:21.4067923Z unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = tensor_2.unsqueeze(0); tensor_2 = None 2025-03-04T21:03:21.4068080Z expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1); unsqueeze_2 = None 2025-03-04T21:03:21.4068215Z anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4); expand_2 = None 2025-03-04T21:03:21.4068283Z 2025-03-04T21:03:21.4068651Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:03:21.4068842Z deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_5.float(); pred_anchor_deltas_i_5 = None 2025-03-04T21:03:21.4068917Z 2025-03-04T21:03:21.4069234Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:03:21.4069382Z boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32); anchors_i_2 = None 2025-03-04T21:03:21.4069446Z 2025-03-04T21:03:21.4069764Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:03:21.4069894Z getitem_42: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)] 2025-03-04T21:03:21.4070024Z getitem_43: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T21:03:21.4070172Z widths_2: "f32[67488][1]cpu" = getitem_42 - getitem_43; getitem_42 = getitem_43 = None 2025-03-04T21:03:21.4070240Z 2025-03-04T21:03:21.4070557Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:03:21.4070685Z getitem_44: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)] 2025-03-04T21:03:21.4070828Z getitem_45: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)] 2025-03-04T21:03:21.4070981Z heights_2: "f32[67488][1]cpu" = getitem_44 - getitem_45; getitem_44 = getitem_45 = None 2025-03-04T21:03:21.4071041Z 2025-03-04T21:03:21.4071356Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:03:21.4071473Z getitem_46: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T21:03:21.4071567Z mul_20: "f32[67488][1]cpu" = 0.5 * widths_2 2025-03-04T21:03:21.4071696Z ctr_x_2: "f32[67488][1]cpu" = getitem_46 + mul_20; getitem_46 = mul_20 = None 2025-03-04T21:03:21.4071766Z 2025-03-04T21:03:21.4072076Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:03:21.4072241Z getitem_47: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]; boxes_2 = None 2025-03-04T21:03:21.4072333Z mul_21: "f32[67488][1]cpu" = 0.5 * heights_2 2025-03-04T21:03:21.4072465Z ctr_y_2: "f32[67488][1]cpu" = getitem_47 + mul_21; getitem_47 = mul_21 = None 2025-03-04T21:03:21.4072529Z 2025-03-04T21:03:21.4072843Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:03:21.4072998Z getitem_48: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:03:21.4073142Z dx_2: "f32[67488, 1][1, 1]cpu" = getitem_48 / 1.0; getitem_48 = None 2025-03-04T21:03:21.4073204Z 2025-03-04T21:03:21.4073509Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:03:21.4073658Z getitem_49: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:03:21.4073845Z dy_2: "f32[67488, 1][1, 1]cpu" = getitem_49 / 1.0; getitem_49 = None 2025-03-04T21:03:21.4073908Z 2025-03-04T21:03:21.4074221Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:03:21.4074369Z getitem_50: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:03:21.4074487Z dw_4: "f32[67488, 1][1, 1]cpu" = getitem_50 / 1.0; getitem_50 = None 2025-03-04T21:03:21.4074549Z 2025-03-04T21:03:21.4074901Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:03:21.4075097Z getitem_51: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))]; deltas_2 = None 2025-03-04T21:03:21.4075214Z dh_4: "f32[67488, 1][1, 1]cpu" = getitem_51 / 1.0; getitem_51 = None 2025-03-04T21:03:21.4075275Z 2025-03-04T21:03:21.4075615Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:03:21.4075759Z dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356); dw_4 = None 2025-03-04T21:03:21.4075819Z 2025-03-04T21:03:21.4076157Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:03:21.4076296Z dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356); dh_4 = None 2025-03-04T21:03:21.4076364Z 2025-03-04T21:03:21.4076703Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:03:21.4076858Z getitem_52: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)] 2025-03-04T21:03:21.4076980Z mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_52; dx_2 = getitem_52 = None 2025-03-04T21:03:21.4077137Z getitem_53: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)]; ctr_x_2 = None 2025-03-04T21:03:21.4077276Z pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_53; mul_22 = getitem_53 = None 2025-03-04T21:03:21.4077341Z 2025-03-04T21:03:21.4077688Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:03:21.4077829Z getitem_54: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)] 2025-03-04T21:03:21.4077960Z mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_54; dy_2 = getitem_54 = None 2025-03-04T21:03:21.4078127Z getitem_55: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)]; ctr_y_2 = None 2025-03-04T21:03:21.4078257Z pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_55; mul_23 = getitem_55 = None 2025-03-04T21:03:21.4078322Z 2025-03-04T21:03:21.4078646Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:03:21.4078765Z exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5); dw_5 = None 2025-03-04T21:03:21.4078923Z getitem_56: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]; widths_2 = None 2025-03-04T21:03:21.4079061Z pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_56; exp_4 = getitem_56 = None 2025-03-04T21:03:21.4079122Z 2025-03-04T21:03:21.4079470Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:03:21.4079575Z exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5); dh_5 = None 2025-03-04T21:03:21.4079739Z getitem_57: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]; heights_2 = None 2025-03-04T21:03:21.4079867Z pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_57; exp_5 = getitem_57 = None 2025-03-04T21:03:21.4079935Z 2025-03-04T21:03:21.4080241Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:03:21.4080392Z mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2 2025-03-04T21:03:21.4080501Z x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26; mul_26 = None 2025-03-04T21:03:21.4080568Z 2025-03-04T21:03:21.4080865Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:03:21.4080958Z mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2 2025-03-04T21:03:21.4081063Z y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27; mul_27 = None 2025-03-04T21:03:21.4081127Z 2025-03-04T21:03:21.4081423Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:03:21.4081539Z mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2; pred_w_2 = None 2025-03-04T21:03:21.4081668Z x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28; pred_ctr_x_2 = mul_28 = None 2025-03-04T21:03:21.4081738Z 2025-03-04T21:03:21.4082037Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:03:21.4082171Z mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2; pred_h_2 = None 2025-03-04T21:03:21.4082299Z y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29; pred_ctr_y_2 = mul_29 = None 2025-03-04T21:03:21.4082367Z 2025-03-04T21:03:21.4082710Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:03:21.4082901Z pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1); x1_2 = y1_2 = x2_2 = y2_2 = None 2025-03-04T21:03:21.4082962Z 2025-03-04T21:03:21.4083298Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:03:21.4083466Z proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4)); pred_boxes_2 = None 2025-03-04T21:03:21.4083529Z 2025-03-04T21:03:21.4083936Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:03:21.4084117Z proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4); proposals_i_2 = None 2025-03-04T21:03:21.4084183Z 2025-03-04T21:03:21.4084576Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:03:21.4084789Z pred_anchor_deltas_i_7: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_6.reshape(-1, 4); pred_anchor_deltas_i_6 = None 2025-03-04T21:03:21.4084852Z 2025-03-04T21:03:21.4085289Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:03:21.4085440Z unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = tensor_3.unsqueeze(0); tensor_3 = None 2025-03-04T21:03:21.4085594Z expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1); unsqueeze_3 = None 2025-03-04T21:03:21.4085726Z anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4); expand_3 = None 2025-03-04T21:03:21.4085795Z 2025-03-04T21:03:21.4086165Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:03:21.4086368Z deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_7.float(); pred_anchor_deltas_i_7 = None 2025-03-04T21:03:21.4086430Z 2025-03-04T21:03:21.4086741Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:03:21.4086882Z boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32); anchors_i_3 = None 2025-03-04T21:03:21.4086950Z 2025-03-04T21:03:21.4087262Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:03:21.4087396Z getitem_58: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)] 2025-03-04T21:03:21.4087520Z getitem_59: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T21:03:21.4087671Z widths_3: "f32[16872][1]cpu" = getitem_58 - getitem_59; getitem_58 = getitem_59 = None 2025-03-04T21:03:21.4087734Z 2025-03-04T21:03:21.4088055Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:03:21.4088193Z getitem_60: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)] 2025-03-04T21:03:21.4088429Z getitem_61: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)] 2025-03-04T21:03:21.4088599Z heights_3: "f32[16872][1]cpu" = getitem_60 - getitem_61; getitem_60 = getitem_61 = None 2025-03-04T21:03:21.4088852Z 2025-03-04T21:03:21.4089194Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:03:21.4089361Z getitem_62: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T21:03:21.4089476Z mul_30: "f32[16872][1]cpu" = 0.5 * widths_3 2025-03-04T21:03:21.4089658Z ctr_x_3: "f32[16872][1]cpu" = getitem_62 + mul_30; getitem_62 = mul_30 = None 2025-03-04T21:03:21.4089730Z 2025-03-04T21:03:21.4090129Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:03:21.4090350Z getitem_63: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]; boxes_3 = None 2025-03-04T21:03:21.4090466Z mul_31: "f32[16872][1]cpu" = 0.5 * heights_3 2025-03-04T21:03:21.4090640Z ctr_y_3: "f32[16872][1]cpu" = getitem_63 + mul_31; getitem_63 = mul_31 = None 2025-03-04T21:03:21.4090713Z 2025-03-04T21:03:21.4091087Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:03:21.4091261Z getitem_64: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:03:21.4091427Z dx_3: "f32[16872, 1][1, 1]cpu" = getitem_64 / 1.0; getitem_64 = None 2025-03-04T21:03:21.4091508Z 2025-03-04T21:03:21.4091843Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:03:21.4092034Z getitem_65: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:03:21.4092203Z dy_3: "f32[16872, 1][1, 1]cpu" = getitem_65 / 1.0; getitem_65 = None 2025-03-04T21:03:21.4092283Z 2025-03-04T21:03:21.4092628Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:03:21.4092797Z getitem_66: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:03:21.4092960Z dw_6: "f32[16872, 1][1, 1]cpu" = getitem_66 / 1.0; getitem_66 = None 2025-03-04T21:03:21.4093095Z 2025-03-04T21:03:21.4093448Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:03:21.4093649Z getitem_67: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))]; deltas_3 = None 2025-03-04T21:03:21.4093803Z dh_6: "f32[16872, 1][1, 1]cpu" = getitem_67 / 1.0; getitem_67 = None 2025-03-04T21:03:21.4093871Z 2025-03-04T21:03:21.4094294Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:03:21.4094452Z dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356); dw_6 = None 2025-03-04T21:03:21.4094560Z 2025-03-04T21:03:21.4094910Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:03:21.4095082Z dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356); dh_6 = None 2025-03-04T21:03:21.4095258Z 2025-03-04T21:03:21.4095621Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:03:21.4095797Z getitem_68: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)] 2025-03-04T21:03:21.4095938Z mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_68; dx_3 = getitem_68 = None 2025-03-04T21:03:21.4096129Z getitem_69: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)]; ctr_x_3 = None 2025-03-04T21:03:21.4096313Z pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_69; mul_32 = getitem_69 = None 2025-03-04T21:03:21.4096435Z 2025-03-04T21:03:21.4096805Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:03:21.4096986Z getitem_70: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)] 2025-03-04T21:03:21.4097149Z mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_70; dy_3 = getitem_70 = None 2025-03-04T21:03:21.4097352Z getitem_71: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)]; ctr_y_3 = None 2025-03-04T21:03:21.4097516Z pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_71; mul_33 = getitem_71 = None 2025-03-04T21:03:21.4097624Z 2025-03-04T21:03:21.4097978Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:03:21.4098143Z exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7); dw_7 = None 2025-03-04T21:03:21.4098311Z getitem_72: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]; widths_3 = None 2025-03-04T21:03:21.4098524Z pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_72; exp_6 = getitem_72 = None 2025-03-04T21:03:21.4098615Z 2025-03-04T21:03:21.4098985Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:03:21.4099115Z exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7); dh_7 = None 2025-03-04T21:03:21.4099311Z getitem_73: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]; heights_3 = None 2025-03-04T21:03:21.4099478Z pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_73; exp_7 = getitem_73 = None 2025-03-04T21:03:21.4099593Z 2025-03-04T21:03:21.4099930Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:03:21.4100087Z mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3 2025-03-04T21:03:21.4100215Z x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36; mul_36 = None 2025-03-04T21:03:21.4100344Z 2025-03-04T21:03:21.4100698Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:03:21.4100811Z mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3 2025-03-04T21:03:21.4100962Z y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37; mul_37 = None 2025-03-04T21:03:21.4101043Z 2025-03-04T21:03:21.4101403Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:03:21.4101543Z mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3; pred_w_3 = None 2025-03-04T21:03:21.4101722Z x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38; pred_ctr_x_3 = mul_38 = None 2025-03-04T21:03:21.4101802Z 2025-03-04T21:03:21.4102311Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:03:21.4102477Z mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3; pred_h_3 = None 2025-03-04T21:03:21.4102683Z y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39; pred_ctr_y_3 = mul_39 = None 2025-03-04T21:03:21.4102764Z 2025-03-04T21:03:21.4103162Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:03:21.4103369Z pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1); x1_3 = y1_3 = x2_3 = y2_3 = None 2025-03-04T21:03:21.4103469Z 2025-03-04T21:03:21.4103846Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:03:21.4104064Z proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4)); pred_boxes_3 = None 2025-03-04T21:03:21.4104145Z 2025-03-04T21:03:21.4104583Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:03:21.4104767Z proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4); proposals_i_3 = None 2025-03-04T21:03:21.4104898Z 2025-03-04T21:03:21.4105318Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:03:21.4105566Z pred_anchor_deltas_i_9: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_8.reshape(-1, 4); pred_anchor_deltas_i_8 = None 2025-03-04T21:03:21.4105647Z 2025-03-04T21:03:21.4106116Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:03:21.4106312Z unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = tensor_4.unsqueeze(0); tensor_4 = None 2025-03-04T21:03:21.4106483Z expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1); unsqueeze_4 = None 2025-03-04T21:03:21.4106656Z anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4); expand_4 = None 2025-03-04T21:03:21.4106738Z 2025-03-04T21:03:21.4107171Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:03:21.4107361Z deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_9.float(); pred_anchor_deltas_i_9 = None 2025-03-04T21:03:21.4107488Z 2025-03-04T21:03:21.4107810Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:03:21.4107989Z boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32); anchors_i_4 = None 2025-03-04T21:03:21.4108075Z 2025-03-04T21:03:21.4108411Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:03:21.4108567Z getitem_74: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)] 2025-03-04T21:03:21.4108740Z getitem_75: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T21:03:21.4108898Z widths_4: "f32[4332][1]cpu" = getitem_74 - getitem_75; getitem_74 = getitem_75 = None 2025-03-04T21:03:21.4109017Z 2025-03-04T21:03:21.4109350Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:03:21.4109537Z getitem_76: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)] 2025-03-04T21:03:21.4109682Z getitem_77: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)] 2025-03-04T21:03:21.4109873Z heights_4: "f32[4332][1]cpu" = getitem_76 - getitem_77; getitem_76 = getitem_77 = None 2025-03-04T21:03:21.4109963Z 2025-03-04T21:03:21.4110320Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:03:21.4110446Z getitem_78: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T21:03:21.4110609Z mul_40: "f32[4332][1]cpu" = 0.5 * widths_4 2025-03-04T21:03:21.4110755Z ctr_x_4: "f32[4332][1]cpu" = getitem_78 + mul_40; getitem_78 = mul_40 = None 2025-03-04T21:03:21.4110869Z 2025-03-04T21:03:21.4111219Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:03:21.4111400Z getitem_79: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]; boxes_4 = None 2025-03-04T21:03:21.4111529Z mul_41: "f32[4332][1]cpu" = 0.5 * heights_4 2025-03-04T21:03:21.4111718Z ctr_y_4: "f32[4332][1]cpu" = getitem_79 + mul_41; getitem_79 = mul_41 = None 2025-03-04T21:03:21.4111798Z 2025-03-04T21:03:21.4112150Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:03:21.4112320Z getitem_80: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:03:21.4112492Z dx_4: "f32[4332, 1][1, 1]cpu" = getitem_80 / 1.0; getitem_80 = None 2025-03-04T21:03:21.4112611Z 2025-03-04T21:03:21.4112929Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:03:21.4113123Z getitem_81: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:03:21.4113249Z dy_4: "f32[4332, 1][1, 1]cpu" = getitem_81 / 1.0; getitem_81 = None 2025-03-04T21:03:21.4113369Z 2025-03-04T21:03:21.4113753Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:03:21.4113980Z getitem_82: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:03:21.4114122Z dw_8: "f32[4332, 1][1, 1]cpu" = getitem_82 / 1.0; getitem_82 = None 2025-03-04T21:03:21.4114244Z 2025-03-04T21:03:21.4114565Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:03:21.4114878Z getitem_83: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))]; deltas_4 = None 2025-03-04T21:03:21.4115002Z dh_8: "f32[4332, 1][1, 1]cpu" = getitem_83 / 1.0; getitem_83 = None 2025-03-04T21:03:21.4115111Z 2025-03-04T21:03:21.4115471Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:03:21.4115642Z dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356); dw_8 = None 2025-03-04T21:03:21.4115756Z 2025-03-04T21:03:21.4116140Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:03:21.4116293Z dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356); dh_8 = None 2025-03-04T21:03:21.4116416Z 2025-03-04T21:03:21.4116784Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:03:21.4116968Z getitem_84: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)] 2025-03-04T21:03:21.4117117Z mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_84; dx_4 = getitem_84 = None 2025-03-04T21:03:21.4117308Z getitem_85: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)]; ctr_x_4 = None 2025-03-04T21:03:21.4117486Z pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_85; mul_42 = getitem_85 = None 2025-03-04T21:03:21.4117575Z 2025-03-04T21:03:21.4117974Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:03:21.4118154Z getitem_86: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)] 2025-03-04T21:03:21.4118327Z mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_86; dy_4 = getitem_86 = None 2025-03-04T21:03:21.4118493Z getitem_87: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)]; ctr_y_4 = None 2025-03-04T21:03:21.4118678Z pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_87; mul_43 = getitem_87 = None 2025-03-04T21:03:21.4118745Z 2025-03-04T21:03:21.4119143Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:03:21.4119279Z exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9); dw_9 = None 2025-03-04T21:03:21.4119483Z getitem_88: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]; widths_4 = None 2025-03-04T21:03:21.4119631Z pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_88; exp_8 = getitem_88 = None 2025-03-04T21:03:21.4119727Z 2025-03-04T21:03:21.4120095Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:03:21.4120257Z exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9); dh_9 = None 2025-03-04T21:03:21.4120440Z getitem_89: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]; heights_4 = None 2025-03-04T21:03:21.4120617Z pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_89; exp_9 = getitem_89 = None 2025-03-04T21:03:21.4120697Z 2025-03-04T21:03:21.4121105Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:03:21.4121242Z mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4 2025-03-04T21:03:21.4121405Z x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46; mul_46 = None 2025-03-04T21:03:21.4121490Z 2025-03-04T21:03:21.4121845Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:03:21.4121945Z mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4 2025-03-04T21:03:21.4122147Z y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47; mul_47 = None 2025-03-04T21:03:21.4122234Z 2025-03-04T21:03:21.4122586Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:03:21.4122715Z mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4; pred_w_4 = None 2025-03-04T21:03:21.4122879Z x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48; pred_ctr_x_4 = mul_48 = None 2025-03-04T21:03:21.4122972Z 2025-03-04T21:03:21.4123352Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:03:21.4123481Z mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4; pred_h_4 = None 2025-03-04T21:03:21.4123657Z y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49; pred_ctr_y_4 = mul_49 = None 2025-03-04T21:03:21.4123739Z 2025-03-04T21:03:21.4124133Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:03:21.4124378Z pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1); x1_4 = y1_4 = x2_4 = y2_4 = None 2025-03-04T21:03:21.4124462Z 2025-03-04T21:03:21.4124833Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:03:21.4125012Z proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4)); pred_boxes_4 = None 2025-03-04T21:03:21.4125145Z 2025-03-04T21:03:21.4125554Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:03:21.4125768Z proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4); proposals_i_4 = None 2025-03-04T21:03:21.4125848Z 2025-03-04T21:03:21.4126401Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T21:03:21.4126549Z arange_10: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T21:03:21.4126712Z 2025-03-04T21:03:21.4127028Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:21.4127216Z batch_idx: "i64[4][1]cpu" = arange_10.to(device(type='cpu')); arange_10 = None 2025-03-04T21:03:21.4127296Z 2025-03-04T21:03:21.4127771Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:03:21.4127918Z topk = logits_i.topk(1000, dim = 1); logits_i = None 2025-03-04T21:03:21.4128076Z topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0] 2025-03-04T21:03:21.4128225Z topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1]; topk = None 2025-03-04T21:03:21.4128354Z 2025-03-04T21:03:21.4128836Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:03:21.4129019Z getitem_92: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:03:21.4129275Z topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_92, topk_idx)]; proposals_i_5 = getitem_92 = topk_idx = None 2025-03-04T21:03:21.4129379Z 2025-03-04T21:03:21.4129895Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:03:21.4130078Z full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:03:21.4130187Z 2025-03-04T21:03:21.4130504Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:21.4130679Z to_21: "i64[1000][1]cpu" = full.to(device(type='cpu')); full = None 2025-03-04T21:03:21.4130781Z 2025-03-04T21:03:21.4131251Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:03:21.4131371Z topk_1 = logits_i_1.topk(1000, dim = 1); logits_i_1 = None 2025-03-04T21:03:21.4131545Z topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0] 2025-03-04T21:03:21.4131676Z topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1]; topk_1 = None 2025-03-04T21:03:21.4131783Z 2025-03-04T21:03:21.4132253Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:03:21.4132414Z getitem_96: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:03:21.4132690Z topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_96, topk_idx_1)]; proposals_i_6 = getitem_96 = topk_idx_1 = None 2025-03-04T21:03:21.4132812Z 2025-03-04T21:03:21.4133274Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:03:21.4133483Z full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:03:21.4133566Z 2025-03-04T21:03:21.4133918Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:21.4134080Z to_22: "i64[1000][1]cpu" = full_1.to(device(type='cpu')); full_1 = None 2025-03-04T21:03:21.4134186Z 2025-03-04T21:03:21.4134636Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:03:21.4134795Z topk_2 = logits_i_2.topk(1000, dim = 1); logits_i_2 = None 2025-03-04T21:03:21.4134951Z topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0] 2025-03-04T21:03:21.4135099Z topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1]; topk_2 = None 2025-03-04T21:03:21.4135227Z 2025-03-04T21:03:21.4135721Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:03:21.4135909Z getitem_100: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:03:21.4136146Z topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_100, topk_idx_2)]; proposals_i_7 = getitem_100 = topk_idx_2 = None 2025-03-04T21:03:21.4136288Z 2025-03-04T21:03:21.4136746Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:03:21.4136947Z full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:03:21.4137028Z 2025-03-04T21:03:21.4137353Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:21.4137512Z to_23: "i64[1000][1]cpu" = full_2.to(device(type='cpu')); full_2 = None 2025-03-04T21:03:21.4137637Z 2025-03-04T21:03:21.4138072Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:03:21.4138224Z topk_3 = logits_i_3.topk(1000, dim = 1); logits_i_3 = None 2025-03-04T21:03:21.4138347Z topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0] 2025-03-04T21:03:21.4138545Z topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1]; topk_3 = None 2025-03-04T21:03:21.4138635Z 2025-03-04T21:03:21.4139120Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:03:21.4139269Z getitem_104: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:03:21.4139546Z topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_104, topk_idx_3)]; proposals_i_8 = getitem_104 = topk_idx_3 = None 2025-03-04T21:03:21.4139682Z 2025-03-04T21:03:21.4140177Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:03:21.4140373Z full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:03:21.4140458Z 2025-03-04T21:03:21.4140792Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:21.4140936Z to_24: "i64[1000][1]cpu" = full_3.to(device(type='cpu')); full_3 = None 2025-03-04T21:03:21.4141072Z 2025-03-04T21:03:21.4141509Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:03:21.4141668Z topk_4 = logits_i_4.topk(1000, dim = 1); logits_i_4 = None 2025-03-04T21:03:21.4141786Z topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0] 2025-03-04T21:03:21.4141926Z topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1]; topk_4 = None 2025-03-04T21:03:21.4142021Z 2025-03-04T21:03:21.4142516Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:03:21.4142727Z getitem_108: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T21:03:21.4142995Z topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_108, topk_idx_4)]; proposals_i_9 = getitem_108 = topk_idx_4 = None 2025-03-04T21:03:21.4143075Z 2025-03-04T21:03:21.4143563Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:03:21.4143748Z full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:03:21.4143860Z 2025-03-04T21:03:21.4144175Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:21.4144343Z to_25: "i64[1000][1]cpu" = full_4.to(device(type='cpu')); full_4 = None 2025-03-04T21:03:21.4144436Z 2025-03-04T21:03:21.4144797Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:03:21.4145224Z topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1); topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None 2025-03-04T21:03:21.4145303Z 2025-03-04T21:03:21.4145621Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:03:21.4146080Z topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1); topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None 2025-03-04T21:03:21.4146214Z 2025-03-04T21:03:21.4146504Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:03:21.4146760Z level_ids: "i64[5000][1]cpu" = torch.cat([to_21, to_22, to_23, to_24, to_25], 0); to_21 = to_22 = to_23 = to_24 = to_25 = level_ids = None 2025-03-04T21:03:21.4146840Z 2025-03-04T21:03:21.4147249Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T21:03:21.4147423Z getitem_110: "f32[5000, 4][4, 1]cpu" = topk_proposals[0]; topk_proposals = None 2025-03-04T21:03:21.4147537Z 2025-03-04T21:03:21.4147852Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:03:21.4148048Z tensor_5: "f32[5000, 4][4, 1]cpu" = getitem_110.to(torch.float32); getitem_110 = None 2025-03-04T21:03:21.4148127Z 2025-03-04T21:03:21.4148565Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T21:03:21.4148726Z scores_per_img: "f32[5000][1]cpu" = topk_scores[0]; topk_scores = None 2025-03-04T21:03:21.4148832Z 2025-03-04T21:03:21.4149339Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T21:03:21.4149540Z isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor_5); tensor_5 = None 2025-03-04T21:03:21.4149711Z all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:03:21.4149921Z isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T21:03:21.4150096Z valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T21:03:21.4150181Z 2025-03-04T21:03:21.4150595Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T21:03:21.4150717Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T21:03:21.4173319Z 2025-03-04T21:03:21.4173758Z 2025-03-04T21:03:21.4173970Z class GraphModule(torch.nn.Module): 2025-03-04T21:03:21.4295776Z def forward(self, L_stack0_tensor: "f32[4, 3, 1184, 1216][4319232, 1439744, 1216, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", 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L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", 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1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_: "f32[512, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_: "f32[2048, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_lateral_convs_0_parameters_weight_: "f32[256, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_0_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_0_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_0_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_1_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_1_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_1_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_2_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_2_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_2_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_3_parameters_weight_: "f32[256, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_3_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_3_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_3_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[3, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[3][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[12, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[12][1]cpu"): 2025-03-04T21:03:21.4297028Z l_stack0_tensor = L_stack0_tensor 2025-03-04T21:03:21.4297388Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.4297793Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4298182Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4298569Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4298922Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4299277Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.4299687Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4300149Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4300601Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4301030Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4301440Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.4302035Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4302519Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4302961Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4303431Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4303880Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.4304338Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4304745Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4305128Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4305518Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4305962Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:03:21.4306451Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4306908Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4307368Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4307845Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4308248Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.4308727Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4309192Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4309620Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4310044Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4310426Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.4310900Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4311368Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4311814Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4312223Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4312627Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.4313102Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4313567Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4314057Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4314505Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4314954Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.4315415Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4315858Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4316294Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4316696Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4317066Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.4317495Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4317942Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4318362Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4318773Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4319153Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.4319583Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4320008Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4320433Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4320835Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4321203Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.4321625Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4322063Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4322460Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4322866Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4323230Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.4323662Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4324082Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4324479Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4324888Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4325265Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.4325682Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4326084Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4326479Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4326862Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4327240Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:03:21.4327669Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4328084Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4328496Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4328902Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4329261Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.4329673Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4330080Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4330475Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4330853Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4331213Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.4331653Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4332062Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4332455Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4332832Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4333191Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.4333615Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4334031Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4334419Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4334818Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4335197Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.4335606Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4336018Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4336403Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4336799Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4337147Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.4337564Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4337992Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4338392Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4338778Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4339128Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.4339545Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4339948Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4340357Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4340741Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4341091Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.4341505Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4341928Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4342328Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4342707Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4343068Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.4343487Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4343897Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4344291Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4344696Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4345053Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.4345460Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4345872Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4346264Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4346657Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4347011Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.4347415Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4347823Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4348229Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4348613Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4348970Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.4349380Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4349795Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4350189Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4350578Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4350953Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.4351378Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4351795Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4352184Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4352601Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4353000Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:03:21.4353480Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4354008Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4354448Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4354882Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4355282Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.4355727Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4356127Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4356517Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4356898Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4357241Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.4357646Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4358053Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4358454Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4358821Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4359174Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.4359587Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4359985Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4360389Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4360759Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4361110Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.4361509Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4361925Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4362308Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4362681Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4363028Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.4363425Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4363827Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4364218Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4364607Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4364960Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.4365362Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4365762Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4366142Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4366534Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4366876Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.4367284Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4367689Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4368081Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4368462Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4368806Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.4369221Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4369634Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4370033Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4370416Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4370775Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.4371197Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4371591Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4371976Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4372354Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4372724Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.4373147Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4373539Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4373926Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4374308Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4374684Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.4375097Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4375513Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4375922Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4376316Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4376687Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.4377105Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4377557Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4377960Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4378350Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4378711Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.4379122Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4379551Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4379936Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4380334Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4380689Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.4381130Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4381544Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4381927Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4382315Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4382664Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.4383083Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4383484Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4383890Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4384304Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4384682Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.4385117Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4385524Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4385921Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4386314Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4386677Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.4387091Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4387495Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4387904Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4388282Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4388642Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.4389054Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4389463Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4389852Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4390229Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4390632Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.4391080Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4391514Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4391920Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4392304Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4392663Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.4393101Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4393534Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4394002Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4394416Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4394807Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.4395247Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4395681Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4396098Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4396511Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4396884Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.4397345Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4397788Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4398206Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4398613Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4398981Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.4399422Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4399868Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4400285Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4400693Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4401066Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.4401499Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4402009Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4402399Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4402771Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4403124Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.4403530Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4403935Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4404366Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4404762Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4405124Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.4405537Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4405937Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4406321Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4406710Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4407058Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.4407460Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4407861Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4408262Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4408642Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4408996Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.4409403Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4409809Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4410192Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4410586Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4410974Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.4411396Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4411807Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4412189Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4412567Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4412927Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.4413340Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4413746Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4414145Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4414549Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4414903Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.4415321Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4415732Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4416134Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4416520Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4416868Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.4417290Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4417705Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4418095Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4418470Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4418829Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.4419236Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4419654Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4420036Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4420411Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4420765Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.4421196Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4421601Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4421980Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4422366Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4422722Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.4423127Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4423528Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4423930Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4424320Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4424672Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.4425069Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4425476Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4425872Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4426247Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4426588Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.4426994Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4427411Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4427790Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4428175Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4428528Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.4428951Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4429351Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4429747Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4430152Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4430520Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.4430941Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4431349Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4431750Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4432141Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4432513Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.4432932Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4433336Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4433816Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4434247Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4434640Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.4435058Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4435471Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4435873Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4436257Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4436618Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.4437086Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4437505Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4437906Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4438291Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4438656Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.4439086Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4439498Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4439890Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4440282Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4440659Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.4441069Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4441481Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4441871Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4442259Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4442612Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.4443027Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4443453Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4443855Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4444243Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4444614Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.4445060Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4445464Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4445877Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4446267Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4446635Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.4447046Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4447470Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4447887Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4448299Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4448681Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.4449128Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4449553Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4449963Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4450361Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4450720Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.4451129Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4451564Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4451984Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4452403Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4452780Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.4453223Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4453684Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4454132Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4454571Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4454944Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.4455355Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4455771Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4456158Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4456544Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4456912Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.4457352Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4457766Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4458158Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4458546Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4458906Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.4459348Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4459768Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4460159Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4460550Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4460923Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.4461339Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4461745Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4462142Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4462532Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4462911Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.4463329Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4463750Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4464151Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4464538Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4464897Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.4465323Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4465741Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4466137Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4466520Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4466881Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.4467309Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4467721Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4468113Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4468499Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4468861Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.4469273Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4469682Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4470086Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4470486Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4470847Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.4471260Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4471696Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4472108Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4472509Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4472862Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.4473299Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4473787Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4474231Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4474660Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4475069Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.4475508Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4475924Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4476326Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4476733Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4477112Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.4477544Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4477965Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4478371Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4478765Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4479168Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.4479624Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4480029Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4480429Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4480827Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4481200Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.4481616Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4482040Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4482449Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4482836Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4483200Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.4483634Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4484076Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4484474Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4484855Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4485217Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.4485632Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4486063Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4486438Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4486818Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4487166Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.4487580Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4487985Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4488362Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4488743Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4489087Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.4489496Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4489912Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4490301Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4490679Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4491019Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.4491425Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4491822Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4492224Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4492598Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4492956Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:03:21.4493379Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4493817Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4494249Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4494657Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4495024Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.4495432Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4495827Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4496208Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4496592Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4496957Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.4497355Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4497755Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4498140Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4498511Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4498882Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.4499292Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4499703Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4500090Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4500489Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4500845Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:03:21.4501255Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4501667Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4502233Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4502626Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4502981Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:03:21.4503456Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4503868Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4504253Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4504638Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4504990Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:03:21.4505427Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:03:21.4505829Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:03:21.4506221Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:03:21.4506607Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:03:21.4506841Z l_self_modules_backbone_lateral_convs_0_parameters_weight_ = L_self_modules_backbone_lateral_convs_0_parameters_weight_ 2025-03-04T21:03:21.4507087Z l_self_modules_backbone_lateral_convs_0_parameters_bias_ = L_self_modules_backbone_lateral_convs_0_parameters_bias_ 2025-03-04T21:03:21.4507314Z l_self_modules_backbone_output_convs_0_parameters_weight_ = L_self_modules_backbone_output_convs_0_parameters_weight_ 2025-03-04T21:03:21.4507534Z l_self_modules_backbone_output_convs_0_parameters_bias_ = L_self_modules_backbone_output_convs_0_parameters_bias_ 2025-03-04T21:03:21.4507757Z l_self_modules_backbone_lateral_convs_1_parameters_weight_ = L_self_modules_backbone_lateral_convs_1_parameters_weight_ 2025-03-04T21:03:21.4507979Z l_self_modules_backbone_lateral_convs_1_parameters_bias_ = L_self_modules_backbone_lateral_convs_1_parameters_bias_ 2025-03-04T21:03:21.4508197Z l_self_modules_backbone_output_convs_1_parameters_weight_ = L_self_modules_backbone_output_convs_1_parameters_weight_ 2025-03-04T21:03:21.4508416Z l_self_modules_backbone_output_convs_1_parameters_bias_ = L_self_modules_backbone_output_convs_1_parameters_bias_ 2025-03-04T21:03:21.4508636Z l_self_modules_backbone_lateral_convs_2_parameters_weight_ = L_self_modules_backbone_lateral_convs_2_parameters_weight_ 2025-03-04T21:03:21.4508858Z l_self_modules_backbone_lateral_convs_2_parameters_bias_ = L_self_modules_backbone_lateral_convs_2_parameters_bias_ 2025-03-04T21:03:21.4509076Z l_self_modules_backbone_output_convs_2_parameters_weight_ = L_self_modules_backbone_output_convs_2_parameters_weight_ 2025-03-04T21:03:21.4509292Z l_self_modules_backbone_output_convs_2_parameters_bias_ = L_self_modules_backbone_output_convs_2_parameters_bias_ 2025-03-04T21:03:21.4509558Z l_self_modules_backbone_lateral_convs_3_parameters_weight_ = L_self_modules_backbone_lateral_convs_3_parameters_weight_ 2025-03-04T21:03:21.4509771Z l_self_modules_backbone_lateral_convs_3_parameters_bias_ = L_self_modules_backbone_lateral_convs_3_parameters_bias_ 2025-03-04T21:03:21.4509998Z l_self_modules_backbone_output_convs_3_parameters_weight_ = L_self_modules_backbone_output_convs_3_parameters_weight_ 2025-03-04T21:03:21.4510204Z l_self_modules_backbone_output_convs_3_parameters_bias_ = L_self_modules_backbone_output_convs_3_parameters_bias_ 2025-03-04T21:03:21.4510567Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T21:03:21.4510920Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ 2025-03-04T21:03:21.4511277Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ 2025-03-04T21:03:21.4511641Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ 2025-03-04T21:03:21.4511997Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ 2025-03-04T21:03:21.4512323Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T21:03:21.4512650Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T21:03:21.4513056Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T21:03:21.4513458Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T21:03:21.4513900Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T21:03:21.4514302Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T21:03:21.4514388Z 2025-03-04T21:03:21.4514727Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4515289Z x: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1); l_stack0_tensor = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.4515358Z 2025-03-04T21:03:21.4515654Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4517438Z x_1: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4517517Z 2025-03-04T21:03:21.4517832Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x) 2025-03-04T21:03:21.4517972Z x_2: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.relu_(x_1); x_1 = None 2025-03-04T21:03:21.4518045Z 2025-03-04T21:03:21.4518425Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1) 2025-03-04T21:03:21.4518691Z x_3: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1); x_2 = None 2025-03-04T21:03:21.4518751Z 2025-03-04T21:03:21.4519011Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4519492Z x_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.4519558Z 2025-03-04T21:03:21.4519831Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4521608Z x_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4521679Z 2025-03-04T21:03:21.4521961Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4522105Z out: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_5); x_5 = None 2025-03-04T21:03:21.4522172Z 2025-03-04T21:03:21.4522421Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4522936Z x_6: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.4523010Z 2025-03-04T21:03:21.4523279Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4525114Z x_7: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_6 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4525206Z 2025-03-04T21:03:21.4525499Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4525632Z out_1: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_7); x_7 = None 2025-03-04T21:03:21.4525698Z 2025-03-04T21:03:21.4525944Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4526464Z x_8: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_1 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.4526527Z 2025-03-04T21:03:21.4526795Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4528598Z x_9: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4528664Z 2025-03-04T21:03:21.4528915Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4529405Z x_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); x_3 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:03:21.4529474Z 2025-03-04T21:03:21.4529744Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4531593Z x_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_10 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4531666Z 2025-03-04T21:03:21.4531958Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.4532109Z x_9 += x_11; out_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_9; x_9 = x_11 = None 2025-03-04T21:03:21.4532168Z 2025-03-04T21:03:21.4532455Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4532599Z out_3: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_2); out_2 = None 2025-03-04T21:03:21.4532666Z 2025-03-04T21:03:21.4532909Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4533401Z x_12: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.4533486Z 2025-03-04T21:03:21.4533743Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4535508Z x_13: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_12 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4535569Z 2025-03-04T21:03:21.4535853Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4535996Z out_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_13); x_13 = None 2025-03-04T21:03:21.4536055Z 2025-03-04T21:03:21.4536313Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4536817Z x_14: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.4536884Z 2025-03-04T21:03:21.4537143Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4538942Z x_15: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_14 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4539022Z 2025-03-04T21:03:21.4539298Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4539438Z out_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_15); x_15 = None 2025-03-04T21:03:21.4539500Z 2025-03-04T21:03:21.4539748Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4540249Z x_16: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_5 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.4540318Z 2025-03-04T21:03:21.4540575Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4542367Z x_17: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_16 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4542437Z 2025-03-04T21:03:21.4542709Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.4542898Z x_17 += out_3; out_6: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_17; x_17 = out_3 = None 2025-03-04T21:03:21.4542959Z 2025-03-04T21:03:21.4543243Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4543390Z out_7: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_6); out_6 = None 2025-03-04T21:03:21.4543458Z 2025-03-04T21:03:21.4543710Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4544207Z x_18: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.4544272Z 2025-03-04T21:03:21.4544543Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4546367Z x_19: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_18 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4546430Z 2025-03-04T21:03:21.4546738Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4546875Z out_8: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_19); x_19 = None 2025-03-04T21:03:21.4546945Z 2025-03-04T21:03:21.4547195Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4547702Z x_20: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.4547770Z 2025-03-04T21:03:21.4548036Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4549892Z x_21: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_20 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4549968Z 2025-03-04T21:03:21.4550261Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4550404Z out_9: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_21); x_21 = None 2025-03-04T21:03:21.4550465Z 2025-03-04T21:03:21.4550716Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4551229Z x_22: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_9 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.4551314Z 2025-03-04T21:03:21.4551582Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4553427Z x_23: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_22 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4553497Z 2025-03-04T21:03:21.4553839Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.4554023Z x_23 += out_7; out_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_23; x_23 = out_7 = None 2025-03-04T21:03:21.4554087Z 2025-03-04T21:03:21.4554410Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4554583Z out_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_10); out_10 = None 2025-03-04T21:03:21.4554656Z 2025-03-04T21:03:21.4554930Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4555455Z x_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.4555516Z 2025-03-04T21:03:21.4555793Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4557622Z x_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4557698Z 2025-03-04T21:03:21.4558004Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4558152Z out_12: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_25); x_25 = None 2025-03-04T21:03:21.4558243Z 2025-03-04T21:03:21.4558503Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4559010Z x_26: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_12 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.4559072Z 2025-03-04T21:03:21.4559343Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4561196Z x_27: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_26 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4561259Z 2025-03-04T21:03:21.4561554Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4561695Z out_13: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_27); x_27 = None 2025-03-04T21:03:21.4561763Z 2025-03-04T21:03:21.4562024Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4562542Z x_28: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_13 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.4562608Z 2025-03-04T21:03:21.4562897Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4564741Z x_29: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_28 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4564803Z 2025-03-04T21:03:21.4565071Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4565603Z x_30: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:03:21.4565664Z 2025-03-04T21:03:21.4565947Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4567834Z x_31: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_30 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4567906Z 2025-03-04T21:03:21.4568206Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.4568352Z x_29 += x_31; out_14: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_29; x_29 = x_31 = None 2025-03-04T21:03:21.4568420Z 2025-03-04T21:03:21.4568706Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4568858Z out_15: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_14); out_14 = None 2025-03-04T21:03:21.4568921Z 2025-03-04T21:03:21.4569178Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4569690Z x_32: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.4569772Z 2025-03-04T21:03:21.4570042Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4571807Z x_33: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_32 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4571888Z 2025-03-04T21:03:21.4572165Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4572308Z out_16: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_33); x_33 = None 2025-03-04T21:03:21.4572368Z 2025-03-04T21:03:21.4572618Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4573106Z x_34: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_16 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.4573174Z 2025-03-04T21:03:21.4573434Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4575277Z x_35: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_34 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4575350Z 2025-03-04T21:03:21.4575646Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4575791Z out_17: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_35); x_35 = None 2025-03-04T21:03:21.4575861Z 2025-03-04T21:03:21.4576112Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4576613Z x_36: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_17 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.4576695Z 2025-03-04T21:03:21.4576968Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4578761Z x_37: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_36 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4578845Z 2025-03-04T21:03:21.4579121Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.4579279Z x_37 += out_15; out_18: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_37; x_37 = out_15 = None 2025-03-04T21:03:21.4579346Z 2025-03-04T21:03:21.4579628Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4579776Z out_19: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_18); out_18 = None 2025-03-04T21:03:21.4579836Z 2025-03-04T21:03:21.4580090Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4580584Z x_38: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.4580650Z 2025-03-04T21:03:21.4580908Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4582678Z x_39: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_38 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4582746Z 2025-03-04T21:03:21.4583054Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4583195Z out_20: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_39); x_39 = None 2025-03-04T21:03:21.4583257Z 2025-03-04T21:03:21.4583507Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4583991Z x_40: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_20 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.4584059Z 2025-03-04T21:03:21.4584321Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4586145Z x_41: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_40 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4586226Z 2025-03-04T21:03:21.4586502Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4586645Z out_21: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_41); x_41 = None 2025-03-04T21:03:21.4586722Z 2025-03-04T21:03:21.4586972Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4587455Z x_42: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_21 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.4587524Z 2025-03-04T21:03:21.4587785Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4589634Z x_43: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_42 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4589718Z 2025-03-04T21:03:21.4590000Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.4590159Z x_43 += out_19; out_22: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_43; x_43 = out_19 = None 2025-03-04T21:03:21.4590220Z 2025-03-04T21:03:21.4590511Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4590656Z out_23: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_22); out_22 = None 2025-03-04T21:03:21.4590726Z 2025-03-04T21:03:21.4590974Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4591473Z x_44: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.4591561Z 2025-03-04T21:03:21.4591828Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4593744Z x_45: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_44 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4593822Z 2025-03-04T21:03:21.4594109Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4594252Z out_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_45); x_45 = None 2025-03-04T21:03:21.4594314Z 2025-03-04T21:03:21.4594576Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4595105Z x_46: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.4595176Z 2025-03-04T21:03:21.4595454Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4597294Z x_47: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_46 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4597381Z 2025-03-04T21:03:21.4597666Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4597810Z out_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_47); x_47 = None 2025-03-04T21:03:21.4597875Z 2025-03-04T21:03:21.4598130Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4598641Z x_48: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_25 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.4598712Z 2025-03-04T21:03:21.4598977Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4600833Z x_49: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_48 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4600909Z 2025-03-04T21:03:21.4601203Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.4601371Z x_49 += out_23; out_26: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_49; x_49 = out_23 = None 2025-03-04T21:03:21.4601435Z 2025-03-04T21:03:21.4601738Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4602032Z out_27: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_26); out_26 = None 2025-03-04T21:03:21.4602115Z 2025-03-04T21:03:21.4602379Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4602901Z x_50: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.4603024Z 2025-03-04T21:03:21.4603314Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4605224Z x_51: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_50 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4605314Z 2025-03-04T21:03:21.4605622Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4605764Z out_28: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_51); x_51 = None 2025-03-04T21:03:21.4605836Z 2025-03-04T21:03:21.4606099Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4606625Z x_52: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_28 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.4606698Z 2025-03-04T21:03:21.4606976Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4608918Z x_53: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_52 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4608995Z 2025-03-04T21:03:21.4609298Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4609443Z out_29: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_53); x_53 = None 2025-03-04T21:03:21.4609509Z 2025-03-04T21:03:21.4609776Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4610329Z x_54: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_29 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.4610419Z 2025-03-04T21:03:21.4610701Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4612597Z x_55: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_54 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4612686Z 2025-03-04T21:03:21.4612950Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4613480Z x_56: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:03:21.4613543Z 2025-03-04T21:03:21.4613848Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4615911Z x_57: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_56 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4615983Z 2025-03-04T21:03:21.4616302Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.4616453Z x_55 += x_57; out_30: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_55; x_55 = x_57 = None 2025-03-04T21:03:21.4616529Z 2025-03-04T21:03:21.4616846Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4617008Z out_31: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_30); out_30 = None 2025-03-04T21:03:21.4617076Z 2025-03-04T21:03:21.4617383Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4617902Z x_58: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.4617974Z 2025-03-04T21:03:21.4618253Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4620154Z x_59: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_58 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4620242Z 2025-03-04T21:03:21.4620546Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4620697Z out_32: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_59); x_59 = None 2025-03-04T21:03:21.4620762Z 2025-03-04T21:03:21.4621046Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4621549Z x_60: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_32 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.4621618Z 2025-03-04T21:03:21.4621887Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4623678Z x_61: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_60 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4623750Z 2025-03-04T21:03:21.4624030Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4624179Z out_33: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_61); x_61 = None 2025-03-04T21:03:21.4624259Z 2025-03-04T21:03:21.4624508Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4625011Z x_62: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_33 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.4625071Z 2025-03-04T21:03:21.4625346Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4627180Z x_63: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_62 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4627263Z 2025-03-04T21:03:21.4627559Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.4627703Z x_63 += out_31; out_34: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_63; x_63 = out_31 = None 2025-03-04T21:03:21.4627770Z 2025-03-04T21:03:21.4628053Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4628214Z out_35: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_34); out_34 = None 2025-03-04T21:03:21.4628275Z 2025-03-04T21:03:21.4628527Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4629002Z x_64: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.4629071Z 2025-03-04T21:03:21.4629335Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4631314Z x_65: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_64 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4631398Z 2025-03-04T21:03:21.4631688Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4631825Z out_36: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_65); x_65 = None 2025-03-04T21:03:21.4631886Z 2025-03-04T21:03:21.4632145Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4632639Z x_66: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_36 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.4632710Z 2025-03-04T21:03:21.4632975Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4634901Z x_67: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_66 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4634980Z 2025-03-04T21:03:21.4635303Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4635449Z out_37: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_67); x_67 = None 2025-03-04T21:03:21.4635513Z 2025-03-04T21:03:21.4635785Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4636294Z x_68: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_37 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.4636363Z 2025-03-04T21:03:21.4636638Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4638442Z x_69: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_68 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4638530Z 2025-03-04T21:03:21.4638811Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.4638961Z x_69 += out_35; out_38: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_69; x_69 = out_35 = None 2025-03-04T21:03:21.4639022Z 2025-03-04T21:03:21.4639315Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4639472Z out_39: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_38); out_38 = None 2025-03-04T21:03:21.4639537Z 2025-03-04T21:03:21.4639812Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4640319Z x_70: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.4640386Z 2025-03-04T21:03:21.4640647Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4642462Z x_71: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_70 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4642534Z 2025-03-04T21:03:21.4642819Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4642954Z out_40: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_71); x_71 = None 2025-03-04T21:03:21.4643014Z 2025-03-04T21:03:21.4643272Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4643759Z x_72: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_40 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.4643826Z 2025-03-04T21:03:21.4644091Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4645962Z x_73: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_72 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4646047Z 2025-03-04T21:03:21.4646340Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4646474Z out_41: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_73); x_73 = None 2025-03-04T21:03:21.4646551Z 2025-03-04T21:03:21.4646800Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4647277Z x_74: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_41 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.4647346Z 2025-03-04T21:03:21.4647609Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4649443Z x_75: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_74 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4649516Z 2025-03-04T21:03:21.4649801Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.4649947Z x_75 += out_39; out_42: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_75; x_75 = out_39 = None 2025-03-04T21:03:21.4650008Z 2025-03-04T21:03:21.4650290Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4650426Z out_43: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_42); out_42 = None 2025-03-04T21:03:21.4650494Z 2025-03-04T21:03:21.4650736Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4651237Z x_76: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.4651321Z 2025-03-04T21:03:21.4651595Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4653373Z x_77: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_76 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4653449Z 2025-03-04T21:03:21.4653742Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4653878Z out_44: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_77); x_77 = None 2025-03-04T21:03:21.4653938Z 2025-03-04T21:03:21.4654196Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4654688Z x_78: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_44 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.4654755Z 2025-03-04T21:03:21.4655034Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4656827Z x_79: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_78 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4656894Z 2025-03-04T21:03:21.4657176Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4657312Z out_45: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_79); x_79 = None 2025-03-04T21:03:21.4657373Z 2025-03-04T21:03:21.4657638Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4658136Z x_80: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_45 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.4658205Z 2025-03-04T21:03:21.4658467Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4660309Z x_81: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_80 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4660405Z 2025-03-04T21:03:21.4660700Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.4660859Z x_81 += out_43; out_46: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_81; x_81 = out_43 = None 2025-03-04T21:03:21.4660923Z 2025-03-04T21:03:21.4661234Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4661386Z out_47: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_46); out_46 = None 2025-03-04T21:03:21.4661458Z 2025-03-04T21:03:21.4661737Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4662253Z x_82: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.4662316Z 2025-03-04T21:03:21.4662590Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4664412Z x_83: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_82 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4664490Z 2025-03-04T21:03:21.4664789Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4664923Z out_48: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_83); x_83 = None 2025-03-04T21:03:21.4664992Z 2025-03-04T21:03:21.4665246Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4665747Z x_84: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_48 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.4665816Z 2025-03-04T21:03:21.4666089Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4667913Z x_85: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_84 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4667990Z 2025-03-04T21:03:21.4668284Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4668434Z out_49: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_85); x_85 = None 2025-03-04T21:03:21.4668496Z 2025-03-04T21:03:21.4668749Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4669231Z x_86: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_49 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.4669308Z 2025-03-04T21:03:21.4669572Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4671377Z x_87: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_86 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4671461Z 2025-03-04T21:03:21.4671739Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.4671887Z x_87 += out_47; out_50: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_87; x_87 = out_47 = None 2025-03-04T21:03:21.4671948Z 2025-03-04T21:03:21.4672236Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4672376Z out_51: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_50); out_50 = None 2025-03-04T21:03:21.4672443Z 2025-03-04T21:03:21.4672695Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4673176Z x_88: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.4673252Z 2025-03-04T21:03:21.4673531Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4675398Z x_89: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_88 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4675471Z 2025-03-04T21:03:21.4675760Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4675887Z out_52: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_89); x_89 = None 2025-03-04T21:03:21.4675956Z 2025-03-04T21:03:21.4676202Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4676689Z x_90: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_52 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.4676749Z 2025-03-04T21:03:21.4677015Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4678806Z x_91: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_90 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4678885Z 2025-03-04T21:03:21.4679183Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4679314Z out_53: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_91); x_91 = None 2025-03-04T21:03:21.4679387Z 2025-03-04T21:03:21.4679643Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4680156Z x_92: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_53 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.4680218Z 2025-03-04T21:03:21.4680498Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4682272Z x_93: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_92 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4682336Z 2025-03-04T21:03:21.4682619Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.4682759Z x_93 += out_51; out_54: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_93; x_93 = out_51 = None 2025-03-04T21:03:21.4682825Z 2025-03-04T21:03:21.4683104Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4683248Z out_55: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_54); out_54 = None 2025-03-04T21:03:21.4683307Z 2025-03-04T21:03:21.4683557Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4684044Z x_94: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.4684124Z 2025-03-04T21:03:21.4684391Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4686178Z x_95: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_94 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4686262Z 2025-03-04T21:03:21.4686547Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4686672Z out_56: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_95); x_95 = None 2025-03-04T21:03:21.4686736Z 2025-03-04T21:03:21.4686979Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4687460Z x_96: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_56 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.4687521Z 2025-03-04T21:03:21.4687785Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4689554Z x_97: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_96 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4689625Z 2025-03-04T21:03:21.4689909Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4690033Z out_57: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_97); x_97 = None 2025-03-04T21:03:21.4690098Z 2025-03-04T21:03:21.4690342Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4690839Z x_98: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_57 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.4690915Z 2025-03-04T21:03:21.4691178Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4692926Z x_99: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_98 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4693001Z 2025-03-04T21:03:21.4693282Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.4693424Z x_99 += out_55; out_58: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_99; x_99 = out_55 = None 2025-03-04T21:03:21.4693490Z 2025-03-04T21:03:21.4693764Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4693910Z out_59: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_58); out_58 = None 2025-03-04T21:03:21.4693969Z 2025-03-04T21:03:21.4694222Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4694719Z x_100: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.4694789Z 2025-03-04T21:03:21.4695054Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4696913Z x_101: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_100 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4696985Z 2025-03-04T21:03:21.4697282Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4697441Z out_60: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_101); x_101 = None 2025-03-04T21:03:21.4697502Z 2025-03-04T21:03:21.4697763Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4698262Z x_102: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_60 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.4698322Z 2025-03-04T21:03:21.4698598Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4700437Z x_103: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_102 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4700525Z 2025-03-04T21:03:21.4700861Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4701000Z out_61: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_103); x_103 = None 2025-03-04T21:03:21.4701066Z 2025-03-04T21:03:21.4701330Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4701832Z x_104: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_61 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.4702037Z 2025-03-04T21:03:21.4702331Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4704140Z x_105: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_104 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4704233Z 2025-03-04T21:03:21.4704530Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.4704687Z x_105 += out_59; out_62: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_105; x_105 = out_59 = None 2025-03-04T21:03:21.4704753Z 2025-03-04T21:03:21.4705042Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4705187Z out_63: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_62); out_62 = None 2025-03-04T21:03:21.4705248Z 2025-03-04T21:03:21.4705507Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4706009Z x_106: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_63, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.4706113Z 2025-03-04T21:03:21.4706389Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4708224Z x_107: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_106 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4708297Z 2025-03-04T21:03:21.4708585Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4708727Z out_64: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_107); x_107 = None 2025-03-04T21:03:21.4708786Z 2025-03-04T21:03:21.4709043Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4709536Z x_108: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_64, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_64 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.4709604Z 2025-03-04T21:03:21.4709870Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4711762Z x_109: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_108, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_108 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4711853Z 2025-03-04T21:03:21.4712164Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4712314Z out_65: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_109); x_109 = None 2025-03-04T21:03:21.4712379Z 2025-03-04T21:03:21.4712654Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4713207Z x_110: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_65, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_65 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.4713291Z 2025-03-04T21:03:21.4713591Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4715596Z x_111: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_110, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_110 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4715674Z 2025-03-04T21:03:21.4715975Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.4716135Z x_111 += out_63; out_66: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_111; x_111 = out_63 = None 2025-03-04T21:03:21.4716207Z 2025-03-04T21:03:21.4716503Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4716657Z out_67: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_66); out_66 = None 2025-03-04T21:03:21.4716719Z 2025-03-04T21:03:21.4716988Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4717511Z x_112: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_67, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.4717582Z 2025-03-04T21:03:21.4717894Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4719833Z x_113: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_112, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_112 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4719907Z 2025-03-04T21:03:21.4720223Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4720371Z out_68: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_113); x_113 = None 2025-03-04T21:03:21.4720434Z 2025-03-04T21:03:21.4720702Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4721232Z x_114: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_68, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_68 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.4721304Z 2025-03-04T21:03:21.4721584Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4723439Z x_115: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_114, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_114 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4723512Z 2025-03-04T21:03:21.4723795Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4723935Z out_69: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_115); x_115 = None 2025-03-04T21:03:21.4723996Z 2025-03-04T21:03:21.4724253Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4724762Z x_116: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_69, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_69 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.4724846Z 2025-03-04T21:03:21.4725119Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4726926Z x_117: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_116, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_116 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4727010Z 2025-03-04T21:03:21.4727283Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.4727435Z x_117 += out_67; out_70: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_117; x_117 = out_67 = None 2025-03-04T21:03:21.4727502Z 2025-03-04T21:03:21.4727776Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4727918Z out_71: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_70); out_70 = None 2025-03-04T21:03:21.4727979Z 2025-03-04T21:03:21.4728226Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4728717Z x_118: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_71, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.4728785Z 2025-03-04T21:03:21.4729053Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4730862Z x_119: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_118, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_118 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4730929Z 2025-03-04T21:03:21.4731236Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4731398Z out_72: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_119); x_119 = None 2025-03-04T21:03:21.4731457Z 2025-03-04T21:03:21.4731714Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4732207Z x_120: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_72, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_72 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.4732272Z 2025-03-04T21:03:21.4732536Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4734344Z x_121: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_120, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_120 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4734426Z 2025-03-04T21:03:21.4734706Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4734843Z out_73: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_121); x_121 = None 2025-03-04T21:03:21.4734902Z 2025-03-04T21:03:21.4735171Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4735651Z x_122: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_73, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_73 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.4735719Z 2025-03-04T21:03:21.4735986Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4737854Z x_123: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_122, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_122 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4737935Z 2025-03-04T21:03:21.4738205Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.4738358Z x_123 += out_71; out_74: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_123; x_123 = out_71 = None 2025-03-04T21:03:21.4738417Z 2025-03-04T21:03:21.4738702Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4738837Z out_75: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_74); out_74 = None 2025-03-04T21:03:21.4738902Z 2025-03-04T21:03:21.4739142Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4739624Z x_124: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_75, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.4739703Z 2025-03-04T21:03:21.4739961Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4741764Z x_125: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_124, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_124 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4741834Z 2025-03-04T21:03:21.4742116Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4742251Z out_76: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_125); x_125 = None 2025-03-04T21:03:21.4742311Z 2025-03-04T21:03:21.4742566Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4743059Z x_126: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_76, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_76 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.4743127Z 2025-03-04T21:03:21.4743390Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4745252Z x_127: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_126, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_126 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4745333Z 2025-03-04T21:03:21.4745616Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4745754Z out_77: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_127); x_127 = None 2025-03-04T21:03:21.4745814Z 2025-03-04T21:03:21.4746071Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4746563Z x_128: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_77, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_77 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.4746644Z 2025-03-04T21:03:21.4746909Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4748740Z x_129: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_128, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_128 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4748811Z 2025-03-04T21:03:21.4749091Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.4749246Z x_129 += out_75; out_78: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_129; x_129 = out_75 = None 2025-03-04T21:03:21.4749308Z 2025-03-04T21:03:21.4749596Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4749737Z out_79: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_78); out_78 = None 2025-03-04T21:03:21.4749806Z 2025-03-04T21:03:21.4750055Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4750573Z x_130: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_79, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.4750638Z 2025-03-04T21:03:21.4750933Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4752931Z x_131: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_130, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_130 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4753001Z 2025-03-04T21:03:21.4753340Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4753486Z out_80: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_131); x_131 = None 2025-03-04T21:03:21.4753560Z 2025-03-04T21:03:21.4753889Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4754448Z x_132: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_80, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_80 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.4754528Z 2025-03-04T21:03:21.4754821Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4756681Z x_133: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_132, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_132 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4756753Z 2025-03-04T21:03:21.4757045Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4757186Z out_81: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_133); x_133 = None 2025-03-04T21:03:21.4757245Z 2025-03-04T21:03:21.4757498Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4758010Z x_134: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_81, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_81 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.4758096Z 2025-03-04T21:03:21.4758362Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4760172Z x_135: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_134, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_134 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4760265Z 2025-03-04T21:03:21.4760544Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.4760702Z x_135 += out_79; out_82: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_135; x_135 = out_79 = None 2025-03-04T21:03:21.4760763Z 2025-03-04T21:03:21.4761052Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4761193Z out_83: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_82); out_82 = None 2025-03-04T21:03:21.4761261Z 2025-03-04T21:03:21.4761508Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4762013Z x_136: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_83, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.4762074Z 2025-03-04T21:03:21.4762352Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4764205Z x_137: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_136, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_136 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4764271Z 2025-03-04T21:03:21.4764566Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4764736Z out_84: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_137); x_137 = None 2025-03-04T21:03:21.4764805Z 2025-03-04T21:03:21.4765056Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4765550Z x_138: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_84, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_84 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.4765608Z 2025-03-04T21:03:21.4765870Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4767699Z x_139: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_138, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_138 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4767777Z 2025-03-04T21:03:21.4768069Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4768201Z out_85: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_139); x_139 = None 2025-03-04T21:03:21.4768271Z 2025-03-04T21:03:21.4768535Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4769028Z x_140: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_85, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_85 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.4769095Z 2025-03-04T21:03:21.4769359Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4771173Z x_141: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_140, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_140 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4771277Z 2025-03-04T21:03:21.4771556Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.4771712Z x_141 += out_83; out_86: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_141; x_141 = out_83 = None 2025-03-04T21:03:21.4771773Z 2025-03-04T21:03:21.4772066Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4772203Z out_87: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_86); out_86 = None 2025-03-04T21:03:21.4772270Z 2025-03-04T21:03:21.4772530Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4773011Z x_142: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_87, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.4773088Z 2025-03-04T21:03:21.4773363Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4775131Z x_143: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_142, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_142 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4775195Z 2025-03-04T21:03:21.4775478Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4775610Z out_88: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_143); x_143 = None 2025-03-04T21:03:21.4775677Z 2025-03-04T21:03:21.4775930Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4776432Z x_144: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_88, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_88 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.4776494Z 2025-03-04T21:03:21.4776765Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4778598Z x_145: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_144, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_144 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4779032Z 2025-03-04T21:03:21.4779320Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4779452Z out_89: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_145); x_145 = None 2025-03-04T21:03:21.4779520Z 2025-03-04T21:03:21.4779769Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4780274Z x_146: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_89, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_89 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.4780365Z 2025-03-04T21:03:21.4780629Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4782447Z x_147: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_146, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_146 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4782510Z 2025-03-04T21:03:21.4782795Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.4782944Z x_147 += out_87; out_90: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_147; x_147 = out_87 = None 2025-03-04T21:03:21.4783005Z 2025-03-04T21:03:21.4783293Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4783431Z out_91: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_90); out_90 = None 2025-03-04T21:03:21.4783499Z 2025-03-04T21:03:21.4783742Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4784225Z x_148: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_91, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.4784284Z 2025-03-04T21:03:21.4784576Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4786402Z x_149: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_148, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_148 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4786471Z 2025-03-04T21:03:21.4786775Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4786935Z out_92: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_149); x_149 = None 2025-03-04T21:03:21.4787007Z 2025-03-04T21:03:21.4787270Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4787820Z x_150: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_92, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_92 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.4787891Z 2025-03-04T21:03:21.4788192Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4790235Z x_151: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_150, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_150 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4790308Z 2025-03-04T21:03:21.4790635Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4790788Z out_93: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_151); x_151 = None 2025-03-04T21:03:21.4790862Z 2025-03-04T21:03:21.4791140Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4791714Z x_152: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_93, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_93 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.4791799Z 2025-03-04T21:03:21.4792102Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4794198Z x_153: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_152, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_152 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4794293Z 2025-03-04T21:03:21.4794629Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.4794791Z x_153 += out_91; out_94: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_153; x_153 = out_91 = None 2025-03-04T21:03:21.4794866Z 2025-03-04T21:03:21.4795184Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4795348Z out_95: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_94); out_94 = None 2025-03-04T21:03:21.4795419Z 2025-03-04T21:03:21.4795710Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4796280Z x_154: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_95, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.4796348Z 2025-03-04T21:03:21.4796652Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4798487Z x_155: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_154, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_154 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4798558Z 2025-03-04T21:03:21.4798843Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4799005Z out_96: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_155); x_155 = None 2025-03-04T21:03:21.4799073Z 2025-03-04T21:03:21.4799315Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4799803Z x_156: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_96, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_96 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.4799863Z 2025-03-04T21:03:21.4800127Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4802013Z x_157: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_156, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_156 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4802127Z 2025-03-04T21:03:21.4802430Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4802565Z out_97: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_157); x_157 = None 2025-03-04T21:03:21.4802638Z 2025-03-04T21:03:21.4802915Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4803417Z x_158: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_97, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_97 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.4803476Z 2025-03-04T21:03:21.4803742Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4805532Z x_159: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_158, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_158 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4805643Z 2025-03-04T21:03:21.4805931Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.4806078Z x_159 += out_95; out_98: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_159; x_159 = out_95 = None 2025-03-04T21:03:21.4806147Z 2025-03-04T21:03:21.4806434Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4806578Z out_99: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_98); out_98 = None 2025-03-04T21:03:21.4806636Z 2025-03-04T21:03:21.4806885Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4807353Z x_160: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_99, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.4807433Z 2025-03-04T21:03:21.4807691Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4809467Z x_161: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_160, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_160 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4809540Z 2025-03-04T21:03:21.4809815Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4809958Z out_100: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_161); x_161 = None 2025-03-04T21:03:21.4810017Z 2025-03-04T21:03:21.4810265Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4810769Z x_162: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_100, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_100 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.4810830Z 2025-03-04T21:03:21.4811102Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4812929Z x_163: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_162, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_162 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4813011Z 2025-03-04T21:03:21.4813293Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4813426Z out_101: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_163); x_163 = None 2025-03-04T21:03:21.4813492Z 2025-03-04T21:03:21.4813734Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4814232Z x_164: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_101, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_101 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.4814308Z 2025-03-04T21:03:21.4814574Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4816408Z x_165: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_164, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_164 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4816479Z 2025-03-04T21:03:21.4816764Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.4816915Z x_165 += out_99; out_102: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_165; x_165 = out_99 = None 2025-03-04T21:03:21.4816986Z 2025-03-04T21:03:21.4817268Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4817422Z out_103: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_102); out_102 = None 2025-03-04T21:03:21.4817482Z 2025-03-04T21:03:21.4817737Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4818223Z x_166: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_103, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.4818293Z 2025-03-04T21:03:21.4818572Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4820422Z x_167: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_166, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_166 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4820493Z 2025-03-04T21:03:21.4820777Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4820943Z out_104: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_167); x_167 = None 2025-03-04T21:03:21.4821003Z 2025-03-04T21:03:21.4821261Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4821750Z x_168: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_104, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_104 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.4821820Z 2025-03-04T21:03:21.4822082Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4823915Z x_169: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_168, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_168 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4823987Z 2025-03-04T21:03:21.4824273Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4824414Z out_105: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_169); x_169 = None 2025-03-04T21:03:21.4824475Z 2025-03-04T21:03:21.4824728Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4825250Z x_170: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_105, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_105 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.4825329Z 2025-03-04T21:03:21.4825599Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4827385Z x_171: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_170, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_170 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4827469Z 2025-03-04T21:03:21.4827759Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.4827919Z x_171 += out_103; out_106: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_171; x_171 = out_103 = None 2025-03-04T21:03:21.4827987Z 2025-03-04T21:03:21.4828272Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4828424Z out_107: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_106); out_106 = None 2025-03-04T21:03:21.4828488Z 2025-03-04T21:03:21.4828746Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4829259Z x_172: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_107, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.4829324Z 2025-03-04T21:03:21.4829584Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4831393Z x_173: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_172, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_172 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4831463Z 2025-03-04T21:03:21.4831746Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4831922Z out_108: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_173); x_173 = None 2025-03-04T21:03:21.4831982Z 2025-03-04T21:03:21.4832237Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4832731Z x_174: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_108, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_108 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.4832797Z 2025-03-04T21:03:21.4833062Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4834975Z x_175: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_174, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_174 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4835080Z 2025-03-04T21:03:21.4835365Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4835507Z out_109: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_175); x_175 = None 2025-03-04T21:03:21.4835568Z 2025-03-04T21:03:21.4835844Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4836342Z x_176: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_109, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_109 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.4836409Z 2025-03-04T21:03:21.4836678Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4838483Z x_177: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_176, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_176 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4838581Z 2025-03-04T21:03:21.4838854Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.4839014Z x_177 += out_107; out_110: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_177; x_177 = out_107 = None 2025-03-04T21:03:21.4839081Z 2025-03-04T21:03:21.4839353Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4839495Z out_111: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_110); out_110 = None 2025-03-04T21:03:21.4839555Z 2025-03-04T21:03:21.4839803Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4840280Z x_178: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_111, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.4840359Z 2025-03-04T21:03:21.4840621Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4842449Z x_179: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_178, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_178 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4842520Z 2025-03-04T21:03:21.4842803Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4842943Z out_112: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_179); x_179 = None 2025-03-04T21:03:21.4843004Z 2025-03-04T21:03:21.4843258Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4843753Z x_180: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_112, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_112 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.4843824Z 2025-03-04T21:03:21.4844087Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4846024Z x_181: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_180, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_180 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4846107Z 2025-03-04T21:03:21.4846394Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4846539Z out_113: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_181); x_181 = None 2025-03-04T21:03:21.4846598Z 2025-03-04T21:03:21.4846858Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4847357Z x_182: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_113, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_113 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.4847441Z 2025-03-04T21:03:21.4847707Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4849544Z x_183: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_182, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_182 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4849618Z 2025-03-04T21:03:21.4849894Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.4850056Z x_183 += out_111; out_114: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_183; x_183 = out_111 = None 2025-03-04T21:03:21.4850119Z 2025-03-04T21:03:21.4850410Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4850553Z out_115: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_114); out_114 = None 2025-03-04T21:03:21.4850623Z 2025-03-04T21:03:21.4850871Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4851366Z x_184: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_115, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.4851433Z 2025-03-04T21:03:21.4851709Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4853543Z x_185: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_184, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_184 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4853614Z 2025-03-04T21:03:21.4853898Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4854061Z out_116: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_185); x_185 = None 2025-03-04T21:03:21.4854121Z 2025-03-04T21:03:21.4854381Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4854877Z x_186: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_116, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_116 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.4854948Z 2025-03-04T21:03:21.4855212Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4857041Z x_187: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_186, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_186 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4857113Z 2025-03-04T21:03:21.4857396Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4857538Z out_117: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_187); x_187 = None 2025-03-04T21:03:21.4857599Z 2025-03-04T21:03:21.4857856Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4858369Z x_188: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_117, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_117 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.4858452Z 2025-03-04T21:03:21.4858718Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4860536Z x_189: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_188, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_188 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4860631Z 2025-03-04T21:03:21.4860916Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.4861075Z x_189 += out_115; out_118: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_189; x_189 = out_115 = None 2025-03-04T21:03:21.4861133Z 2025-03-04T21:03:21.4861414Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4861554Z out_119: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_118); out_118 = None 2025-03-04T21:03:21.4861623Z 2025-03-04T21:03:21.4861864Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4862365Z x_190: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.4862426Z 2025-03-04T21:03:21.4862697Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4864488Z x_191: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_190, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_190 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4864550Z 2025-03-04T21:03:21.4864833Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4865012Z out_120: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_191); x_191 = None 2025-03-04T21:03:21.4865077Z 2025-03-04T21:03:21.4865320Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4865803Z x_192: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_120, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_120 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.4865868Z 2025-03-04T21:03:21.4866123Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4867898Z x_193: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_192, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_192 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4867982Z 2025-03-04T21:03:21.4868270Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4868410Z out_121: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_193); x_193 = None 2025-03-04T21:03:21.4868472Z 2025-03-04T21:03:21.4868741Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4869233Z x_194: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_121, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_121 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.4869300Z 2025-03-04T21:03:21.4869566Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4871397Z x_195: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_194, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_194 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4871488Z 2025-03-04T21:03:21.4871770Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4872306Z x_196: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:03:21.4872370Z 2025-03-04T21:03:21.4872655Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4874636Z x_197: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_196, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_196 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4874732Z 2025-03-04T21:03:21.4875033Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.4875184Z x_195 += x_197; out_122: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_195; x_195 = x_197 = None 2025-03-04T21:03:21.4875253Z 2025-03-04T21:03:21.4875547Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4875710Z out_123: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_122); out_122 = None 2025-03-04T21:03:21.4875772Z 2025-03-04T21:03:21.4876027Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4876497Z x_198: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_123, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.4876566Z 2025-03-04T21:03:21.4876829Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4878617Z x_199: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_198, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_198 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4878704Z 2025-03-04T21:03:21.4878983Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4879122Z out_124: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_199); x_199 = None 2025-03-04T21:03:21.4879180Z 2025-03-04T21:03:21.4879429Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4879912Z x_200: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_124, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_124 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.4879973Z 2025-03-04T21:03:21.4880237Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4882002Z x_201: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_200, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_200 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4882073Z 2025-03-04T21:03:21.4882374Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4882505Z out_125: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_201); x_201 = None 2025-03-04T21:03:21.4882570Z 2025-03-04T21:03:21.4882809Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4883300Z x_202: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_125, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_125 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.4883362Z 2025-03-04T21:03:21.4883626Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4885432Z x_203: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_202, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_202 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4885521Z 2025-03-04T21:03:21.4885799Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.4885949Z x_203 += out_123; out_126: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_203; x_203 = out_123 = None 2025-03-04T21:03:21.4886015Z 2025-03-04T21:03:21.4886288Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4886435Z out_127: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_126); out_126 = None 2025-03-04T21:03:21.4886496Z 2025-03-04T21:03:21.4886746Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4887224Z x_204: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_127, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:03:21.4887293Z 2025-03-04T21:03:21.4887550Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4889320Z x_205: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_204, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_204 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4889389Z 2025-03-04T21:03:21.4889667Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4889808Z out_128: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_205); x_205 = None 2025-03-04T21:03:21.4889867Z 2025-03-04T21:03:21.4890119Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4890597Z x_206: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_128, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_128 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:03:21.4890663Z 2025-03-04T21:03:21.4890921Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4892699Z x_207: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_206, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_206 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4892781Z 2025-03-04T21:03:21.4893062Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4893197Z out_129: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_207); x_207 = None 2025-03-04T21:03:21.4893269Z 2025-03-04T21:03:21.4893523Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4894012Z x_208: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_129, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_129 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:03:21.4894071Z 2025-03-04T21:03:21.4894348Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:03:21.4896123Z x_209: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_208, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_208 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:03:21.4896192Z 2025-03-04T21:03:21.4896472Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:03:21.4896621Z x_209 += out_127; out_130: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_209; x_209 = out_127 = None 2025-03-04T21:03:21.4896689Z 2025-03-04T21:03:21.4896964Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:03:21.4897105Z out_131: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_130); out_130 = None 2025-03-04T21:03:21.4897163Z 2025-03-04T21:03:21.4897412Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4897988Z x_210: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(out_131, l_self_modules_backbone_lateral_convs_0_parameters_weight_, l_self_modules_backbone_lateral_convs_0_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_131 = l_self_modules_backbone_lateral_convs_0_parameters_weight_ = l_self_modules_backbone_lateral_convs_0_parameters_bias_ = None 2025-03-04T21:03:21.4898068Z 2025-03-04T21:03:21.4898312Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4898852Z x_211: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_210, l_self_modules_backbone_output_convs_0_parameters_weight_, l_self_modules_backbone_output_convs_0_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_self_modules_backbone_output_convs_0_parameters_weight_ = l_self_modules_backbone_output_convs_0_parameters_bias_ = None 2025-03-04T21:03:21.4898911Z 2025-03-04T21:03:21.4899333Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest") 2025-03-04T21:03:21.4899604Z top_down_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.interpolate(x_210, scale_factor = 2.0, mode = 'nearest'); x_210 = None 2025-03-04T21:03:21.4899685Z 2025-03-04T21:03:21.4899938Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4900525Z x_212: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_lateral_convs_1_parameters_weight_, l_self_modules_backbone_lateral_convs_1_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_119 = l_self_modules_backbone_lateral_convs_1_parameters_weight_ = l_self_modules_backbone_lateral_convs_1_parameters_bias_ = None 2025-03-04T21:03:21.4900594Z 2025-03-04T21:03:21.4900948Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features 2025-03-04T21:03:21.4901154Z prev_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = x_212 + top_down_features; x_212 = top_down_features = None 2025-03-04T21:03:21.4901218Z 2025-03-04T21:03:21.4901491Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4902503Z x_213: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(prev_features, l_self_modules_backbone_output_convs_1_parameters_weight_, l_self_modules_backbone_output_convs_1_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_self_modules_backbone_output_convs_1_parameters_weight_ = l_self_modules_backbone_output_convs_1_parameters_bias_ = None 2025-03-04T21:03:21.4902580Z 2025-03-04T21:03:21.4902988Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest") 2025-03-04T21:03:21.4903322Z top_down_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.interpolate(prev_features, scale_factor = 2.0, mode = 'nearest'); prev_features = None 2025-03-04T21:03:21.4903389Z 2025-03-04T21:03:21.4903652Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4904255Z x_214: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_lateral_convs_2_parameters_weight_, l_self_modules_backbone_lateral_convs_2_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_27 = l_self_modules_backbone_lateral_convs_2_parameters_weight_ = l_self_modules_backbone_lateral_convs_2_parameters_bias_ = None 2025-03-04T21:03:21.4904316Z 2025-03-04T21:03:21.4904726Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features 2025-03-04T21:03:21.4904935Z prev_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = x_214 + top_down_features_1; x_214 = top_down_features_1 = None 2025-03-04T21:03:21.4905004Z 2025-03-04T21:03:21.4905251Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4905841Z x_215: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(prev_features_1, l_self_modules_backbone_output_convs_2_parameters_weight_, l_self_modules_backbone_output_convs_2_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_self_modules_backbone_output_convs_2_parameters_weight_ = l_self_modules_backbone_output_convs_2_parameters_bias_ = None 2025-03-04T21:03:21.4905902Z 2025-03-04T21:03:21.4906321Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest") 2025-03-04T21:03:21.4906679Z top_down_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.interpolate(prev_features_1, scale_factor = 2.0, mode = 'nearest'); prev_features_1 = None 2025-03-04T21:03:21.4906751Z 2025-03-04T21:03:21.4907004Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4907608Z x_216: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_lateral_convs_3_parameters_weight_, l_self_modules_backbone_lateral_convs_3_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_11 = l_self_modules_backbone_lateral_convs_3_parameters_weight_ = l_self_modules_backbone_lateral_convs_3_parameters_bias_ = None 2025-03-04T21:03:21.4907679Z 2025-03-04T21:03:21.4908029Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features 2025-03-04T21:03:21.4908275Z prev_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_216 + top_down_features_2; x_216 = top_down_features_2 = None 2025-03-04T21:03:21.4908337Z 2025-03-04T21:03:21.4908592Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4909222Z x_217: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(prev_features_2, l_self_modules_backbone_output_convs_3_parameters_weight_, l_self_modules_backbone_output_convs_3_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); prev_features_2 = l_self_modules_backbone_output_convs_3_parameters_weight_ = l_self_modules_backbone_output_convs_3_parameters_bias_ = None 2025-03-04T21:03:21.4909292Z 2025-03-04T21:03:21.4909665Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:200 in forward, code: return [F.max_pool2d(x, kernel_size=1, stride=2, padding=0)] 2025-03-04T21:03:21.4909889Z res: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.max_pool2d(x_211, kernel_size = 1, stride = 2, padding = 0) 2025-03-04T21:03:21.4909950Z 2025-03-04T21:03:21.4910413Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:03:21.4910567Z arange: "f32[304][1]cpu" = torch.arange(0.0, 1216, step = 4, dtype = torch.float32) 2025-03-04T21:03:21.4910637Z 2025-03-04T21:03:21.4910957Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:21.4911119Z shifts_x: "f32[304][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T21:03:21.4911181Z 2025-03-04T21:03:21.4911629Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:03:21.4911779Z arange_1: "f32[296][1]cpu" = torch.arange(0.0, 1184, step = 4, dtype = torch.float32) 2025-03-04T21:03:21.4911847Z 2025-03-04T21:03:21.4912138Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:21.4912282Z shifts_y: "f32[296][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T21:03:21.4912344Z 2025-03-04T21:03:21.4912730Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:03:21.4912935Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T21:03:21.4913033Z shift_y: "f32[296, 304][1, 0]cpu" = meshgrid[0] 2025-03-04T21:03:21.4913158Z shift_x: "f32[296, 304][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T21:03:21.4913218Z 2025-03-04T21:03:21.4913556Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:03:21.4913735Z shift_x_1: "f32[89984][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T21:03:21.4913809Z 2025-03-04T21:03:21.4914141Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:03:21.4914263Z shift_y_1: "f32[89984][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T21:03:21.4914324Z 2025-03-04T21:03:21.4914747Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:03:21.4914968Z shifts: "f32[89984, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T21:03:21.4915036Z 2025-03-04T21:03:21.4915471Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:03:21.4915604Z view: "f32[89984, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T21:03:21.4916056Z view_1: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T21:03:21.4916188Z add_3: "f32[89984, 3, 4][12, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T21:03:21.4916309Z x_218: "f32[269952, 4][4, 1]cpu" = add_3.reshape(-1, 4); add_3 = None 2025-03-04T21:03:21.4916378Z 2025-03-04T21:03:21.4916813Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:03:21.4916967Z arange_2: "f32[152][1]cpu" = torch.arange(0.0, 1216, step = 8, dtype = torch.float32) 2025-03-04T21:03:21.4917030Z 2025-03-04T21:03:21.4917342Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:21.4917497Z shifts_x_1: "f32[152][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T21:03:21.4917567Z 2025-03-04T21:03:21.4918001Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:03:21.4918153Z arange_3: "f32[148][1]cpu" = torch.arange(0.0, 1184, step = 8, dtype = torch.float32) 2025-03-04T21:03:21.4918213Z 2025-03-04T21:03:21.4918517Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:21.4918651Z shifts_y_1: "f32[148][1]cpu" = arange_3.to(device(type='cpu')); arange_3 = None 2025-03-04T21:03:21.4918716Z 2025-03-04T21:03:21.4919094Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:03:21.4919312Z meshgrid_1 = torch.functional.meshgrid(shifts_y_1, shifts_x_1); shifts_y_1 = shifts_x_1 = None 2025-03-04T21:03:21.4919419Z shift_y_2: "f32[148, 152][1, 0]cpu" = meshgrid_1[0] 2025-03-04T21:03:21.4919540Z shift_x_2: "f32[148, 152][0, 1]cpu" = meshgrid_1[1]; meshgrid_1 = None 2025-03-04T21:03:21.4919606Z 2025-03-04T21:03:21.4919932Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:03:21.4920065Z shift_x_3: "f32[22496][1]cpu" = shift_x_2.reshape(-1); shift_x_2 = None 2025-03-04T21:03:21.4920126Z 2025-03-04T21:03:21.4920459Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:03:21.4920582Z shift_y_3: "f32[22496][1]cpu" = shift_y_2.reshape(-1); shift_y_2 = None 2025-03-04T21:03:21.4920652Z 2025-03-04T21:03:21.4921049Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:03:21.4921269Z shifts_1: "f32[22496, 4][4, 1]cpu" = torch.stack((shift_x_3, shift_y_3, shift_x_3, shift_y_3), dim = 1); shift_x_3 = shift_y_3 = None 2025-03-04T21:03:21.4921329Z 2025-03-04T21:03:21.4921751Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:03:21.4921880Z view_2: "f32[22496, 1, 4][4, 4, 1]cpu" = shifts_1.view(-1, 1, 4); shifts_1 = None 2025-03-04T21:03:21.4922310Z view_3: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = None 2025-03-04T21:03:21.4922436Z add_4: "f32[22496, 3, 4][12, 4, 1]cpu" = view_2 + view_3; view_2 = view_3 = None 2025-03-04T21:03:21.4922554Z x_219: "f32[67488, 4][4, 1]cpu" = add_4.reshape(-1, 4); add_4 = None 2025-03-04T21:03:21.4922615Z 2025-03-04T21:03:21.4923055Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:03:21.4923199Z arange_4: "f32[76][1]cpu" = torch.arange(0.0, 1216, step = 16, dtype = torch.float32) 2025-03-04T21:03:21.4923268Z 2025-03-04T21:03:21.4923591Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:21.4923734Z shifts_x_2: "f32[76][1]cpu" = arange_4.to(device(type='cpu')); arange_4 = None 2025-03-04T21:03:21.4923796Z 2025-03-04T21:03:21.4924235Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:03:21.4924375Z arange_5: "f32[74][1]cpu" = torch.arange(0.0, 1184, step = 16, dtype = torch.float32) 2025-03-04T21:03:21.4924443Z 2025-03-04T21:03:21.4924729Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:21.4924865Z shifts_y_2: "f32[74][1]cpu" = arange_5.to(device(type='cpu')); arange_5 = None 2025-03-04T21:03:21.4924926Z 2025-03-04T21:03:21.4925305Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:03:21.4925519Z meshgrid_2 = torch.functional.meshgrid(shifts_y_2, shifts_x_2); shifts_y_2 = shifts_x_2 = None 2025-03-04T21:03:21.4925618Z shift_y_4: "f32[74, 76][1, 0]cpu" = meshgrid_2[0] 2025-03-04T21:03:21.4925740Z shift_x_4: "f32[74, 76][0, 1]cpu" = meshgrid_2[1]; meshgrid_2 = None 2025-03-04T21:03:21.4925800Z 2025-03-04T21:03:21.4926132Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:03:21.4926252Z shift_x_5: "f32[5624][1]cpu" = shift_x_4.reshape(-1); shift_x_4 = None 2025-03-04T21:03:21.4926318Z 2025-03-04T21:03:21.4926644Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:03:21.4926770Z shift_y_5: "f32[5624][1]cpu" = shift_y_4.reshape(-1); shift_y_4 = None 2025-03-04T21:03:21.4926831Z 2025-03-04T21:03:21.4927230Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:03:21.4927439Z shifts_2: "f32[5624, 4][4, 1]cpu" = torch.stack((shift_x_5, shift_y_5, shift_x_5, shift_y_5), dim = 1); shift_x_5 = shift_y_5 = None 2025-03-04T21:03:21.4927506Z 2025-03-04T21:03:21.4927913Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:03:21.4928045Z view_4: "f32[5624, 1, 4][4, 4, 1]cpu" = shifts_2.view(-1, 1, 4); shifts_2 = None 2025-03-04T21:03:21.4928459Z view_5: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = None 2025-03-04T21:03:21.4928588Z add_5: "f32[5624, 3, 4][12, 4, 1]cpu" = view_4 + view_5; view_4 = view_5 = None 2025-03-04T21:03:21.4928698Z x_220: "f32[16872, 4][4, 1]cpu" = add_5.reshape(-1, 4); add_5 = None 2025-03-04T21:03:21.4928768Z 2025-03-04T21:03:21.4929190Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:03:21.4929338Z arange_6: "f32[38][1]cpu" = torch.arange(0.0, 1216, step = 32, dtype = torch.float32) 2025-03-04T21:03:21.4929430Z 2025-03-04T21:03:21.4929730Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:21.4929864Z shifts_x_3: "f32[38][1]cpu" = arange_6.to(device(type='cpu')); arange_6 = None 2025-03-04T21:03:21.4929931Z 2025-03-04T21:03:21.4930362Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:03:21.4930508Z arange_7: "f32[37][1]cpu" = torch.arange(0.0, 1184, step = 32, dtype = torch.float32) 2025-03-04T21:03:21.4930569Z 2025-03-04T21:03:21.4930864Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:21.4930994Z shifts_y_3: "f32[37][1]cpu" = arange_7.to(device(type='cpu')); arange_7 = None 2025-03-04T21:03:21.4931063Z 2025-03-04T21:03:21.4931431Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:03:21.4931645Z meshgrid_3 = torch.functional.meshgrid(shifts_y_3, shifts_x_3); shifts_y_3 = shifts_x_3 = None 2025-03-04T21:03:21.4931746Z shift_y_6: "f32[37, 38][1, 0]cpu" = meshgrid_3[0] 2025-03-04T21:03:21.4931860Z shift_x_6: "f32[37, 38][0, 1]cpu" = meshgrid_3[1]; meshgrid_3 = None 2025-03-04T21:03:21.4931924Z 2025-03-04T21:03:21.4932253Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:03:21.4932388Z shift_x_7: "f32[1406][1]cpu" = shift_x_6.reshape(-1); shift_x_6 = None 2025-03-04T21:03:21.4932450Z 2025-03-04T21:03:21.4932775Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:03:21.4932890Z shift_y_7: "f32[1406][1]cpu" = shift_y_6.reshape(-1); shift_y_6 = None 2025-03-04T21:03:21.4932956Z 2025-03-04T21:03:21.4933352Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:03:21.4933561Z shifts_3: "f32[1406, 4][4, 1]cpu" = torch.stack((shift_x_7, shift_y_7, shift_x_7, shift_y_7), dim = 1); shift_x_7 = shift_y_7 = None 2025-03-04T21:03:21.4933621Z 2025-03-04T21:03:21.4934042Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:03:21.4934166Z view_6: "f32[1406, 1, 4][4, 4, 1]cpu" = shifts_3.view(-1, 1, 4); shifts_3 = None 2025-03-04T21:03:21.4934592Z view_7: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = None 2025-03-04T21:03:21.4934710Z add_6: "f32[1406, 3, 4][12, 4, 1]cpu" = view_6 + view_7; view_6 = view_7 = None 2025-03-04T21:03:21.4934827Z x_221: "f32[4218, 4][4, 1]cpu" = add_6.reshape(-1, 4); add_6 = None 2025-03-04T21:03:21.4934888Z 2025-03-04T21:03:21.4935328Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:03:21.4935483Z arange_8: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32) 2025-03-04T21:03:21.4935568Z 2025-03-04T21:03:21.4935852Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:21.4935991Z shifts_x_4: "f32[19][1]cpu" = arange_8.to(device(type='cpu')); arange_8 = None 2025-03-04T21:03:21.4936050Z 2025-03-04T21:03:21.4936474Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:03:21.4936610Z arange_9: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32) 2025-03-04T21:03:21.4936680Z 2025-03-04T21:03:21.4936971Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:21.4937113Z shifts_y_4: "f32[19][1]cpu" = arange_9.to(device(type='cpu')); arange_9 = None 2025-03-04T21:03:21.4937176Z 2025-03-04T21:03:21.4937562Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:03:21.4937770Z meshgrid_4 = torch.functional.meshgrid(shifts_y_4, shifts_x_4); shifts_y_4 = shifts_x_4 = None 2025-03-04T21:03:21.4937865Z shift_y_8: "f32[19, 19][1, 0]cpu" = meshgrid_4[0] 2025-03-04T21:03:21.4937986Z shift_x_8: "f32[19, 19][0, 1]cpu" = meshgrid_4[1]; meshgrid_4 = None 2025-03-04T21:03:21.4938048Z 2025-03-04T21:03:21.4938384Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:03:21.4938506Z shift_x_9: "f32[361][1]cpu" = shift_x_8.reshape(-1); shift_x_8 = None 2025-03-04T21:03:21.4938576Z 2025-03-04T21:03:21.4938901Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:03:21.4939038Z shift_y_9: "f32[361][1]cpu" = shift_y_8.reshape(-1); shift_y_8 = None 2025-03-04T21:03:21.4939100Z 2025-03-04T21:03:21.4939483Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:03:21.4939692Z shifts_4: "f32[361, 4][4, 1]cpu" = torch.stack((shift_x_9, shift_y_9, shift_x_9, shift_y_9), dim = 1); shift_x_9 = shift_y_9 = None 2025-03-04T21:03:21.4939759Z 2025-03-04T21:03:21.4940171Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:03:21.4940301Z view_8: "f32[361, 1, 4][4, 4, 1]cpu" = shifts_4.view(-1, 1, 4); shifts_4 = None 2025-03-04T21:03:21.4940720Z view_9: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = None 2025-03-04T21:03:21.4940844Z add_7: "f32[361, 3, 4][12, 4, 1]cpu" = view_8 + view_9; view_8 = view_9 = None 2025-03-04T21:03:21.4940953Z x_222: "f32[1083, 4][4, 1]cpu" = add_7.reshape(-1, 4); add_7 = None 2025-03-04T21:03:21.4941020Z 2025-03-04T21:03:21.4941320Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:03:21.4941471Z tensor: "f32[269952, 4][4, 1]cpu" = x_218.to(torch.float32); x_218 = None 2025-03-04T21:03:21.4941615Z tensor_1: "f32[67488, 4][4, 1]cpu" = x_219.to(torch.float32); x_219 = None 2025-03-04T21:03:21.4941742Z tensor_2: "f32[16872, 4][4, 1]cpu" = x_220.to(torch.float32); x_220 = None 2025-03-04T21:03:21.4941861Z tensor_3: "f32[4218, 4][4, 1]cpu" = x_221.to(torch.float32); x_221 = None 2025-03-04T21:03:21.4941987Z tensor_4: "f32[1083, 4][4, 1]cpu" = x_222.to(torch.float32); x_222 = None 2025-03-04T21:03:21.4942048Z 2025-03-04T21:03:21.4942311Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4942818Z x_223: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_217, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_217 = None 2025-03-04T21:03:21.4942890Z 2025-03-04T21:03:21.4943166Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:03:21.4943382Z x_224: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.relu(x_223, inplace = False); x_223 = None 2025-03-04T21:03:21.4943442Z 2025-03-04T21:03:21.4943843Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:03:21.4944388Z score: "f32[4, 3, 296, 304][269952, 89984, 304, 1]cpu" = torch.conv2d(x_224, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:03:21.4944450Z 2025-03-04T21:03:21.4944821Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:03:21.4945360Z x_233: "f32[4, 12, 296, 304][1079808, 89984, 304, 1]cpu" = torch.conv2d(x_224, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_224 = None 2025-03-04T21:03:21.4945432Z 2025-03-04T21:03:21.4945682Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4946179Z x_225: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(x_215, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_215 = None 2025-03-04T21:03:21.4946242Z 2025-03-04T21:03:21.4946519Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:03:21.4946711Z x_226: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.relu(x_225, inplace = False); x_225 = None 2025-03-04T21:03:21.4946782Z 2025-03-04T21:03:21.4947156Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:03:21.4947676Z score_1: "f32[4, 3, 148, 152][67488, 22496, 152, 1]cpu" = torch.conv2d(x_226, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:03:21.4947744Z 2025-03-04T21:03:21.4948132Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:03:21.4948662Z x_234: "f32[4, 12, 148, 152][269952, 22496, 152, 1]cpu" = torch.conv2d(x_226, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_226 = None 2025-03-04T21:03:21.4948722Z 2025-03-04T21:03:21.4948981Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4949464Z x_227: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(x_213, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_213 = None 2025-03-04T21:03:21.4949534Z 2025-03-04T21:03:21.4949807Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:03:21.4950018Z x_228: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.relu(x_227, inplace = False); x_227 = None 2025-03-04T21:03:21.4950079Z 2025-03-04T21:03:21.4950458Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:03:21.4950960Z score_2: "f32[4, 3, 74, 76][16872, 5624, 76, 1]cpu" = torch.conv2d(x_228, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:03:21.4951030Z 2025-03-04T21:03:21.4951392Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:03:21.4951908Z x_235: "f32[4, 12, 74, 76][67488, 5624, 76, 1]cpu" = torch.conv2d(x_228, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_228 = None 2025-03-04T21:03:21.4951978Z 2025-03-04T21:03:21.4952228Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4952708Z x_229: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_211, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_211 = None 2025-03-04T21:03:21.4952770Z 2025-03-04T21:03:21.4953044Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:03:21.4953225Z x_230: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.nn.functional.relu(x_229, inplace = False); x_229 = None 2025-03-04T21:03:21.4953294Z 2025-03-04T21:03:21.4953718Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:03:21.4954243Z score_3: "f32[4, 3, 37, 38][4218, 1406, 38, 1]cpu" = torch.conv2d(x_230, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:03:21.4954345Z 2025-03-04T21:03:21.4954723Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:03:21.4955248Z x_236: "f32[4, 12, 37, 38][16872, 1406, 38, 1]cpu" = torch.conv2d(x_230, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_230 = None 2025-03-04T21:03:21.4955310Z 2025-03-04T21:03:21.4955569Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:21.4956336Z x_231: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.conv2d(res, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); res = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T21:03:21.4956433Z 2025-03-04T21:03:21.4956708Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:03:21.4956890Z x_232: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.relu(x_231, inplace = False); x_231 = None 2025-03-04T21:03:21.4956952Z 2025-03-04T21:03:21.4957330Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:03:21.4958197Z score_4: "f32[4, 3, 19, 19][1083, 361, 19, 1]cpu" = torch.conv2d(x_232, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T21:03:21.4958284Z 2025-03-04T21:03:21.4958646Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:03:21.4959479Z x_237: "f32[4, 12, 19, 19][4332, 361, 19, 1]cpu" = torch.conv2d(x_232, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_232 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T21:03:21.4959549Z 2025-03-04T21:03:21.4959890Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T21:03:21.4960065Z permute: "f32[4, 296, 304, 3][269952, 304, 1, 89984]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T21:03:21.4960209Z logits_i: "f32[4, 269952][269952, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T21:03:21.4960379Z permute_1: "f32[4, 148, 152, 3][67488, 152, 1, 22496]cpu" = score_1.permute(0, 2, 3, 1); score_1 = None 2025-03-04T21:03:21.4960521Z logits_i_1: "f32[4, 67488][67488, 1]cpu" = permute_1.flatten(1); permute_1 = None 2025-03-04T21:03:21.4960679Z permute_2: "f32[4, 74, 76, 3][16872, 76, 1, 5624]cpu" = score_2.permute(0, 2, 3, 1); score_2 = None 2025-03-04T21:03:21.4960851Z logits_i_2: "f32[4, 16872][16872, 1]cpu" = permute_2.flatten(1); permute_2 = None 2025-03-04T21:03:21.4961004Z permute_3: "f32[4, 37, 38, 3][4218, 38, 1, 1406]cpu" = score_3.permute(0, 2, 3, 1); score_3 = None 2025-03-04T21:03:21.4961135Z logits_i_3: "f32[4, 4218][4218, 1]cpu" = permute_3.flatten(1); permute_3 = None 2025-03-04T21:03:21.4961286Z permute_4: "f32[4, 19, 19, 3][1083, 19, 1, 361]cpu" = score_4.permute(0, 2, 3, 1); score_4 = None 2025-03-04T21:03:21.4961412Z logits_i_4: "f32[4, 1083][1083, 1]cpu" = permute_4.flatten(1); permute_4 = None 2025-03-04T21:03:21.4961481Z 2025-03-04T21:03:21.4961905Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T21:03:21.4962088Z view_10: "f32[4, 3, 4, 296, 304][1079808, 359936, 89984, 304, 1]cpu" = x_233.view(4, -1, 4, 296, 304); x_233 = None 2025-03-04T21:03:21.4962282Z permute_5: "f32[4, 296, 304, 3, 4][1079808, 304, 1, 359936, 89984]cpu" = view_10.permute(0, 3, 4, 1, 2); view_10 = None 2025-03-04T21:03:21.4962480Z pred_anchor_deltas_i: "f32[4, 269952, 4][1079808, 4, 1]cpu" = permute_5.flatten(1, -2); permute_5 = None 2025-03-04T21:03:21.4962650Z view_11: "f32[4, 3, 4, 148, 152][269952, 89984, 22496, 152, 1]cpu" = x_234.view(4, -1, 4, 148, 152); x_234 = None 2025-03-04T21:03:21.4962820Z permute_6: "f32[4, 148, 152, 3, 4][269952, 152, 1, 89984, 22496]cpu" = view_11.permute(0, 3, 4, 1, 2); view_11 = None 2025-03-04T21:03:21.4962992Z pred_anchor_deltas_i_2: "f32[4, 67488, 4][269952, 4, 1]cpu" = permute_6.flatten(1, -2); permute_6 = None 2025-03-04T21:03:21.4963138Z view_12: "f32[4, 3, 4, 74, 76][67488, 22496, 5624, 76, 1]cpu" = x_235.view(4, -1, 4, 74, 76); x_235 = None 2025-03-04T21:03:21.4963307Z permute_7: "f32[4, 74, 76, 3, 4][67488, 76, 1, 22496, 5624]cpu" = view_12.permute(0, 3, 4, 1, 2); view_12 = None 2025-03-04T21:03:21.4963476Z pred_anchor_deltas_i_4: "f32[4, 16872, 4][67488, 4, 1]cpu" = permute_7.flatten(1, -2); permute_7 = None 2025-03-04T21:03:21.4963626Z view_13: "f32[4, 3, 4, 37, 38][16872, 5624, 1406, 38, 1]cpu" = x_236.view(4, -1, 4, 37, 38); x_236 = None 2025-03-04T21:03:21.4963797Z permute_8: "f32[4, 37, 38, 3, 4][16872, 38, 1, 5624, 1406]cpu" = view_13.permute(0, 3, 4, 1, 2); view_13 = None 2025-03-04T21:03:21.4963976Z pred_anchor_deltas_i_6: "f32[4, 4218, 4][16872, 4, 1]cpu" = permute_8.flatten(1, -2); permute_8 = None 2025-03-04T21:03:21.4964107Z view_14: "f32[4, 3, 4, 19, 19][4332, 1444, 361, 19, 1]cpu" = x_237.view(4, -1, 4, 19, 19); x_237 = None 2025-03-04T21:03:21.4964265Z permute_9: "f32[4, 19, 19, 3, 4][4332, 19, 1, 1444, 361]cpu" = view_14.permute(0, 3, 4, 1, 2); view_14 = None 2025-03-04T21:03:21.4964423Z pred_anchor_deltas_i_8: "f32[4, 1083, 4][4332, 4, 1]cpu" = permute_9.flatten(1, -2); permute_9 = None 2025-03-04T21:03:21.4964492Z 2025-03-04T21:03:21.4964889Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:03:21.4965093Z pred_anchor_deltas_i_1: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T21:03:21.4965152Z 2025-03-04T21:03:21.4965583Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:03:21.4965731Z unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T21:03:21.4965878Z expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T21:03:21.4966050Z anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T21:03:21.4966110Z 2025-03-04T21:03:21.4966481Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:03:21.4966646Z deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T21:03:21.4966713Z 2025-03-04T21:03:21.4967022Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:03:21.4967164Z boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T21:03:21.4967224Z 2025-03-04T21:03:21.4967540Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:03:21.4967666Z getitem_10: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:03:21.4967793Z getitem_11: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:03:21.4967955Z widths: "f32[1079808][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T21:03:21.4968021Z 2025-03-04T21:03:21.4968330Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:03:21.4968457Z getitem_12: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:03:21.4968574Z getitem_13: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:03:21.4968724Z heights: "f32[1079808][1]cpu" = getitem_12 - getitem_13; getitem_12 = getitem_13 = None 2025-03-04T21:03:21.4968784Z 2025-03-04T21:03:21.4969096Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:03:21.4969213Z getitem_14: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:03:21.4969303Z mul: "f32[1079808][1]cpu" = 0.5 * widths 2025-03-04T21:03:21.4969437Z ctr_x: "f32[1079808][1]cpu" = getitem_14 + mul; getitem_14 = mul = None 2025-03-04T21:03:21.4969505Z 2025-03-04T21:03:21.4969808Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:03:21.4969952Z getitem_15: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:03:21.4970036Z mul_1: "f32[1079808][1]cpu" = 0.5 * heights 2025-03-04T21:03:21.4970164Z ctr_y: "f32[1079808][1]cpu" = getitem_15 + mul_1; getitem_15 = mul_1 = None 2025-03-04T21:03:21.4970225Z 2025-03-04T21:03:21.4970570Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:03:21.4970724Z getitem_16: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:03:21.4970842Z dx: "f32[1079808, 1][1, 1]cpu" = getitem_16 / 1.0; getitem_16 = None 2025-03-04T21:03:21.4970903Z 2025-03-04T21:03:21.4971204Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:03:21.4971352Z getitem_17: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:03:21.4971466Z dy: "f32[1079808, 1][1, 1]cpu" = getitem_17 / 1.0; getitem_17 = None 2025-03-04T21:03:21.4971525Z 2025-03-04T21:03:21.4971836Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:03:21.4971997Z getitem_18: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:03:21.4972115Z dw: "f32[1079808, 1][1, 1]cpu" = getitem_18 / 1.0; getitem_18 = None 2025-03-04T21:03:21.4972175Z 2025-03-04T21:03:21.4972478Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:03:21.4972664Z getitem_19: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:03:21.4972771Z dh: "f32[1079808, 1][1, 1]cpu" = getitem_19 / 1.0; getitem_19 = None 2025-03-04T21:03:21.4972838Z 2025-03-04T21:03:21.4973167Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:03:21.4973315Z dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:03:21.4973389Z 2025-03-04T21:03:21.4973717Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:03:21.4973847Z dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:03:21.4973914Z 2025-03-04T21:03:21.4974267Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:03:21.4974408Z getitem_20: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:03:21.4974533Z mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_20; dx = getitem_20 = None 2025-03-04T21:03:21.4974691Z getitem_21: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:03:21.4974827Z pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_21; mul_2 = getitem_21 = None 2025-03-04T21:03:21.4974897Z 2025-03-04T21:03:21.4975265Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:03:21.4975408Z getitem_22: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:03:21.4975528Z mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_22; dy = getitem_22 = None 2025-03-04T21:03:21.4975682Z getitem_23: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:03:21.4975817Z pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_23; mul_3 = getitem_23 = None 2025-03-04T21:03:21.4975897Z 2025-03-04T21:03:21.4976217Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:03:21.4976337Z exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:03:21.4976494Z getitem_24: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:03:21.4976631Z pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_24; exp = getitem_24 = None 2025-03-04T21:03:21.4976690Z 2025-03-04T21:03:21.4977019Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:03:21.4977129Z exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:03:21.4977311Z getitem_25: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:03:21.4977463Z pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_25; exp_1 = getitem_25 = None 2025-03-04T21:03:21.4977531Z 2025-03-04T21:03:21.4977855Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:03:21.4977958Z mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T21:03:21.4978074Z x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:03:21.4978146Z 2025-03-04T21:03:21.4978455Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:03:21.4978552Z mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T21:03:21.4978662Z y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:03:21.4978733Z 2025-03-04T21:03:21.4979037Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:03:21.4979175Z mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:03:21.4979303Z x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:03:21.4979371Z 2025-03-04T21:03:21.4979671Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:03:21.4979787Z mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:03:21.4979911Z y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:03:21.4979981Z 2025-03-04T21:03:21.4980328Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:03:21.4980520Z pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:03:21.4980582Z 2025-03-04T21:03:21.4980933Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:03:21.4981102Z proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4)); pred_boxes = None 2025-03-04T21:03:21.4981163Z 2025-03-04T21:03:21.4981551Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:03:21.4981726Z proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T21:03:21.4981795Z 2025-03-04T21:03:21.4982196Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:03:21.4982413Z pred_anchor_deltas_i_3: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_2.reshape(-1, 4); pred_anchor_deltas_i_2 = None 2025-03-04T21:03:21.4982474Z 2025-03-04T21:03:21.4982914Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:03:21.4983067Z unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = tensor_1.unsqueeze(0); tensor_1 = None 2025-03-04T21:03:21.4983216Z expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1); unsqueeze_1 = None 2025-03-04T21:03:21.4983381Z anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4); expand_1 = None 2025-03-04T21:03:21.4983449Z 2025-03-04T21:03:21.4983815Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:03:21.4983991Z deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float(); pred_anchor_deltas_i_3 = None 2025-03-04T21:03:21.4984052Z 2025-03-04T21:03:21.4984366Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:03:21.4984508Z boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32); anchors_i_1 = None 2025-03-04T21:03:21.4984577Z 2025-03-04T21:03:21.4984887Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:03:21.4985025Z getitem_26: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)] 2025-03-04T21:03:21.4985150Z getitem_27: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T21:03:21.4985324Z widths_1: "f32[269952][1]cpu" = getitem_26 - getitem_27; getitem_26 = getitem_27 = None 2025-03-04T21:03:21.4985386Z 2025-03-04T21:03:21.4985712Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:03:21.4985836Z getitem_28: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)] 2025-03-04T21:03:21.4985963Z getitem_29: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)] 2025-03-04T21:03:21.4986112Z heights_1: "f32[269952][1]cpu" = getitem_28 - getitem_29; getitem_28 = getitem_29 = None 2025-03-04T21:03:21.4986180Z 2025-03-04T21:03:21.4986492Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:03:21.4986622Z getitem_30: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T21:03:21.4986712Z mul_10: "f32[269952][1]cpu" = 0.5 * widths_1 2025-03-04T21:03:21.4986866Z ctr_x_1: "f32[269952][1]cpu" = getitem_30 + mul_10; getitem_30 = mul_10 = None 2025-03-04T21:03:21.4986929Z 2025-03-04T21:03:21.4987253Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:03:21.4987400Z getitem_31: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]; boxes_1 = None 2025-03-04T21:03:21.4987499Z mul_11: "f32[269952][1]cpu" = 0.5 * heights_1 2025-03-04T21:03:21.4987629Z ctr_y_1: "f32[269952][1]cpu" = getitem_31 + mul_11; getitem_31 = mul_11 = None 2025-03-04T21:03:21.4987699Z 2025-03-04T21:03:21.4988009Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:03:21.4988166Z getitem_32: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:03:21.4988287Z dx_1: "f32[269952, 1][1, 1]cpu" = getitem_32 / 1.0; getitem_32 = None 2025-03-04T21:03:21.4988348Z 2025-03-04T21:03:21.4988655Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:03:21.4988806Z getitem_33: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:03:21.4988925Z dy_1: "f32[269952, 1][1, 1]cpu" = getitem_33 / 1.0; getitem_33 = None 2025-03-04T21:03:21.4988986Z 2025-03-04T21:03:21.4989320Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:03:21.4989469Z getitem_34: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:03:21.4989589Z dw_2: "f32[269952, 1][1, 1]cpu" = getitem_34 / 1.0; getitem_34 = None 2025-03-04T21:03:21.4989648Z 2025-03-04T21:03:21.4989956Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:03:21.4990137Z getitem_35: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))]; deltas_1 = None 2025-03-04T21:03:21.4990249Z dh_2: "f32[269952, 1][1, 1]cpu" = getitem_35 / 1.0; getitem_35 = None 2025-03-04T21:03:21.4990310Z 2025-03-04T21:03:21.4990651Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:03:21.4990792Z dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356); dw_2 = None 2025-03-04T21:03:21.4990875Z 2025-03-04T21:03:21.4991205Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:03:21.4991347Z dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356); dh_2 = None 2025-03-04T21:03:21.4991407Z 2025-03-04T21:03:21.4991752Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:03:21.4991885Z getitem_36: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)] 2025-03-04T21:03:21.4992019Z mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_36; dx_1 = getitem_36 = None 2025-03-04T21:03:21.4992173Z getitem_37: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)]; ctr_x_1 = None 2025-03-04T21:03:21.4992323Z pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_37; mul_12 = getitem_37 = None 2025-03-04T21:03:21.4992383Z 2025-03-04T21:03:21.4992747Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:03:21.4992882Z getitem_38: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)] 2025-03-04T21:03:21.4993008Z mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_38; dy_1 = getitem_38 = None 2025-03-04T21:03:21.4993159Z getitem_39: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)]; ctr_y_1 = None 2025-03-04T21:03:21.4993305Z pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_39; mul_13 = getitem_39 = None 2025-03-04T21:03:21.4993374Z 2025-03-04T21:03:21.4993762Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:03:21.4993894Z exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3); dw_3 = None 2025-03-04T21:03:21.4994059Z getitem_40: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]; widths_1 = None 2025-03-04T21:03:21.4994201Z pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_40; exp_2 = getitem_40 = None 2025-03-04T21:03:21.4994265Z 2025-03-04T21:03:21.4994613Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:03:21.4994727Z exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3); dh_3 = None 2025-03-04T21:03:21.4994944Z getitem_41: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]; heights_1 = None 2025-03-04T21:03:21.4995084Z pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_41; exp_3 = getitem_41 = None 2025-03-04T21:03:21.4995158Z 2025-03-04T21:03:21.4995488Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:03:21.4995591Z mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1 2025-03-04T21:03:21.4995706Z x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16; mul_16 = None 2025-03-04T21:03:21.4995774Z 2025-03-04T21:03:21.4996082Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:03:21.4996180Z mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1 2025-03-04T21:03:21.4996296Z y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17; mul_17 = None 2025-03-04T21:03:21.4996363Z 2025-03-04T21:03:21.4996666Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:03:21.4996808Z mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1; pred_w_1 = None 2025-03-04T21:03:21.4996939Z x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18; pred_ctr_x_1 = mul_18 = None 2025-03-04T21:03:21.4997006Z 2025-03-04T21:03:21.4997304Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:03:21.4997422Z mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1; pred_h_1 = None 2025-03-04T21:03:21.4997549Z y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19; pred_ctr_y_1 = mul_19 = None 2025-03-04T21:03:21.4997620Z 2025-03-04T21:03:21.4997973Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:03:21.4998195Z pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1); x1_1 = y1_1 = x2_1 = y2_1 = None 2025-03-04T21:03:21.4998257Z 2025-03-04T21:03:21.4998601Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:03:21.4998764Z proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4)); pred_boxes_1 = None 2025-03-04T21:03:21.4998833Z 2025-03-04T21:03:21.4999224Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:03:21.4999409Z proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4); proposals_i_1 = None 2025-03-04T21:03:21.4999472Z 2025-03-04T21:03:21.4999889Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:03:21.5000096Z pred_anchor_deltas_i_5: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_4.reshape(-1, 4); pred_anchor_deltas_i_4 = None 2025-03-04T21:03:21.5000166Z 2025-03-04T21:03:21.5000603Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:03:21.5000764Z unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = tensor_2.unsqueeze(0); tensor_2 = None 2025-03-04T21:03:21.5000966Z expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1); unsqueeze_2 = None 2025-03-04T21:03:21.5001105Z anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4); expand_2 = None 2025-03-04T21:03:21.5001176Z 2025-03-04T21:03:21.5001556Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:03:21.5001732Z deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_5.float(); pred_anchor_deltas_i_5 = None 2025-03-04T21:03:21.5001796Z 2025-03-04T21:03:21.5002256Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:03:21.5002407Z boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32); anchors_i_2 = None 2025-03-04T21:03:21.5002480Z 2025-03-04T21:03:21.5002806Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:03:21.5002979Z getitem_42: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)] 2025-03-04T21:03:21.5003108Z getitem_43: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T21:03:21.5003267Z widths_2: "f32[67488][1]cpu" = getitem_42 - getitem_43; getitem_42 = getitem_43 = None 2025-03-04T21:03:21.5003329Z 2025-03-04T21:03:21.5003663Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:03:21.5003786Z getitem_44: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)] 2025-03-04T21:03:21.5003912Z getitem_45: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)] 2025-03-04T21:03:21.5004064Z heights_2: "f32[67488][1]cpu" = getitem_44 - getitem_45; getitem_44 = getitem_45 = None 2025-03-04T21:03:21.5004135Z 2025-03-04T21:03:21.5004454Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:03:21.5004609Z getitem_46: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T21:03:21.5004701Z mul_20: "f32[67488][1]cpu" = 0.5 * widths_2 2025-03-04T21:03:21.5004840Z ctr_x_2: "f32[67488][1]cpu" = getitem_46 + mul_20; getitem_46 = mul_20 = None 2025-03-04T21:03:21.5004903Z 2025-03-04T21:03:21.5005229Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:03:21.5005377Z getitem_47: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]; boxes_2 = None 2025-03-04T21:03:21.5005479Z mul_21: "f32[67488][1]cpu" = 0.5 * heights_2 2025-03-04T21:03:21.5005611Z ctr_y_2: "f32[67488][1]cpu" = getitem_47 + mul_21; getitem_47 = mul_21 = None 2025-03-04T21:03:21.5005680Z 2025-03-04T21:03:21.5005995Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:03:21.5006163Z getitem_48: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:03:21.5006275Z dx_2: "f32[67488, 1][1, 1]cpu" = getitem_48 / 1.0; getitem_48 = None 2025-03-04T21:03:21.5006345Z 2025-03-04T21:03:21.5006652Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:03:21.5006810Z getitem_49: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:03:21.5006945Z dy_2: "f32[67488, 1][1, 1]cpu" = getitem_49 / 1.0; getitem_49 = None 2025-03-04T21:03:21.5007044Z 2025-03-04T21:03:21.5007331Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:03:21.5007483Z getitem_50: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:03:21.5007586Z dw_4: "f32[67488, 1][1, 1]cpu" = getitem_50 / 1.0; getitem_50 = None 2025-03-04T21:03:21.5007653Z 2025-03-04T21:03:21.5007949Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:03:21.5008121Z getitem_51: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))]; deltas_2 = None 2025-03-04T21:03:21.5008229Z dh_4: "f32[67488, 1][1, 1]cpu" = getitem_51 / 1.0; getitem_51 = None 2025-03-04T21:03:21.5008291Z 2025-03-04T21:03:21.5008619Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:03:21.5008767Z dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356); dw_4 = None 2025-03-04T21:03:21.5008832Z 2025-03-04T21:03:21.5009156Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:03:21.5009293Z dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356); dh_4 = None 2025-03-04T21:03:21.5009351Z 2025-03-04T21:03:21.5009695Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:03:21.5009823Z getitem_52: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)] 2025-03-04T21:03:21.5009950Z mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_52; dx_2 = getitem_52 = None 2025-03-04T21:03:21.5010101Z getitem_53: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)]; ctr_x_2 = None 2025-03-04T21:03:21.5010256Z pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_53; mul_22 = getitem_53 = None 2025-03-04T21:03:21.5010314Z 2025-03-04T21:03:21.5010659Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:03:21.5010788Z getitem_54: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)] 2025-03-04T21:03:21.5010911Z mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_54; dy_2 = getitem_54 = None 2025-03-04T21:03:21.5011057Z getitem_55: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)]; ctr_y_2 = None 2025-03-04T21:03:21.5011194Z pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_55; mul_23 = getitem_55 = None 2025-03-04T21:03:21.5011254Z 2025-03-04T21:03:21.5011583Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:03:21.5011693Z exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5); dw_5 = None 2025-03-04T21:03:21.5011852Z getitem_56: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]; widths_2 = None 2025-03-04T21:03:21.5011980Z pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_56; exp_4 = getitem_56 = None 2025-03-04T21:03:21.5012046Z 2025-03-04T21:03:21.5012369Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:03:21.5012513Z exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5); dh_5 = None 2025-03-04T21:03:21.5012671Z getitem_57: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]; heights_2 = None 2025-03-04T21:03:21.5012808Z pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_57; exp_5 = getitem_57 = None 2025-03-04T21:03:21.5012866Z 2025-03-04T21:03:21.5013172Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:03:21.5013262Z mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2 2025-03-04T21:03:21.5013379Z x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26; mul_26 = None 2025-03-04T21:03:21.5013437Z 2025-03-04T21:03:21.5013744Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:03:21.5013835Z mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2 2025-03-04T21:03:21.5013950Z y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27; mul_27 = None 2025-03-04T21:03:21.5014009Z 2025-03-04T21:03:21.5014323Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:03:21.5014432Z mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2; pred_w_2 = None 2025-03-04T21:03:21.5014563Z x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28; pred_ctr_x_2 = mul_28 = None 2025-03-04T21:03:21.5014623Z 2025-03-04T21:03:21.5014924Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:03:21.5015031Z mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2; pred_h_2 = None 2025-03-04T21:03:21.5015161Z y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29; pred_ctr_y_2 = mul_29 = None 2025-03-04T21:03:21.5015221Z 2025-03-04T21:03:21.5015565Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:03:21.5015770Z pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1); x1_2 = y1_2 = x2_2 = y2_2 = None 2025-03-04T21:03:21.5015830Z 2025-03-04T21:03:21.5016156Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:03:21.5016309Z proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4)); pred_boxes_2 = None 2025-03-04T21:03:21.5016374Z 2025-03-04T21:03:21.5016747Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:03:21.5016922Z proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4); proposals_i_2 = None 2025-03-04T21:03:21.5016982Z 2025-03-04T21:03:21.5017381Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:03:21.5017580Z pred_anchor_deltas_i_7: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_6.reshape(-1, 4); pred_anchor_deltas_i_6 = None 2025-03-04T21:03:21.5017648Z 2025-03-04T21:03:21.5018068Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:03:21.5018233Z unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = tensor_3.unsqueeze(0); tensor_3 = None 2025-03-04T21:03:21.5018388Z expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1); unsqueeze_3 = None 2025-03-04T21:03:21.5018525Z anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4); expand_3 = None 2025-03-04T21:03:21.5018586Z 2025-03-04T21:03:21.5018953Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:03:21.5019114Z deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_7.float(); pred_anchor_deltas_i_7 = None 2025-03-04T21:03:21.5019183Z 2025-03-04T21:03:21.5019481Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:03:21.5019627Z boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32); anchors_i_3 = None 2025-03-04T21:03:21.5019690Z 2025-03-04T21:03:21.5019998Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:03:21.5020136Z getitem_58: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)] 2025-03-04T21:03:21.5020265Z getitem_59: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T21:03:21.5020407Z widths_3: "f32[16872][1]cpu" = getitem_58 - getitem_59; getitem_58 = getitem_59 = None 2025-03-04T21:03:21.5020475Z 2025-03-04T21:03:21.5020783Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:03:21.5020908Z getitem_60: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)] 2025-03-04T21:03:21.5021025Z getitem_61: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)] 2025-03-04T21:03:21.5021177Z heights_3: "f32[16872][1]cpu" = getitem_60 - getitem_61; getitem_60 = getitem_61 = None 2025-03-04T21:03:21.5021237Z 2025-03-04T21:03:21.5021548Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:03:21.5021688Z getitem_62: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T21:03:21.5021780Z mul_30: "f32[16872][1]cpu" = 0.5 * widths_3 2025-03-04T21:03:21.5021904Z ctr_x_3: "f32[16872][1]cpu" = getitem_62 + mul_30; getitem_62 = mul_30 = None 2025-03-04T21:03:21.5021969Z 2025-03-04T21:03:21.5022274Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:03:21.5022427Z getitem_63: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]; boxes_3 = None 2025-03-04T21:03:21.5022524Z mul_31: "f32[16872][1]cpu" = 0.5 * heights_3 2025-03-04T21:03:21.5022649Z ctr_y_3: "f32[16872][1]cpu" = getitem_63 + mul_31; getitem_63 = mul_31 = None 2025-03-04T21:03:21.5022719Z 2025-03-04T21:03:21.5023022Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:03:21.5023179Z getitem_64: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:03:21.5023289Z dx_3: "f32[16872, 1][1, 1]cpu" = getitem_64 / 1.0; getitem_64 = None 2025-03-04T21:03:21.5023356Z 2025-03-04T21:03:21.5023654Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:03:21.5023811Z getitem_65: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:03:21.5024212Z dy_3: "f32[16872, 1][1, 1]cpu" = getitem_65 / 1.0; getitem_65 = None 2025-03-04T21:03:21.5024283Z 2025-03-04T21:03:21.5024591Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:03:21.5024748Z getitem_66: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:03:21.5024854Z dw_6: "f32[16872, 1][1, 1]cpu" = getitem_66 / 1.0; getitem_66 = None 2025-03-04T21:03:21.5024921Z 2025-03-04T21:03:21.5025219Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:03:21.5025409Z getitem_67: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))]; deltas_3 = None 2025-03-04T21:03:21.5025518Z dh_6: "f32[16872, 1][1, 1]cpu" = getitem_67 / 1.0; getitem_67 = None 2025-03-04T21:03:21.5025587Z 2025-03-04T21:03:21.5025921Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:03:21.5026084Z dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356); dw_6 = None 2025-03-04T21:03:21.5026145Z 2025-03-04T21:03:21.5026476Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:03:21.5026607Z dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356); dh_6 = None 2025-03-04T21:03:21.5026674Z 2025-03-04T21:03:21.5027013Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:03:21.5027152Z getitem_68: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)] 2025-03-04T21:03:21.5027272Z mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_68; dx_3 = getitem_68 = None 2025-03-04T21:03:21.5027427Z getitem_69: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)]; ctr_x_3 = None 2025-03-04T21:03:21.5027577Z pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_69; mul_32 = getitem_69 = None 2025-03-04T21:03:21.5027644Z 2025-03-04T21:03:21.5027988Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:03:21.5028126Z getitem_70: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)] 2025-03-04T21:03:21.5028249Z mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_70; dy_3 = getitem_70 = None 2025-03-04T21:03:21.5028398Z getitem_71: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)]; ctr_y_3 = None 2025-03-04T21:03:21.5028537Z pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_71; mul_33 = getitem_71 = None 2025-03-04T21:03:21.5028599Z 2025-03-04T21:03:21.5028939Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:03:21.5029049Z exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7); dw_7 = None 2025-03-04T21:03:21.5029210Z getitem_72: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]; widths_3 = None 2025-03-04T21:03:21.5029342Z pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_72; exp_6 = getitem_72 = None 2025-03-04T21:03:21.5029410Z 2025-03-04T21:03:21.5029755Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:03:21.5029885Z exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7); dh_7 = None 2025-03-04T21:03:21.5030045Z getitem_73: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]; heights_3 = None 2025-03-04T21:03:21.5030185Z pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_73; exp_7 = getitem_73 = None 2025-03-04T21:03:21.5030246Z 2025-03-04T21:03:21.5030560Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:03:21.5030653Z mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3 2025-03-04T21:03:21.5030770Z x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36; mul_36 = None 2025-03-04T21:03:21.5030831Z 2025-03-04T21:03:21.5031144Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:03:21.5031234Z mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3 2025-03-04T21:03:21.5031352Z y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37; mul_37 = None 2025-03-04T21:03:21.5031428Z 2025-03-04T21:03:21.5031742Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:03:21.5031856Z mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3; pred_w_3 = None 2025-03-04T21:03:21.5031995Z x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38; pred_ctr_x_3 = mul_38 = None 2025-03-04T21:03:21.5032058Z 2025-03-04T21:03:21.5032366Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:03:21.5032478Z mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3; pred_h_3 = None 2025-03-04T21:03:21.5032612Z y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39; pred_ctr_y_3 = mul_39 = None 2025-03-04T21:03:21.5032672Z 2025-03-04T21:03:21.5033021Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:03:21.5033220Z pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1); x1_3 = y1_3 = x2_3 = y2_3 = None 2025-03-04T21:03:21.5033290Z 2025-03-04T21:03:21.5033618Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:03:21.5033886Z proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4)); pred_boxes_3 = None 2025-03-04T21:03:21.5033954Z 2025-03-04T21:03:21.5034358Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:03:21.5034532Z proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4); proposals_i_3 = None 2025-03-04T21:03:21.5034608Z 2025-03-04T21:03:21.5035019Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:03:21.5035237Z pred_anchor_deltas_i_9: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_8.reshape(-1, 4); pred_anchor_deltas_i_8 = None 2025-03-04T21:03:21.5035312Z 2025-03-04T21:03:21.5035756Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:03:21.5035943Z unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = tensor_4.unsqueeze(0); tensor_4 = None 2025-03-04T21:03:21.5036092Z expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1); unsqueeze_4 = None 2025-03-04T21:03:21.5036231Z anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4); expand_4 = None 2025-03-04T21:03:21.5036295Z 2025-03-04T21:03:21.5036674Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:03:21.5036836Z deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_9.float(); pred_anchor_deltas_i_9 = None 2025-03-04T21:03:21.5036906Z 2025-03-04T21:03:21.5037215Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:03:21.5037361Z boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32); anchors_i_4 = None 2025-03-04T21:03:21.5037422Z 2025-03-04T21:03:21.5037741Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:03:21.5037883Z getitem_74: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)] 2025-03-04T21:03:21.5038010Z getitem_75: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T21:03:21.5038155Z widths_4: "f32[4332][1]cpu" = getitem_74 - getitem_75; getitem_74 = getitem_75 = None 2025-03-04T21:03:21.5038222Z 2025-03-04T21:03:21.5038539Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:03:21.5038666Z getitem_76: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)] 2025-03-04T21:03:21.5038785Z getitem_77: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)] 2025-03-04T21:03:21.5038935Z heights_4: "f32[4332][1]cpu" = getitem_76 - getitem_77; getitem_76 = getitem_77 = None 2025-03-04T21:03:21.5038997Z 2025-03-04T21:03:21.5039329Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:03:21.5039446Z getitem_78: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T21:03:21.5039539Z mul_40: "f32[4332][1]cpu" = 0.5 * widths_4 2025-03-04T21:03:21.5039665Z ctr_x_4: "f32[4332][1]cpu" = getitem_78 + mul_40; getitem_78 = mul_40 = None 2025-03-04T21:03:21.5039732Z 2025-03-04T21:03:21.5040041Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:03:21.5040192Z getitem_79: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]; boxes_4 = None 2025-03-04T21:03:21.5040278Z mul_41: "f32[4332][1]cpu" = 0.5 * heights_4 2025-03-04T21:03:21.5040406Z ctr_y_4: "f32[4332][1]cpu" = getitem_79 + mul_41; getitem_79 = mul_41 = None 2025-03-04T21:03:21.5040466Z 2025-03-04T21:03:21.5040775Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:03:21.5040922Z getitem_80: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:03:21.5041038Z dx_4: "f32[4332, 1][1, 1]cpu" = getitem_80 / 1.0; getitem_80 = None 2025-03-04T21:03:21.5041098Z 2025-03-04T21:03:21.5041403Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:03:21.5041563Z getitem_81: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:03:21.5041692Z dy_4: "f32[4332, 1][1, 1]cpu" = getitem_81 / 1.0; getitem_81 = None 2025-03-04T21:03:21.5041755Z 2025-03-04T21:03:21.5042066Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:03:21.5042217Z getitem_82: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:03:21.5042321Z dw_8: "f32[4332, 1][1, 1]cpu" = getitem_82 / 1.0; getitem_82 = None 2025-03-04T21:03:21.5042387Z 2025-03-04T21:03:21.5042688Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:03:21.5042873Z getitem_83: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))]; deltas_4 = None 2025-03-04T21:03:21.5042979Z dh_8: "f32[4332, 1][1, 1]cpu" = getitem_83 / 1.0; getitem_83 = None 2025-03-04T21:03:21.5043054Z 2025-03-04T21:03:21.5043382Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:03:21.5043544Z dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356); dw_8 = None 2025-03-04T21:03:21.5043604Z 2025-03-04T21:03:21.5043935Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:03:21.5044063Z dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356); dh_8 = None 2025-03-04T21:03:21.5044130Z 2025-03-04T21:03:21.5044472Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:03:21.5044611Z getitem_84: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)] 2025-03-04T21:03:21.5044730Z mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_84; dx_4 = getitem_84 = None 2025-03-04T21:03:21.5044899Z getitem_85: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)]; ctr_x_4 = None 2025-03-04T21:03:21.5045033Z pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_85; mul_42 = getitem_85 = None 2025-03-04T21:03:21.5045101Z 2025-03-04T21:03:21.5045449Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:03:21.5045587Z getitem_86: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)] 2025-03-04T21:03:21.5045706Z mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_86; dy_4 = getitem_86 = None 2025-03-04T21:03:21.5045862Z getitem_87: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)]; ctr_y_4 = None 2025-03-04T21:03:21.5046007Z pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_87; mul_43 = getitem_87 = None 2025-03-04T21:03:21.5046077Z 2025-03-04T21:03:21.5046400Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:03:21.5046516Z exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9); dw_9 = None 2025-03-04T21:03:21.5046666Z getitem_88: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]; widths_4 = None 2025-03-04T21:03:21.5046797Z pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_88; exp_8 = getitem_88 = None 2025-03-04T21:03:21.5046856Z 2025-03-04T21:03:21.5047209Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:03:21.5047331Z exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9); dh_9 = None 2025-03-04T21:03:21.5047495Z getitem_89: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]; heights_4 = None 2025-03-04T21:03:21.5047618Z pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_89; exp_9 = getitem_89 = None 2025-03-04T21:03:21.5047686Z 2025-03-04T21:03:21.5047983Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:03:21.5048085Z mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4 2025-03-04T21:03:21.5048193Z x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46; mul_46 = None 2025-03-04T21:03:21.5048263Z 2025-03-04T21:03:21.5048564Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:03:21.5048661Z mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4 2025-03-04T21:03:21.5048768Z y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47; mul_47 = None 2025-03-04T21:03:21.5048853Z 2025-03-04T21:03:21.5049157Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:03:21.5049278Z mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4; pred_w_4 = None 2025-03-04T21:03:21.5049405Z x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48; pred_ctr_x_4 = mul_48 = None 2025-03-04T21:03:21.5049473Z 2025-03-04T21:03:21.5049776Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:03:21.5049894Z mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4; pred_h_4 = None 2025-03-04T21:03:21.5050027Z y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49; pred_ctr_y_4 = mul_49 = None 2025-03-04T21:03:21.5050088Z 2025-03-04T21:03:21.5050467Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:03:21.5050654Z pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1); x1_4 = y1_4 = x2_4 = y2_4 = None 2025-03-04T21:03:21.5050723Z 2025-03-04T21:03:21.5051065Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:03:21.5051233Z proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4)); pred_boxes_4 = None 2025-03-04T21:03:21.5051295Z 2025-03-04T21:03:21.5051705Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:03:21.5051886Z proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4); proposals_i_4 = None 2025-03-04T21:03:21.5051957Z 2025-03-04T21:03:21.5052478Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T21:03:21.5052625Z arange_10: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T21:03:21.5052689Z 2025-03-04T21:03:21.5053016Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:21.5053172Z batch_idx: "i64[4][1]cpu" = arange_10.to(device(type='cpu')); arange_10 = None 2025-03-04T21:03:21.5053258Z 2025-03-04T21:03:21.5053716Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:03:21.5053841Z topk = logits_i.topk(1000, dim = 1); logits_i = None 2025-03-04T21:03:21.5053947Z topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0] 2025-03-04T21:03:21.5054070Z topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1]; topk = None 2025-03-04T21:03:21.5054131Z 2025-03-04T21:03:21.5054625Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:03:21.5054758Z getitem_92: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:03:21.5055013Z topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_92, topk_idx)]; proposals_i_5 = getitem_92 = topk_idx = None 2025-03-04T21:03:21.5055088Z 2025-03-04T21:03:21.5055568Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:03:21.5055734Z full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:03:21.5055801Z 2025-03-04T21:03:21.5056108Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:21.5056234Z to_21: "i64[1000][1]cpu" = full.to(device(type='cpu')); full = None 2025-03-04T21:03:21.5056294Z 2025-03-04T21:03:21.5056749Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:03:21.5056870Z topk_1 = logits_i_1.topk(1000, dim = 1); logits_i_1 = None 2025-03-04T21:03:21.5056987Z topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0] 2025-03-04T21:03:21.5057108Z topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1]; topk_1 = None 2025-03-04T21:03:21.5057169Z 2025-03-04T21:03:21.5057632Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:03:21.5057756Z getitem_96: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:03:21.5057993Z topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_96, topk_idx_1)]; proposals_i_6 = getitem_96 = topk_idx_1 = None 2025-03-04T21:03:21.5058055Z 2025-03-04T21:03:21.5058515Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:03:21.5058684Z full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:03:21.5058749Z 2025-03-04T21:03:21.5059032Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:21.5059161Z to_22: "i64[1000][1]cpu" = full_1.to(device(type='cpu')); full_1 = None 2025-03-04T21:03:21.5059220Z 2025-03-04T21:03:21.5059673Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:03:21.5059796Z topk_2 = logits_i_2.topk(1000, dim = 1); logits_i_2 = None 2025-03-04T21:03:21.5059906Z topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0] 2025-03-04T21:03:21.5060018Z topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1]; topk_2 = None 2025-03-04T21:03:21.5060086Z 2025-03-04T21:03:21.5060543Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:03:21.5060679Z getitem_100: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:03:21.5060914Z topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_100, topk_idx_2)]; proposals_i_7 = getitem_100 = topk_idx_2 = None 2025-03-04T21:03:21.5060985Z 2025-03-04T21:03:21.5061438Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:03:21.5061625Z full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:03:21.5061686Z 2025-03-04T21:03:21.5061989Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:21.5062112Z to_23: "i64[1000][1]cpu" = full_2.to(device(type='cpu')); full_2 = None 2025-03-04T21:03:21.5062180Z 2025-03-04T21:03:21.5062610Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:03:21.5062728Z topk_3 = logits_i_3.topk(1000, dim = 1); logits_i_3 = None 2025-03-04T21:03:21.5062837Z topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0] 2025-03-04T21:03:21.5062948Z topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1]; topk_3 = None 2025-03-04T21:03:21.5063032Z 2025-03-04T21:03:21.5063485Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:03:21.5063621Z getitem_104: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:03:21.5063852Z topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_104, topk_idx_3)]; proposals_i_8 = getitem_104 = topk_idx_3 = None 2025-03-04T21:03:21.5063919Z 2025-03-04T21:03:21.5064374Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:03:21.5064542Z full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:03:21.5064604Z 2025-03-04T21:03:21.5064900Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:21.5065015Z to_24: "i64[1000][1]cpu" = full_3.to(device(type='cpu')); full_3 = None 2025-03-04T21:03:21.5065082Z 2025-03-04T21:03:21.5065507Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:03:21.5065660Z topk_4 = logits_i_4.topk(1000, dim = 1); logits_i_4 = None 2025-03-04T21:03:21.5065759Z topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0] 2025-03-04T21:03:21.5065874Z topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1]; topk_4 = None 2025-03-04T21:03:21.5065938Z 2025-03-04T21:03:21.5066397Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:03:21.5066557Z getitem_108: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T21:03:21.5066795Z topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_108, topk_idx_4)]; proposals_i_9 = getitem_108 = topk_idx_4 = None 2025-03-04T21:03:21.5066857Z 2025-03-04T21:03:21.5067319Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:03:21.5067494Z full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:03:21.5067563Z 2025-03-04T21:03:21.5067856Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:21.5067982Z to_25: "i64[1000][1]cpu" = full_4.to(device(type='cpu')); full_4 = None 2025-03-04T21:03:21.5068042Z 2025-03-04T21:03:21.5068330Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:03:21.5068713Z topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1); topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None 2025-03-04T21:03:21.5068775Z 2025-03-04T21:03:21.5069059Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:03:21.5069538Z topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1); topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None 2025-03-04T21:03:21.5069608Z 2025-03-04T21:03:21.5069891Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:03:21.5070093Z level_ids: "i64[5000][1]cpu" = torch.cat([to_21, to_22, to_23, to_24, to_25], 0); to_21 = to_22 = to_23 = to_24 = to_25 = level_ids = None 2025-03-04T21:03:21.5070158Z 2025-03-04T21:03:21.5070563Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T21:03:21.5070703Z getitem_110: "f32[5000, 4][4, 1]cpu" = topk_proposals[0]; topk_proposals = None 2025-03-04T21:03:21.5070781Z 2025-03-04T21:03:21.5071074Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:03:21.5071221Z tensor_5: "f32[5000, 4][4, 1]cpu" = getitem_110.to(torch.float32); getitem_110 = None 2025-03-04T21:03:21.5071279Z 2025-03-04T21:03:21.5071651Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T21:03:21.5071793Z scores_per_img: "f32[5000][1]cpu" = topk_scores[0]; topk_scores = None 2025-03-04T21:03:21.5071874Z 2025-03-04T21:03:21.5072345Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T21:03:21.5072484Z isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor_5); tensor_5 = None 2025-03-04T21:03:21.5072599Z all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:03:21.5072753Z isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T21:03:21.5072876Z valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T21:03:21.5072946Z 2025-03-04T21:03:21.5073304Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T21:03:21.5073424Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T21:03:21.5073483Z 2025-03-04T21:03:30.5467480Z 2025-03-04T21:03:30.5468356Z class GraphModule(torch.nn.Module): 2025-03-04T21:03:30.5471460Z def forward(self, L_features_p2_: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu", L_features_p3_: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu", L_features_p4_: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu", L_features_p5_: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu", L_features_p6_: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_1_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_2_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_3_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_4_: "f32[3, 4][4, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_bias_: "f32[256][1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[3, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[3][1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[12, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[12][1]cpu"): 2025-03-04T21:03:30.5474054Z l_features_p2_ = L_features_p2_ 2025-03-04T21:03:30.5474286Z l_features_p3_ = L_features_p3_ 2025-03-04T21:03:30.5474502Z l_features_p4_ = L_features_p4_ 2025-03-04T21:03:30.5474719Z l_features_p5_ = L_features_p5_ 2025-03-04T21:03:30.5474940Z l_features_p6_ = L_features_p6_ 2025-03-04T21:03:30.5475344Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T21:03:30.5476031Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_1_ 2025-03-04T21:03:30.5476650Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_2_ 2025-03-04T21:03:30.5477264Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_3_ 2025-03-04T21:03:30.5477873Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_4_ 2025-03-04T21:03:30.5478446Z l_self_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T21:03:30.5479028Z l_self_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T21:03:30.5479649Z l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T21:03:30.5480296Z l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T21:03:30.5480930Z l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T21:03:30.5481555Z l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T21:03:30.5481986Z 2025-03-04T21:03:30.5482586Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:03:30.5483241Z arange: "f32[304][1]cpu" = torch.arange(0.0, 1216, step = 4, dtype = torch.float32) 2025-03-04T21:03:30.5483493Z 2025-03-04T21:03:30.5483925Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:30.5484416Z shifts_x: "f32[304][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T21:03:30.5484669Z 2025-03-04T21:03:30.5485190Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:03:30.5485825Z arange_1: "f32[296][1]cpu" = torch.arange(0.0, 1184, step = 4, dtype = torch.float32) 2025-03-04T21:03:30.5486086Z 2025-03-04T21:03:30.5486454Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:30.5486937Z shifts_y: "f32[296][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T21:03:30.5487198Z 2025-03-04T21:03:30.5487674Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:03:30.5488265Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T21:03:30.5488585Z shift_y: "f32[296, 304][1, 0]cpu" = meshgrid[0] 2025-03-04T21:03:30.5488847Z shift_x: "f32[296, 304][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T21:03:30.5489074Z 2025-03-04T21:03:30.5489477Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:03:30.5489976Z shift_x_1: "f32[89984][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T21:03:30.5490210Z 2025-03-04T21:03:30.5490605Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:03:30.5491094Z shift_y_1: "f32[89984][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T21:03:30.5491323Z 2025-03-04T21:03:30.5491772Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:03:30.5492398Z shifts: "f32[89984, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T21:03:30.5492709Z 2025-03-04T21:03:30.5493877Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:03:30.5494492Z view: "f32[89984, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T21:03:30.5494990Z view_1: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T21:03:30.5495479Z add: "f32[89984, 3, 4][12, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T21:03:30.5495774Z x: "f32[269952, 4][4, 1]cpu" = add.reshape(-1, 4); add = None 2025-03-04T21:03:30.5496001Z 2025-03-04T21:03:30.5496523Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:03:30.5497157Z arange_2: "f32[152][1]cpu" = torch.arange(0.0, 1216, step = 8, dtype = torch.float32) 2025-03-04T21:03:30.5497424Z 2025-03-04T21:03:30.5497805Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:30.5498315Z shifts_x_1: "f32[152][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T21:03:30.5498581Z 2025-03-04T21:03:30.5499078Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:03:30.5499693Z arange_3: "f32[148][1]cpu" = torch.arange(0.0, 1184, step = 8, dtype = torch.float32) 2025-03-04T21:03:30.5499958Z 2025-03-04T21:03:30.5500334Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:30.5500834Z shifts_y_1: "f32[148][1]cpu" = arange_3.to(device(type='cpu')); arange_3 = None 2025-03-04T21:03:30.5501106Z 2025-03-04T21:03:30.5501600Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:03:30.5502469Z meshgrid_1 = torch.functional.meshgrid(shifts_y_1, shifts_x_1); shifts_y_1 = shifts_x_1 = None 2025-03-04T21:03:30.5502845Z shift_y_2: "f32[148, 152][1, 0]cpu" = meshgrid_1[0] 2025-03-04T21:03:30.5503139Z shift_x_2: "f32[148, 152][0, 1]cpu" = meshgrid_1[1]; meshgrid_1 = None 2025-03-04T21:03:30.5503397Z 2025-03-04T21:03:30.5503848Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:03:30.5504383Z shift_x_3: "f32[22496][1]cpu" = shift_x_2.reshape(-1); shift_x_2 = None 2025-03-04T21:03:30.5504638Z 2025-03-04T21:03:30.5505060Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:03:30.5505587Z shift_y_3: "f32[22496][1]cpu" = shift_y_2.reshape(-1); shift_y_2 = None 2025-03-04T21:03:30.5505851Z 2025-03-04T21:03:30.5506321Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:03:30.5507008Z shifts_1: "f32[22496, 4][4, 1]cpu" = torch.stack((shift_x_3, shift_y_3, shift_x_3, shift_y_3), dim = 1); shift_x_3 = shift_y_3 = None 2025-03-04T21:03:30.5507354Z 2025-03-04T21:03:30.5507915Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:03:30.5508577Z view_2: "f32[22496, 1, 4][4, 4, 1]cpu" = shifts_1.view(-1, 1, 4); shifts_1 = None 2025-03-04T21:03:30.5509105Z view_3: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_ = None 2025-03-04T21:03:30.5509617Z add_1: "f32[22496, 3, 4][12, 4, 1]cpu" = view_2 + view_3; view_2 = view_3 = None 2025-03-04T21:03:30.5509928Z x_1: "f32[67488, 4][4, 1]cpu" = add_1.reshape(-1, 4); add_1 = None 2025-03-04T21:03:30.5510165Z 2025-03-04T21:03:30.5510706Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:03:30.5511427Z arange_4: "f32[76][1]cpu" = torch.arange(0.0, 1216, step = 16, dtype = torch.float32) 2025-03-04T21:03:30.5511720Z 2025-03-04T21:03:30.5512151Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:30.5512718Z shifts_x_2: "f32[76][1]cpu" = arange_4.to(device(type='cpu')); arange_4 = None 2025-03-04T21:03:30.5512998Z 2025-03-04T21:03:30.5513621Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:03:30.5514292Z arange_5: "f32[74][1]cpu" = torch.arange(0.0, 1184, step = 16, dtype = torch.float32) 2025-03-04T21:03:30.5514570Z 2025-03-04T21:03:30.5514966Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:30.5515465Z shifts_y_2: "f32[74][1]cpu" = arange_5.to(device(type='cpu')); arange_5 = None 2025-03-04T21:03:30.5515726Z 2025-03-04T21:03:30.5516230Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:03:30.5516846Z meshgrid_2 = torch.functional.meshgrid(shifts_y_2, shifts_x_2); shifts_y_2 = shifts_x_2 = None 2025-03-04T21:03:30.5517186Z shift_y_4: "f32[74, 76][1, 0]cpu" = meshgrid_2[0] 2025-03-04T21:03:30.5517452Z shift_x_4: "f32[74, 76][0, 1]cpu" = meshgrid_2[1]; meshgrid_2 = None 2025-03-04T21:03:30.5517681Z 2025-03-04T21:03:30.5518085Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:03:30.5518583Z shift_x_5: "f32[5624][1]cpu" = shift_x_4.reshape(-1); shift_x_4 = None 2025-03-04T21:03:30.5518819Z 2025-03-04T21:03:30.5519217Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:03:30.5519715Z shift_y_5: "f32[5624][1]cpu" = shift_y_4.reshape(-1); shift_y_4 = None 2025-03-04T21:03:30.5519950Z 2025-03-04T21:03:30.5520413Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:03:30.5521054Z shifts_2: "f32[5624, 4][4, 1]cpu" = torch.stack((shift_x_5, shift_y_5, shift_x_5, shift_y_5), dim = 1); shift_x_5 = shift_y_5 = None 2025-03-04T21:03:30.5521368Z 2025-03-04T21:03:30.5521862Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:03:30.5522475Z view_4: "f32[5624, 1, 4][4, 4, 1]cpu" = shifts_2.view(-1, 1, 4); shifts_2 = None 2025-03-04T21:03:30.5522956Z view_5: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_ = None 2025-03-04T21:03:30.5523428Z add_2: "f32[5624, 3, 4][12, 4, 1]cpu" = view_4 + view_5; view_4 = view_5 = None 2025-03-04T21:03:30.5523712Z x_2: "f32[16872, 4][4, 1]cpu" = add_2.reshape(-1, 4); add_2 = None 2025-03-04T21:03:30.5523934Z 2025-03-04T21:03:30.5524439Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:03:30.5525055Z arange_6: "f32[38][1]cpu" = torch.arange(0.0, 1216, step = 32, dtype = torch.float32) 2025-03-04T21:03:30.5525312Z 2025-03-04T21:03:30.5525681Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:30.5526173Z shifts_x_3: "f32[38][1]cpu" = arange_6.to(device(type='cpu')); arange_6 = None 2025-03-04T21:03:30.5526421Z 2025-03-04T21:03:30.5526922Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:03:30.5527534Z arange_7: "f32[37][1]cpu" = torch.arange(0.0, 1184, step = 32, dtype = torch.float32) 2025-03-04T21:03:30.5527790Z 2025-03-04T21:03:30.5528159Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:30.5528629Z shifts_y_3: "f32[37][1]cpu" = arange_7.to(device(type='cpu')); arange_7 = None 2025-03-04T21:03:30.5528878Z 2025-03-04T21:03:30.5529338Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:03:30.5529936Z meshgrid_3 = torch.functional.meshgrid(shifts_y_3, shifts_x_3); shifts_y_3 = shifts_x_3 = None 2025-03-04T21:03:30.5530273Z shift_y_6: "f32[37, 38][1, 0]cpu" = meshgrid_3[0] 2025-03-04T21:03:30.5530530Z shift_x_6: "f32[37, 38][0, 1]cpu" = meshgrid_3[1]; meshgrid_3 = None 2025-03-04T21:03:30.5530764Z 2025-03-04T21:03:30.5531161Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:03:30.5531657Z shift_x_7: "f32[1406][1]cpu" = shift_x_6.reshape(-1); shift_x_6 = None 2025-03-04T21:03:30.5531889Z 2025-03-04T21:03:30.5532281Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:03:30.5532772Z shift_y_7: "f32[1406][1]cpu" = shift_y_6.reshape(-1); shift_y_6 = None 2025-03-04T21:03:30.5533002Z 2025-03-04T21:03:30.5533444Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:03:30.5534076Z shifts_3: "f32[1406, 4][4, 1]cpu" = torch.stack((shift_x_7, shift_y_7, shift_x_7, shift_y_7), dim = 1); shift_x_7 = shift_y_7 = None 2025-03-04T21:03:30.5534394Z 2025-03-04T21:03:30.5534896Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:03:30.5535510Z view_6: "f32[1406, 1, 4][4, 4, 1]cpu" = shifts_3.view(-1, 1, 4); shifts_3 = None 2025-03-04T21:03:30.5536007Z view_7: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_ = None 2025-03-04T21:03:30.5536501Z add_3: "f32[1406, 3, 4][12, 4, 1]cpu" = view_6 + view_7; view_6 = view_7 = None 2025-03-04T21:03:30.5536788Z x_3: "f32[4218, 4][4, 1]cpu" = add_3.reshape(-1, 4); add_3 = None 2025-03-04T21:03:30.5537009Z 2025-03-04T21:03:30.5537531Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:03:30.5538176Z arange_8: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32) 2025-03-04T21:03:30.5538436Z 2025-03-04T21:03:30.5538855Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:30.5539358Z shifts_x_4: "f32[19][1]cpu" = arange_8.to(device(type='cpu')); arange_8 = None 2025-03-04T21:03:30.5539612Z 2025-03-04T21:03:30.5540120Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:03:30.5540738Z arange_9: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32) 2025-03-04T21:03:30.5540997Z 2025-03-04T21:03:30.5541373Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:30.5541850Z shifts_y_4: "f32[19][1]cpu" = arange_9.to(device(type='cpu')); arange_9 = None 2025-03-04T21:03:30.5542102Z 2025-03-04T21:03:30.5542571Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:03:30.5543184Z meshgrid_4 = torch.functional.meshgrid(shifts_y_4, shifts_x_4); shifts_y_4 = shifts_x_4 = None 2025-03-04T21:03:30.5543526Z shift_y_8: "f32[19, 19][1, 0]cpu" = meshgrid_4[0] 2025-03-04T21:03:30.5543793Z shift_x_8: "f32[19, 19][0, 1]cpu" = meshgrid_4[1]; meshgrid_4 = None 2025-03-04T21:03:30.5544031Z 2025-03-04T21:03:30.5544443Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:03:30.5544946Z shift_x_9: "f32[361][1]cpu" = shift_x_8.reshape(-1); shift_x_8 = None 2025-03-04T21:03:30.5545184Z 2025-03-04T21:03:30.5545591Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:03:30.5546092Z shift_y_9: "f32[361][1]cpu" = shift_y_8.reshape(-1); shift_y_8 = None 2025-03-04T21:03:30.5546321Z 2025-03-04T21:03:30.5546780Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:03:30.5547422Z shifts_4: "f32[361, 4][4, 1]cpu" = torch.stack((shift_x_9, shift_y_9, shift_x_9, shift_y_9), dim = 1); shift_x_9 = shift_y_9 = None 2025-03-04T21:03:30.5547733Z 2025-03-04T21:03:30.5548250Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:03:30.5548894Z view_8: "f32[361, 1, 4][4, 4, 1]cpu" = shifts_4.view(-1, 1, 4); shifts_4 = None 2025-03-04T21:03:30.5549423Z view_9: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_ = None 2025-03-04T21:03:30.5549900Z add_4: "f32[361, 3, 4][12, 4, 1]cpu" = view_8 + view_9; view_8 = view_9 = None 2025-03-04T21:03:30.5550185Z x_4: "f32[1083, 4][4, 1]cpu" = add_4.reshape(-1, 4); add_4 = None 2025-03-04T21:03:30.5550404Z 2025-03-04T21:03:30.5550789Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:03:30.5551273Z tensor: "f32[269952, 4][4, 1]cpu" = x.to(torch.float32); x = None 2025-03-04T21:03:30.5551590Z tensor_1: "f32[67488, 4][4, 1]cpu" = x_1.to(torch.float32); x_1 = None 2025-03-04T21:03:30.5551902Z tensor_2: "f32[16872, 4][4, 1]cpu" = x_2.to(torch.float32); x_2 = None 2025-03-04T21:03:30.5552254Z tensor_3: "f32[4218, 4][4, 1]cpu" = x_3.to(torch.float32); x_3 = None 2025-03-04T21:03:30.5552555Z tensor_4: "f32[1083, 4][4, 1]cpu" = x_4.to(torch.float32); x_4 = None 2025-03-04T21:03:30.5552804Z 2025-03-04T21:03:30.5553166Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:30.5554031Z x_5: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(l_features_p2_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_p2_ = None 2025-03-04T21:03:30.5554621Z 2025-03-04T21:03:30.5554989Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:03:30.5555514Z x_6: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.relu(x_5, inplace = False); x_5 = None 2025-03-04T21:03:30.5555832Z 2025-03-04T21:03:30.5556352Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:03:30.5557246Z score: "f32[4, 3, 296, 304][269952, 89984, 304, 1]cpu" = torch.conv2d(x_6, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:03:30.5557798Z 2025-03-04T21:03:30.5558267Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:03:30.5559136Z x_15: "f32[4, 12, 296, 304][1079808, 89984, 304, 1]cpu" = torch.conv2d(x_6, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_6 = None 2025-03-04T21:03:30.5559694Z 2025-03-04T21:03:30.5560050Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:30.5560807Z x_7: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(l_features_p3_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_p3_ = None 2025-03-04T21:03:30.5561359Z 2025-03-04T21:03:30.5561734Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:03:30.5562309Z x_8: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.relu(x_7, inplace = False); x_7 = None 2025-03-04T21:03:30.5562630Z 2025-03-04T21:03:30.5563118Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:03:30.5563958Z score_1: "f32[4, 3, 148, 152][67488, 22496, 152, 1]cpu" = torch.conv2d(x_8, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:03:30.5564465Z 2025-03-04T21:03:30.5564894Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:03:30.5565684Z x_16: "f32[4, 12, 148, 152][269952, 22496, 152, 1]cpu" = torch.conv2d(x_8, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_8 = None 2025-03-04T21:03:30.5566206Z 2025-03-04T21:03:30.5566536Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:30.5567223Z x_9: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(l_features_p4_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_p4_ = None 2025-03-04T21:03:30.5567720Z 2025-03-04T21:03:30.5568063Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:03:30.5568548Z x_10: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.relu(x_9, inplace = False); x_9 = None 2025-03-04T21:03:30.5568832Z 2025-03-04T21:03:30.5569399Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:03:30.5570244Z score_2: "f32[4, 3, 74, 76][16872, 5624, 76, 1]cpu" = torch.conv2d(x_10, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:03:30.5570748Z 2025-03-04T21:03:30.5571177Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:03:30.5571972Z x_17: "f32[4, 12, 74, 76][67488, 5624, 76, 1]cpu" = torch.conv2d(x_10, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_10 = None 2025-03-04T21:03:30.5572468Z 2025-03-04T21:03:30.5572799Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:30.5573488Z x_11: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(l_features_p5_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_p5_ = None 2025-03-04T21:03:30.5573992Z 2025-03-04T21:03:30.5574340Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:03:30.5574833Z x_12: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.nn.functional.relu(x_11, inplace = False); x_11 = None 2025-03-04T21:03:30.5575123Z 2025-03-04T21:03:30.5575604Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:03:30.5576424Z score_3: "f32[4, 3, 37, 38][4218, 1406, 38, 1]cpu" = torch.conv2d(x_12, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:03:30.5576922Z 2025-03-04T21:03:30.5577356Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:03:30.5578131Z x_18: "f32[4, 12, 37, 38][16872, 1406, 38, 1]cpu" = torch.conv2d(x_12, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_12 = None 2025-03-04T21:03:30.5578629Z 2025-03-04T21:03:30.5578960Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:30.5579828Z x_13: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.conv2d(l_features_p6_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_p6_ = l_self_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T21:03:30.5580516Z 2025-03-04T21:03:30.5580865Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:03:30.5581349Z x_14: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.relu(x_13, inplace = False); x_13 = None 2025-03-04T21:03:30.5581627Z 2025-03-04T21:03:30.5582077Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:03:30.5583125Z score_4: "f32[4, 3, 19, 19][1083, 361, 19, 1]cpu" = torch.conv2d(x_14, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T21:03:30.5583856Z 2025-03-04T21:03:30.5584282Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:03:30.5585247Z x_19: "f32[4, 12, 19, 19][4332, 361, 19, 1]cpu" = torch.conv2d(x_14, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_14 = l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T21:03:30.5585932Z 2025-03-04T21:03:30.5586343Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T21:03:30.5586882Z permute: "f32[4, 296, 304, 3][269952, 304, 1, 89984]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T21:03:30.5587227Z logits_i: "f32[4, 269952][269952, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T21:03:30.5587575Z permute_1: "f32[4, 148, 152, 3][67488, 152, 1, 22496]cpu" = score_1.permute(0, 2, 3, 1); score_1 = None 2025-03-04T21:03:30.5587925Z logits_i_1: "f32[4, 67488][67488, 1]cpu" = permute_1.flatten(1); permute_1 = None 2025-03-04T21:03:30.5588275Z permute_2: "f32[4, 74, 76, 3][16872, 76, 1, 5624]cpu" = score_2.permute(0, 2, 3, 1); score_2 = None 2025-03-04T21:03:30.5588624Z logits_i_2: "f32[4, 16872][16872, 1]cpu" = permute_2.flatten(1); permute_2 = None 2025-03-04T21:03:30.5588958Z permute_3: "f32[4, 37, 38, 3][4218, 38, 1, 1406]cpu" = score_3.permute(0, 2, 3, 1); score_3 = None 2025-03-04T21:03:30.5589283Z logits_i_3: "f32[4, 4218][4218, 1]cpu" = permute_3.flatten(1); permute_3 = None 2025-03-04T21:03:30.5589613Z permute_4: "f32[4, 19, 19, 3][1083, 19, 1, 361]cpu" = score_4.permute(0, 2, 3, 1); score_4 = None 2025-03-04T21:03:30.5589937Z logits_i_4: "f32[4, 1083][1083, 1]cpu" = permute_4.flatten(1); permute_4 = None 2025-03-04T21:03:30.5590185Z 2025-03-04T21:03:30.5590685Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T21:03:30.5591314Z view_10: "f32[4, 3, 4, 296, 304][1079808, 359936, 89984, 304, 1]cpu" = x_15.view(4, -1, 4, 296, 304); x_15 = None 2025-03-04T21:03:30.5591715Z permute_5: "f32[4, 296, 304, 3, 4][1079808, 304, 1, 359936, 89984]cpu" = view_10.permute(0, 3, 4, 1, 2); view_10 = None 2025-03-04T21:03:30.5592131Z pred_anchor_deltas_i: "f32[4, 269952, 4][1079808, 4, 1]cpu" = permute_5.flatten(1, -2); permute_5 = None 2025-03-04T21:03:30.5592509Z view_11: "f32[4, 3, 4, 148, 152][269952, 89984, 22496, 152, 1]cpu" = x_16.view(4, -1, 4, 148, 152); x_16 = None 2025-03-04T21:03:30.5592887Z permute_6: "f32[4, 148, 152, 3, 4][269952, 152, 1, 89984, 22496]cpu" = view_11.permute(0, 3, 4, 1, 2); view_11 = None 2025-03-04T21:03:30.5593279Z pred_anchor_deltas_i_2: "f32[4, 67488, 4][269952, 4, 1]cpu" = permute_6.flatten(1, -2); permute_6 = None 2025-03-04T21:03:30.5594573Z view_12: "f32[4, 3, 4, 74, 76][67488, 22496, 5624, 76, 1]cpu" = x_17.view(4, -1, 4, 74, 76); x_17 = None 2025-03-04T21:03:30.5595005Z permute_7: "f32[4, 74, 76, 3, 4][67488, 76, 1, 22496, 5624]cpu" = view_12.permute(0, 3, 4, 1, 2); view_12 = None 2025-03-04T21:03:30.5595394Z pred_anchor_deltas_i_4: "f32[4, 16872, 4][67488, 4, 1]cpu" = permute_7.flatten(1, -2); permute_7 = None 2025-03-04T21:03:30.5595785Z view_13: "f32[4, 3, 4, 37, 38][16872, 5624, 1406, 38, 1]cpu" = x_18.view(4, -1, 4, 37, 38); x_18 = None 2025-03-04T21:03:30.5596134Z permute_8: "f32[4, 37, 38, 3, 4][16872, 38, 1, 5624, 1406]cpu" = view_13.permute(0, 3, 4, 1, 2); view_13 = None 2025-03-04T21:03:30.5596504Z pred_anchor_deltas_i_6: "f32[4, 4218, 4][16872, 4, 1]cpu" = permute_8.flatten(1, -2); permute_8 = None 2025-03-04T21:03:30.5596857Z view_14: "f32[4, 3, 4, 19, 19][4332, 1444, 361, 19, 1]cpu" = x_19.view(4, -1, 4, 19, 19); x_19 = None 2025-03-04T21:03:30.5597201Z permute_9: "f32[4, 19, 19, 3, 4][4332, 19, 1, 1444, 361]cpu" = view_14.permute(0, 3, 4, 1, 2); view_14 = None 2025-03-04T21:03:30.5597571Z pred_anchor_deltas_i_8: "f32[4, 1083, 4][4332, 4, 1]cpu" = permute_9.flatten(1, -2); permute_9 = None 2025-03-04T21:03:30.5597852Z 2025-03-04T21:03:30.5598349Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:03:30.5599012Z pred_anchor_deltas_i_1: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T21:03:30.5599323Z 2025-03-04T21:03:30.5599840Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:03:30.5600466Z unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T21:03:30.5600832Z expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T21:03:30.5601224Z anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T21:03:30.5601475Z 2025-03-04T21:03:30.5602094Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:03:30.5602693Z deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T21:03:30.5603168Z 2025-03-04T21:03:30.5603610Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:03:30.5604112Z boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T21:03:30.5604363Z 2025-03-04T21:03:30.5604755Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:03:30.5605245Z getitem_10: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:03:30.5605606Z getitem_11: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:03:30.5605932Z widths: "f32[1079808][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T21:03:30.5606193Z 2025-03-04T21:03:30.5606582Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:03:30.5607066Z getitem_12: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:03:30.5607362Z getitem_13: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:03:30.5607690Z heights: "f32[1079808][1]cpu" = getitem_12 - getitem_13; getitem_12 = getitem_13 = None 2025-03-04T21:03:30.5607961Z 2025-03-04T21:03:30.5608348Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:03:30.5608831Z getitem_14: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:03:30.5609118Z mul: "f32[1079808][1]cpu" = 0.5 * widths 2025-03-04T21:03:30.5609379Z ctr_x: "f32[1079808][1]cpu" = getitem_14 + mul; getitem_14 = mul = None 2025-03-04T21:03:30.5609615Z 2025-03-04T21:03:30.5610001Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:03:30.5610504Z getitem_15: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:03:30.5610789Z mul_1: "f32[1079808][1]cpu" = 0.5 * heights 2025-03-04T21:03:30.5611056Z ctr_y: "f32[1079808][1]cpu" = getitem_15 + mul_1; getitem_15 = mul_1 = None 2025-03-04T21:03:30.5611299Z 2025-03-04T21:03:30.5611720Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:03:30.5612224Z getitem_16: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:03:30.5612546Z dx: "f32[1079808, 1][1, 1]cpu" = getitem_16 / 1.0; getitem_16 = None 2025-03-04T21:03:30.5612993Z 2025-03-04T21:03:30.5613441Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:03:30.5613959Z getitem_17: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:03:30.5614283Z dy: "f32[1079808, 1][1, 1]cpu" = getitem_17 / 1.0; getitem_17 = None 2025-03-04T21:03:30.5614551Z 2025-03-04T21:03:30.5614954Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:03:30.5615451Z getitem_18: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:03:30.5615768Z dw: "f32[1079808, 1][1, 1]cpu" = getitem_18 / 1.0; getitem_18 = None 2025-03-04T21:03:30.5616013Z 2025-03-04T21:03:30.5616392Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:03:30.5616917Z getitem_19: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:03:30.5617256Z dh: "f32[1079808, 1][1, 1]cpu" = getitem_19 / 1.0; getitem_19 = None 2025-03-04T21:03:30.5617480Z 2025-03-04T21:03:30.5617893Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:03:30.5618421Z dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:03:30.5618695Z 2025-03-04T21:03:30.5619099Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:03:30.5619605Z dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:03:30.5619849Z 2025-03-04T21:03:30.5620263Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:03:30.5620784Z getitem_20: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:03:30.5621098Z mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_20; dx = getitem_20 = None 2025-03-04T21:03:30.5621422Z getitem_21: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:03:30.5621762Z pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_21; mul_2 = getitem_21 = None 2025-03-04T21:03:30.5622048Z 2025-03-04T21:03:30.5622472Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:03:30.5623257Z getitem_22: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:03:30.5623573Z mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_22; dy = getitem_22 = None 2025-03-04T21:03:30.5623892Z getitem_23: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:03:30.5624235Z pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_23; mul_3 = getitem_23 = None 2025-03-04T21:03:30.5624487Z 2025-03-04T21:03:30.5624884Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:03:30.5625383Z exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:03:30.5625698Z getitem_24: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:03:30.5626033Z pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_24; exp = getitem_24 = None 2025-03-04T21:03:30.5626278Z 2025-03-04T21:03:30.5626682Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:03:30.5627167Z exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:03:30.5627534Z getitem_25: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:03:30.5627882Z pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_25; exp_1 = getitem_25 = None 2025-03-04T21:03:30.5628130Z 2025-03-04T21:03:30.5628522Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:03:30.5628973Z mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T21:03:30.5629230Z x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:03:30.5629458Z 2025-03-04T21:03:30.5629838Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:03:30.5630280Z mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T21:03:30.5630532Z y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:03:30.5630757Z 2025-03-04T21:03:30.5631133Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:03:30.5631615Z mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:03:30.5631901Z x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:03:30.5632146Z 2025-03-04T21:03:30.5632532Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:03:30.5633274Z mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:03:30.5633623Z y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:03:30.5633884Z 2025-03-04T21:03:30.5634326Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:03:30.5634932Z pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:03:30.5635235Z 2025-03-04T21:03:30.5635753Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:03:30.5636315Z proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4)); pred_boxes = None 2025-03-04T21:03:30.5636598Z 2025-03-04T21:03:30.5637066Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:03:30.5637677Z proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T21:03:30.5637973Z 2025-03-04T21:03:30.5638453Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:03:30.5639125Z pred_anchor_deltas_i_3: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_2.reshape(-1, 4); pred_anchor_deltas_i_2 = None 2025-03-04T21:03:30.5639447Z 2025-03-04T21:03:30.5639961Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:03:30.5640604Z unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = tensor_1.unsqueeze(0); tensor_1 = None 2025-03-04T21:03:30.5640976Z expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1); unsqueeze_1 = None 2025-03-04T21:03:30.5641335Z anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4); expand_1 = None 2025-03-04T21:03:30.5641593Z 2025-03-04T21:03:30.5642051Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:03:30.5642650Z deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float(); pred_anchor_deltas_i_3 = None 2025-03-04T21:03:30.5643184Z 2025-03-04T21:03:30.5643605Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:03:30.5644123Z boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32); anchors_i_1 = None 2025-03-04T21:03:30.5644386Z 2025-03-04T21:03:30.5644794Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:03:30.5645304Z getitem_26: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)] 2025-03-04T21:03:30.5645634Z getitem_27: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T21:03:30.5645967Z widths_1: "f32[269952][1]cpu" = getitem_26 - getitem_27; getitem_26 = getitem_27 = None 2025-03-04T21:03:30.5646234Z 2025-03-04T21:03:30.5646637Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:03:30.5647140Z getitem_28: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)] 2025-03-04T21:03:30.5647435Z getitem_29: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)] 2025-03-04T21:03:30.5647759Z heights_1: "f32[269952][1]cpu" = getitem_28 - getitem_29; getitem_28 = getitem_29 = None 2025-03-04T21:03:30.5648026Z 2025-03-04T21:03:30.5648414Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:03:30.5648896Z getitem_30: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T21:03:30.5649182Z mul_10: "f32[269952][1]cpu" = 0.5 * widths_1 2025-03-04T21:03:30.5649455Z ctr_x_1: "f32[269952][1]cpu" = getitem_30 + mul_10; getitem_30 = mul_10 = None 2025-03-04T21:03:30.5649699Z 2025-03-04T21:03:30.5650086Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:03:30.5650592Z getitem_31: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]; boxes_1 = None 2025-03-04T21:03:30.5650887Z mul_11: "f32[269952][1]cpu" = 0.5 * heights_1 2025-03-04T21:03:30.5651170Z ctr_y_1: "f32[269952][1]cpu" = getitem_31 + mul_11; getitem_31 = mul_11 = None 2025-03-04T21:03:30.5651424Z 2025-03-04T21:03:30.5651842Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:03:30.5652369Z getitem_32: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:03:30.5652704Z dx_1: "f32[269952, 1][1, 1]cpu" = getitem_32 / 1.0; getitem_32 = None 2025-03-04T21:03:30.5652945Z 2025-03-04T21:03:30.5653343Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:03:30.5653869Z getitem_33: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:03:30.5654505Z dy_1: "f32[269952, 1][1, 1]cpu" = getitem_33 / 1.0; getitem_33 = None 2025-03-04T21:03:30.5654819Z 2025-03-04T21:03:30.5655224Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:03:30.5655744Z getitem_34: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:03:30.5656065Z dw_2: "f32[269952, 1][1, 1]cpu" = getitem_34 / 1.0; getitem_34 = None 2025-03-04T21:03:30.5656299Z 2025-03-04T21:03:30.5656693Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:03:30.5657235Z getitem_35: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))]; deltas_1 = None 2025-03-04T21:03:30.5657581Z dh_2: "f32[269952, 1][1, 1]cpu" = getitem_35 / 1.0; getitem_35 = None 2025-03-04T21:03:30.5657812Z 2025-03-04T21:03:30.5658243Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:03:30.5658777Z dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356); dw_2 = None 2025-03-04T21:03:30.5659057Z 2025-03-04T21:03:30.5659482Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:03:30.5660027Z dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356); dh_2 = None 2025-03-04T21:03:30.5660274Z 2025-03-04T21:03:30.5660695Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:03:30.5661234Z getitem_36: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)] 2025-03-04T21:03:30.5661556Z mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_36; dx_1 = getitem_36 = None 2025-03-04T21:03:30.5661895Z getitem_37: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)]; ctr_x_1 = None 2025-03-04T21:03:30.5662271Z pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_37; mul_12 = getitem_37 = None 2025-03-04T21:03:30.5662533Z 2025-03-04T21:03:30.5663224Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:03:30.5663805Z getitem_38: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)] 2025-03-04T21:03:30.5664134Z mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_38; dy_1 = getitem_38 = None 2025-03-04T21:03:30.5664470Z getitem_39: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)]; ctr_y_1 = None 2025-03-04T21:03:30.5664825Z pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_39; mul_13 = getitem_39 = None 2025-03-04T21:03:30.5665081Z 2025-03-04T21:03:30.5665508Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:03:30.5666020Z exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3); dw_3 = None 2025-03-04T21:03:30.5666360Z getitem_40: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]; widths_1 = None 2025-03-04T21:03:30.5666721Z pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_40; exp_2 = getitem_40 = None 2025-03-04T21:03:30.5666981Z 2025-03-04T21:03:30.5667405Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:03:30.5667930Z exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3); dh_3 = None 2025-03-04T21:03:30.5668286Z getitem_41: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]; heights_1 = None 2025-03-04T21:03:30.5668643Z pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_41; exp_3 = getitem_41 = None 2025-03-04T21:03:30.5668894Z 2025-03-04T21:03:30.5669294Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:03:30.5669756Z mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1 2025-03-04T21:03:30.5670022Z x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16; mul_16 = None 2025-03-04T21:03:30.5670252Z 2025-03-04T21:03:30.5670644Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:03:30.5671097Z mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1 2025-03-04T21:03:30.5671359Z y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17; mul_17 = None 2025-03-04T21:03:30.5671590Z 2025-03-04T21:03:30.5672005Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:03:30.5672489Z mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1; pred_w_1 = None 2025-03-04T21:03:30.5673117Z x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18; pred_ctr_x_1 = mul_18 = None 2025-03-04T21:03:30.5673447Z 2025-03-04T21:03:30.5673969Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:03:30.5674481Z mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1; pred_h_1 = None 2025-03-04T21:03:30.5674803Z y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19; pred_ctr_y_1 = mul_19 = None 2025-03-04T21:03:30.5675068Z 2025-03-04T21:03:30.5675725Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:03:30.5676734Z pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1); x1_1 = y1_1 = x2_1 = y2_1 = None 2025-03-04T21:03:30.5677057Z 2025-03-04T21:03:30.5677477Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:03:30.5678028Z proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4)); pred_boxes_1 = None 2025-03-04T21:03:30.5678310Z 2025-03-04T21:03:30.5678777Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:03:30.5679387Z proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4); proposals_i_1 = None 2025-03-04T21:03:30.5679680Z 2025-03-04T21:03:30.5680164Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:03:30.5680922Z pred_anchor_deltas_i_5: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_4.reshape(-1, 4); pred_anchor_deltas_i_4 = None 2025-03-04T21:03:30.5681374Z 2025-03-04T21:03:30.5682065Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:03:30.5682942Z unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = tensor_2.unsqueeze(0); tensor_2 = None 2025-03-04T21:03:30.5683385Z expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1); unsqueeze_2 = None 2025-03-04T21:03:30.5683756Z anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4); expand_2 = None 2025-03-04T21:03:30.5684022Z 2025-03-04T21:03:30.5684489Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:03:30.5685312Z deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_5.float(); pred_anchor_deltas_i_5 = None 2025-03-04T21:03:30.5685717Z 2025-03-04T21:03:30.5686256Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:03:30.5686927Z boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32); anchors_i_2 = None 2025-03-04T21:03:30.5687207Z 2025-03-04T21:03:30.5687709Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:03:30.5688402Z getitem_42: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)] 2025-03-04T21:03:30.5688799Z getitem_43: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T21:03:30.5689201Z widths_2: "f32[67488][1]cpu" = getitem_42 - getitem_43; getitem_42 = getitem_43 = None 2025-03-04T21:03:30.5689477Z 2025-03-04T21:03:30.5689890Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:03:30.5690418Z getitem_44: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)] 2025-03-04T21:03:30.5690734Z getitem_45: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)] 2025-03-04T21:03:30.5691076Z heights_2: "f32[67488][1]cpu" = getitem_44 - getitem_45; getitem_44 = getitem_45 = None 2025-03-04T21:03:30.5691357Z 2025-03-04T21:03:30.5691763Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:03:30.5692286Z getitem_46: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T21:03:30.5692571Z mul_20: "f32[67488][1]cpu" = 0.5 * widths_2 2025-03-04T21:03:30.5692846Z ctr_x_2: "f32[67488][1]cpu" = getitem_46 + mul_20; getitem_46 = mul_20 = None 2025-03-04T21:03:30.5693095Z 2025-03-04T21:03:30.5693494Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:03:30.5694010Z getitem_47: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]; boxes_2 = None 2025-03-04T21:03:30.5694318Z mul_21: "f32[67488][1]cpu" = 0.5 * heights_2 2025-03-04T21:03:30.5694585Z ctr_y_2: "f32[67488][1]cpu" = getitem_47 + mul_21; getitem_47 = mul_21 = None 2025-03-04T21:03:30.5694830Z 2025-03-04T21:03:30.5695467Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:03:30.5695974Z getitem_48: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:03:30.5696298Z dx_2: "f32[67488, 1][1, 1]cpu" = getitem_48 / 1.0; getitem_48 = None 2025-03-04T21:03:30.5696529Z 2025-03-04T21:03:30.5696909Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:03:30.5697401Z getitem_49: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:03:30.5697750Z dy_2: "f32[67488, 1][1, 1]cpu" = getitem_49 / 1.0; getitem_49 = None 2025-03-04T21:03:30.5697981Z 2025-03-04T21:03:30.5698366Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:03:30.5698869Z getitem_50: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:03:30.5699182Z dw_4: "f32[67488, 1][1, 1]cpu" = getitem_50 / 1.0; getitem_50 = None 2025-03-04T21:03:30.5699413Z 2025-03-04T21:03:30.5699802Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:03:30.5700342Z getitem_51: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))]; deltas_2 = None 2025-03-04T21:03:30.5700686Z dh_4: "f32[67488, 1][1, 1]cpu" = getitem_51 / 1.0; getitem_51 = None 2025-03-04T21:03:30.5700916Z 2025-03-04T21:03:30.5701341Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:03:30.5702053Z dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356); dw_4 = None 2025-03-04T21:03:30.5702328Z 2025-03-04T21:03:30.5702751Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:03:30.5703287Z dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356); dh_4 = None 2025-03-04T21:03:30.5703987Z 2025-03-04T21:03:30.5704474Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:03:30.5705025Z getitem_52: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)] 2025-03-04T21:03:30.5705346Z mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_52; dx_2 = getitem_52 = None 2025-03-04T21:03:30.5705682Z getitem_53: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)]; ctr_x_2 = None 2025-03-04T21:03:30.5706088Z pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_53; mul_22 = getitem_53 = None 2025-03-04T21:03:30.5706353Z 2025-03-04T21:03:30.5706788Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:03:30.5707324Z getitem_54: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)] 2025-03-04T21:03:30.5707644Z mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_54; dy_2 = getitem_54 = None 2025-03-04T21:03:30.5707976Z getitem_55: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)]; ctr_y_2 = None 2025-03-04T21:03:30.5708325Z pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_55; mul_23 = getitem_55 = None 2025-03-04T21:03:30.5708586Z 2025-03-04T21:03:30.5709000Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:03:30.5709503Z exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5); dw_5 = None 2025-03-04T21:03:30.5709826Z getitem_56: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]; widths_2 = None 2025-03-04T21:03:30.5710175Z pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_56; exp_4 = getitem_56 = None 2025-03-04T21:03:30.5710436Z 2025-03-04T21:03:30.5710896Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:03:30.5711439Z exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5); dh_5 = None 2025-03-04T21:03:30.5711784Z getitem_57: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]; heights_2 = None 2025-03-04T21:03:30.5712151Z pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_57; exp_5 = getitem_57 = None 2025-03-04T21:03:30.5712414Z 2025-03-04T21:03:30.5713022Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:03:30.5713611Z mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2 2025-03-04T21:03:30.5713900Z x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26; mul_26 = None 2025-03-04T21:03:30.5714149Z 2025-03-04T21:03:30.5714570Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:03:30.5715056Z mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2 2025-03-04T21:03:30.5715335Z y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27; mul_27 = None 2025-03-04T21:03:30.5715615Z 2025-03-04T21:03:30.5716028Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:03:30.5716515Z mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2; pred_w_2 = None 2025-03-04T21:03:30.5716824Z x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28; pred_ctr_x_2 = mul_28 = None 2025-03-04T21:03:30.5717082Z 2025-03-04T21:03:30.5717481Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:03:30.5717961Z mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2; pred_h_2 = None 2025-03-04T21:03:30.5718267Z y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29; pred_ctr_y_2 = mul_29 = None 2025-03-04T21:03:30.5718523Z 2025-03-04T21:03:30.5718963Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:03:30.5719587Z pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1); x1_2 = y1_2 = x2_2 = y2_2 = None 2025-03-04T21:03:30.5719889Z 2025-03-04T21:03:30.5720309Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:03:30.5720844Z proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4)); pred_boxes_2 = None 2025-03-04T21:03:30.5721118Z 2025-03-04T21:03:30.5721574Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:03:30.5722170Z proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4); proposals_i_2 = None 2025-03-04T21:03:30.5722455Z 2025-03-04T21:03:30.5723137Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:03:30.5723808Z pred_anchor_deltas_i_7: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_6.reshape(-1, 4); pred_anchor_deltas_i_6 = None 2025-03-04T21:03:30.5724120Z 2025-03-04T21:03:30.5724620Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:03:30.5725283Z unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = tensor_3.unsqueeze(0); tensor_3 = None 2025-03-04T21:03:30.5725627Z expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1); unsqueeze_3 = None 2025-03-04T21:03:30.5725957Z anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4); expand_3 = None 2025-03-04T21:03:30.5726206Z 2025-03-04T21:03:30.5726649Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:03:30.5727222Z deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_7.float(); pred_anchor_deltas_i_7 = None 2025-03-04T21:03:30.5727501Z 2025-03-04T21:03:30.5727886Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:03:30.5728387Z boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32); anchors_i_3 = None 2025-03-04T21:03:30.5728648Z 2025-03-04T21:03:30.5729037Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:03:30.5729544Z getitem_58: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)] 2025-03-04T21:03:30.5729845Z getitem_59: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T21:03:30.5730165Z widths_3: "f32[16872][1]cpu" = getitem_58 - getitem_59; getitem_58 = getitem_59 = None 2025-03-04T21:03:30.5730418Z 2025-03-04T21:03:30.5730813Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:03:30.5731304Z getitem_60: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)] 2025-03-04T21:03:30.5731599Z getitem_61: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)] 2025-03-04T21:03:30.5731917Z heights_3: "f32[16872][1]cpu" = getitem_60 - getitem_61; getitem_60 = getitem_61 = None 2025-03-04T21:03:30.5732181Z 2025-03-04T21:03:30.5732582Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:03:30.5733275Z getitem_62: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T21:03:30.5733540Z mul_30: "f32[16872][1]cpu" = 0.5 * widths_3 2025-03-04T21:03:30.5733806Z ctr_x_3: "f32[16872][1]cpu" = getitem_62 + mul_30; getitem_62 = mul_30 = None 2025-03-04T21:03:30.5734050Z 2025-03-04T21:03:30.5734440Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:03:30.5734945Z getitem_63: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]; boxes_3 = None 2025-03-04T21:03:30.5735236Z mul_31: "f32[16872][1]cpu" = 0.5 * heights_3 2025-03-04T21:03:30.5735500Z ctr_y_3: "f32[16872][1]cpu" = getitem_63 + mul_31; getitem_63 = mul_31 = None 2025-03-04T21:03:30.5735744Z 2025-03-04T21:03:30.5736140Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:03:30.5736634Z getitem_64: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:03:30.5736945Z dx_3: "f32[16872, 1][1, 1]cpu" = getitem_64 / 1.0; getitem_64 = None 2025-03-04T21:03:30.5737174Z 2025-03-04T21:03:30.5737549Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:03:30.5738052Z getitem_65: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:03:30.5738380Z dy_3: "f32[16872, 1][1, 1]cpu" = getitem_65 / 1.0; getitem_65 = None 2025-03-04T21:03:30.5738605Z 2025-03-04T21:03:30.5738979Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:03:30.5739462Z getitem_66: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:03:30.5739770Z dw_6: "f32[16872, 1][1, 1]cpu" = getitem_66 / 1.0; getitem_66 = None 2025-03-04T21:03:30.5739994Z 2025-03-04T21:03:30.5740368Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:03:30.5740893Z getitem_67: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))]; deltas_3 = None 2025-03-04T21:03:30.5741232Z dh_6: "f32[16872, 1][1, 1]cpu" = getitem_67 / 1.0; getitem_67 = None 2025-03-04T21:03:30.5741453Z 2025-03-04T21:03:30.5741864Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:03:30.5742391Z dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356); dw_6 = None 2025-03-04T21:03:30.5742643Z 2025-03-04T21:03:30.5743310Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:03:30.5743841Z dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356); dh_6 = None 2025-03-04T21:03:30.5744098Z 2025-03-04T21:03:30.5744519Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:03:30.5745045Z getitem_68: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)] 2025-03-04T21:03:30.5745359Z mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_68; dx_3 = getitem_68 = None 2025-03-04T21:03:30.5745712Z getitem_69: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)]; ctr_x_3 = None 2025-03-04T21:03:30.5746059Z pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_69; mul_32 = getitem_69 = None 2025-03-04T21:03:30.5746314Z 2025-03-04T21:03:30.5746747Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:03:30.5747274Z getitem_70: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)] 2025-03-04T21:03:30.5747584Z mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_70; dy_3 = getitem_70 = None 2025-03-04T21:03:30.5747909Z getitem_71: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)]; ctr_y_3 = None 2025-03-04T21:03:30.5748251Z pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_71; mul_33 = getitem_71 = None 2025-03-04T21:03:30.5748522Z 2025-03-04T21:03:30.5748932Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:03:30.5749445Z exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7); dw_7 = None 2025-03-04T21:03:30.5749768Z getitem_72: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]; widths_3 = None 2025-03-04T21:03:30.5750119Z pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_72; exp_6 = getitem_72 = None 2025-03-04T21:03:30.5750373Z 2025-03-04T21:03:30.5750806Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:03:30.5751338Z exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7); dh_7 = None 2025-03-04T21:03:30.5751682Z getitem_73: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]; heights_3 = None 2025-03-04T21:03:30.5752046Z pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_73; exp_7 = getitem_73 = None 2025-03-04T21:03:30.5752304Z 2025-03-04T21:03:30.5752720Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:03:30.5753418Z mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3 2025-03-04T21:03:30.5753749Z x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36; mul_36 = None 2025-03-04T21:03:30.5753992Z 2025-03-04T21:03:30.5754388Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:03:30.5754844Z mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3 2025-03-04T21:03:30.5755126Z y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37; mul_37 = None 2025-03-04T21:03:30.5755359Z 2025-03-04T21:03:30.5755748Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:03:30.5756224Z mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3; pred_w_3 = None 2025-03-04T21:03:30.5756525Z x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38; pred_ctr_x_3 = mul_38 = None 2025-03-04T21:03:30.5756869Z 2025-03-04T21:03:30.5757348Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:03:30.5758064Z mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3; pred_h_3 = None 2025-03-04T21:03:30.5758437Z y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39; pred_ctr_y_3 = mul_39 = None 2025-03-04T21:03:30.5758756Z 2025-03-04T21:03:30.5759310Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:03:30.5760015Z pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1); x1_3 = y1_3 = x2_3 = y2_3 = None 2025-03-04T21:03:30.5760375Z 2025-03-04T21:03:30.5760897Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:03:30.5761509Z proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4)); pred_boxes_3 = None 2025-03-04T21:03:30.5761867Z 2025-03-04T21:03:30.5762420Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:03:30.5763356Z proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4); proposals_i_3 = None 2025-03-04T21:03:30.5763750Z 2025-03-04T21:03:30.5764313Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:03:30.5765024Z pred_anchor_deltas_i_9: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_8.reshape(-1, 4); pred_anchor_deltas_i_8 = None 2025-03-04T21:03:30.5765439Z 2025-03-04T21:03:30.5766081Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:03:30.5766800Z unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = tensor_4.unsqueeze(0); tensor_4 = None 2025-03-04T21:03:30.5767213Z expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1); unsqueeze_4 = None 2025-03-04T21:03:30.5767614Z anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4); expand_4 = None 2025-03-04T21:03:30.5767965Z 2025-03-04T21:03:30.5768482Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:03:30.5769121Z deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_9.float(); pred_anchor_deltas_i_9 = None 2025-03-04T21:03:30.5769489Z 2025-03-04T21:03:30.5769943Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:03:30.5770508Z boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32); anchors_i_4 = None 2025-03-04T21:03:30.5770854Z 2025-03-04T21:03:30.5771332Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:03:30.5771900Z getitem_74: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)] 2025-03-04T21:03:30.5772261Z getitem_75: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T21:03:30.5772627Z widths_4: "f32[4332][1]cpu" = getitem_74 - getitem_75; getitem_74 = getitem_75 = None 2025-03-04T21:03:30.5773238Z 2025-03-04T21:03:30.5773747Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:03:30.5774333Z getitem_76: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)] 2025-03-04T21:03:30.5774705Z getitem_77: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)] 2025-03-04T21:03:30.5775085Z heights_4: "f32[4332][1]cpu" = getitem_76 - getitem_77; getitem_76 = getitem_77 = None 2025-03-04T21:03:30.5775462Z 2025-03-04T21:03:30.5775912Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:03:30.5776450Z getitem_78: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T21:03:30.5776788Z mul_40: "f32[4332][1]cpu" = 0.5 * widths_4 2025-03-04T21:03:30.5777110Z ctr_x_4: "f32[4332][1]cpu" = getitem_78 + mul_40; getitem_78 = mul_40 = None 2025-03-04T21:03:30.5777407Z 2025-03-04T21:03:30.5777890Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:03:30.5778454Z getitem_79: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]; boxes_4 = None 2025-03-04T21:03:30.5778808Z mul_41: "f32[4332][1]cpu" = 0.5 * heights_4 2025-03-04T21:03:30.5779141Z ctr_y_4: "f32[4332][1]cpu" = getitem_79 + mul_41; getitem_79 = mul_41 = None 2025-03-04T21:03:30.5779438Z 2025-03-04T21:03:30.5779927Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:03:30.5780484Z getitem_80: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:03:30.5780849Z dx_4: "f32[4332, 1][1, 1]cpu" = getitem_80 / 1.0; getitem_80 = None 2025-03-04T21:03:30.5781166Z 2025-03-04T21:03:30.5781600Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:03:30.5782202Z getitem_81: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:03:30.5782586Z dy_4: "f32[4332, 1][1, 1]cpu" = getitem_81 / 1.0; getitem_81 = None 2025-03-04T21:03:30.5783096Z 2025-03-04T21:03:30.5783625Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:03:30.5784195Z getitem_82: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:03:30.5784590Z dw_8: "f32[4332, 1][1, 1]cpu" = getitem_82 / 1.0; getitem_82 = None 2025-03-04T21:03:30.5784913Z 2025-03-04T21:03:30.5785360Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:03:30.5785958Z getitem_83: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))]; deltas_4 = None 2025-03-04T21:03:30.5786364Z dh_8: "f32[4332, 1][1, 1]cpu" = getitem_83 / 1.0; getitem_83 = None 2025-03-04T21:03:30.5786651Z 2025-03-04T21:03:30.5787191Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:03:30.5787781Z dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356); dw_8 = None 2025-03-04T21:03:30.5788084Z 2025-03-04T21:03:30.5788599Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:03:30.5789195Z dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356); dh_8 = None 2025-03-04T21:03:30.5789516Z 2025-03-04T21:03:30.5790014Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:03:30.5790603Z getitem_84: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)] 2025-03-04T21:03:30.5791012Z mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_84; dx_4 = getitem_84 = None 2025-03-04T21:03:30.5791420Z getitem_85: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)]; ctr_x_4 = None 2025-03-04T21:03:30.5791812Z pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_85; mul_42 = getitem_85 = None 2025-03-04T21:03:30.5792200Z 2025-03-04T21:03:30.5792701Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:03:30.5793612Z getitem_86: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)] 2025-03-04T21:03:30.5794018Z mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_86; dy_4 = getitem_86 = None 2025-03-04T21:03:30.5794447Z getitem_87: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)]; ctr_y_4 = None 2025-03-04T21:03:30.5794871Z pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_87; mul_43 = getitem_87 = None 2025-03-04T21:03:30.5795195Z 2025-03-04T21:03:30.5795675Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:03:30.5796271Z exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9); dw_9 = None 2025-03-04T21:03:30.5796670Z getitem_88: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]; widths_4 = None 2025-03-04T21:03:30.5797096Z pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_88; exp_8 = getitem_88 = None 2025-03-04T21:03:30.5797426Z 2025-03-04T21:03:30.5797939Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:03:30.5798533Z exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9); dh_9 = None 2025-03-04T21:03:30.5798967Z getitem_89: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]; heights_4 = None 2025-03-04T21:03:30.5799375Z pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_89; exp_9 = getitem_89 = None 2025-03-04T21:03:30.5799718Z 2025-03-04T21:03:30.5800197Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:03:30.5800738Z mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4 2025-03-04T21:03:30.5801095Z x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46; mul_46 = None 2025-03-04T21:03:30.5801404Z 2025-03-04T21:03:30.5802126Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:03:30.5802684Z mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4 2025-03-04T21:03:30.5803053Z y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47; mul_47 = None 2025-03-04T21:03:30.5803430Z 2025-03-04T21:03:30.5803897Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:03:30.5804477Z mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4; pred_w_4 = None 2025-03-04T21:03:30.5804872Z x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48; pred_ctr_x_4 = mul_48 = None 2025-03-04T21:03:30.5805215Z 2025-03-04T21:03:30.5805704Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:03:30.5806261Z mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4; pred_h_4 = None 2025-03-04T21:03:30.5807349Z y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49; pred_ctr_y_4 = mul_49 = None 2025-03-04T21:03:30.5807724Z 2025-03-04T21:03:30.5808278Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:03:30.5808956Z pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1); x1_4 = y1_4 = x2_4 = y2_4 = None 2025-03-04T21:03:30.5809356Z 2025-03-04T21:03:30.5809847Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:03:30.5810471Z proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4)); pred_boxes_4 = None 2025-03-04T21:03:30.5810814Z 2025-03-04T21:03:30.5811340Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:03:30.5812041Z proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4); proposals_i_4 = None 2025-03-04T21:03:30.5812398Z 2025-03-04T21:03:30.5816391Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T21:03:30.5817197Z arange_10: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T21:03:30.5817522Z 2025-03-04T21:03:30.5818015Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:30.5818678Z batch_idx: "i64[4][1]cpu" = arange_10.to(device(type='cpu')); arange_10 = None 2025-03-04T21:03:30.5818997Z 2025-03-04T21:03:30.5819626Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:03:30.5820281Z topk = logits_i.topk(1000, dim = 1); logits_i = None 2025-03-04T21:03:30.5820612Z topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0] 2025-03-04T21:03:30.5820949Z topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1]; topk = None 2025-03-04T21:03:30.5821230Z 2025-03-04T21:03:30.5821847Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:03:30.5822543Z getitem_92: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:03:30.5823286Z topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_92, topk_idx)]; proposals_i_5 = getitem_92 = topk_idx = None 2025-03-04T21:03:30.5823791Z 2025-03-04T21:03:30.5824410Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:03:30.5825156Z full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:03:30.5825501Z 2025-03-04T21:03:30.5825932Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:30.5826509Z to_21: "i64[1000][1]cpu" = full.to(device(type='cpu')); full = None 2025-03-04T21:03:30.5826805Z 2025-03-04T21:03:30.5827364Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:03:30.5828057Z topk_1 = logits_i_1.topk(1000, dim = 1); logits_i_1 = None 2025-03-04T21:03:30.5828408Z topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0] 2025-03-04T21:03:30.5828746Z topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1]; topk_1 = None 2025-03-04T21:03:30.5829040Z 2025-03-04T21:03:30.5829642Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:03:30.5830357Z getitem_96: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:03:30.5830829Z topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_96, topk_idx_1)]; proposals_i_6 = getitem_96 = topk_idx_1 = None 2025-03-04T21:03:30.5831228Z 2025-03-04T21:03:30.5831860Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:03:30.5832581Z full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:03:30.5833194Z 2025-03-04T21:03:30.5833733Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:30.5834334Z to_22: "i64[1000][1]cpu" = full_1.to(device(type='cpu')); full_1 = None 2025-03-04T21:03:30.5834669Z 2025-03-04T21:03:30.5835292Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:03:30.5835947Z topk_2 = logits_i_2.topk(1000, dim = 1); logits_i_2 = None 2025-03-04T21:03:30.5836305Z topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0] 2025-03-04T21:03:30.5836633Z topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1]; topk_2 = None 2025-03-04T21:03:30.5836924Z 2025-03-04T21:03:30.5837530Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:03:30.5838222Z getitem_100: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:03:30.5838725Z topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_100, topk_idx_2)]; proposals_i_7 = getitem_100 = topk_idx_2 = None 2025-03-04T21:03:30.5839149Z 2025-03-04T21:03:30.5839729Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:03:30.5840498Z full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:03:30.5840831Z 2025-03-04T21:03:30.5841279Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:30.5841802Z to_23: "i64[1000][1]cpu" = full_2.to(device(type='cpu')); full_2 = None 2025-03-04T21:03:30.5842096Z 2025-03-04T21:03:30.5842885Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:03:30.5843606Z topk_3 = logits_i_3.topk(1000, dim = 1); logits_i_3 = None 2025-03-04T21:03:30.5843935Z topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0] 2025-03-04T21:03:30.5863887Z topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1]; topk_3 = None 2025-03-04T21:03:30.5864217Z 2025-03-04T21:03:30.5864807Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:03:30.5865480Z getitem_104: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:03:30.5865914Z topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_104, topk_idx_3)]; proposals_i_8 = getitem_104 = topk_idx_3 = None 2025-03-04T21:03:30.5866276Z 2025-03-04T21:03:30.5866821Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:03:30.5867497Z full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:03:30.5867773Z 2025-03-04T21:03:30.5868155Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:30.5868635Z to_24: "i64[1000][1]cpu" = full_3.to(device(type='cpu')); full_3 = None 2025-03-04T21:03:30.5868876Z 2025-03-04T21:03:30.5869419Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:03:30.5870039Z topk_4 = logits_i_4.topk(1000, dim = 1); logits_i_4 = None 2025-03-04T21:03:30.5870313Z topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0] 2025-03-04T21:03:30.5870584Z topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1]; topk_4 = None 2025-03-04T21:03:30.5870812Z 2025-03-04T21:03:30.5871351Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:03:30.5872015Z getitem_108: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T21:03:30.5872461Z topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_108, topk_idx_4)]; proposals_i_9 = getitem_108 = topk_idx_4 = None 2025-03-04T21:03:30.5873032Z 2025-03-04T21:03:30.5873684Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:03:30.5874446Z full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:03:30.5874728Z 2025-03-04T21:03:30.5875116Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:30.5875578Z to_25: "i64[1000][1]cpu" = full_4.to(device(type='cpu')); full_4 = None 2025-03-04T21:03:30.5875811Z 2025-03-04T21:03:30.5876165Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:03:30.5876852Z topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1); topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None 2025-03-04T21:03:30.5877325Z 2025-03-04T21:03:30.5877678Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:03:30.5878486Z topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1); topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None 2025-03-04T21:03:30.5879045Z 2025-03-04T21:03:30.5879395Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:03:30.5879911Z level_ids: "i64[5000][1]cpu" = torch.cat([to_21, to_22, to_23, to_24, to_25], 0); to_21 = to_22 = to_23 = to_24 = to_25 = level_ids = None 2025-03-04T21:03:30.5880214Z 2025-03-04T21:03:30.5880679Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T21:03:30.5881252Z getitem_110: "f32[5000, 4][4, 1]cpu" = topk_proposals[0]; topk_proposals = None 2025-03-04T21:03:30.5881513Z 2025-03-04T21:03:30.5881888Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:03:30.5882371Z tensor_5: "f32[5000, 4][4, 1]cpu" = getitem_110.to(torch.float32); getitem_110 = None 2025-03-04T21:03:30.5882630Z 2025-03-04T21:03:30.5883343Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T21:03:30.5883937Z scores_per_img: "f32[5000][1]cpu" = topk_scores[0]; topk_scores = None 2025-03-04T21:03:30.5884180Z 2025-03-04T21:03:30.5884740Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T21:03:30.5885404Z isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor_5); tensor_5 = None 2025-03-04T21:03:30.5885715Z all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:03:30.5886036Z isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T21:03:30.5886372Z valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T21:03:30.5886622Z 2025-03-04T21:03:30.5887073Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T21:03:30.5887614Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T21:03:30.5887863Z 2025-03-04T21:03:30.5888124Z 2025-03-04T21:03:30.5888214Z class GraphModule(torch.nn.Module): 2025-03-04T21:03:30.5890380Z def forward(self, L_features_p2_: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu", L_features_p3_: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu", L_features_p4_: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu", L_features_p5_: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu", L_features_p6_: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_1_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_2_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_3_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_4_: "f32[3, 4][4, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_bias_: "f32[256][1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[3, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[3][1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[12, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[12][1]cpu"): 2025-03-04T21:03:30.5892957Z l_features_p2_ = L_features_p2_ 2025-03-04T21:03:30.5893227Z l_features_p3_ = L_features_p3_ 2025-03-04T21:03:30.5893445Z l_features_p4_ = L_features_p4_ 2025-03-04T21:03:30.5893659Z l_features_p5_ = L_features_p5_ 2025-03-04T21:03:30.5893871Z l_features_p6_ = L_features_p6_ 2025-03-04T21:03:30.5894284Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T21:03:30.5894888Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_1_ 2025-03-04T21:03:30.5895474Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_2_ 2025-03-04T21:03:30.5896077Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_3_ 2025-03-04T21:03:30.5896608Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_4_ 2025-03-04T21:03:30.5897115Z l_self_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T21:03:30.5897618Z l_self_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T21:03:30.5898136Z l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T21:03:30.5898713Z l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T21:03:30.5899265Z l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T21:03:30.5899799Z l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T21:03:30.5900155Z 2025-03-04T21:03:30.5900692Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:03:30.5901336Z arange: "f32[304][1]cpu" = torch.arange(0.0, 1216, step = 4, dtype = torch.float32) 2025-03-04T21:03:30.5901614Z 2025-03-04T21:03:30.5902184Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:30.5902671Z shifts_x: "f32[304][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T21:03:30.5903177Z 2025-03-04T21:03:30.5903715Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:03:30.5904356Z arange_1: "f32[296][1]cpu" = torch.arange(0.0, 1184, step = 4, dtype = torch.float32) 2025-03-04T21:03:30.5904619Z 2025-03-04T21:03:30.5904998Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:30.5905477Z shifts_y: "f32[296][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T21:03:30.5905734Z 2025-03-04T21:03:30.5906255Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:03:30.5906850Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T21:03:30.5907174Z shift_y: "f32[296, 304][1, 0]cpu" = meshgrid[0] 2025-03-04T21:03:30.5907437Z shift_x: "f32[296, 304][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T21:03:30.5907671Z 2025-03-04T21:03:30.5908081Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:03:30.5908589Z shift_x_1: "f32[89984][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T21:03:30.5908831Z 2025-03-04T21:03:30.5909238Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:03:30.5909733Z shift_y_1: "f32[89984][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T21:03:30.5909961Z 2025-03-04T21:03:30.5910415Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:03:30.5911055Z shifts: "f32[89984, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T21:03:30.5911374Z 2025-03-04T21:03:30.5911958Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:03:30.5912554Z view: "f32[89984, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T21:03:30.5913355Z view_1: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T21:03:30.5913919Z add: "f32[89984, 3, 4][12, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T21:03:30.5914221Z x: "f32[269952, 4][4, 1]cpu" = add.reshape(-1, 4); add = None 2025-03-04T21:03:30.5914455Z 2025-03-04T21:03:30.5914996Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:03:30.5915637Z arange_2: "f32[152][1]cpu" = torch.arange(0.0, 1216, step = 8, dtype = torch.float32) 2025-03-04T21:03:30.5915904Z 2025-03-04T21:03:30.5916316Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:30.5916802Z shifts_x_1: "f32[152][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T21:03:30.5917060Z 2025-03-04T21:03:30.5917569Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:03:30.5918192Z arange_3: "f32[148][1]cpu" = torch.arange(0.0, 1184, step = 8, dtype = torch.float32) 2025-03-04T21:03:30.5918453Z 2025-03-04T21:03:30.5918825Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:30.5919305Z shifts_y_1: "f32[148][1]cpu" = arange_3.to(device(type='cpu')); arange_3 = None 2025-03-04T21:03:30.5919560Z 2025-03-04T21:03:30.5920032Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:03:30.5920657Z meshgrid_1 = torch.functional.meshgrid(shifts_y_1, shifts_x_1); shifts_y_1 = shifts_x_1 = None 2025-03-04T21:03:30.5921007Z shift_y_2: "f32[148, 152][1, 0]cpu" = meshgrid_1[0] 2025-03-04T21:03:30.5921282Z shift_x_2: "f32[148, 152][0, 1]cpu" = meshgrid_1[1]; meshgrid_1 = None 2025-03-04T21:03:30.5921522Z 2025-03-04T21:03:30.5921938Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:03:30.5922452Z shift_x_3: "f32[22496][1]cpu" = shift_x_2.reshape(-1); shift_x_2 = None 2025-03-04T21:03:30.5922709Z 2025-03-04T21:03:30.5923361Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:03:30.5923878Z shift_y_3: "f32[22496][1]cpu" = shift_y_2.reshape(-1); shift_y_2 = None 2025-03-04T21:03:30.5924125Z 2025-03-04T21:03:30.5924586Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:03:30.5925252Z shifts_1: "f32[22496, 4][4, 1]cpu" = torch.stack((shift_x_3, shift_y_3, shift_x_3, shift_y_3), dim = 1); shift_x_3 = shift_y_3 = None 2025-03-04T21:03:30.5925581Z 2025-03-04T21:03:30.5926144Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:03:30.5926735Z view_2: "f32[22496, 1, 4][4, 4, 1]cpu" = shifts_1.view(-1, 1, 4); shifts_1 = None 2025-03-04T21:03:30.5927222Z view_3: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_ = None 2025-03-04T21:03:30.5927698Z add_1: "f32[22496, 3, 4][12, 4, 1]cpu" = view_2 + view_3; view_2 = view_3 = None 2025-03-04T21:03:30.5927984Z x_1: "f32[67488, 4][4, 1]cpu" = add_1.reshape(-1, 4); add_1 = None 2025-03-04T21:03:30.5928203Z 2025-03-04T21:03:30.5928709Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:03:30.5929329Z arange_4: "f32[76][1]cpu" = torch.arange(0.0, 1216, step = 16, dtype = torch.float32) 2025-03-04T21:03:30.5929603Z 2025-03-04T21:03:30.5929975Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:30.5930448Z shifts_x_2: "f32[76][1]cpu" = arange_4.to(device(type='cpu')); arange_4 = None 2025-03-04T21:03:30.5930695Z 2025-03-04T21:03:30.5931199Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:03:30.5931816Z arange_5: "f32[74][1]cpu" = torch.arange(0.0, 1184, step = 16, dtype = torch.float32) 2025-03-04T21:03:30.5932061Z 2025-03-04T21:03:30.5932431Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:30.5933138Z shifts_y_2: "f32[74][1]cpu" = arange_5.to(device(type='cpu')); arange_5 = None 2025-03-04T21:03:30.5933404Z 2025-03-04T21:03:30.5933889Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:03:30.5934490Z meshgrid_2 = torch.functional.meshgrid(shifts_y_2, shifts_x_2); shifts_y_2 = shifts_x_2 = None 2025-03-04T21:03:30.5934826Z shift_y_4: "f32[74, 76][1, 0]cpu" = meshgrid_2[0] 2025-03-04T21:03:30.5935092Z shift_x_4: "f32[74, 76][0, 1]cpu" = meshgrid_2[1]; meshgrid_2 = None 2025-03-04T21:03:30.5935324Z 2025-03-04T21:03:30.5935731Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:03:30.5936230Z shift_x_5: "f32[5624][1]cpu" = shift_x_4.reshape(-1); shift_x_4 = None 2025-03-04T21:03:30.5936464Z 2025-03-04T21:03:30.5936864Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:03:30.5937351Z shift_y_5: "f32[5624][1]cpu" = shift_y_4.reshape(-1); shift_y_4 = None 2025-03-04T21:03:30.5937581Z 2025-03-04T21:03:30.5938027Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:03:30.5938647Z shifts_2: "f32[5624, 4][4, 1]cpu" = torch.stack((shift_x_5, shift_y_5, shift_x_5, shift_y_5), dim = 1); shift_x_5 = shift_y_5 = None 2025-03-04T21:03:30.5938961Z 2025-03-04T21:03:30.5939476Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:03:30.5940055Z view_4: "f32[5624, 1, 4][4, 4, 1]cpu" = shifts_2.view(-1, 1, 4); shifts_2 = None 2025-03-04T21:03:30.5940535Z view_5: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_ = None 2025-03-04T21:03:30.5941011Z add_2: "f32[5624, 3, 4][12, 4, 1]cpu" = view_4 + view_5; view_4 = view_5 = None 2025-03-04T21:03:30.5941291Z x_2: "f32[16872, 4][4, 1]cpu" = add_2.reshape(-1, 4); add_2 = None 2025-03-04T21:03:30.5941510Z 2025-03-04T21:03:30.5942010Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:03:30.5942628Z arange_6: "f32[38][1]cpu" = torch.arange(0.0, 1216, step = 32, dtype = torch.float32) 2025-03-04T21:03:30.5943153Z 2025-03-04T21:03:30.5943549Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:30.5944030Z shifts_x_3: "f32[38][1]cpu" = arange_6.to(device(type='cpu')); arange_6 = None 2025-03-04T21:03:30.5944282Z 2025-03-04T21:03:30.5944786Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:03:30.5945401Z arange_7: "f32[37][1]cpu" = torch.arange(0.0, 1184, step = 32, dtype = torch.float32) 2025-03-04T21:03:30.5945655Z 2025-03-04T21:03:30.5946024Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:30.5946499Z shifts_y_3: "f32[37][1]cpu" = arange_7.to(device(type='cpu')); arange_7 = None 2025-03-04T21:03:30.5946747Z 2025-03-04T21:03:30.5947129Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:03:30.5947326Z meshgrid_3 = torch.functional.meshgrid(shifts_y_3, shifts_x_3); shifts_y_3 = shifts_x_3 = None 2025-03-04T21:03:30.5947419Z shift_y_6: "f32[37, 38][1, 0]cpu" = meshgrid_3[0] 2025-03-04T21:03:30.5947542Z shift_x_6: "f32[37, 38][0, 1]cpu" = meshgrid_3[1]; meshgrid_3 = None 2025-03-04T21:03:30.5947602Z 2025-03-04T21:03:30.5947932Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:03:30.5948053Z shift_x_7: "f32[1406][1]cpu" = shift_x_6.reshape(-1); shift_x_6 = None 2025-03-04T21:03:30.5948119Z 2025-03-04T21:03:30.5948438Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:03:30.5948561Z shift_y_7: "f32[1406][1]cpu" = shift_y_6.reshape(-1); shift_y_6 = None 2025-03-04T21:03:30.5948620Z 2025-03-04T21:03:30.5948999Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:03:30.5949211Z shifts_3: "f32[1406, 4][4, 1]cpu" = torch.stack((shift_x_7, shift_y_7, shift_x_7, shift_y_7), dim = 1); shift_x_7 = shift_y_7 = None 2025-03-04T21:03:30.5949270Z 2025-03-04T21:03:30.5949712Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:03:30.5949836Z view_6: "f32[1406, 1, 4][4, 4, 1]cpu" = shifts_3.view(-1, 1, 4); shifts_3 = None 2025-03-04T21:03:30.5950142Z view_7: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_ = None 2025-03-04T21:03:30.5950257Z add_3: "f32[1406, 3, 4][12, 4, 1]cpu" = view_6 + view_7; view_6 = view_7 = None 2025-03-04T21:03:30.5950369Z x_3: "f32[4218, 4][4, 1]cpu" = add_3.reshape(-1, 4); add_3 = None 2025-03-04T21:03:30.5950430Z 2025-03-04T21:03:30.5950865Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:03:30.5951007Z arange_8: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32) 2025-03-04T21:03:30.5951095Z 2025-03-04T21:03:30.5951399Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:30.5951544Z shifts_x_4: "f32[19][1]cpu" = arange_8.to(device(type='cpu')); arange_8 = None 2025-03-04T21:03:30.5951607Z 2025-03-04T21:03:30.5952059Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:03:30.5952207Z arange_9: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32) 2025-03-04T21:03:30.5952278Z 2025-03-04T21:03:30.5952590Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:30.5952737Z shifts_y_4: "f32[19][1]cpu" = arange_9.to(device(type='cpu')); arange_9 = None 2025-03-04T21:03:30.5952801Z 2025-03-04T21:03:30.5953220Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:03:30.5953423Z meshgrid_4 = torch.functional.meshgrid(shifts_y_4, shifts_x_4); shifts_y_4 = shifts_x_4 = None 2025-03-04T21:03:30.5953622Z shift_y_8: "f32[19, 19][1, 0]cpu" = meshgrid_4[0] 2025-03-04T21:03:30.5953757Z shift_x_8: "f32[19, 19][0, 1]cpu" = meshgrid_4[1]; meshgrid_4 = None 2025-03-04T21:03:30.5953829Z 2025-03-04T21:03:30.5954182Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:03:30.5954318Z shift_x_9: "f32[361][1]cpu" = shift_x_8.reshape(-1); shift_x_8 = None 2025-03-04T21:03:30.5954387Z 2025-03-04T21:03:30.5955392Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:03:30.5955556Z shift_y_9: "f32[361][1]cpu" = shift_y_8.reshape(-1); shift_y_8 = None 2025-03-04T21:03:30.5955637Z 2025-03-04T21:03:30.5956050Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:03:30.5956264Z shifts_4: "f32[361, 4][4, 1]cpu" = torch.stack((shift_x_9, shift_y_9, shift_x_9, shift_y_9), dim = 1); shift_x_9 = shift_y_9 = None 2025-03-04T21:03:30.5956332Z 2025-03-04T21:03:30.5956781Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:03:30.5956908Z view_8: "f32[361, 1, 4][4, 4, 1]cpu" = shifts_4.view(-1, 1, 4); shifts_4 = None 2025-03-04T21:03:30.5957213Z view_9: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_ = None 2025-03-04T21:03:30.5957334Z add_4: "f32[361, 3, 4][12, 4, 1]cpu" = view_8 + view_9; view_8 = view_9 = None 2025-03-04T21:03:30.5957439Z x_4: "f32[1083, 4][4, 1]cpu" = add_4.reshape(-1, 4); add_4 = None 2025-03-04T21:03:30.5957504Z 2025-03-04T21:03:30.5957793Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:03:30.5957917Z tensor: "f32[269952, 4][4, 1]cpu" = x.to(torch.float32); x = None 2025-03-04T21:03:30.5958036Z tensor_1: "f32[67488, 4][4, 1]cpu" = x_1.to(torch.float32); x_1 = None 2025-03-04T21:03:30.5958173Z tensor_2: "f32[16872, 4][4, 1]cpu" = x_2.to(torch.float32); x_2 = None 2025-03-04T21:03:30.5958286Z tensor_3: "f32[4218, 4][4, 1]cpu" = x_3.to(torch.float32); x_3 = None 2025-03-04T21:03:30.5958402Z tensor_4: "f32[1083, 4][4, 1]cpu" = x_4.to(torch.float32); x_4 = None 2025-03-04T21:03:30.5958461Z 2025-03-04T21:03:30.5958716Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:30.5959131Z x_5: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(l_features_p2_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_p2_ = None 2025-03-04T21:03:30.5959198Z 2025-03-04T21:03:30.5959466Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:03:30.5959659Z x_6: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.relu(x_5, inplace = False); x_5 = None 2025-03-04T21:03:30.5959734Z 2025-03-04T21:03:30.5960123Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:03:30.5960529Z score: "f32[4, 3, 296, 304][269952, 89984, 304, 1]cpu" = torch.conv2d(x_6, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:03:30.5960596Z 2025-03-04T21:03:30.5960960Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:03:30.5961375Z x_15: "f32[4, 12, 296, 304][1079808, 89984, 304, 1]cpu" = torch.conv2d(x_6, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_6 = None 2025-03-04T21:03:30.5961443Z 2025-03-04T21:03:30.5961699Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:30.5962109Z x_7: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(l_features_p3_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_p3_ = None 2025-03-04T21:03:30.5962170Z 2025-03-04T21:03:30.5962465Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:03:30.5962673Z x_8: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.relu(x_7, inplace = False); x_7 = None 2025-03-04T21:03:30.5962904Z 2025-03-04T21:03:30.5963356Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:03:30.5963781Z score_1: "f32[4, 3, 148, 152][67488, 22496, 152, 1]cpu" = torch.conv2d(x_8, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:03:30.5963845Z 2025-03-04T21:03:30.5964213Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:03:30.5964616Z x_16: "f32[4, 12, 148, 152][269952, 22496, 152, 1]cpu" = torch.conv2d(x_8, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_8 = None 2025-03-04T21:03:30.5964708Z 2025-03-04T21:03:30.5964963Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:30.5965369Z x_9: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(l_features_p4_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_p4_ = None 2025-03-04T21:03:30.5965436Z 2025-03-04T21:03:30.5965704Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:03:30.5965885Z x_10: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.relu(x_9, inplace = False); x_9 = None 2025-03-04T21:03:30.5965949Z 2025-03-04T21:03:30.5966331Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:03:30.5966743Z score_2: "f32[4, 3, 74, 76][16872, 5624, 76, 1]cpu" = torch.conv2d(x_10, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:03:30.5966811Z 2025-03-04T21:03:30.5967165Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:03:30.5967559Z x_17: "f32[4, 12, 74, 76][67488, 5624, 76, 1]cpu" = torch.conv2d(x_10, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_10 = None 2025-03-04T21:03:30.5967621Z 2025-03-04T21:03:30.5967874Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:30.5968268Z x_11: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(l_features_p5_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_p5_ = None 2025-03-04T21:03:30.5968337Z 2025-03-04T21:03:30.5968606Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:03:30.5968787Z x_12: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.nn.functional.relu(x_11, inplace = False); x_11 = None 2025-03-04T21:03:30.5968866Z 2025-03-04T21:03:30.5969261Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:03:30.5969659Z score_3: "f32[4, 3, 37, 38][4218, 1406, 38, 1]cpu" = torch.conv2d(x_12, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:03:30.5969720Z 2025-03-04T21:03:30.5970082Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:03:30.5970465Z x_18: "f32[4, 12, 37, 38][16872, 1406, 38, 1]cpu" = torch.conv2d(x_12, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_12 = None 2025-03-04T21:03:30.5970533Z 2025-03-04T21:03:30.5970786Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:03:30.5971381Z x_13: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.conv2d(l_features_p6_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_p6_ = l_self_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T21:03:30.5971441Z 2025-03-04T21:03:30.5971721Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:03:30.5971889Z x_14: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.relu(x_13, inplace = False); x_13 = None 2025-03-04T21:03:30.5971957Z 2025-03-04T21:03:30.5972335Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:03:30.5973227Z score_4: "f32[4, 3, 19, 19][1083, 361, 19, 1]cpu" = torch.conv2d(x_14, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T21:03:30.5973308Z 2025-03-04T21:03:30.5973693Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:03:30.5974294Z x_19: "f32[4, 12, 19, 19][4332, 361, 19, 1]cpu" = torch.conv2d(x_14, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_14 = l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T21:03:30.5974357Z 2025-03-04T21:03:30.5974706Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T21:03:30.5974871Z permute: "f32[4, 296, 304, 3][269952, 304, 1, 89984]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T21:03:30.5975022Z logits_i: "f32[4, 269952][269952, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T21:03:30.5975183Z permute_1: "f32[4, 148, 152, 3][67488, 152, 1, 22496]cpu" = score_1.permute(0, 2, 3, 1); score_1 = None 2025-03-04T21:03:30.5975335Z logits_i_1: "f32[4, 67488][67488, 1]cpu" = permute_1.flatten(1); permute_1 = None 2025-03-04T21:03:30.5975524Z permute_2: "f32[4, 74, 76, 3][16872, 76, 1, 5624]cpu" = score_2.permute(0, 2, 3, 1); score_2 = None 2025-03-04T21:03:30.5975671Z logits_i_2: "f32[4, 16872][16872, 1]cpu" = permute_2.flatten(1); permute_2 = None 2025-03-04T21:03:30.5975818Z permute_3: "f32[4, 37, 38, 3][4218, 38, 1, 1406]cpu" = score_3.permute(0, 2, 3, 1); score_3 = None 2025-03-04T21:03:30.5975959Z logits_i_3: "f32[4, 4218][4218, 1]cpu" = permute_3.flatten(1); permute_3 = None 2025-03-04T21:03:30.5976107Z permute_4: "f32[4, 19, 19, 3][1083, 19, 1, 361]cpu" = score_4.permute(0, 2, 3, 1); score_4 = None 2025-03-04T21:03:30.5976242Z logits_i_4: "f32[4, 1083][1083, 1]cpu" = permute_4.flatten(1); permute_4 = None 2025-03-04T21:03:30.5976301Z 2025-03-04T21:03:30.5976720Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T21:03:30.5976890Z view_10: "f32[4, 3, 4, 296, 304][1079808, 359936, 89984, 304, 1]cpu" = x_15.view(4, -1, 4, 296, 304); x_15 = None 2025-03-04T21:03:30.5977093Z permute_5: "f32[4, 296, 304, 3, 4][1079808, 304, 1, 359936, 89984]cpu" = view_10.permute(0, 3, 4, 1, 2); view_10 = None 2025-03-04T21:03:30.5977273Z pred_anchor_deltas_i: "f32[4, 269952, 4][1079808, 4, 1]cpu" = permute_5.flatten(1, -2); permute_5 = None 2025-03-04T21:03:30.5977426Z view_11: "f32[4, 3, 4, 148, 152][269952, 89984, 22496, 152, 1]cpu" = x_16.view(4, -1, 4, 148, 152); x_16 = None 2025-03-04T21:03:30.5977597Z permute_6: "f32[4, 148, 152, 3, 4][269952, 152, 1, 89984, 22496]cpu" = view_11.permute(0, 3, 4, 1, 2); view_11 = None 2025-03-04T21:03:30.5977760Z pred_anchor_deltas_i_2: "f32[4, 67488, 4][269952, 4, 1]cpu" = permute_6.flatten(1, -2); permute_6 = None 2025-03-04T21:03:30.5977906Z view_12: "f32[4, 3, 4, 74, 76][67488, 22496, 5624, 76, 1]cpu" = x_17.view(4, -1, 4, 74, 76); x_17 = None 2025-03-04T21:03:30.5978066Z permute_7: "f32[4, 74, 76, 3, 4][67488, 76, 1, 22496, 5624]cpu" = view_12.permute(0, 3, 4, 1, 2); view_12 = None 2025-03-04T21:03:30.5978235Z pred_anchor_deltas_i_4: "f32[4, 16872, 4][67488, 4, 1]cpu" = permute_7.flatten(1, -2); permute_7 = None 2025-03-04T21:03:30.5978394Z view_13: "f32[4, 3, 4, 37, 38][16872, 5624, 1406, 38, 1]cpu" = x_18.view(4, -1, 4, 37, 38); x_18 = None 2025-03-04T21:03:30.5978554Z permute_8: "f32[4, 37, 38, 3, 4][16872, 38, 1, 5624, 1406]cpu" = view_13.permute(0, 3, 4, 1, 2); view_13 = None 2025-03-04T21:03:30.5978713Z pred_anchor_deltas_i_6: "f32[4, 4218, 4][16872, 4, 1]cpu" = permute_8.flatten(1, -2); permute_8 = None 2025-03-04T21:03:30.5978849Z view_14: "f32[4, 3, 4, 19, 19][4332, 1444, 361, 19, 1]cpu" = x_19.view(4, -1, 4, 19, 19); x_19 = None 2025-03-04T21:03:30.5979000Z permute_9: "f32[4, 19, 19, 3, 4][4332, 19, 1, 1444, 361]cpu" = view_14.permute(0, 3, 4, 1, 2); view_14 = None 2025-03-04T21:03:30.5979166Z pred_anchor_deltas_i_8: "f32[4, 1083, 4][4332, 4, 1]cpu" = permute_9.flatten(1, -2); permute_9 = None 2025-03-04T21:03:30.5979230Z 2025-03-04T21:03:30.5979633Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:03:30.5979830Z pred_anchor_deltas_i_1: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T21:03:30.5979895Z 2025-03-04T21:03:30.5980317Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:03:30.5980476Z unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T21:03:30.5980650Z expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T21:03:30.5980795Z anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T21:03:30.5980857Z 2025-03-04T21:03:30.5981237Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:03:30.5981402Z deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T21:03:30.5981467Z 2025-03-04T21:03:30.5981774Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:03:30.5981916Z boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T21:03:30.5981983Z 2025-03-04T21:03:30.5982290Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:03:30.5982443Z getitem_10: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:03:30.5982568Z getitem_11: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:03:30.5982721Z widths: "f32[1079808][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T21:03:30.5982779Z 2025-03-04T21:03:30.5983098Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:03:30.5983216Z getitem_12: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:03:30.5983339Z getitem_13: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:03:30.5983917Z heights: "f32[1079808][1]cpu" = getitem_12 - getitem_13; getitem_12 = getitem_13 = None 2025-03-04T21:03:30.5984000Z 2025-03-04T21:03:30.5984339Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:03:30.5984493Z getitem_14: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:03:30.5984580Z mul: "f32[1079808][1]cpu" = 0.5 * widths 2025-03-04T21:03:30.5984714Z ctr_x: "f32[1079808][1]cpu" = getitem_14 + mul; getitem_14 = mul = None 2025-03-04T21:03:30.5984775Z 2025-03-04T21:03:30.5985091Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:03:30.5985234Z getitem_15: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:03:30.5985328Z mul_1: "f32[1079808][1]cpu" = 0.5 * heights 2025-03-04T21:03:30.5985454Z ctr_y: "f32[1079808][1]cpu" = getitem_15 + mul_1; getitem_15 = mul_1 = None 2025-03-04T21:03:30.5985522Z 2025-03-04T21:03:30.5985850Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:03:30.5986009Z getitem_16: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:03:30.5986120Z dx: "f32[1079808, 1][1, 1]cpu" = getitem_16 / 1.0; getitem_16 = None 2025-03-04T21:03:30.5986186Z 2025-03-04T21:03:30.5986479Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:03:30.5986634Z getitem_17: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:03:30.5986760Z dy: "f32[1079808, 1][1, 1]cpu" = getitem_17 / 1.0; getitem_17 = None 2025-03-04T21:03:30.5986842Z 2025-03-04T21:03:30.5987133Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:03:30.5987287Z getitem_18: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:03:30.5987392Z dw: "f32[1079808, 1][1, 1]cpu" = getitem_18 / 1.0; getitem_18 = None 2025-03-04T21:03:30.5987458Z 2025-03-04T21:03:30.5987750Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:03:30.5987934Z getitem_19: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:03:30.5988038Z dh: "f32[1079808, 1][1, 1]cpu" = getitem_19 / 1.0; getitem_19 = None 2025-03-04T21:03:30.5988105Z 2025-03-04T21:03:30.5988433Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:03:30.5988591Z dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:03:30.5988651Z 2025-03-04T21:03:30.5988984Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:03:30.5989121Z dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:03:30.5989181Z 2025-03-04T21:03:30.5989526Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:03:30.5989658Z getitem_20: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:03:30.5989783Z mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_20; dx = getitem_20 = None 2025-03-04T21:03:30.5989928Z getitem_21: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:03:30.5990081Z pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_21; mul_2 = getitem_21 = None 2025-03-04T21:03:30.5990142Z 2025-03-04T21:03:30.5990489Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:03:30.5990621Z getitem_22: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:03:30.5990744Z mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_22; dy = getitem_22 = None 2025-03-04T21:03:30.5990890Z getitem_23: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:03:30.5991031Z pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_23; mul_3 = getitem_23 = None 2025-03-04T21:03:30.5991093Z 2025-03-04T21:03:30.5991431Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:03:30.5991548Z exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:03:30.5991712Z getitem_24: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:03:30.5991842Z pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_24; exp = getitem_24 = None 2025-03-04T21:03:30.5991909Z 2025-03-04T21:03:30.5992241Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:03:30.5992372Z exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:03:30.5992550Z getitem_25: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:03:30.5992692Z pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_25; exp_1 = getitem_25 = None 2025-03-04T21:03:30.5992922Z 2025-03-04T21:03:30.5993328Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:03:30.5993431Z mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T21:03:30.5993604Z x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:03:30.5993675Z 2025-03-04T21:03:30.5994010Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:03:30.5994101Z mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T21:03:30.5994240Z y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:03:30.5994302Z 2025-03-04T21:03:30.5994618Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:03:30.5994753Z mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:03:30.5994888Z x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:03:30.5994949Z 2025-03-04T21:03:30.5995259Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:03:30.5995366Z mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:03:30.5995495Z y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:03:30.5995559Z 2025-03-04T21:03:30.5995915Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:03:30.5996835Z pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:03:30.5996910Z 2025-03-04T21:03:30.5997243Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:03:30.5997410Z proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4)); pred_boxes = None 2025-03-04T21:03:30.5997472Z 2025-03-04T21:03:30.5997865Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:03:30.5998052Z proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T21:03:30.5998114Z 2025-03-04T21:03:30.5998519Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:03:30.5998729Z pred_anchor_deltas_i_3: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_2.reshape(-1, 4); pred_anchor_deltas_i_2 = None 2025-03-04T21:03:30.5998798Z 2025-03-04T21:03:30.5999234Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:03:30.5999395Z unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = tensor_1.unsqueeze(0); tensor_1 = None 2025-03-04T21:03:30.5999577Z expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1); unsqueeze_1 = None 2025-03-04T21:03:30.5999726Z anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4); expand_1 = None 2025-03-04T21:03:30.5999788Z 2025-03-04T21:03:30.6000165Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:03:30.6000332Z deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float(); pred_anchor_deltas_i_3 = None 2025-03-04T21:03:30.6000400Z 2025-03-04T21:03:30.6000706Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:03:30.6000853Z boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32); anchors_i_1 = None 2025-03-04T21:03:30.6000921Z 2025-03-04T21:03:30.6001239Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:03:30.6001397Z getitem_26: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)] 2025-03-04T21:03:30.6001529Z getitem_27: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T21:03:30.6001680Z widths_1: "f32[269952][1]cpu" = getitem_26 - getitem_27; getitem_26 = getitem_27 = None 2025-03-04T21:03:30.6001746Z 2025-03-04T21:03:30.6002241Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:03:30.6002379Z getitem_28: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)] 2025-03-04T21:03:30.6002496Z getitem_29: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)] 2025-03-04T21:03:30.6002655Z heights_1: "f32[269952][1]cpu" = getitem_28 - getitem_29; getitem_28 = getitem_29 = None 2025-03-04T21:03:30.6002728Z 2025-03-04T21:03:30.6003277Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:03:30.6003462Z getitem_30: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T21:03:30.6003566Z mul_10: "f32[269952][1]cpu" = 0.5 * widths_1 2025-03-04T21:03:30.6003698Z ctr_x_1: "f32[269952][1]cpu" = getitem_30 + mul_10; getitem_30 = mul_10 = None 2025-03-04T21:03:30.6003772Z 2025-03-04T21:03:30.6004088Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:03:30.6004246Z getitem_31: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]; boxes_1 = None 2025-03-04T21:03:30.6004345Z mul_11: "f32[269952][1]cpu" = 0.5 * heights_1 2025-03-04T21:03:30.6004484Z ctr_y_1: "f32[269952][1]cpu" = getitem_31 + mul_11; getitem_31 = mul_11 = None 2025-03-04T21:03:30.6004550Z 2025-03-04T21:03:30.6004880Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:03:30.6005036Z getitem_32: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:03:30.6005161Z dx_1: "f32[269952, 1][1, 1]cpu" = getitem_32 / 1.0; getitem_32 = None 2025-03-04T21:03:30.6005225Z 2025-03-04T21:03:30.6005539Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:03:30.6005700Z getitem_33: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:03:30.6005838Z dy_1: "f32[269952, 1][1, 1]cpu" = getitem_33 / 1.0; getitem_33 = None 2025-03-04T21:03:30.6005926Z 2025-03-04T21:03:30.6006223Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:03:30.6006378Z getitem_34: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:03:30.6006485Z dw_2: "f32[269952, 1][1, 1]cpu" = getitem_34 / 1.0; getitem_34 = None 2025-03-04T21:03:30.6006552Z 2025-03-04T21:03:30.6006852Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:03:30.6007037Z getitem_35: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))]; deltas_1 = None 2025-03-04T21:03:30.6007142Z dh_2: "f32[269952, 1][1, 1]cpu" = getitem_35 / 1.0; getitem_35 = None 2025-03-04T21:03:30.6007209Z 2025-03-04T21:03:30.6007554Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:03:30.6007716Z dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356); dw_2 = None 2025-03-04T21:03:30.6007776Z 2025-03-04T21:03:30.6008103Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:03:30.6008233Z dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356); dh_2 = None 2025-03-04T21:03:30.6008296Z 2025-03-04T21:03:30.6008628Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:03:30.6008770Z getitem_36: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)] 2025-03-04T21:03:30.6008892Z mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_36; dx_1 = getitem_36 = None 2025-03-04T21:03:30.6009048Z getitem_37: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)]; ctr_x_1 = None 2025-03-04T21:03:30.6009202Z pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_37; mul_12 = getitem_37 = None 2025-03-04T21:03:30.6009269Z 2025-03-04T21:03:30.6009604Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:03:30.6009742Z getitem_38: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)] 2025-03-04T21:03:30.6009857Z mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_38; dy_1 = getitem_38 = None 2025-03-04T21:03:30.6010009Z getitem_39: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)]; ctr_y_1 = None 2025-03-04T21:03:30.6010142Z pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_39; mul_13 = getitem_39 = None 2025-03-04T21:03:30.6010211Z 2025-03-04T21:03:30.6010530Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:03:30.6010648Z exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3); dw_3 = None 2025-03-04T21:03:30.6010803Z getitem_40: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]; widths_1 = None 2025-03-04T21:03:30.6010939Z pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_40; exp_2 = getitem_40 = None 2025-03-04T21:03:30.6010998Z 2025-03-04T21:03:30.6011326Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:03:30.6011472Z exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3); dh_3 = None 2025-03-04T21:03:30.6011639Z getitem_41: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]; heights_1 = None 2025-03-04T21:03:30.6011774Z pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_41; exp_3 = getitem_41 = None 2025-03-04T21:03:30.6011837Z 2025-03-04T21:03:30.6012156Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:03:30.6012251Z mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1 2025-03-04T21:03:30.6012380Z x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16; mul_16 = None 2025-03-04T21:03:30.6012439Z 2025-03-04T21:03:30.6013039Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:03:30.6013171Z mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1 2025-03-04T21:03:30.6013314Z y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17; mul_17 = None 2025-03-04T21:03:30.6013399Z 2025-03-04T21:03:30.6013747Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:03:30.6013863Z mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1; pred_w_1 = None 2025-03-04T21:03:30.6014004Z x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18; pred_ctr_x_1 = mul_18 = None 2025-03-04T21:03:30.6014064Z 2025-03-04T21:03:30.6014370Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:03:30.6014482Z mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1; pred_h_1 = None 2025-03-04T21:03:30.6014620Z y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19; pred_ctr_y_1 = mul_19 = None 2025-03-04T21:03:30.6014679Z 2025-03-04T21:03:30.6015029Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:03:30.6015236Z pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1); x1_1 = y1_1 = x2_1 = y2_1 = None 2025-03-04T21:03:30.6015302Z 2025-03-04T21:03:30.6015636Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:03:30.6015802Z proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4)); pred_boxes_1 = None 2025-03-04T21:03:30.6015862Z 2025-03-04T21:03:30.6016247Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:03:30.6016417Z proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4); proposals_i_1 = None 2025-03-04T21:03:30.6016483Z 2025-03-04T21:03:30.6016875Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:03:30.6017081Z pred_anchor_deltas_i_5: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_4.reshape(-1, 4); pred_anchor_deltas_i_4 = None 2025-03-04T21:03:30.6017139Z 2025-03-04T21:03:30.6017572Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:03:30.6017752Z unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = tensor_2.unsqueeze(0); tensor_2 = None 2025-03-04T21:03:30.6017907Z expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1); unsqueeze_2 = None 2025-03-04T21:03:30.6018040Z anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4); expand_2 = None 2025-03-04T21:03:30.6018109Z 2025-03-04T21:03:30.6018470Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:03:30.6018639Z deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_5.float(); pred_anchor_deltas_i_5 = None 2025-03-04T21:03:30.6018706Z 2025-03-04T21:03:30.6019009Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:03:30.6019154Z boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32); anchors_i_2 = None 2025-03-04T21:03:30.6019215Z 2025-03-04T21:03:30.6019527Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:03:30.6019670Z getitem_42: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)] 2025-03-04T21:03:30.6019796Z getitem_43: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T21:03:30.6019939Z widths_2: "f32[67488][1]cpu" = getitem_42 - getitem_43; getitem_42 = getitem_43 = None 2025-03-04T21:03:30.6020007Z 2025-03-04T21:03:30.6020319Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:03:30.6020443Z getitem_44: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)] 2025-03-04T21:03:30.6020562Z getitem_45: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)] 2025-03-04T21:03:30.6020712Z heights_2: "f32[67488][1]cpu" = getitem_44 - getitem_45; getitem_44 = getitem_45 = None 2025-03-04T21:03:30.6020772Z 2025-03-04T21:03:30.6021095Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:03:30.6021213Z getitem_46: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T21:03:30.6021304Z mul_20: "f32[67488][1]cpu" = 0.5 * widths_2 2025-03-04T21:03:30.6021429Z ctr_x_2: "f32[67488][1]cpu" = getitem_46 + mul_20; getitem_46 = mul_20 = None 2025-03-04T21:03:30.6021496Z 2025-03-04T21:03:30.6021800Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:03:30.6021949Z getitem_47: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]; boxes_2 = None 2025-03-04T21:03:30.6022039Z mul_21: "f32[67488][1]cpu" = 0.5 * heights_2 2025-03-04T21:03:30.6022170Z ctr_y_2: "f32[67488][1]cpu" = getitem_47 + mul_21; getitem_47 = mul_21 = None 2025-03-04T21:03:30.6022230Z 2025-03-04T21:03:30.6022539Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:03:30.6022691Z getitem_48: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:03:30.6023030Z dx_2: "f32[67488, 1][1, 1]cpu" = getitem_48 / 1.0; getitem_48 = None 2025-03-04T21:03:30.6023101Z 2025-03-04T21:03:30.6023435Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:03:30.6023611Z getitem_49: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:03:30.6023751Z dy_2: "f32[67488, 1][1, 1]cpu" = getitem_49 / 1.0; getitem_49 = None 2025-03-04T21:03:30.6023811Z 2025-03-04T21:03:30.6024113Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:03:30.6024256Z getitem_50: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:03:30.6024369Z dw_4: "f32[67488, 1][1, 1]cpu" = getitem_50 / 1.0; getitem_50 = None 2025-03-04T21:03:30.6024430Z 2025-03-04T21:03:30.6024733Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:03:30.6024908Z getitem_51: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))]; deltas_2 = None 2025-03-04T21:03:30.6025022Z dh_4: "f32[67488, 1][1, 1]cpu" = getitem_51 / 1.0; getitem_51 = None 2025-03-04T21:03:30.6025082Z 2025-03-04T21:03:30.6025418Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:03:30.6025582Z dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356); dw_4 = None 2025-03-04T21:03:30.6025643Z 2025-03-04T21:03:30.6025975Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:03:30.6026108Z dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356); dh_4 = None 2025-03-04T21:03:30.6026174Z 2025-03-04T21:03:30.6026513Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:03:30.6026652Z getitem_52: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)] 2025-03-04T21:03:30.6026773Z mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_52; dx_2 = getitem_52 = None 2025-03-04T21:03:30.6026944Z getitem_53: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)]; ctr_x_2 = None 2025-03-04T21:03:30.6027082Z pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_53; mul_22 = getitem_53 = None 2025-03-04T21:03:30.6027149Z 2025-03-04T21:03:30.6027488Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:03:30.6027627Z getitem_54: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)] 2025-03-04T21:03:30.6027745Z mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_54; dy_2 = getitem_54 = None 2025-03-04T21:03:30.6027898Z getitem_55: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)]; ctr_y_2 = None 2025-03-04T21:03:30.6028030Z pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_55; mul_23 = getitem_55 = None 2025-03-04T21:03:30.6028098Z 2025-03-04T21:03:30.6028421Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:03:30.6028538Z exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5); dw_5 = None 2025-03-04T21:03:30.6028690Z getitem_56: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]; widths_2 = None 2025-03-04T21:03:30.6028825Z pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_56; exp_4 = getitem_56 = None 2025-03-04T21:03:30.6028885Z 2025-03-04T21:03:30.6029238Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:03:30.6029362Z exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5); dh_5 = None 2025-03-04T21:03:30.6029529Z getitem_57: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]; heights_2 = None 2025-03-04T21:03:30.6029656Z pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_57; exp_5 = getitem_57 = None 2025-03-04T21:03:30.6029723Z 2025-03-04T21:03:30.6030024Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:03:30.6030126Z mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2 2025-03-04T21:03:30.6030239Z x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26; mul_26 = None 2025-03-04T21:03:30.6030308Z 2025-03-04T21:03:30.6030616Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:03:30.6030719Z mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2 2025-03-04T21:03:30.6030848Z y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27; mul_27 = None 2025-03-04T21:03:30.6030917Z 2025-03-04T21:03:30.6031235Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:03:30.6031355Z mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2; pred_w_2 = None 2025-03-04T21:03:30.6031486Z x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28; pred_ctr_x_2 = mul_28 = None 2025-03-04T21:03:30.6031555Z 2025-03-04T21:03:30.6031870Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:03:30.6031993Z mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2; pred_h_2 = None 2025-03-04T21:03:30.6032122Z y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29; pred_ctr_y_2 = mul_29 = None 2025-03-04T21:03:30.6032192Z 2025-03-04T21:03:30.6032575Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:03:30.6032974Z pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1); x1_2 = y1_2 = x2_2 = y2_2 = None 2025-03-04T21:03:30.6033053Z 2025-03-04T21:03:30.6033427Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:03:30.6033656Z proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4)); pred_boxes_2 = None 2025-03-04T21:03:30.6033724Z 2025-03-04T21:03:30.6034127Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:03:30.6034301Z proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4); proposals_i_2 = None 2025-03-04T21:03:30.6034373Z 2025-03-04T21:03:30.6034774Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:03:30.6034984Z pred_anchor_deltas_i_7: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_6.reshape(-1, 4); pred_anchor_deltas_i_6 = None 2025-03-04T21:03:30.6035046Z 2025-03-04T21:03:30.6035513Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:03:30.6035678Z unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = tensor_3.unsqueeze(0); tensor_3 = None 2025-03-04T21:03:30.6035838Z expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1); unsqueeze_3 = None 2025-03-04T21:03:30.6035977Z anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4); expand_3 = None 2025-03-04T21:03:30.6036048Z 2025-03-04T21:03:30.6036427Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:03:30.6036599Z deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_7.float(); pred_anchor_deltas_i_7 = None 2025-03-04T21:03:30.6036660Z 2025-03-04T21:03:30.6036978Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:03:30.6037124Z boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32); anchors_i_3 = None 2025-03-04T21:03:30.6037190Z 2025-03-04T21:03:30.6037519Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:03:30.6037654Z getitem_58: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)] 2025-03-04T21:03:30.6037777Z getitem_59: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T21:03:30.6037930Z widths_3: "f32[16872][1]cpu" = getitem_58 - getitem_59; getitem_58 = getitem_59 = None 2025-03-04T21:03:30.6037991Z 2025-03-04T21:03:30.6038320Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:03:30.6038441Z getitem_60: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)] 2025-03-04T21:03:30.6038567Z getitem_61: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)] 2025-03-04T21:03:30.6038714Z heights_3: "f32[16872][1]cpu" = getitem_60 - getitem_61; getitem_60 = getitem_61 = None 2025-03-04T21:03:30.6038784Z 2025-03-04T21:03:30.6039110Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:03:30.6039238Z getitem_62: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T21:03:30.6039325Z mul_30: "f32[16872][1]cpu" = 0.5 * widths_3 2025-03-04T21:03:30.6039460Z ctr_x_3: "f32[16872][1]cpu" = getitem_62 + mul_30; getitem_62 = mul_30 = None 2025-03-04T21:03:30.6039522Z 2025-03-04T21:03:30.6039843Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:03:30.6039989Z getitem_63: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]; boxes_3 = None 2025-03-04T21:03:30.6040086Z mul_31: "f32[16872][1]cpu" = 0.5 * heights_3 2025-03-04T21:03:30.6040215Z ctr_y_3: "f32[16872][1]cpu" = getitem_63 + mul_31; getitem_63 = mul_31 = None 2025-03-04T21:03:30.6040285Z 2025-03-04T21:03:30.6040595Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:03:30.6040752Z getitem_64: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:03:30.6040869Z dx_3: "f32[16872, 1][1, 1]cpu" = getitem_64 / 1.0; getitem_64 = None 2025-03-04T21:03:30.6040931Z 2025-03-04T21:03:30.6041238Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:03:30.6041417Z getitem_65: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:03:30.6041535Z dy_3: "f32[16872, 1][1, 1]cpu" = getitem_65 / 1.0; getitem_65 = None 2025-03-04T21:03:30.6041598Z 2025-03-04T21:03:30.6041908Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:03:30.6042054Z getitem_66: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:03:30.6042166Z dw_6: "f32[16872, 1][1, 1]cpu" = getitem_66 / 1.0; getitem_66 = None 2025-03-04T21:03:30.6042228Z 2025-03-04T21:03:30.6042540Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:03:30.6042950Z getitem_67: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))]; deltas_3 = None 2025-03-04T21:03:30.6043100Z dh_6: "f32[16872, 1][1, 1]cpu" = getitem_67 / 1.0; getitem_67 = None 2025-03-04T21:03:30.6043189Z 2025-03-04T21:03:30.6043549Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:03:30.6043689Z dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356); dw_6 = None 2025-03-04T21:03:30.6043760Z 2025-03-04T21:03:30.6044092Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:03:30.6044235Z dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356); dh_6 = None 2025-03-04T21:03:30.6044297Z 2025-03-04T21:03:30.6044653Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:03:30.6044789Z getitem_68: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)] 2025-03-04T21:03:30.6044921Z mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_68; dx_3 = getitem_68 = None 2025-03-04T21:03:30.6045088Z getitem_69: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)]; ctr_x_3 = None 2025-03-04T21:03:30.6045237Z pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_69; mul_32 = getitem_69 = None 2025-03-04T21:03:30.6045296Z 2025-03-04T21:03:30.6045660Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:03:30.6045789Z getitem_70: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)] 2025-03-04T21:03:30.6045918Z mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_70; dy_3 = getitem_70 = None 2025-03-04T21:03:30.6046062Z getitem_71: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)]; ctr_y_3 = None 2025-03-04T21:03:30.6046200Z pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_71; mul_33 = getitem_71 = None 2025-03-04T21:03:30.6046261Z 2025-03-04T21:03:30.6046598Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:03:30.6046713Z exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7); dw_7 = None 2025-03-04T21:03:30.6046865Z getitem_72: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]; widths_3 = None 2025-03-04T21:03:30.6047000Z pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_72; exp_6 = getitem_72 = None 2025-03-04T21:03:30.6047061Z 2025-03-04T21:03:30.6047437Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:03:30.6047546Z exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7); dh_7 = None 2025-03-04T21:03:30.6047712Z getitem_73: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]; heights_3 = None 2025-03-04T21:03:30.6047838Z pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_73; exp_7 = getitem_73 = None 2025-03-04T21:03:30.6047904Z 2025-03-04T21:03:30.6048208Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:03:30.6048306Z mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3 2025-03-04T21:03:30.6048417Z x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36; mul_36 = None 2025-03-04T21:03:30.6048485Z 2025-03-04T21:03:30.6048796Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:03:30.6048909Z mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3 2025-03-04T21:03:30.6049020Z y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37; mul_37 = None 2025-03-04T21:03:30.6049088Z 2025-03-04T21:03:30.6049397Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:03:30.6049516Z mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3; pred_w_3 = None 2025-03-04T21:03:30.6049644Z x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38; pred_ctr_x_3 = mul_38 = None 2025-03-04T21:03:30.6049712Z 2025-03-04T21:03:30.6050024Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:03:30.6050140Z mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3; pred_h_3 = None 2025-03-04T21:03:30.6050264Z y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39; pred_ctr_y_3 = mul_39 = None 2025-03-04T21:03:30.6050332Z 2025-03-04T21:03:30.6050691Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:03:30.6050880Z pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1); x1_3 = y1_3 = x2_3 = y2_3 = None 2025-03-04T21:03:30.6050937Z 2025-03-04T21:03:30.6051270Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:03:30.6051426Z proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4)); pred_boxes_3 = None 2025-03-04T21:03:30.6051493Z 2025-03-04T21:03:30.6051861Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:03:30.6052037Z proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4); proposals_i_3 = None 2025-03-04T21:03:30.6052096Z 2025-03-04T21:03:30.6052490Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:03:30.6052843Z pred_anchor_deltas_i_9: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_8.reshape(-1, 4); pred_anchor_deltas_i_8 = None 2025-03-04T21:03:30.6052931Z 2025-03-04T21:03:30.6053410Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:03:30.6053585Z unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = tensor_4.unsqueeze(0); tensor_4 = None 2025-03-04T21:03:30.6053730Z expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1); unsqueeze_4 = None 2025-03-04T21:03:30.6053869Z anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4); expand_4 = None 2025-03-04T21:03:30.6053929Z 2025-03-04T21:03:30.6054301Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:03:30.6054469Z deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_9.float(); pred_anchor_deltas_i_9 = None 2025-03-04T21:03:30.6054529Z 2025-03-04T21:03:30.6054839Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:03:30.6054977Z boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32); anchors_i_4 = None 2025-03-04T21:03:30.6055062Z 2025-03-04T21:03:30.6055366Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:03:30.6055499Z getitem_74: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)] 2025-03-04T21:03:30.6055617Z getitem_75: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T21:03:30.6055765Z widths_4: "f32[4332][1]cpu" = getitem_74 - getitem_75; getitem_74 = getitem_75 = None 2025-03-04T21:03:30.6055825Z 2025-03-04T21:03:30.6056141Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:03:30.6056261Z getitem_76: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)] 2025-03-04T21:03:30.6056381Z getitem_77: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)] 2025-03-04T21:03:30.6056525Z heights_4: "f32[4332][1]cpu" = getitem_76 - getitem_77; getitem_76 = getitem_77 = None 2025-03-04T21:03:30.6056592Z 2025-03-04T21:03:30.6056909Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:03:30.6057032Z getitem_78: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T21:03:30.6057114Z mul_40: "f32[4332][1]cpu" = 0.5 * widths_4 2025-03-04T21:03:30.6057247Z ctr_x_4: "f32[4332][1]cpu" = getitem_78 + mul_40; getitem_78 = mul_40 = None 2025-03-04T21:03:30.6057307Z 2025-03-04T21:03:30.6057620Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:03:30.6057759Z getitem_79: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]; boxes_4 = None 2025-03-04T21:03:30.6057852Z mul_41: "f32[4332][1]cpu" = 0.5 * heights_4 2025-03-04T21:03:30.6057973Z ctr_y_4: "f32[4332][1]cpu" = getitem_79 + mul_41; getitem_79 = mul_41 = None 2025-03-04T21:03:30.6058043Z 2025-03-04T21:03:30.6058341Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:03:30.6058493Z getitem_80: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:03:30.6058599Z dx_4: "f32[4332, 1][1, 1]cpu" = getitem_80 / 1.0; getitem_80 = None 2025-03-04T21:03:30.6058667Z 2025-03-04T21:03:30.6058975Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:03:30.6059139Z getitem_81: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:03:30.6059244Z dy_4: "f32[4332, 1][1, 1]cpu" = getitem_81 / 1.0; getitem_81 = None 2025-03-04T21:03:30.6059321Z 2025-03-04T21:03:30.6059611Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:03:30.6059762Z getitem_82: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:03:30.6059864Z dw_8: "f32[4332, 1][1, 1]cpu" = getitem_82 / 1.0; getitem_82 = None 2025-03-04T21:03:30.6059931Z 2025-03-04T21:03:30.6060224Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:03:30.6060407Z getitem_83: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))]; deltas_4 = None 2025-03-04T21:03:30.6060514Z dh_8: "f32[4332, 1][1, 1]cpu" = getitem_83 / 1.0; getitem_83 = None 2025-03-04T21:03:30.6060591Z 2025-03-04T21:03:30.6060927Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:03:30.6061059Z dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356); dw_8 = None 2025-03-04T21:03:30.6061125Z 2025-03-04T21:03:30.6061449Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:03:30.6061581Z dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356); dh_8 = None 2025-03-04T21:03:30.6061640Z 2025-03-04T21:03:30.6061985Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:03:30.6062114Z getitem_84: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)] 2025-03-04T21:03:30.6062251Z mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_84; dx_4 = getitem_84 = None 2025-03-04T21:03:30.6062398Z getitem_85: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)]; ctr_x_4 = None 2025-03-04T21:03:30.6062536Z pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_85; mul_42 = getitem_85 = None 2025-03-04T21:03:30.6062594Z 2025-03-04T21:03:30.6063153Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:03:30.6063303Z getitem_86: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)] 2025-03-04T21:03:30.6063429Z mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_86; dy_4 = getitem_86 = None 2025-03-04T21:03:30.6063571Z getitem_87: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)]; ctr_y_4 = None 2025-03-04T21:03:30.6063713Z pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_87; mul_43 = getitem_87 = None 2025-03-04T21:03:30.6063776Z 2025-03-04T21:03:30.6064110Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:03:30.6064217Z exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9); dw_9 = None 2025-03-04T21:03:30.6064377Z getitem_88: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]; widths_4 = None 2025-03-04T21:03:30.6064503Z pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_88; exp_8 = getitem_88 = None 2025-03-04T21:03:30.6064596Z 2025-03-04T21:03:30.6064940Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:03:30.6065060Z exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9); dh_9 = None 2025-03-04T21:03:30.6065220Z getitem_89: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]; heights_4 = None 2025-03-04T21:03:30.6065354Z pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_89; exp_9 = getitem_89 = None 2025-03-04T21:03:30.6065414Z 2025-03-04T21:03:30.6065723Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:03:30.6065813Z mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4 2025-03-04T21:03:30.6065932Z x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46; mul_46 = None 2025-03-04T21:03:30.6065992Z 2025-03-04T21:03:30.6066298Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:03:30.6066403Z mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4 2025-03-04T21:03:30.6066519Z y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47; mul_47 = None 2025-03-04T21:03:30.6066577Z 2025-03-04T21:03:30.6066882Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:03:30.6066990Z mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4; pred_w_4 = None 2025-03-04T21:03:30.6067123Z x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48; pred_ctr_x_4 = mul_48 = None 2025-03-04T21:03:30.6067183Z 2025-03-04T21:03:30.6067490Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:03:30.6067595Z mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4; pred_h_4 = None 2025-03-04T21:03:30.6067721Z y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49; pred_ctr_y_4 = mul_49 = None 2025-03-04T21:03:30.6067781Z 2025-03-04T21:03:30.6068150Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:03:30.6068336Z pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1); x1_4 = y1_4 = x2_4 = y2_4 = None 2025-03-04T21:03:30.6068396Z 2025-03-04T21:03:30.6068726Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:03:30.6068882Z proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4)); pred_boxes_4 = None 2025-03-04T21:03:30.6068954Z 2025-03-04T21:03:30.6069331Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:03:30.6069508Z proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4); proposals_i_4 = None 2025-03-04T21:03:30.6069569Z 2025-03-04T21:03:30.6070069Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T21:03:30.6070198Z arange_10: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T21:03:30.6070266Z 2025-03-04T21:03:30.6070574Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:30.6070741Z batch_idx: "i64[4][1]cpu" = arange_10.to(device(type='cpu')); arange_10 = None 2025-03-04T21:03:30.6070802Z 2025-03-04T21:03:30.6071249Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:03:30.6071359Z topk = logits_i.topk(1000, dim = 1); logits_i = None 2025-03-04T21:03:30.6071464Z topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0] 2025-03-04T21:03:30.6071575Z topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1]; topk = None 2025-03-04T21:03:30.6071645Z 2025-03-04T21:03:30.6072107Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:03:30.6072244Z getitem_92: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:03:30.6072471Z topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_92, topk_idx)]; proposals_i_5 = getitem_92 = topk_idx = None 2025-03-04T21:03:30.6072556Z 2025-03-04T21:03:30.6073256Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:03:30.6073449Z full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:03:30.6073513Z 2025-03-04T21:03:30.6073888Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:30.6074018Z to_21: "i64[1000][1]cpu" = full.to(device(type='cpu')); full = None 2025-03-04T21:03:30.6074089Z 2025-03-04T21:03:30.6074523Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:03:30.6074670Z topk_1 = logits_i_1.topk(1000, dim = 1); logits_i_1 = None 2025-03-04T21:03:30.6074776Z topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0] 2025-03-04T21:03:30.6074899Z topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1]; topk_1 = None 2025-03-04T21:03:30.6074960Z 2025-03-04T21:03:30.6075426Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:03:30.6075565Z getitem_96: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:03:30.6075798Z topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_96, topk_idx_1)]; proposals_i_6 = getitem_96 = topk_idx_1 = None 2025-03-04T21:03:30.6075868Z 2025-03-04T21:03:30.6076330Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:03:30.6076500Z full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:03:30.6076563Z 2025-03-04T21:03:30.6076864Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:30.6076988Z to_22: "i64[1000][1]cpu" = full_1.to(device(type='cpu')); full_1 = None 2025-03-04T21:03:30.6077089Z 2025-03-04T21:03:30.6077522Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:03:30.6077641Z topk_2 = logits_i_2.topk(1000, dim = 1); logits_i_2 = None 2025-03-04T21:03:30.6077749Z topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0] 2025-03-04T21:03:30.6077868Z topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1]; topk_2 = None 2025-03-04T21:03:30.6077929Z 2025-03-04T21:03:30.6078392Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:03:30.6078522Z getitem_100: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:03:30.6078767Z topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_100, topk_idx_2)]; proposals_i_7 = getitem_100 = topk_idx_2 = None 2025-03-04T21:03:30.6078829Z 2025-03-04T21:03:30.6079307Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:03:30.6079467Z full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:03:30.6079536Z 2025-03-04T21:03:30.6079824Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:30.6079950Z to_23: "i64[1000][1]cpu" = full_2.to(device(type='cpu')); full_2 = None 2025-03-04T21:03:30.6080012Z 2025-03-04T21:03:30.6080446Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:03:30.6080557Z topk_3 = logits_i_3.topk(1000, dim = 1); logits_i_3 = None 2025-03-04T21:03:30.6080668Z topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0] 2025-03-04T21:03:30.6080793Z topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1]; topk_3 = None 2025-03-04T21:03:30.6080861Z 2025-03-04T21:03:30.6081311Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:03:30.6081448Z getitem_104: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:03:30.6081684Z topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_104, topk_idx_3)]; proposals_i_8 = getitem_104 = topk_idx_3 = None 2025-03-04T21:03:30.6081747Z 2025-03-04T21:03:30.6082202Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:03:30.6082362Z full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:03:30.6082427Z 2025-03-04T21:03:30.6082882Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:30.6083066Z to_24: "i64[1000][1]cpu" = full_3.to(device(type='cpu')); full_3 = None 2025-03-04T21:03:30.6083131Z 2025-03-04T21:03:30.6083609Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:03:30.6083735Z topk_4 = logits_i_4.topk(1000, dim = 1); logits_i_4 = None 2025-03-04T21:03:30.6083843Z topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0] 2025-03-04T21:03:30.6083956Z topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1]; topk_4 = None 2025-03-04T21:03:30.6084025Z 2025-03-04T21:03:30.6084485Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:03:30.6084654Z getitem_108: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T21:03:30.6084885Z topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_108, topk_idx_4)]; proposals_i_9 = getitem_108 = topk_idx_4 = None 2025-03-04T21:03:30.6084957Z 2025-03-04T21:03:30.6085422Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:03:30.6085606Z full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:03:30.6085665Z 2025-03-04T21:03:30.6085962Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:30.6086080Z to_25: "i64[1000][1]cpu" = full_4.to(device(type='cpu')); full_4 = None 2025-03-04T21:03:30.6086146Z 2025-03-04T21:03:30.6086419Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:03:30.6086800Z topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1); topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None 2025-03-04T21:03:30.6086861Z 2025-03-04T21:03:30.6087158Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:03:30.6087622Z topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1); topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None 2025-03-04T21:03:30.6087683Z 2025-03-04T21:03:30.6087960Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:03:30.6088154Z level_ids: "i64[5000][1]cpu" = torch.cat([to_21, to_22, to_23, to_24, to_25], 0); to_21 = to_22 = to_23 = to_24 = to_25 = level_ids = None 2025-03-04T21:03:30.6088222Z 2025-03-04T21:03:30.6088601Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T21:03:30.6088748Z getitem_110: "f32[5000, 4][4, 1]cpu" = topk_proposals[0]; topk_proposals = None 2025-03-04T21:03:30.6088808Z 2025-03-04T21:03:30.6089104Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:03:30.6089248Z tensor_5: "f32[5000, 4][4, 1]cpu" = getitem_110.to(torch.float32); getitem_110 = None 2025-03-04T21:03:30.6089314Z 2025-03-04T21:03:30.6089702Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T21:03:30.6089859Z scores_per_img: "f32[5000][1]cpu" = topk_scores[0]; topk_scores = None 2025-03-04T21:03:30.6089920Z 2025-03-04T21:03:30.6090396Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T21:03:30.6090528Z isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor_5); tensor_5 = None 2025-03-04T21:03:30.6090648Z all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:03:30.6090797Z isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T21:03:30.6090930Z valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T21:03:30.6090991Z 2025-03-04T21:03:30.6091356Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T21:03:30.6091495Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T21:03:30.6091563Z 2025-03-04T21:03:31.8711774Z 2025-03-04T21:03:31.8712375Z class GraphModule(torch.nn.Module): 2025-03-04T21:03:31.8714437Z def forward(self, L_pred_anchor_deltas_0_: "f32[4, 269952, 4][1079808, 4, 1]cpu", L_anchors_0_tensor: "f32[269952, 4][4, 1]cpu", L_pred_anchor_deltas_1_: "f32[4, 67488, 4][269952, 4, 1]cpu", L_anchors_1_tensor: "f32[67488, 4][4, 1]cpu", L_pred_anchor_deltas_2_: "f32[4, 16872, 4][67488, 4, 1]cpu", L_anchors_2_tensor: "f32[16872, 4][4, 1]cpu", L_pred_anchor_deltas_3_: "f32[4, 4218, 4][16872, 4, 1]cpu", L_anchors_3_tensor: "f32[4218, 4][4, 1]cpu", L_pred_anchor_deltas_4_: "f32[4, 1083, 4][4332, 4, 1]cpu", L_anchors_4_tensor: "f32[1083, 4][4, 1]cpu", L_pred_objectness_logits_0_: "f32[4, 269952][269952, 1]cpu", L_pred_objectness_logits_1_: "f32[4, 67488][67488, 1]cpu", L_pred_objectness_logits_2_: "f32[4, 16872][16872, 1]cpu", L_pred_objectness_logits_3_: "f32[4, 4218][4218, 1]cpu", L_pred_objectness_logits_4_: "f32[4, 1083][1083, 1]cpu"): 2025-03-04T21:03:31.8716065Z l_pred_anchor_deltas_0_ = L_pred_anchor_deltas_0_ 2025-03-04T21:03:31.8716704Z l_anchors_0_tensor = L_anchors_0_tensor 2025-03-04T21:03:31.8716987Z l_pred_anchor_deltas_1_ = L_pred_anchor_deltas_1_ 2025-03-04T21:03:31.8717239Z l_anchors_1_tensor = L_anchors_1_tensor 2025-03-04T21:03:31.8717488Z l_pred_anchor_deltas_2_ = L_pred_anchor_deltas_2_ 2025-03-04T21:03:31.8717753Z l_anchors_2_tensor = L_anchors_2_tensor 2025-03-04T21:03:31.8718014Z l_pred_anchor_deltas_3_ = L_pred_anchor_deltas_3_ 2025-03-04T21:03:31.8718262Z l_anchors_3_tensor = L_anchors_3_tensor 2025-03-04T21:03:31.8718503Z l_pred_anchor_deltas_4_ = L_pred_anchor_deltas_4_ 2025-03-04T21:03:31.8718754Z l_anchors_4_tensor = L_anchors_4_tensor 2025-03-04T21:03:31.8719018Z l_pred_objectness_logits_0_ = L_pred_objectness_logits_0_ 2025-03-04T21:03:31.8719323Z l_pred_objectness_logits_1_ = L_pred_objectness_logits_1_ 2025-03-04T21:03:31.8719616Z l_pred_objectness_logits_2_ = L_pred_objectness_logits_2_ 2025-03-04T21:03:31.8719916Z l_pred_objectness_logits_3_ = L_pred_objectness_logits_3_ 2025-03-04T21:03:31.8720202Z l_pred_objectness_logits_4_ = L_pred_objectness_logits_4_ 2025-03-04T21:03:31.8720458Z 2025-03-04T21:03:31.8721094Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:03:31.8722018Z pred_anchor_deltas_i: "f32[1079808, 4][4, 1]cpu" = l_pred_anchor_deltas_0_.reshape(-1, 4); l_pred_anchor_deltas_0_ = None 2025-03-04T21:03:31.8722499Z 2025-03-04T21:03:31.8723114Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:03:31.8723910Z unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = l_anchors_0_tensor.unsqueeze(0); l_anchors_0_tensor = None 2025-03-04T21:03:31.8724338Z expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T21:03:31.8724715Z anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T21:03:31.8724976Z 2025-03-04T21:03:31.8725878Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:03:31.8726541Z deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.float(); pred_anchor_deltas_i = None 2025-03-04T21:03:31.8726837Z 2025-03-04T21:03:31.8727255Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:03:31.8727826Z boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T21:03:31.8728083Z 2025-03-04T21:03:31.8728494Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:03:31.8728998Z getitem: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:03:31.8729321Z getitem_1: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:03:31.8729660Z widths: "f32[1079808][1]cpu" = getitem - getitem_1; getitem = getitem_1 = None 2025-03-04T21:03:31.8729932Z 2025-03-04T21:03:31.8730426Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:03:31.8731038Z getitem_2: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:03:31.8731364Z getitem_3: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:03:31.8731732Z heights: "f32[1079808][1]cpu" = getitem_2 - getitem_3; getitem_2 = getitem_3 = None 2025-03-04T21:03:31.8732017Z 2025-03-04T21:03:31.8732444Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:03:31.8733192Z getitem_4: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:03:31.8733500Z mul: "f32[1079808][1]cpu" = 0.5 * widths 2025-03-04T21:03:31.8733772Z ctr_x: "f32[1079808][1]cpu" = getitem_4 + mul; getitem_4 = mul = None 2025-03-04T21:03:31.8734021Z 2025-03-04T21:03:31.8734439Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:03:31.8734961Z getitem_5: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:03:31.8735297Z mul_1: "f32[1079808][1]cpu" = 0.5 * heights 2025-03-04T21:03:31.8736657Z ctr_y: "f32[1079808][1]cpu" = getitem_5 + mul_1; getitem_5 = mul_1 = None 2025-03-04T21:03:31.8739432Z 2025-03-04T21:03:31.8739892Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:03:31.8740408Z getitem_6: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:03:31.8740737Z dx: "f32[1079808, 1][1, 1]cpu" = getitem_6 / 1.0; getitem_6 = None 2025-03-04T21:03:31.8741042Z 2025-03-04T21:03:31.8741454Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:03:31.8741953Z getitem_7: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:03:31.8742273Z dy: "f32[1079808, 1][1, 1]cpu" = getitem_7 / 1.0; getitem_7 = None 2025-03-04T21:03:31.8742499Z 2025-03-04T21:03:31.8743104Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:03:31.8743622Z getitem_8: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:03:31.8743935Z dw: "f32[1079808, 1][1, 1]cpu" = getitem_8 / 1.0; getitem_8 = None 2025-03-04T21:03:31.8744156Z 2025-03-04T21:03:31.8744537Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:03:31.8745056Z getitem_9: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:03:31.8745419Z dh: "f32[1079808, 1][1, 1]cpu" = getitem_9 / 1.0; getitem_9 = None 2025-03-04T21:03:31.8745645Z 2025-03-04T21:03:31.8746059Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:03:31.8746583Z dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:03:31.8746833Z 2025-03-04T21:03:31.8747241Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:03:31.8747755Z dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:03:31.8748004Z 2025-03-04T21:03:31.8748421Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:03:31.8748976Z getitem_10: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:03:31.8749294Z mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_10; dx = getitem_10 = None 2025-03-04T21:03:31.8749625Z getitem_11: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:03:31.8749964Z pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_11; mul_2 = getitem_11 = None 2025-03-04T21:03:31.8750216Z 2025-03-04T21:03:31.8750643Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:03:31.8751175Z getitem_12: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:03:31.8751482Z mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_12; dy = getitem_12 = None 2025-03-04T21:03:31.8751807Z getitem_13: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:03:31.8752145Z pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_13; mul_3 = getitem_13 = None 2025-03-04T21:03:31.8752395Z 2025-03-04T21:03:31.8753025Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:03:31.8753596Z exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:03:31.8753949Z getitem_14: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:03:31.8754457Z pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_14; exp = getitem_14 = None 2025-03-04T21:03:31.8754768Z 2025-03-04T21:03:31.8755228Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:03:31.8755737Z exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:03:31.8756074Z getitem_15: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:03:31.8756437Z pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_15; exp_1 = getitem_15 = None 2025-03-04T21:03:31.8756686Z 2025-03-04T21:03:31.8757080Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:03:31.8757538Z mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T21:03:31.8757808Z x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:03:31.8758040Z 2025-03-04T21:03:31.8758435Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:03:31.8758920Z mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T21:03:31.8759177Z y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:03:31.8759411Z 2025-03-04T21:03:31.8759799Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:03:31.8760270Z mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:03:31.8760565Z x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:03:31.8760812Z 2025-03-04T21:03:31.8761201Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:03:31.8761673Z mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:03:31.8761966Z y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:03:31.8762224Z 2025-03-04T21:03:31.8762667Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:03:31.8763571Z pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:03:31.8763866Z 2025-03-04T21:03:31.8764278Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:03:31.8764823Z proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4)); pred_boxes = None 2025-03-04T21:03:31.8765101Z 2025-03-04T21:03:31.8765559Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:03:31.8766160Z proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T21:03:31.8766447Z 2025-03-04T21:03:31.8766911Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:03:31.8767563Z pred_anchor_deltas_i_1: "f32[269952, 4][4, 1]cpu" = l_pred_anchor_deltas_1_.reshape(-1, 4); l_pred_anchor_deltas_1_ = None 2025-03-04T21:03:31.8767880Z 2025-03-04T21:03:31.8768436Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:03:31.8769105Z unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = l_anchors_1_tensor.unsqueeze(0); l_anchors_1_tensor = None 2025-03-04T21:03:31.8769487Z expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1); unsqueeze_1 = None 2025-03-04T21:03:31.8769822Z anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4); expand_1 = None 2025-03-04T21:03:31.8770068Z 2025-03-04T21:03:31.8770514Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:03:31.8771097Z deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T21:03:31.8771380Z 2025-03-04T21:03:31.8771763Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:03:31.8772285Z boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32); anchors_i_1 = None 2025-03-04T21:03:31.8772545Z 2025-03-04T21:03:31.8773162Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:03:31.8773693Z getitem_16: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)] 2025-03-04T21:03:31.8774007Z getitem_17: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T21:03:31.8774335Z widths_1: "f32[269952][1]cpu" = getitem_16 - getitem_17; getitem_16 = getitem_17 = None 2025-03-04T21:03:31.8774602Z 2025-03-04T21:03:31.8774999Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:03:31.8775489Z getitem_18: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)] 2025-03-04T21:03:31.8775786Z getitem_19: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)] 2025-03-04T21:03:31.8776127Z heights_1: "f32[269952][1]cpu" = getitem_18 - getitem_19; getitem_18 = getitem_19 = None 2025-03-04T21:03:31.8776393Z 2025-03-04T21:03:31.8776781Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:03:31.8777255Z getitem_20: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T21:03:31.8777513Z mul_10: "f32[269952][1]cpu" = 0.5 * widths_1 2025-03-04T21:03:31.8777779Z ctr_x_1: "f32[269952][1]cpu" = getitem_20 + mul_10; getitem_20 = mul_10 = None 2025-03-04T21:03:31.8778023Z 2025-03-04T21:03:31.8778408Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:03:31.8778911Z getitem_21: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]; boxes_1 = None 2025-03-04T21:03:31.8779201Z mul_11: "f32[269952][1]cpu" = 0.5 * heights_1 2025-03-04T21:03:31.8779467Z ctr_y_1: "f32[269952][1]cpu" = getitem_21 + mul_11; getitem_21 = mul_11 = None 2025-03-04T21:03:31.8779709Z 2025-03-04T21:03:31.8780107Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:03:31.8780609Z getitem_22: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:03:31.8780926Z dx_1: "f32[269952, 1][1, 1]cpu" = getitem_22 / 1.0; getitem_22 = None 2025-03-04T21:03:31.8781189Z 2025-03-04T21:03:31.8781564Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:03:31.8782058Z getitem_23: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:03:31.8782373Z dy_1: "f32[269952, 1][1, 1]cpu" = getitem_23 / 1.0; getitem_23 = None 2025-03-04T21:03:31.8782601Z 2025-03-04T21:03:31.8782975Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:03:31.8783465Z getitem_24: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:03:31.8783772Z dw_2: "f32[269952, 1][1, 1]cpu" = getitem_24 / 1.0; getitem_24 = None 2025-03-04T21:03:31.8784007Z 2025-03-04T21:03:31.8784941Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:03:31.8785491Z getitem_25: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))]; deltas_1 = None 2025-03-04T21:03:31.8785861Z dh_2: "f32[269952, 1][1, 1]cpu" = getitem_25 / 1.0; getitem_25 = None 2025-03-04T21:03:31.8786095Z 2025-03-04T21:03:31.8786518Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:03:31.8787039Z dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356); dw_2 = None 2025-03-04T21:03:31.8787292Z 2025-03-04T21:03:31.8787699Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:03:31.8788214Z dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356); dh_2 = None 2025-03-04T21:03:31.8788463Z 2025-03-04T21:03:31.8788872Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:03:31.8789418Z getitem_26: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)] 2025-03-04T21:03:31.8789725Z mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_26; dx_1 = getitem_26 = None 2025-03-04T21:03:31.8790051Z getitem_27: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)]; ctr_x_1 = None 2025-03-04T21:03:31.8790400Z pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_27; mul_12 = getitem_27 = None 2025-03-04T21:03:31.8790655Z 2025-03-04T21:03:31.8791082Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:03:31.8791615Z getitem_28: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)] 2025-03-04T21:03:31.8791927Z mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_28; dy_1 = getitem_28 = None 2025-03-04T21:03:31.8792254Z getitem_29: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)]; ctr_y_1 = None 2025-03-04T21:03:31.8792593Z pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_29; mul_13 = getitem_29 = None 2025-03-04T21:03:31.8793088Z 2025-03-04T21:03:31.8793539Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:03:31.8794049Z exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3); dw_3 = None 2025-03-04T21:03:31.8794487Z getitem_30: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]; widths_1 = None 2025-03-04T21:03:31.8794878Z pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_30; exp_2 = getitem_30 = None 2025-03-04T21:03:31.8795137Z 2025-03-04T21:03:31.8795576Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:03:31.8796134Z exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3); dh_3 = None 2025-03-04T21:03:31.8796478Z getitem_31: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]; heights_1 = None 2025-03-04T21:03:31.8796848Z pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_31; exp_3 = getitem_31 = None 2025-03-04T21:03:31.8797112Z 2025-03-04T21:03:31.8797526Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:03:31.8798008Z mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1 2025-03-04T21:03:31.8798281Z x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16; mul_16 = None 2025-03-04T21:03:31.8798545Z 2025-03-04T21:03:31.8798955Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:03:31.8799428Z mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1 2025-03-04T21:03:31.8799697Z y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17; mul_17 = None 2025-03-04T21:03:31.8799937Z 2025-03-04T21:03:31.8800338Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:03:31.8800831Z mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1; pred_w_1 = None 2025-03-04T21:03:31.8801143Z x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18; pred_ctr_x_1 = mul_18 = None 2025-03-04T21:03:31.8801400Z 2025-03-04T21:03:31.8801801Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:03:31.8802490Z mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1; pred_h_1 = None 2025-03-04T21:03:31.8803131Z y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19; pred_ctr_y_1 = mul_19 = None 2025-03-04T21:03:31.8803412Z 2025-03-04T21:03:31.8803869Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:03:31.8804492Z pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1); x1_1 = y1_1 = x2_1 = y2_1 = None 2025-03-04T21:03:31.8804800Z 2025-03-04T21:03:31.8805231Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:03:31.8805797Z proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4)); pred_boxes_1 = None 2025-03-04T21:03:31.8806074Z 2025-03-04T21:03:31.8806527Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:03:31.8807119Z proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4); proposals_i_1 = None 2025-03-04T21:03:31.8807401Z 2025-03-04T21:03:31.8807873Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:03:31.8808548Z pred_anchor_deltas_i_2: "f32[67488, 4][4, 1]cpu" = l_pred_anchor_deltas_2_.reshape(-1, 4); l_pred_anchor_deltas_2_ = None 2025-03-04T21:03:31.8808885Z 2025-03-04T21:03:31.8809391Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:03:31.8810046Z unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = l_anchors_2_tensor.unsqueeze(0); l_anchors_2_tensor = None 2025-03-04T21:03:31.8810425Z expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1); unsqueeze_2 = None 2025-03-04T21:03:31.8810759Z anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4); expand_2 = None 2025-03-04T21:03:31.8811002Z 2025-03-04T21:03:31.8811446Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:03:31.8812025Z deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_2.float(); pred_anchor_deltas_i_2 = None 2025-03-04T21:03:31.8812304Z 2025-03-04T21:03:31.8813081Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:03:31.8813664Z boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32); anchors_i_2 = None 2025-03-04T21:03:31.8813931Z 2025-03-04T21:03:31.8814328Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:03:31.8814815Z getitem_32: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)] 2025-03-04T21:03:31.8815115Z getitem_33: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T21:03:31.8815438Z widths_2: "f32[67488][1]cpu" = getitem_32 - getitem_33; getitem_32 = getitem_33 = None 2025-03-04T21:03:31.8815703Z 2025-03-04T21:03:31.8816096Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:03:31.8816608Z getitem_34: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)] 2025-03-04T21:03:31.8816904Z getitem_35: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)] 2025-03-04T21:03:31.8817223Z heights_2: "f32[67488][1]cpu" = getitem_34 - getitem_35; getitem_34 = getitem_35 = None 2025-03-04T21:03:31.8817486Z 2025-03-04T21:03:31.8817879Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:03:31.8818358Z getitem_36: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T21:03:31.8818621Z mul_20: "f32[67488][1]cpu" = 0.5 * widths_2 2025-03-04T21:03:31.8818890Z ctr_x_2: "f32[67488][1]cpu" = getitem_36 + mul_20; getitem_36 = mul_20 = None 2025-03-04T21:03:31.8819135Z 2025-03-04T21:03:31.8819527Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:03:31.8820037Z getitem_37: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]; boxes_2 = None 2025-03-04T21:03:31.8820329Z mul_21: "f32[67488][1]cpu" = 0.5 * heights_2 2025-03-04T21:03:31.8820596Z ctr_y_2: "f32[67488][1]cpu" = getitem_37 + mul_21; getitem_37 = mul_21 = None 2025-03-04T21:03:31.8820837Z 2025-03-04T21:03:31.8821232Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:03:31.8821765Z getitem_38: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:03:31.8822096Z dx_2: "f32[67488, 1][1, 1]cpu" = getitem_38 / 1.0; getitem_38 = None 2025-03-04T21:03:31.8822324Z 2025-03-04T21:03:31.8823258Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:03:31.8823827Z getitem_39: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:03:31.8824145Z dy_2: "f32[67488, 1][1, 1]cpu" = getitem_39 / 1.0; getitem_39 = None 2025-03-04T21:03:31.8824376Z 2025-03-04T21:03:31.8824759Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:03:31.8825254Z getitem_40: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:03:31.8825568Z dw_4: "f32[67488, 1][1, 1]cpu" = getitem_40 / 1.0; getitem_40 = None 2025-03-04T21:03:31.8825800Z 2025-03-04T21:03:31.8826183Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:03:31.8826731Z getitem_41: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))]; deltas_2 = None 2025-03-04T21:03:31.8827066Z dh_4: "f32[67488, 1][1, 1]cpu" = getitem_41 / 1.0; getitem_41 = None 2025-03-04T21:03:31.8827285Z 2025-03-04T21:03:31.8827698Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:03:31.8828215Z dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356); dw_4 = None 2025-03-04T21:03:31.8828462Z 2025-03-04T21:03:31.8828869Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:03:31.8829381Z dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356); dh_4 = None 2025-03-04T21:03:31.8829629Z 2025-03-04T21:03:31.8830060Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:03:31.8830605Z getitem_42: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)] 2025-03-04T21:03:31.8830913Z mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_42; dx_2 = getitem_42 = None 2025-03-04T21:03:31.8831243Z getitem_43: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)]; ctr_x_2 = None 2025-03-04T21:03:31.8831593Z pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_43; mul_22 = getitem_43 = None 2025-03-04T21:03:31.8831851Z 2025-03-04T21:03:31.8832288Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:03:31.8833054Z getitem_44: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)] 2025-03-04T21:03:31.8833393Z mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_44; dy_2 = getitem_44 = None 2025-03-04T21:03:31.8833729Z getitem_45: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)]; ctr_y_2 = None 2025-03-04T21:03:31.8834078Z pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_45; mul_23 = getitem_45 = None 2025-03-04T21:03:31.8834402Z 2025-03-04T21:03:31.8834831Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:03:31.8835357Z exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5); dw_5 = None 2025-03-04T21:03:31.8835712Z getitem_46: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]; widths_2 = None 2025-03-04T21:03:31.8836070Z pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_46; exp_4 = getitem_46 = None 2025-03-04T21:03:31.8836330Z 2025-03-04T21:03:31.8836751Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:03:31.8837248Z exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5); dh_5 = None 2025-03-04T21:03:31.8837578Z getitem_47: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]; heights_2 = None 2025-03-04T21:03:31.8837935Z pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_47; exp_5 = getitem_47 = None 2025-03-04T21:03:31.8838185Z 2025-03-04T21:03:31.8838588Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:03:31.8839050Z mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2 2025-03-04T21:03:31.8839330Z x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26; mul_26 = None 2025-03-04T21:03:31.8839567Z 2025-03-04T21:03:31.8839960Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:03:31.8840418Z mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2 2025-03-04T21:03:31.8840677Z y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27; mul_27 = None 2025-03-04T21:03:31.8840913Z 2025-03-04T21:03:31.8841302Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:03:31.8841789Z mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2; pred_w_2 = None 2025-03-04T21:03:31.8842091Z x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28; pred_ctr_x_2 = mul_28 = None 2025-03-04T21:03:31.8842341Z 2025-03-04T21:03:31.8842944Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:03:31.8843507Z mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2; pred_h_2 = None 2025-03-04T21:03:31.8843813Z y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29; pred_ctr_y_2 = mul_29 = None 2025-03-04T21:03:31.8844060Z 2025-03-04T21:03:31.8844498Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:03:31.8845096Z pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1); x1_2 = y1_2 = x2_2 = y2_2 = None 2025-03-04T21:03:31.8845400Z 2025-03-04T21:03:31.8845818Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:03:31.8846372Z proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4)); pred_boxes_2 = None 2025-03-04T21:03:31.8846654Z 2025-03-04T21:03:31.8847128Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:03:31.8847801Z proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4); proposals_i_2 = None 2025-03-04T21:03:31.8848090Z 2025-03-04T21:03:31.8848580Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:03:31.8849253Z pred_anchor_deltas_i_3: "f32[16872, 4][4, 1]cpu" = l_pred_anchor_deltas_3_.reshape(-1, 4); l_pred_anchor_deltas_3_ = None 2025-03-04T21:03:31.8849582Z 2025-03-04T21:03:31.8850096Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:03:31.8850758Z unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = l_anchors_3_tensor.unsqueeze(0); l_anchors_3_tensor = None 2025-03-04T21:03:31.8851144Z expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1); unsqueeze_3 = None 2025-03-04T21:03:31.8851492Z anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4); expand_3 = None 2025-03-04T21:03:31.8851758Z 2025-03-04T21:03:31.8852218Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:03:31.8853039Z deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float(); pred_anchor_deltas_i_3 = None 2025-03-04T21:03:31.8853367Z 2025-03-04T21:03:31.8853766Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:03:31.8854269Z boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32); anchors_i_3 = None 2025-03-04T21:03:31.8854531Z 2025-03-04T21:03:31.8854926Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:03:31.8855412Z getitem_48: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)] 2025-03-04T21:03:31.8855712Z getitem_49: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T21:03:31.8856038Z widths_3: "f32[16872][1]cpu" = getitem_48 - getitem_49; getitem_48 = getitem_49 = None 2025-03-04T21:03:31.8856299Z 2025-03-04T21:03:31.8856693Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:03:31.8857197Z getitem_50: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)] 2025-03-04T21:03:31.8857490Z getitem_51: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)] 2025-03-04T21:03:31.8857814Z heights_3: "f32[16872][1]cpu" = getitem_50 - getitem_51; getitem_50 = getitem_51 = None 2025-03-04T21:03:31.8858081Z 2025-03-04T21:03:31.8858473Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:03:31.8858952Z getitem_52: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T21:03:31.8859225Z mul_30: "f32[16872][1]cpu" = 0.5 * widths_3 2025-03-04T21:03:31.8859506Z ctr_x_3: "f32[16872][1]cpu" = getitem_52 + mul_30; getitem_52 = mul_30 = None 2025-03-04T21:03:31.8859763Z 2025-03-04T21:03:31.8860165Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:03:31.8860683Z getitem_53: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]; boxes_3 = None 2025-03-04T21:03:31.8860988Z mul_31: "f32[16872][1]cpu" = 0.5 * heights_3 2025-03-04T21:03:31.8861260Z ctr_y_3: "f32[16872][1]cpu" = getitem_53 + mul_31; getitem_53 = mul_31 = None 2025-03-04T21:03:31.8861507Z 2025-03-04T21:03:31.8861906Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:03:31.8862435Z getitem_54: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:03:31.8863032Z dx_3: "f32[16872, 1][1, 1]cpu" = getitem_54 / 1.0; getitem_54 = None 2025-03-04T21:03:31.8863302Z 2025-03-04T21:03:31.8863691Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:03:31.8864204Z getitem_55: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:03:31.8864534Z dy_3: "f32[16872, 1][1, 1]cpu" = getitem_55 / 1.0; getitem_55 = None 2025-03-04T21:03:31.8864758Z 2025-03-04T21:03:31.8865137Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:03:31.8865632Z getitem_56: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:03:31.8865946Z dw_6: "f32[16872, 1][1, 1]cpu" = getitem_56 / 1.0; getitem_56 = None 2025-03-04T21:03:31.8866169Z 2025-03-04T21:03:31.8866572Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:03:31.8867095Z getitem_57: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))]; deltas_3 = None 2025-03-04T21:03:31.8867435Z dh_6: "f32[16872, 1][1, 1]cpu" = getitem_57 / 1.0; getitem_57 = None 2025-03-04T21:03:31.8867659Z 2025-03-04T21:03:31.8868077Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:03:31.8868608Z dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356); dw_6 = None 2025-03-04T21:03:31.8868868Z 2025-03-04T21:03:31.8869285Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:03:31.8869811Z dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356); dh_6 = None 2025-03-04T21:03:31.8870068Z 2025-03-04T21:03:31.8870537Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:03:31.8871071Z getitem_58: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)] 2025-03-04T21:03:31.8871386Z mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_58; dx_3 = getitem_58 = None 2025-03-04T21:03:31.8871728Z getitem_59: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)]; ctr_x_3 = None 2025-03-04T21:03:31.8872086Z pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_59; mul_32 = getitem_59 = None 2025-03-04T21:03:31.8872345Z 2025-03-04T21:03:31.8872790Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:03:31.8873346Z getitem_60: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)] 2025-03-04T21:03:31.8873666Z mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_60; dy_3 = getitem_60 = None 2025-03-04T21:03:31.8874002Z getitem_61: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)]; ctr_y_3 = None 2025-03-04T21:03:31.8875826Z pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_61; mul_33 = getitem_61 = None 2025-03-04T21:03:31.8876110Z 2025-03-04T21:03:31.8876578Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:03:31.8877103Z exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7); dw_7 = None 2025-03-04T21:03:31.8877431Z getitem_62: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]; widths_3 = None 2025-03-04T21:03:31.8877781Z pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_62; exp_6 = getitem_62 = None 2025-03-04T21:03:31.8878038Z 2025-03-04T21:03:31.8878452Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:03:31.8878951Z exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7); dh_7 = None 2025-03-04T21:03:31.8879281Z getitem_63: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]; heights_3 = None 2025-03-04T21:03:31.8879635Z pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_63; exp_7 = getitem_63 = None 2025-03-04T21:03:31.8879888Z 2025-03-04T21:03:31.8880286Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:03:31.8880769Z mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3 2025-03-04T21:03:31.8881034Z x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36; mul_36 = None 2025-03-04T21:03:31.8881270Z 2025-03-04T21:03:31.8881665Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:03:31.8882123Z mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3 2025-03-04T21:03:31.8882380Z y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37; mul_37 = None 2025-03-04T21:03:31.8882612Z 2025-03-04T21:03:31.8883233Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:03:31.8883741Z mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3; pred_w_3 = None 2025-03-04T21:03:31.8884044Z x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38; pred_ctr_x_3 = mul_38 = None 2025-03-04T21:03:31.8884297Z 2025-03-04T21:03:31.8884710Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:03:31.8885190Z mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3; pred_h_3 = None 2025-03-04T21:03:31.8885489Z y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39; pred_ctr_y_3 = mul_39 = None 2025-03-04T21:03:31.8885741Z 2025-03-04T21:03:31.8886174Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:03:31.8886772Z pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1); x1_3 = y1_3 = x2_3 = y2_3 = None 2025-03-04T21:03:31.8887080Z 2025-03-04T21:03:31.8887484Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:03:31.8888022Z proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4)); pred_boxes_3 = None 2025-03-04T21:03:31.8888305Z 2025-03-04T21:03:31.8888771Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:03:31.8889379Z proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4); proposals_i_3 = None 2025-03-04T21:03:31.8889669Z 2025-03-04T21:03:31.8890155Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:03:31.8890813Z pred_anchor_deltas_i_4: "f32[4332, 4][4, 1]cpu" = l_pred_anchor_deltas_4_.reshape(-1, 4); l_pred_anchor_deltas_4_ = None 2025-03-04T21:03:31.8891136Z 2025-03-04T21:03:31.8891653Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:03:31.8892323Z unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = l_anchors_4_tensor.unsqueeze(0); l_anchors_4_tensor = None 2025-03-04T21:03:31.8892865Z expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1); unsqueeze_4 = None 2025-03-04T21:03:31.8893264Z anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4); expand_4 = None 2025-03-04T21:03:31.8893521Z 2025-03-04T21:03:31.8893989Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:03:31.8894609Z deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_4.float(); pred_anchor_deltas_i_4 = None 2025-03-04T21:03:31.8894894Z 2025-03-04T21:03:31.8895304Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:03:31.8895806Z boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32); anchors_i_4 = None 2025-03-04T21:03:31.8896072Z 2025-03-04T21:03:31.8896477Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:03:31.8896983Z getitem_64: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)] 2025-03-04T21:03:31.8897295Z getitem_65: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T21:03:31.8897627Z widths_4: "f32[4332][1]cpu" = getitem_64 - getitem_65; getitem_64 = getitem_65 = None 2025-03-04T21:03:31.8897902Z 2025-03-04T21:03:31.8898340Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:03:31.8898823Z getitem_66: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)] 2025-03-04T21:03:31.8899111Z getitem_67: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)] 2025-03-04T21:03:31.8899427Z heights_4: "f32[4332][1]cpu" = getitem_66 - getitem_67; getitem_66 = getitem_67 = None 2025-03-04T21:03:31.8899688Z 2025-03-04T21:03:31.8900084Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:03:31.8900573Z getitem_68: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T21:03:31.8900830Z mul_40: "f32[4332][1]cpu" = 0.5 * widths_4 2025-03-04T21:03:31.8901091Z ctr_x_4: "f32[4332][1]cpu" = getitem_68 + mul_40; getitem_68 = mul_40 = None 2025-03-04T21:03:31.8901333Z 2025-03-04T21:03:31.8901723Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:03:31.8902350Z getitem_69: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]; boxes_4 = None 2025-03-04T21:03:31.8902645Z mul_41: "f32[4332][1]cpu" = 0.5 * heights_4 2025-03-04T21:03:31.8903157Z ctr_y_4: "f32[4332][1]cpu" = getitem_69 + mul_41; getitem_69 = mul_41 = None 2025-03-04T21:03:31.8903407Z 2025-03-04T21:03:31.8903866Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:03:31.8904387Z getitem_70: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:03:31.8904700Z dx_4: "f32[4332, 1][1, 1]cpu" = getitem_70 / 1.0; getitem_70 = None 2025-03-04T21:03:31.8904934Z 2025-03-04T21:03:31.8905334Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:03:31.8905848Z getitem_71: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:03:31.8906176Z dy_4: "f32[4332, 1][1, 1]cpu" = getitem_71 / 1.0; getitem_71 = None 2025-03-04T21:03:31.8906400Z 2025-03-04T21:03:31.8906773Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:03:31.8907266Z getitem_72: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:03:31.8907569Z dw_8: "f32[4332, 1][1, 1]cpu" = getitem_72 / 1.0; getitem_72 = None 2025-03-04T21:03:31.8907841Z 2025-03-04T21:03:31.8908233Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:03:31.8908778Z getitem_73: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))]; deltas_4 = None 2025-03-04T21:03:31.8909117Z dh_8: "f32[4332, 1][1, 1]cpu" = getitem_73 / 1.0; getitem_73 = None 2025-03-04T21:03:31.8909345Z 2025-03-04T21:03:31.8909775Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:03:31.8910309Z dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356); dw_8 = None 2025-03-04T21:03:31.8910569Z 2025-03-04T21:03:31.8910991Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:03:31.8911546Z dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356); dh_8 = None 2025-03-04T21:03:31.8911799Z 2025-03-04T21:03:31.8912234Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:03:31.8913024Z getitem_74: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)] 2025-03-04T21:03:31.8913375Z mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_74; dx_4 = getitem_74 = None 2025-03-04T21:03:31.8913703Z getitem_75: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)]; ctr_x_4 = None 2025-03-04T21:03:31.8914061Z pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_75; mul_42 = getitem_75 = None 2025-03-04T21:03:31.8914407Z 2025-03-04T21:03:31.8914862Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:03:31.8915430Z getitem_76: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)] 2025-03-04T21:03:31.8915744Z mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_76; dy_4 = getitem_76 = None 2025-03-04T21:03:31.8916072Z getitem_77: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)]; ctr_y_4 = None 2025-03-04T21:03:31.8916416Z pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_77; mul_43 = getitem_77 = None 2025-03-04T21:03:31.8916672Z 2025-03-04T21:03:31.8917115Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:03:31.8917633Z exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9); dw_9 = None 2025-03-04T21:03:31.8917959Z getitem_78: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]; widths_4 = None 2025-03-04T21:03:31.8918309Z pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_78; exp_8 = getitem_78 = None 2025-03-04T21:03:31.8918562Z 2025-03-04T21:03:31.8918983Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:03:31.8919480Z exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9); dh_9 = None 2025-03-04T21:03:31.8919812Z getitem_79: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]; heights_4 = None 2025-03-04T21:03:31.8920159Z pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_79; exp_9 = getitem_79 = None 2025-03-04T21:03:31.8920408Z 2025-03-04T21:03:31.8920808Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:03:31.8921303Z mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4 2025-03-04T21:03:31.8921567Z x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46; mul_46 = None 2025-03-04T21:03:31.8921797Z 2025-03-04T21:03:31.8922191Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:03:31.8922648Z mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4 2025-03-04T21:03:31.8924109Z y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47; mul_47 = None 2025-03-04T21:03:31.8924357Z 2025-03-04T21:03:31.8924762Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:03:31.8925248Z mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4; pred_w_4 = None 2025-03-04T21:03:31.8925541Z x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48; pred_ctr_x_4 = mul_48 = None 2025-03-04T21:03:31.8925786Z 2025-03-04T21:03:31.8926197Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:03:31.8926665Z mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4; pred_h_4 = None 2025-03-04T21:03:31.8926955Z y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49; pred_ctr_y_4 = mul_49 = None 2025-03-04T21:03:31.8927195Z 2025-03-04T21:03:31.8927620Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:03:31.8928200Z pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1); x1_4 = y1_4 = x2_4 = y2_4 = None 2025-03-04T21:03:31.8928489Z 2025-03-04T21:03:31.8928895Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:03:31.8929429Z proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4)); pred_boxes_4 = None 2025-03-04T21:03:31.8929699Z 2025-03-04T21:03:31.8930157Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:03:31.8930762Z proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4); proposals_i_4 = None 2025-03-04T21:03:31.8931045Z 2025-03-04T21:03:31.8931647Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T21:03:31.8932337Z arange: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T21:03:31.8932577Z 2025-03-04T21:03:31.8933169Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:31.8933667Z batch_idx: "i64[4][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T21:03:31.8933911Z 2025-03-04T21:03:31.8934425Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:03:31.8935070Z topk = l_pred_objectness_logits_0_.topk(1000, dim = 1); l_pred_objectness_logits_0_ = None 2025-03-04T21:03:31.8935400Z topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0] 2025-03-04T21:03:31.8935661Z topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1]; topk = None 2025-03-04T21:03:31.8935913Z 2025-03-04T21:03:31.8936473Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:03:31.8937111Z getitem_82: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:03:31.8937520Z topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_82, topk_idx)]; proposals_i_5 = getitem_82 = topk_idx = None 2025-03-04T21:03:31.8937857Z 2025-03-04T21:03:31.8938401Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:03:31.8939082Z full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:03:31.8939364Z 2025-03-04T21:03:31.8939764Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:31.8940232Z to_6: "i64[1000][1]cpu" = full.to(device(type='cpu')); full = None 2025-03-04T21:03:31.8940469Z 2025-03-04T21:03:31.8940987Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:03:31.8941641Z topk_1 = l_pred_objectness_logits_1_.topk(1000, dim = 1); l_pred_objectness_logits_1_ = None 2025-03-04T21:03:31.8941968Z topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0] 2025-03-04T21:03:31.8942238Z topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1]; topk_1 = None 2025-03-04T21:03:31.8942463Z 2025-03-04T21:03:31.8943222Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:03:31.8943878Z getitem_86: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:03:31.8944290Z topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_86, topk_idx_1)]; proposals_i_6 = getitem_86 = topk_idx_1 = None 2025-03-04T21:03:31.8944631Z 2025-03-04T21:03:31.8945176Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:03:31.8945856Z full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:03:31.8946130Z 2025-03-04T21:03:31.8946503Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:31.8946971Z to_7: "i64[1000][1]cpu" = full_1.to(device(type='cpu')); full_1 = None 2025-03-04T21:03:31.8947202Z 2025-03-04T21:03:31.8947709Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:03:31.8948371Z topk_2 = l_pred_objectness_logits_2_.topk(1000, dim = 1); l_pred_objectness_logits_2_ = None 2025-03-04T21:03:31.8948701Z topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0] 2025-03-04T21:03:31.8948975Z topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1]; topk_2 = None 2025-03-04T21:03:31.8949206Z 2025-03-04T21:03:31.8949750Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:03:31.8950423Z getitem_90: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:03:31.8950843Z topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_90, topk_idx_2)]; proposals_i_7 = getitem_90 = topk_idx_2 = None 2025-03-04T21:03:31.8951187Z 2025-03-04T21:03:31.8951735Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:03:31.8952418Z full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:03:31.8952870Z 2025-03-04T21:03:31.8953319Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:31.8953834Z to_8: "i64[1000][1]cpu" = full_2.to(device(type='cpu')); full_2 = None 2025-03-04T21:03:31.8954079Z 2025-03-04T21:03:31.8954654Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:03:31.8955344Z topk_3 = l_pred_objectness_logits_3_.topk(1000, dim = 1); l_pred_objectness_logits_3_ = None 2025-03-04T21:03:31.8955679Z topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0] 2025-03-04T21:03:31.8955956Z topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1]; topk_3 = None 2025-03-04T21:03:31.8956192Z 2025-03-04T21:03:31.8956736Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:03:31.8957392Z getitem_94: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:03:31.8957820Z topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_94, topk_idx_3)]; proposals_i_8 = getitem_94 = topk_idx_3 = None 2025-03-04T21:03:31.8958172Z 2025-03-04T21:03:31.8958715Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:03:31.8959416Z full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:03:31.8959725Z 2025-03-04T21:03:31.8960107Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:31.8960580Z to_9: "i64[1000][1]cpu" = full_3.to(device(type='cpu')); full_3 = None 2025-03-04T21:03:31.8960819Z 2025-03-04T21:03:31.8961328Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:03:31.8961983Z topk_4 = l_pred_objectness_logits_4_.topk(1000, dim = 1); l_pred_objectness_logits_4_ = None 2025-03-04T21:03:31.8962309Z topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0] 2025-03-04T21:03:31.8962580Z topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1]; topk_4 = None 2025-03-04T21:03:31.8963038Z 2025-03-04T21:03:31.8963635Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:03:31.8964343Z getitem_98: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T21:03:31.8964789Z topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_98, topk_idx_4)]; proposals_i_9 = getitem_98 = topk_idx_4 = None 2025-03-04T21:03:31.8965140Z 2025-03-04T21:03:31.8965681Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:03:31.8966349Z full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:03:31.8966625Z 2025-03-04T21:03:31.8966994Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:03:31.8967461Z to_10: "i64[1000][1]cpu" = full_4.to(device(type='cpu')); full_4 = None 2025-03-04T21:03:31.8967713Z 2025-03-04T21:03:31.8968057Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:03:31.8968756Z topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1); topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None 2025-03-04T21:03:31.8969226Z 2025-03-04T21:03:31.8969581Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:03:31.8970357Z topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1); topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None 2025-03-04T21:03:31.8970922Z 2025-03-04T21:03:31.8971269Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:03:31.8971769Z level_ids: "i64[5000][1]cpu" = torch.cat([to_6, to_7, to_8, to_9, to_10], 0); to_6 = to_7 = to_8 = to_9 = to_10 = level_ids = None 2025-03-04T21:03:31.8972061Z 2025-03-04T21:03:31.8972517Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T21:03:31.8973382Z getitem_100: "f32[5000, 4][4, 1]cpu" = topk_proposals[0]; topk_proposals = None 2025-03-04T21:03:31.8973655Z 2025-03-04T21:03:31.8974035Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:03:31.8974530Z tensor: "f32[5000, 4][4, 1]cpu" = getitem_100.to(torch.float32); getitem_100 = None 2025-03-04T21:03:31.8974791Z 2025-03-04T21:03:31.8975252Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T21:03:31.8975814Z scores_per_img: "f32[5000][1]cpu" = topk_scores[0]; topk_scores = None 2025-03-04T21:03:31.8976062Z 2025-03-04T21:03:31.8976629Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T21:03:31.8977290Z isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor); tensor = None 2025-03-04T21:03:31.8977617Z all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:03:31.8977941Z isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T21:03:31.8978272Z valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T21:03:31.8978522Z 2025-03-04T21:03:31.8978970Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T21:03:31.8979495Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T21:03:31.8979719Z 2025-03-04T21:03:38.1508381Z 2025-03-04T21:03:38.1511157Z class GraphModule(torch.nn.Module): 2025-03-04T21:03:38.1513738Z def forward(self, L_stack0_: "f32[4000, 256, 7, 7][12544, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 12544][12544, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"): 2025-03-04T21:03:38.1516020Z l_stack0_ = L_stack0_ 2025-03-04T21:03:38.1516370Z l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_ 2025-03-04T21:03:38.1516856Z l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_ 2025-03-04T21:03:38.1517328Z l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_ 2025-03-04T21:03:38.1517797Z l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_ 2025-03-04T21:03:38.1518318Z l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_ 2025-03-04T21:03:38.1518952Z l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_ 2025-03-04T21:03:38.1519567Z l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ 2025-03-04T21:03:38.1520185Z l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ 2025-03-04T21:03:38.1520652Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:03:38.1521050Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:03:38.1521443Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:03:38.1521826Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:03:38.1522114Z 2025-03-04T21:03:38.1522502Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x) 2025-03-04T21:03:38.1522979Z x: "f32[4000, 12544][12544, 1]cpu" = l_stack0_.flatten(1, -1); l_stack0_ = None 2025-03-04T21:03:38.1523708Z x_1: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_); x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None 2025-03-04T21:03:38.1524425Z x_2: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False); x_1 = None 2025-03-04T21:03:38.1525133Z x_3: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_); x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None 2025-03-04T21:03:38.1525831Z x_4: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False); x_3 = None 2025-03-04T21:03:38.1526104Z 2025-03-04T21:03:38.1526502Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x) 2025-03-04T21:03:38.1527475Z scores: "f32[4000, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_); l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None 2025-03-04T21:03:38.1528165Z 2025-03-04T21:03:38.1528577Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x) 2025-03-04T21:03:38.1529576Z proposal_deltas: "f32[4000, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_); x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None 2025-03-04T21:03:38.1530303Z 2025-03-04T21:03:38.1530696Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:03:38.1531147Z size = l_proposals_0_fields_proposal_boxes_tensor.size() 2025-03-04T21:03:38.1531397Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:03:38.1531624Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:03:38.1531890Z size_1 = l_proposals_1_fields_proposal_boxes_tensor.size() 2025-03-04T21:03:38.1532130Z getitem_2: "Sym(s1)" = size_1[0] 2025-03-04T21:03:38.1532357Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:03:38.1532657Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:03:38.1532893Z getitem_4: "Sym(s2)" = size_2[0] 2025-03-04T21:03:38.1533112Z getitem_5 = size_2[1]; size_2 = getitem_5 = None 2025-03-04T21:03:38.1533368Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:03:38.1533602Z getitem_6: "Sym(s3)" = size_3[0] 2025-03-04T21:03:38.1533819Z getitem_7 = size_3[1]; size_3 = getitem_7 = None 2025-03-04T21:03:38.1534027Z 2025-03-04T21:03:38.1534389Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:03:38.1535138Z proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0) 2025-03-04T21:03:38.1535673Z 2025-03-04T21:03:38.1536119Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:03:38.1536706Z deltas: "f32[4000, 320][320, 1]cpu" = proposal_deltas.float(); proposal_deltas = None 2025-03-04T21:03:38.1536969Z 2025-03-04T21:03:38.1537360Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:03:38.1537879Z boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:03:38.1538153Z 2025-03-04T21:03:38.1538545Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:03:38.1539036Z getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:03:38.1539341Z getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:03:38.1539660Z widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9; getitem_8 = getitem_9 = None 2025-03-04T21:03:38.1539913Z 2025-03-04T21:03:38.1540348Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:03:38.1540856Z getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:03:38.1541167Z getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:03:38.1541499Z heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T21:03:38.1541767Z 2025-03-04T21:03:38.1542159Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:03:38.1542648Z getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:03:38.1542925Z mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths 2025-03-04T21:03:38.1543194Z ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul; getitem_12 = mul = None 2025-03-04T21:03:38.1543437Z 2025-03-04T21:03:38.1543831Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:03:38.1544349Z getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:03:38.1544649Z mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights 2025-03-04T21:03:38.1544928Z ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1; getitem_13 = mul_1 = None 2025-03-04T21:03:38.1545215Z 2025-03-04T21:03:38.1545646Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:03:38.1546159Z getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:03:38.1546480Z dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0; getitem_14 = None 2025-03-04T21:03:38.1546709Z 2025-03-04T21:03:38.1547088Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:03:38.1547596Z getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:03:38.1547909Z dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0; getitem_15 = None 2025-03-04T21:03:38.1548135Z 2025-03-04T21:03:38.1548516Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:03:38.1549023Z getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:03:38.1549356Z dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0; getitem_16 = None 2025-03-04T21:03:38.1549586Z 2025-03-04T21:03:38.1549971Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:03:38.1550512Z getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:03:38.1550849Z dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0; getitem_17 = None 2025-03-04T21:03:38.1551075Z 2025-03-04T21:03:38.1551495Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:03:38.1552029Z dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:03:38.1552279Z 2025-03-04T21:03:38.1552735Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:03:38.1553282Z dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:03:38.1553531Z 2025-03-04T21:03:38.1553966Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:03:38.1554608Z getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:03:38.1554935Z mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18; dx = getitem_18 = None 2025-03-04T21:03:38.1555273Z getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:03:38.1555628Z pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19; mul_2 = getitem_19 = None 2025-03-04T21:03:38.1555889Z 2025-03-04T21:03:38.1556339Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:03:38.1556903Z getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:03:38.1557230Z mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20; dy = getitem_20 = None 2025-03-04T21:03:38.1557566Z getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:03:38.1557939Z pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21; mul_3 = getitem_21 = None 2025-03-04T21:03:38.1558232Z 2025-03-04T21:03:38.1558655Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:03:38.1559186Z exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:03:38.1559524Z getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:03:38.1559877Z pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22; exp = getitem_22 = None 2025-03-04T21:03:38.1560133Z 2025-03-04T21:03:38.1560581Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:03:38.1561108Z exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:03:38.1561454Z getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:03:38.1561821Z pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23; exp_1 = getitem_23 = None 2025-03-04T21:03:38.1562080Z 2025-03-04T21:03:38.1562472Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:03:38.1562921Z mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:03:38.1563168Z x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:03:38.1563391Z 2025-03-04T21:03:38.1563775Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:03:38.1564219Z mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:03:38.1564465Z y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:03:38.1564687Z 2025-03-04T21:03:38.1565067Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:03:38.1565529Z mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:03:38.1565824Z x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:03:38.1566058Z 2025-03-04T21:03:38.1566433Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:03:38.1566887Z mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:03:38.1567161Z y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:03:38.1567395Z 2025-03-04T21:03:38.1567814Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:03:38.1568381Z pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:03:38.1568663Z 2025-03-04T21:03:38.1569071Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:03:38.1569609Z predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320)); pred_boxes = None 2025-03-04T21:03:38.1569884Z 2025-03-04T21:03:38.1570313Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:03:38.1570989Z split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]); predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None 2025-03-04T21:03:38.1571419Z boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0] 2025-03-04T21:03:38.1571702Z getitem_25: "f32[s1, 320][320, 1]cpu" = split[1]; getitem_25 = None 2025-03-04T21:03:38.1571995Z getitem_26: "f32[s2, 320][320, 1]cpu" = split[2]; getitem_26 = None 2025-03-04T21:03:38.1572295Z getitem_27: "f32[s3, 320][320, 1]cpu" = split[3]; split = getitem_27 = None 2025-03-04T21:03:38.1572538Z 2025-03-04T21:03:38.1572914Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:03:38.1573463Z size_4 = l_proposals_0_fields_proposal_boxes_tensor.size(); l_proposals_0_fields_proposal_boxes_tensor = None 2025-03-04T21:03:38.1573806Z getitem_28: "Sym(s0)" = size_4[0] 2025-03-04T21:03:38.1574038Z getitem_29 = size_4[1]; size_4 = getitem_29 = None 2025-03-04T21:03:38.1574404Z size_5 = l_proposals_1_fields_proposal_boxes_tensor.size(); l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:03:38.1574742Z getitem_30: "Sym(s1)" = size_5[0] 2025-03-04T21:03:38.1574984Z getitem_31 = size_5[1]; size_5 = getitem_31 = None 2025-03-04T21:03:38.1575336Z size_6 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:03:38.1575664Z getitem_32: "Sym(s2)" = size_6[0] 2025-03-04T21:03:38.1575889Z getitem_33 = size_6[1]; size_6 = getitem_33 = None 2025-03-04T21:03:38.1576234Z size_7 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:03:38.1576560Z getitem_34: "Sym(s3)" = size_7[0] 2025-03-04T21:03:38.1576779Z getitem_35 = size_7[1]; size_7 = getitem_35 = None 2025-03-04T21:03:38.1576985Z 2025-03-04T21:03:38.1577395Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:03:38.1577937Z probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1); scores = None 2025-03-04T21:03:38.1578212Z 2025-03-04T21:03:38.1578679Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:03:38.1579327Z split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0); probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None 2025-03-04T21:03:38.1579725Z scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:03:38.1580001Z getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1]; getitem_37 = None 2025-03-04T21:03:38.1580280Z getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2]; getitem_38 = None 2025-03-04T21:03:38.1580576Z getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_39 = None 2025-03-04T21:03:38.1580906Z 2025-03-04T21:03:38.1582394Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:03:38.1583132Z isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:03:38.1583475Z all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:03:38.1583829Z isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:03:38.1584302Z all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:03:38.1584719Z valid_mask: "b8[s0][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:03:38.1585047Z 2025-03-04T21:03:38.1585657Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:03:38.1586186Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:03:38.1586415Z 2025-03-04T21:03:38.1592115Z 2025-03-04T21:03:38.1592681Z class GraphModule(torch.nn.Module): 2025-03-04T21:03:38.1594880Z def forward(self, L_stack0_: "f32[4000, 256, 7, 7][12544, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 12544][12544, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"): 2025-03-04T21:03:38.1597263Z l_stack0_ = L_stack0_ 2025-03-04T21:03:38.1597612Z l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_ 2025-03-04T21:03:38.1598095Z l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_ 2025-03-04T21:03:38.1598575Z l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_ 2025-03-04T21:03:38.1599045Z l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_ 2025-03-04T21:03:38.1599570Z l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_ 2025-03-04T21:03:38.1600171Z l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_ 2025-03-04T21:03:38.1600736Z l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ 2025-03-04T21:03:38.1601296Z l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ 2025-03-04T21:03:38.1601777Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:03:38.1602388Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:03:38.1602790Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:03:38.1603189Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:03:38.1603483Z 2025-03-04T21:03:38.1603880Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x) 2025-03-04T21:03:38.1604375Z x: "f32[4000, 12544][12544, 1]cpu" = l_stack0_.flatten(1, -1); l_stack0_ = None 2025-03-04T21:03:38.1605082Z x_1: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_); x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None 2025-03-04T21:03:38.1606421Z x_2: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False); x_1 = None 2025-03-04T21:03:38.1607161Z x_3: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_); x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None 2025-03-04T21:03:38.1607856Z x_4: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False); x_3 = None 2025-03-04T21:03:38.1608119Z 2025-03-04T21:03:38.1608505Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x) 2025-03-04T21:03:38.1609491Z scores: "f32[4000, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_); l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None 2025-03-04T21:03:38.1610196Z 2025-03-04T21:03:38.1610644Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x) 2025-03-04T21:03:38.1611680Z proposal_deltas: "f32[4000, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_); x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None 2025-03-04T21:03:38.1612455Z 2025-03-04T21:03:38.1612854Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:03:38.1613368Z size = l_proposals_0_fields_proposal_boxes_tensor.size() 2025-03-04T21:03:38.1613651Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:03:38.1613909Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:03:38.1614215Z size_1 = l_proposals_1_fields_proposal_boxes_tensor.size() 2025-03-04T21:03:38.1614494Z getitem_2: "Sym(s1)" = size_1[0] 2025-03-04T21:03:38.1614800Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:03:38.1615076Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:03:38.1615328Z getitem_4: "Sym(s2)" = size_2[0] 2025-03-04T21:03:38.1615564Z getitem_5 = size_2[1]; size_2 = getitem_5 = None 2025-03-04T21:03:38.1615838Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:03:38.1616086Z getitem_6: "Sym(s3)" = size_3[0] 2025-03-04T21:03:38.1616325Z getitem_7 = size_3[1]; size_3 = getitem_7 = None 2025-03-04T21:03:38.1616535Z 2025-03-04T21:03:38.1616905Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:03:38.1617679Z proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0) 2025-03-04T21:03:38.1618235Z 2025-03-04T21:03:38.1618735Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:03:38.1619306Z deltas: "f32[4000, 320][320, 1]cpu" = proposal_deltas.float(); proposal_deltas = None 2025-03-04T21:03:38.1619575Z 2025-03-04T21:03:38.1619976Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:03:38.1620537Z boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:03:38.1620818Z 2025-03-04T21:03:38.1621221Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:03:38.1621760Z getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:03:38.1622063Z getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:03:38.1622386Z widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9; getitem_8 = getitem_9 = None 2025-03-04T21:03:38.1622645Z 2025-03-04T21:03:38.1623056Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:03:38.1623550Z getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:03:38.1623853Z getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:03:38.1624201Z heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T21:03:38.1624467Z 2025-03-04T21:03:38.1624856Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:03:38.1625341Z getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:03:38.1625614Z mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths 2025-03-04T21:03:38.1625879Z ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul; getitem_12 = mul = None 2025-03-04T21:03:38.1626114Z 2025-03-04T21:03:38.1626501Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:03:38.1627009Z getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:03:38.1627306Z mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights 2025-03-04T21:03:38.1627593Z ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1; getitem_13 = mul_1 = None 2025-03-04T21:03:38.1627838Z 2025-03-04T21:03:38.1628259Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:03:38.1628766Z getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:03:38.1629090Z dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0; getitem_14 = None 2025-03-04T21:03:38.1629325Z 2025-03-04T21:03:38.1629708Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:03:38.1630210Z getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:03:38.1630537Z dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0; getitem_15 = None 2025-03-04T21:03:38.1630779Z 2025-03-04T21:03:38.1631168Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:03:38.1631676Z getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:03:38.1632000Z dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0; getitem_16 = None 2025-03-04T21:03:38.1632240Z 2025-03-04T21:03:38.1632681Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:03:38.1633271Z getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:03:38.1633633Z dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0; getitem_17 = None 2025-03-04T21:03:38.1633872Z 2025-03-04T21:03:38.1634433Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:03:38.1635013Z dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:03:38.1635289Z 2025-03-04T21:03:38.1635714Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:03:38.1636238Z dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:03:38.1636493Z 2025-03-04T21:03:38.1636932Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:03:38.1637504Z getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:03:38.1637829Z mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18; dx = getitem_18 = None 2025-03-04T21:03:38.1638157Z getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:03:38.1638503Z pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19; mul_2 = getitem_19 = None 2025-03-04T21:03:38.1638757Z 2025-03-04T21:03:38.1639195Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:03:38.1639740Z getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:03:38.1640058Z mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20; dy = getitem_20 = None 2025-03-04T21:03:38.1640387Z getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:03:38.1640744Z pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21; mul_3 = getitem_21 = None 2025-03-04T21:03:38.1640995Z 2025-03-04T21:03:38.1641414Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:03:38.1641917Z exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:03:38.1642240Z getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:03:38.1642581Z pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22; exp = getitem_22 = None 2025-03-04T21:03:38.1642821Z 2025-03-04T21:03:38.1643238Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:03:38.1643743Z exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:03:38.1644071Z getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:03:38.1644419Z pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23; exp_1 = getitem_23 = None 2025-03-04T21:03:38.1644667Z 2025-03-04T21:03:38.1645070Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:03:38.1645533Z mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:03:38.1645828Z x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:03:38.1646078Z 2025-03-04T21:03:38.1646471Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:03:38.1646933Z mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:03:38.1647188Z y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:03:38.1647414Z 2025-03-04T21:03:38.1647795Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:03:38.1648257Z mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:03:38.1648542Z x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:03:38.1648786Z 2025-03-04T21:03:38.1649165Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:03:38.1649621Z mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:03:38.1649915Z y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:03:38.1650155Z 2025-03-04T21:03:38.1650577Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:03:38.1651142Z pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:03:38.1651433Z 2025-03-04T21:03:38.1651857Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:03:38.1652401Z predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320)); pred_boxes = None 2025-03-04T21:03:38.1652676Z 2025-03-04T21:03:38.1653107Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:03:38.1653798Z split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]); predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None 2025-03-04T21:03:38.1654219Z boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0] 2025-03-04T21:03:38.1654500Z getitem_25: "f32[s1, 320][320, 1]cpu" = split[1]; getitem_25 = None 2025-03-04T21:03:38.1654800Z getitem_26: "f32[s2, 320][320, 1]cpu" = split[2]; getitem_26 = None 2025-03-04T21:03:38.1655092Z getitem_27: "f32[s3, 320][320, 1]cpu" = split[3]; split = getitem_27 = None 2025-03-04T21:03:38.1655334Z 2025-03-04T21:03:38.1655701Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:03:38.1656241Z size_4 = l_proposals_0_fields_proposal_boxes_tensor.size(); l_proposals_0_fields_proposal_boxes_tensor = None 2025-03-04T21:03:38.1657625Z getitem_28: "Sym(s0)" = size_4[0] 2025-03-04T21:03:38.1657875Z getitem_29 = size_4[1]; size_4 = getitem_29 = None 2025-03-04T21:03:38.1658236Z size_5 = l_proposals_1_fields_proposal_boxes_tensor.size(); l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:03:38.1658575Z getitem_30: "Sym(s1)" = size_5[0] 2025-03-04T21:03:38.1658793Z getitem_31 = size_5[1]; size_5 = getitem_31 = None 2025-03-04T21:03:38.1659142Z size_6 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:03:38.1659471Z getitem_32: "Sym(s2)" = size_6[0] 2025-03-04T21:03:38.1659732Z getitem_33 = size_6[1]; size_6 = getitem_33 = None 2025-03-04T21:03:38.1660098Z size_7 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:03:38.1660425Z getitem_34: "Sym(s3)" = size_7[0] 2025-03-04T21:03:38.1660644Z getitem_35 = size_7[1]; size_7 = getitem_35 = None 2025-03-04T21:03:38.1660857Z 2025-03-04T21:03:38.1661281Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:03:38.1661848Z probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1); scores = None 2025-03-04T21:03:38.1662134Z 2025-03-04T21:03:38.1662582Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:03:38.1663276Z split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0); probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None 2025-03-04T21:03:38.1663685Z scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:03:38.1663985Z getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1]; getitem_37 = None 2025-03-04T21:03:38.1664697Z getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2]; getitem_38 = None 2025-03-04T21:03:38.1665011Z getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_39 = None 2025-03-04T21:03:38.1665260Z 2025-03-04T21:03:38.1665815Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:03:38.1666506Z isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:03:38.1666863Z all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:03:38.1667294Z isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:03:38.1667628Z all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:03:38.1668044Z valid_mask: "b8[s0][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:03:38.1668278Z 2025-03-04T21:03:38.1668810Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:03:38.1669332Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:03:38.1669561Z 2025-03-04T21:03:38.1674223Z 2025-03-04T21:03:38.1674569Z class GraphModule(torch.nn.Module): 2025-03-04T21:03:38.1676719Z def forward(self, L_stack0_: "f32[4000, 256, 7, 7][12544, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 12544][12544, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"): 2025-03-04T21:03:38.1678942Z l_stack0_ = L_stack0_ 2025-03-04T21:03:38.1684795Z l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_ 2025-03-04T21:03:38.1686924Z l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_ 2025-03-04T21:03:38.1690173Z l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_ 2025-03-04T21:03:38.1690786Z l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_ 2025-03-04T21:03:38.1696104Z l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_ 2025-03-04T21:03:38.1698769Z l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_ 2025-03-04T21:03:38.1699472Z l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ 2025-03-04T21:03:38.1704274Z l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ 2025-03-04T21:03:38.1709722Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:03:38.1710374Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:03:38.1710865Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:03:38.1711375Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:03:38.1711677Z 2025-03-04T21:03:38.1712447Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x) 2025-03-04T21:03:38.1713056Z x: "f32[4000, 12544][12544, 1]cpu" = l_stack0_.flatten(1, -1); l_stack0_ = None 2025-03-04T21:03:38.1713976Z x_1: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_); x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None 2025-03-04T21:03:38.1714838Z x_2: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False); x_1 = None 2025-03-04T21:03:38.1715664Z x_3: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_); x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None 2025-03-04T21:03:38.1716405Z x_4: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False); x_3 = None 2025-03-04T21:03:38.1716704Z 2025-03-04T21:03:38.1717138Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x) 2025-03-04T21:03:38.1718189Z scores: "f32[4000, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_); l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None 2025-03-04T21:03:38.1718924Z 2025-03-04T21:03:38.1719356Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x) 2025-03-04T21:03:38.1720451Z proposal_deltas: "f32[4000, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_); x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None 2025-03-04T21:03:38.1721251Z 2025-03-04T21:03:38.1721649Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:03:38.1722127Z size = l_proposals_0_fields_proposal_boxes_tensor.size() 2025-03-04T21:03:38.1722387Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:03:38.1722626Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:03:38.1722909Z size_1 = l_proposals_1_fields_proposal_boxes_tensor.size() 2025-03-04T21:03:38.1723175Z getitem_2: "Sym(s1)" = size_1[0] 2025-03-04T21:03:38.1723416Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:03:38.1723688Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:03:38.1723938Z getitem_4: "Sym(s2)" = size_2[0] 2025-03-04T21:03:38.1724169Z getitem_5 = size_2[1]; size_2 = getitem_5 = None 2025-03-04T21:03:38.1724439Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:03:38.1724706Z getitem_6: "Sym(s3)" = size_3[0] 2025-03-04T21:03:38.1724934Z getitem_7 = size_3[1]; size_3 = getitem_7 = None 2025-03-04T21:03:38.1725155Z 2025-03-04T21:03:38.1725544Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:03:38.1726312Z proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0) 2025-03-04T21:03:38.1726849Z 2025-03-04T21:03:38.1727305Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:03:38.1727880Z deltas: "f32[4000, 320][320, 1]cpu" = proposal_deltas.float(); proposal_deltas = None 2025-03-04T21:03:38.1728149Z 2025-03-04T21:03:38.1728556Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:03:38.1729074Z boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:03:38.1729351Z 2025-03-04T21:03:38.1729748Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:03:38.1730242Z getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:03:38.1730553Z getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:03:38.1730877Z widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9; getitem_8 = getitem_9 = None 2025-03-04T21:03:38.1731138Z 2025-03-04T21:03:38.1731543Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:03:38.1732039Z getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:03:38.1732339Z getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:03:38.1732671Z heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T21:03:38.1732942Z 2025-03-04T21:03:38.1733332Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:03:38.1733848Z getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:03:38.1734118Z mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths 2025-03-04T21:03:38.1734380Z ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul; getitem_12 = mul = None 2025-03-04T21:03:38.1734616Z 2025-03-04T21:03:38.1735003Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:03:38.1735499Z getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:03:38.1735791Z mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights 2025-03-04T21:03:38.1736062Z ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1; getitem_13 = mul_1 = None 2025-03-04T21:03:38.1736304Z 2025-03-04T21:03:38.1736710Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:03:38.1737207Z getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:03:38.1737540Z dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0; getitem_14 = None 2025-03-04T21:03:38.1737764Z 2025-03-04T21:03:38.1738147Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:03:38.1738638Z getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:03:38.1738948Z dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0; getitem_15 = None 2025-03-04T21:03:38.1739170Z 2025-03-04T21:03:38.1739543Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:03:38.1740032Z getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:03:38.1740337Z dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0; getitem_16 = None 2025-03-04T21:03:38.1740559Z 2025-03-04T21:03:38.1740956Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:03:38.1741479Z getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:03:38.1741809Z dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0; getitem_17 = None 2025-03-04T21:03:38.1742028Z 2025-03-04T21:03:38.1742445Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:03:38.1742963Z dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:03:38.1743210Z 2025-03-04T21:03:38.1743615Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:03:38.1744120Z dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:03:38.1744359Z 2025-03-04T21:03:38.1744771Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:03:38.1745302Z getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:03:38.1745614Z mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18; dx = getitem_18 = None 2025-03-04T21:03:38.1745956Z getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:03:38.1746307Z pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19; mul_2 = getitem_19 = None 2025-03-04T21:03:38.1746553Z 2025-03-04T21:03:38.1746982Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:03:38.1747516Z getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:03:38.1747821Z mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20; dy = getitem_20 = None 2025-03-04T21:03:38.1748142Z getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:03:38.1748472Z pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21; mul_3 = getitem_21 = None 2025-03-04T21:03:38.1748718Z 2025-03-04T21:03:38.1749130Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:03:38.1749630Z exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:03:38.1749977Z getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:03:38.1750322Z pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22; exp = getitem_22 = None 2025-03-04T21:03:38.1750565Z 2025-03-04T21:03:38.1750988Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:03:38.1751494Z exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:03:38.1751828Z getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:03:38.1752183Z pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23; exp_1 = getitem_23 = None 2025-03-04T21:03:38.1752433Z 2025-03-04T21:03:38.1752844Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:03:38.1753333Z mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:03:38.1753599Z x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:03:38.1753843Z 2025-03-04T21:03:38.1754360Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:03:38.1754849Z mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:03:38.1755113Z y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:03:38.1755358Z 2025-03-04T21:03:38.1755769Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:03:38.1756244Z mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:03:38.1756536Z x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:03:38.1756782Z 2025-03-04T21:03:38.1757178Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:03:38.1757653Z mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:03:38.1757937Z y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:03:38.1758183Z 2025-03-04T21:03:38.1758659Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:03:38.1759298Z pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:03:38.1759601Z 2025-03-04T21:03:38.1760039Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:03:38.1760629Z predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320)); pred_boxes = None 2025-03-04T21:03:38.1760914Z 2025-03-04T21:03:38.1761367Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:03:38.1762053Z split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]); predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None 2025-03-04T21:03:38.1762474Z boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0] 2025-03-04T21:03:38.1762752Z getitem_25: "f32[s1, 320][320, 1]cpu" = split[1]; getitem_25 = None 2025-03-04T21:03:38.1763037Z getitem_26: "f32[s2, 320][320, 1]cpu" = split[2]; getitem_26 = None 2025-03-04T21:03:38.1763358Z getitem_27: "f32[s3, 320][320, 1]cpu" = split[3]; split = getitem_27 = None 2025-03-04T21:03:38.1763613Z 2025-03-04T21:03:38.1763998Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:03:38.1764563Z size_4 = l_proposals_0_fields_proposal_boxes_tensor.size(); l_proposals_0_fields_proposal_boxes_tensor = None 2025-03-04T21:03:38.1764915Z getitem_28: "Sym(s0)" = size_4[0] 2025-03-04T21:03:38.1765162Z getitem_29 = size_4[1]; size_4 = getitem_29 = None 2025-03-04T21:03:38.1765536Z size_5 = l_proposals_1_fields_proposal_boxes_tensor.size(); l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:03:38.1765890Z getitem_30: "Sym(s1)" = size_5[0] 2025-03-04T21:03:38.1766129Z getitem_31 = size_5[1]; size_5 = getitem_31 = None 2025-03-04T21:03:38.1766531Z size_6 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:03:38.1766926Z getitem_32: "Sym(s2)" = size_6[0] 2025-03-04T21:03:38.1767168Z getitem_33 = size_6[1]; size_6 = getitem_33 = None 2025-03-04T21:03:38.1767514Z size_7 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:03:38.1767842Z getitem_34: "Sym(s3)" = size_7[0] 2025-03-04T21:03:38.1768063Z getitem_35 = size_7[1]; size_7 = getitem_35 = None 2025-03-04T21:03:38.1768270Z 2025-03-04T21:03:38.1768680Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:03:38.1769228Z probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1); scores = None 2025-03-04T21:03:38.1769511Z 2025-03-04T21:03:38.1769952Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:03:38.1770602Z split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0); probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None 2025-03-04T21:03:38.1771001Z scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:03:38.1771274Z getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1]; getitem_37 = None 2025-03-04T21:03:38.1771559Z getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2]; getitem_38 = None 2025-03-04T21:03:38.1771851Z getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_39 = None 2025-03-04T21:03:38.1772111Z 2025-03-04T21:03:38.1772658Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:03:38.1773332Z isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:03:38.1773657Z all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:03:38.1773976Z isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:03:38.1774299Z all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:03:38.1774577Z valid_mask: "b8[s0][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:03:38.1774804Z 2025-03-04T21:03:38.1775236Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:03:38.1775751Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:03:38.1775991Z 2025-03-04T21:03:38.1776117Z 2025-03-04T21:03:38.1776204Z class GraphModule(torch.nn.Module): 2025-03-04T21:03:38.1778085Z def forward(self, L_stack0_: "f32[4000, 256, 7, 7][12544, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 12544][12544, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"): 2025-03-04T21:03:38.1780079Z l_stack0_ = L_stack0_ 2025-03-04T21:03:38.1780418Z l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_ 2025-03-04T21:03:38.1780888Z l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_ 2025-03-04T21:03:38.1781352Z l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_ 2025-03-04T21:03:38.1781820Z l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_ 2025-03-04T21:03:38.1782332Z l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_ 2025-03-04T21:03:38.1782897Z l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_ 2025-03-04T21:03:38.1783462Z l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ 2025-03-04T21:03:38.1784020Z l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ 2025-03-04T21:03:38.1784502Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:03:38.1784905Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:03:38.1785320Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:03:38.1785736Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:03:38.1786031Z 2025-03-04T21:03:38.1786400Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x) 2025-03-04T21:03:38.1786874Z x: "f32[4000, 12544][12544, 1]cpu" = l_stack0_.flatten(1, -1); l_stack0_ = None 2025-03-04T21:03:38.1787570Z x_1: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_); x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None 2025-03-04T21:03:38.1788267Z x_2: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False); x_1 = None 2025-03-04T21:03:38.1788997Z x_3: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_); x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None 2025-03-04T21:03:38.1789734Z x_4: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False); x_3 = None 2025-03-04T21:03:38.1790009Z 2025-03-04T21:03:38.1790421Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x) 2025-03-04T21:03:38.1791378Z scores: "f32[4000, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_); l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None 2025-03-04T21:03:38.1792099Z 2025-03-04T21:03:38.1792525Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x) 2025-03-04T21:03:38.1793609Z proposal_deltas: "f32[4000, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_); x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None 2025-03-04T21:03:38.1794520Z 2025-03-04T21:03:38.1794959Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:03:38.1795491Z size = l_proposals_0_fields_proposal_boxes_tensor.size() 2025-03-04T21:03:38.1795780Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:03:38.1796053Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:03:38.1796338Z size_1 = l_proposals_1_fields_proposal_boxes_tensor.size() 2025-03-04T21:03:38.1796584Z getitem_2: "Sym(s1)" = size_1[0] 2025-03-04T21:03:38.1796815Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:03:38.1797083Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:03:38.1797323Z getitem_4: "Sym(s2)" = size_2[0] 2025-03-04T21:03:38.1797546Z getitem_5 = size_2[1]; size_2 = getitem_5 = None 2025-03-04T21:03:38.1797804Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:03:38.1798043Z getitem_6: "Sym(s3)" = size_3[0] 2025-03-04T21:03:38.1798261Z getitem_7 = size_3[1]; size_3 = getitem_7 = None 2025-03-04T21:03:38.1798469Z 2025-03-04T21:03:38.1798830Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:03:38.1799628Z proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0) 2025-03-04T21:03:38.1800158Z 2025-03-04T21:03:38.1800597Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:03:38.1801154Z deltas: "f32[4000, 320][320, 1]cpu" = proposal_deltas.float(); proposal_deltas = None 2025-03-04T21:03:38.1801413Z 2025-03-04T21:03:38.1801794Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:03:38.1803098Z boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:03:38.1803386Z 2025-03-04T21:03:38.1803789Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:03:38.1804368Z getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:03:38.1804678Z getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:03:38.1805002Z widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9; getitem_8 = getitem_9 = None 2025-03-04T21:03:38.1805263Z 2025-03-04T21:03:38.1805668Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:03:38.1806162Z getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:03:38.1806468Z getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:03:38.1806801Z heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T21:03:38.1807066Z 2025-03-04T21:03:38.1807479Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:03:38.1807968Z getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:03:38.1808242Z mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths 2025-03-04T21:03:38.1808501Z ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul; getitem_12 = mul = None 2025-03-04T21:03:38.1808738Z 2025-03-04T21:03:38.1809127Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:03:38.1809639Z getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:03:38.1809934Z mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights 2025-03-04T21:03:38.1810214Z ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1; getitem_13 = mul_1 = None 2025-03-04T21:03:38.1810464Z 2025-03-04T21:03:38.1810880Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:03:38.1811397Z getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:03:38.1811720Z dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0; getitem_14 = None 2025-03-04T21:03:38.1811963Z 2025-03-04T21:03:38.1812338Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:03:38.1812916Z getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:03:38.1813232Z dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0; getitem_15 = None 2025-03-04T21:03:38.1813458Z 2025-03-04T21:03:38.1813840Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:03:38.1814342Z getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:03:38.1814653Z dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0; getitem_16 = None 2025-03-04T21:03:38.1814877Z 2025-03-04T21:03:38.1815265Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:03:38.1815804Z getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:03:38.1816149Z dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0; getitem_17 = None 2025-03-04T21:03:38.1816375Z 2025-03-04T21:03:38.1816812Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:03:38.1817340Z dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:03:38.1817591Z 2025-03-04T21:03:38.1818007Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:03:38.1818532Z dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:03:38.1818781Z 2025-03-04T21:03:38.1819212Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:03:38.1819756Z getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:03:38.1820076Z mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18; dx = getitem_18 = None 2025-03-04T21:03:38.1820433Z getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:03:38.1820781Z pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19; mul_2 = getitem_19 = None 2025-03-04T21:03:38.1821036Z 2025-03-04T21:03:38.1821471Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:03:38.1822012Z getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:03:38.1822332Z mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20; dy = getitem_20 = None 2025-03-04T21:03:38.1822658Z getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:03:38.1823000Z pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21; mul_3 = getitem_21 = None 2025-03-04T21:03:38.1823248Z 2025-03-04T21:03:38.1823662Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:03:38.1824163Z exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:03:38.1824486Z getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:03:38.1824830Z pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22; exp = getitem_22 = None 2025-03-04T21:03:38.1825065Z 2025-03-04T21:03:38.1825480Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:03:38.1825983Z exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:03:38.1826315Z getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:03:38.1826649Z pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23; exp_1 = getitem_23 = None 2025-03-04T21:03:38.1826889Z 2025-03-04T21:03:38.1827298Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:03:38.1827757Z mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:03:38.1828006Z x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:03:38.1828236Z 2025-03-04T21:03:38.1828627Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:03:38.1829089Z mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:03:38.1829357Z y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:03:38.1829586Z 2025-03-04T21:03:38.1829978Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:03:38.1830458Z mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:03:38.1830746Z x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:03:38.1830990Z 2025-03-04T21:03:38.1831372Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:03:38.1831851Z mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:03:38.1832132Z y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:03:38.1832376Z 2025-03-04T21:03:38.1832834Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:03:38.1833417Z pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:03:38.1833703Z 2025-03-04T21:03:38.1834203Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:03:38.1834786Z predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320)); pred_boxes = None 2025-03-04T21:03:38.1835081Z 2025-03-04T21:03:38.1835538Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:03:38.1836219Z split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]); predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None 2025-03-04T21:03:38.1836631Z boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0] 2025-03-04T21:03:38.1836907Z getitem_25: "f32[s1, 320][320, 1]cpu" = split[1]; getitem_25 = None 2025-03-04T21:03:38.1837196Z getitem_26: "f32[s2, 320][320, 1]cpu" = split[2]; getitem_26 = None 2025-03-04T21:03:38.1837494Z getitem_27: "f32[s3, 320][320, 1]cpu" = split[3]; split = getitem_27 = None 2025-03-04T21:03:38.1837736Z 2025-03-04T21:03:38.1838106Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:03:38.1838689Z size_4 = l_proposals_0_fields_proposal_boxes_tensor.size(); l_proposals_0_fields_proposal_boxes_tensor = None 2025-03-04T21:03:38.1839025Z getitem_28: "Sym(s0)" = size_4[0] 2025-03-04T21:03:38.1839266Z getitem_29 = size_4[1]; size_4 = getitem_29 = None 2025-03-04T21:03:38.1839626Z size_5 = l_proposals_1_fields_proposal_boxes_tensor.size(); l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:03:38.1839962Z getitem_30: "Sym(s1)" = size_5[0] 2025-03-04T21:03:38.1840195Z getitem_31 = size_5[1]; size_5 = getitem_31 = None 2025-03-04T21:03:38.1840551Z size_6 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:03:38.1840881Z getitem_32: "Sym(s2)" = size_6[0] 2025-03-04T21:03:38.1841106Z getitem_33 = size_6[1]; size_6 = getitem_33 = None 2025-03-04T21:03:38.1841459Z size_7 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:03:38.1841792Z getitem_34: "Sym(s3)" = size_7[0] 2025-03-04T21:03:38.1842013Z getitem_35 = size_7[1]; size_7 = getitem_35 = None 2025-03-04T21:03:38.1842238Z 2025-03-04T21:03:38.1842648Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:03:38.1843192Z probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1); scores = None 2025-03-04T21:03:38.1843474Z 2025-03-04T21:03:38.1843910Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:03:38.1844560Z split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0); probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None 2025-03-04T21:03:38.1844965Z scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:03:38.1845239Z getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1]; getitem_37 = None 2025-03-04T21:03:38.1845523Z getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2]; getitem_38 = None 2025-03-04T21:03:38.1845832Z getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_39 = None 2025-03-04T21:03:38.1846073Z 2025-03-04T21:03:38.1846610Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:03:38.1847282Z isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:03:38.1847608Z all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:03:38.1847927Z isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:03:38.1848250Z all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:03:38.1848524Z valid_mask: "b8[s0][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:03:38.1848749Z 2025-03-04T21:03:38.1849179Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:03:38.1849689Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:03:38.1849912Z 2025-03-04T21:03:38.6522883Z 2025-03-04T21:03:38.6523494Z class GraphModule(torch.nn.Module): 2025-03-04T21:03:38.6525012Z def forward(self, L_predictions_0_: "f32[4000, 81][81, 1]cpu", L_predictions_1_: "f32[4000, 320][320, 1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"): 2025-03-04T21:03:38.6530190Z l_predictions_0_ = L_predictions_0_ 2025-03-04T21:03:38.6535528Z l_predictions_1_ = L_predictions_1_ 2025-03-04T21:03:38.6535956Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:03:38.6536365Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:03:38.6536860Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:03:38.6541574Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:03:38.6546116Z 2025-03-04T21:03:38.6548326Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:03:38.6548991Z size = l_proposals_0_fields_proposal_boxes_tensor.size() 2025-03-04T21:03:38.6549348Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:03:38.6549911Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:03:38.6550335Z size_1 = l_proposals_1_fields_proposal_boxes_tensor.size() 2025-03-04T21:03:38.6550668Z getitem_2: "Sym(s1)" = size_1[0] 2025-03-04T21:03:38.6550996Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:03:38.6551285Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:03:38.6551659Z getitem_4: "Sym(s2)" = size_2[0] 2025-03-04T21:03:38.6551994Z getitem_5 = size_2[1]; size_2 = getitem_5 = None 2025-03-04T21:03:38.6552528Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:03:38.6552861Z getitem_6: "Sym(s3)" = size_3[0] 2025-03-04T21:03:38.6553166Z getitem_7 = size_3[1]; size_3 = getitem_7 = None 2025-03-04T21:03:38.6553484Z 2025-03-04T21:03:38.6553921Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:03:38.6554996Z proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0) 2025-03-04T21:03:38.6555695Z 2025-03-04T21:03:38.6556258Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:03:38.6556909Z deltas: "f32[4000, 320][320, 1]cpu" = l_predictions_1_.float(); l_predictions_1_ = None 2025-03-04T21:03:38.6557259Z 2025-03-04T21:03:38.6557721Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:03:38.6558343Z boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:03:38.6558683Z 2025-03-04T21:03:38.6559131Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:03:38.6559730Z getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:03:38.6560107Z getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:03:38.6560507Z widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9; getitem_8 = getitem_9 = None 2025-03-04T21:03:38.6560843Z 2025-03-04T21:03:38.6561339Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:03:38.6561966Z getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:03:38.6562309Z getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:03:38.6562713Z heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T21:03:38.6563054Z 2025-03-04T21:03:38.6563515Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:03:38.6564053Z getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:03:38.6564420Z mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths 2025-03-04T21:03:38.6564763Z ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul; getitem_12 = mul = None 2025-03-04T21:03:38.6565097Z 2025-03-04T21:03:38.6565586Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:03:38.6566175Z getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:03:38.6566552Z mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights 2025-03-04T21:03:38.6566894Z ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1; getitem_13 = mul_1 = None 2025-03-04T21:03:38.6567182Z 2025-03-04T21:03:38.6567689Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:03:38.6568248Z getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:03:38.6568625Z dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0; getitem_14 = None 2025-03-04T21:03:38.6568933Z 2025-03-04T21:03:38.6569368Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:03:38.6569950Z getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:03:38.6570364Z dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0; getitem_15 = None 2025-03-04T21:03:38.6570638Z 2025-03-04T21:03:38.6571100Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:03:38.6571651Z getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:03:38.6572043Z dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0; getitem_16 = None 2025-03-04T21:03:38.6572336Z 2025-03-04T21:03:38.6572776Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:03:38.6573389Z getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:03:38.6573784Z dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0; getitem_17 = None 2025-03-04T21:03:38.6574062Z 2025-03-04T21:03:38.6574589Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:03:38.6575166Z dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:03:38.6575492Z 2025-03-04T21:03:38.6575959Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:03:38.6576565Z dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:03:38.6576884Z 2025-03-04T21:03:38.6577366Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:03:38.6577944Z getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:03:38.6578345Z mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18; dx = getitem_18 = None 2025-03-04T21:03:38.6578734Z getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:03:38.6579175Z pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19; mul_2 = getitem_19 = None 2025-03-04T21:03:38.6579495Z 2025-03-04T21:03:38.6579980Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:03:38.6580600Z getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:03:38.6580989Z mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20; dy = getitem_20 = None 2025-03-04T21:03:38.6581354Z getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:03:38.6581774Z pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21; mul_3 = getitem_21 = None 2025-03-04T21:03:38.6582080Z 2025-03-04T21:03:38.6582552Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:03:38.6583112Z exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:03:38.6583494Z getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:03:38.6583930Z pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22; exp = getitem_22 = None 2025-03-04T21:03:38.6584227Z 2025-03-04T21:03:38.6585642Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:03:38.6586238Z exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:03:38.6586626Z getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:03:38.6587032Z pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23; exp_1 = getitem_23 = None 2025-03-04T21:03:38.6587344Z 2025-03-04T21:03:38.6587826Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:03:38.6588567Z mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:03:38.6590527Z x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:03:38.6591298Z 2025-03-04T21:03:38.6591903Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:03:38.6592440Z mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:03:38.6592781Z y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:03:38.6593092Z 2025-03-04T21:03:38.6593558Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:03:38.6594205Z mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:03:38.6594592Z x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:03:38.6595498Z 2025-03-04T21:03:38.6596041Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:03:38.6596652Z mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:03:38.6597008Z y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:03:38.6597337Z 2025-03-04T21:03:38.6597831Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:03:38.6598468Z pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:03:38.6598879Z 2025-03-04T21:03:38.6599363Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:03:38.6599993Z predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320)); pred_boxes = None 2025-03-04T21:03:38.6600374Z 2025-03-04T21:03:38.6600893Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:03:38.6601670Z split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]); predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None 2025-03-04T21:03:38.6602507Z boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0] 2025-03-04T21:03:38.6602856Z getitem_25: "f32[s1, 320][320, 1]cpu" = split[1]; getitem_25 = None 2025-03-04T21:03:38.6603248Z getitem_26: "f32[s2, 320][320, 1]cpu" = split[2]; getitem_26 = None 2025-03-04T21:03:38.6603626Z getitem_27: "f32[s3, 320][320, 1]cpu" = split[3]; split = getitem_27 = None 2025-03-04T21:03:38.6603940Z 2025-03-04T21:03:38.6604389Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:03:38.6605089Z size_4 = l_proposals_0_fields_proposal_boxes_tensor.size(); l_proposals_0_fields_proposal_boxes_tensor = None 2025-03-04T21:03:38.6605514Z getitem_28: "Sym(s0)" = size_4[0] 2025-03-04T21:03:38.6605820Z getitem_29 = size_4[1]; size_4 = getitem_29 = None 2025-03-04T21:03:38.6606232Z size_5 = l_proposals_1_fields_proposal_boxes_tensor.size(); l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:03:38.6606689Z getitem_30: "Sym(s1)" = size_5[0] 2025-03-04T21:03:38.6606993Z getitem_31 = size_5[1]; size_5 = getitem_31 = None 2025-03-04T21:03:38.6607423Z size_6 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:03:38.6607843Z getitem_32: "Sym(s2)" = size_6[0] 2025-03-04T21:03:38.6608130Z getitem_33 = size_6[1]; size_6 = getitem_33 = None 2025-03-04T21:03:38.6608562Z size_7 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:03:38.6608957Z getitem_34: "Sym(s3)" = size_7[0] 2025-03-04T21:03:38.6609213Z getitem_35 = size_7[1]; size_7 = getitem_35 = None 2025-03-04T21:03:38.6609506Z 2025-03-04T21:03:38.6609985Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:03:38.6610626Z probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1); l_predictions_0_ = None 2025-03-04T21:03:38.6611069Z 2025-03-04T21:03:38.6611591Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:03:38.6612341Z split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0); probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None 2025-03-04T21:03:38.6612815Z scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:03:38.6613153Z getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1]; getitem_37 = None 2025-03-04T21:03:38.6613529Z getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2]; getitem_38 = None 2025-03-04T21:03:38.6613883Z getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_39 = None 2025-03-04T21:03:38.6614177Z 2025-03-04T21:03:38.6614823Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:03:38.6615574Z isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:03:38.6615993Z all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:03:38.6616408Z isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:03:38.6616798Z all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:03:38.6617165Z valid_mask: "b8[s0][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:03:38.6617462Z 2025-03-04T21:03:38.6617966Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:03:38.6618575Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:03:38.6618876Z 2025-03-04T21:03:38.6619044Z 2025-03-04T21:03:38.6619204Z class GraphModule(torch.nn.Module): 2025-03-04T21:03:38.6620098Z def forward(self, L_predictions_0_: "f32[4000, 81][81, 1]cpu", L_predictions_1_: "f32[4000, 320][320, 1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"): 2025-03-04T21:03:38.6620961Z l_predictions_0_ = L_predictions_0_ 2025-03-04T21:03:38.6621336Z l_predictions_1_ = L_predictions_1_ 2025-03-04T21:03:38.6621713Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:03:38.6622232Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:03:38.6622702Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:03:38.6623151Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:03:38.6623513Z 2025-03-04T21:03:38.6623963Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:03:38.6624484Z size = l_proposals_0_fields_proposal_boxes_tensor.size() 2025-03-04T21:03:38.6624823Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:03:38.6625127Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:03:38.6625465Z size_1 = l_proposals_1_fields_proposal_boxes_tensor.size() 2025-03-04T21:03:38.6625784Z getitem_2: "Sym(s1)" = size_1[0] 2025-03-04T21:03:38.6626072Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:03:38.6626415Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:03:38.6626710Z getitem_4: "Sym(s2)" = size_2[0] 2025-03-04T21:03:38.6626999Z getitem_5 = size_2[1]; size_2 = getitem_5 = None 2025-03-04T21:03:38.6627368Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:03:38.6627663Z getitem_6: "Sym(s3)" = size_3[0] 2025-03-04T21:03:38.6627957Z getitem_7 = size_3[1]; size_3 = getitem_7 = None 2025-03-04T21:03:38.6628211Z 2025-03-04T21:03:38.6628653Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:03:38.6629537Z proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0) 2025-03-04T21:03:38.6630185Z 2025-03-04T21:03:38.6630688Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:03:38.6631403Z deltas: "f32[4000, 320][320, 1]cpu" = l_predictions_1_.float(); l_predictions_1_ = None 2025-03-04T21:03:38.6631709Z 2025-03-04T21:03:38.6632180Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:03:38.6632861Z boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:03:38.6633182Z 2025-03-04T21:03:38.6633669Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:03:38.6634374Z getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:03:38.6634768Z getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:03:38.6635199Z widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9; getitem_8 = getitem_9 = None 2025-03-04T21:03:38.6635537Z 2025-03-04T21:03:38.6635999Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:03:38.6636619Z getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:03:38.6636997Z getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:03:38.6637401Z heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T21:03:38.6637749Z 2025-03-04T21:03:38.6638211Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:03:38.6638817Z getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:03:38.6639166Z mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths 2025-03-04T21:03:38.6639489Z ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul; getitem_12 = mul = None 2025-03-04T21:03:38.6639831Z 2025-03-04T21:03:38.6640298Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:03:38.6640894Z getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:03:38.6641264Z mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights 2025-03-04T21:03:38.6641608Z ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1; getitem_13 = mul_1 = None 2025-03-04T21:03:38.6641935Z 2025-03-04T21:03:38.6653586Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:03:38.6654224Z getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:03:38.6654551Z dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0; getitem_14 = None 2025-03-04T21:03:38.6654791Z 2025-03-04T21:03:38.6655218Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:03:38.6655725Z getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:03:38.6656037Z dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0; getitem_15 = None 2025-03-04T21:03:38.6656263Z 2025-03-04T21:03:38.6656644Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:03:38.6657141Z getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:03:38.6657462Z dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0; getitem_16 = None 2025-03-04T21:03:38.6657688Z 2025-03-04T21:03:38.6658102Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:03:38.6658626Z getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:03:38.6658957Z dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0; getitem_17 = None 2025-03-04T21:03:38.6659181Z 2025-03-04T21:03:38.6659596Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:03:38.6660117Z dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:03:38.6660369Z 2025-03-04T21:03:38.6660774Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:03:38.6661284Z dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:03:38.6661558Z 2025-03-04T21:03:38.6661980Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:03:38.6662517Z getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:03:38.6662832Z mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18; dx = getitem_18 = None 2025-03-04T21:03:38.6663162Z getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:03:38.6663504Z pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19; mul_2 = getitem_19 = None 2025-03-04T21:03:38.6663749Z 2025-03-04T21:03:38.6664185Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:03:38.6664738Z getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:03:38.6665059Z mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20; dy = getitem_20 = None 2025-03-04T21:03:38.6665481Z getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:03:38.6665949Z pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21; mul_3 = getitem_21 = None 2025-03-04T21:03:38.6666762Z 2025-03-04T21:03:38.6667296Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:03:38.6667831Z exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:03:38.6668164Z getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:03:38.6668519Z pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22; exp = getitem_22 = None 2025-03-04T21:03:38.6668836Z 2025-03-04T21:03:38.6669684Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:03:38.6670774Z exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:03:38.6671120Z getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:03:38.6671472Z pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23; exp_1 = getitem_23 = None 2025-03-04T21:03:38.6671732Z 2025-03-04T21:03:38.6672141Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:03:38.6672649Z mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:03:38.6672913Z x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:03:38.6673148Z 2025-03-04T21:03:38.6673545Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:03:38.6674002Z mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:03:38.6674321Z y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:03:38.6674548Z 2025-03-04T21:03:38.6674940Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:03:38.6675414Z mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:03:38.6675700Z x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:03:38.6675940Z 2025-03-04T21:03:38.6676360Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:03:38.6676833Z mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:03:38.6677120Z y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:03:38.6677360Z 2025-03-04T21:03:38.6677793Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:03:38.6678380Z pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:03:38.6678670Z 2025-03-04T21:03:38.6679293Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:03:38.6679957Z predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320)); pred_boxes = None 2025-03-04T21:03:38.6680247Z 2025-03-04T21:03:38.6680695Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:03:38.6681376Z split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]); predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None 2025-03-04T21:03:38.6681800Z boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0] 2025-03-04T21:03:38.6682126Z getitem_25: "f32[s1, 320][320, 1]cpu" = split[1]; getitem_25 = None 2025-03-04T21:03:38.6682420Z getitem_26: "f32[s2, 320][320, 1]cpu" = split[2]; getitem_26 = None 2025-03-04T21:03:38.6682722Z getitem_27: "f32[s3, 320][320, 1]cpu" = split[3]; split = getitem_27 = None 2025-03-04T21:03:38.6682958Z 2025-03-04T21:03:38.6683340Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:03:38.6683893Z size_4 = l_proposals_0_fields_proposal_boxes_tensor.size(); l_proposals_0_fields_proposal_boxes_tensor = None 2025-03-04T21:03:38.6684238Z getitem_28: "Sym(s0)" = size_4[0] 2025-03-04T21:03:38.6684475Z getitem_29 = size_4[1]; size_4 = getitem_29 = None 2025-03-04T21:03:38.6684837Z size_5 = l_proposals_1_fields_proposal_boxes_tensor.size(); l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:03:38.6685181Z getitem_30: "Sym(s1)" = size_5[0] 2025-03-04T21:03:38.6685418Z getitem_31 = size_5[1]; size_5 = getitem_31 = None 2025-03-04T21:03:38.6685773Z size_6 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:03:38.6686168Z getitem_32: "Sym(s2)" = size_6[0] 2025-03-04T21:03:38.6686395Z getitem_33 = size_6[1]; size_6 = getitem_33 = None 2025-03-04T21:03:38.6686741Z size_7 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:03:38.6687066Z getitem_34: "Sym(s3)" = size_7[0] 2025-03-04T21:03:38.6687287Z getitem_35 = size_7[1]; size_7 = getitem_35 = None 2025-03-04T21:03:38.6687497Z 2025-03-04T21:03:38.6687907Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:03:38.6688494Z probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1); l_predictions_0_ = None 2025-03-04T21:03:38.6688813Z 2025-03-04T21:03:38.6689516Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:03:38.6690216Z split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0); probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None 2025-03-04T21:03:38.6690623Z scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:03:38.6690900Z getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1]; getitem_37 = None 2025-03-04T21:03:38.6691184Z getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2]; getitem_38 = None 2025-03-04T21:03:38.6691479Z getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_39 = None 2025-03-04T21:03:38.6691721Z 2025-03-04T21:03:38.6692256Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:03:38.6692934Z isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:03:38.6693255Z all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:03:38.6693574Z isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:03:38.6693891Z all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:03:38.6694170Z valid_mask: "b8[s0][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:03:38.6694396Z 2025-03-04T21:03:38.6694835Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:03:38.6695359Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:03:38.6695576Z 2025-03-04T21:03:38.6695716Z 2025-03-04T21:03:38.6695805Z class GraphModule(torch.nn.Module): 2025-03-04T21:03:38.6696583Z def forward(self, L_predictions_0_: "f32[4000, 81][81, 1]cpu", L_predictions_1_: "f32[4000, 320][320, 1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"): 2025-03-04T21:03:38.6697347Z l_predictions_0_ = L_predictions_0_ 2025-03-04T21:03:38.6697566Z l_predictions_1_ = L_predictions_1_ 2025-03-04T21:03:38.6697870Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:03:38.6698268Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:03:38.6698657Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:03:38.6699429Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:03:38.6699776Z 2025-03-04T21:03:38.6700169Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:03:38.6700629Z size = l_proposals_0_fields_proposal_boxes_tensor.size() 2025-03-04T21:03:38.6701167Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:03:38.6701426Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:03:38.6701700Z size_1 = l_proposals_1_fields_proposal_boxes_tensor.size() 2025-03-04T21:03:38.6702136Z getitem_2: "Sym(s1)" = size_1[0] 2025-03-04T21:03:38.6702371Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:03:38.6702640Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:03:38.6702880Z getitem_4: "Sym(s2)" = size_2[0] 2025-03-04T21:03:38.6703108Z getitem_5 = size_2[1]; size_2 = getitem_5 = None 2025-03-04T21:03:38.6703439Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:03:38.6703672Z getitem_6: "Sym(s3)" = size_3[0] 2025-03-04T21:03:38.6703897Z getitem_7 = size_3[1]; size_3 = getitem_7 = None 2025-03-04T21:03:38.6704114Z 2025-03-04T21:03:38.6704493Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:03:38.6705261Z proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0) 2025-03-04T21:03:38.6705811Z 2025-03-04T21:03:38.6706272Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:03:38.6706849Z deltas: "f32[4000, 320][320, 1]cpu" = l_predictions_1_.float(); l_predictions_1_ = None 2025-03-04T21:03:38.6707129Z 2025-03-04T21:03:38.6707523Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:03:38.6708057Z boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:03:38.6708335Z 2025-03-04T21:03:38.6708734Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:03:38.6709284Z getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:03:38.6709601Z getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:03:38.6709921Z widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9; getitem_8 = getitem_9 = None 2025-03-04T21:03:38.6710180Z 2025-03-04T21:03:38.6710575Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:03:38.6711081Z getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:03:38.6711395Z getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:03:38.6711740Z heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T21:03:38.6712012Z 2025-03-04T21:03:38.6712417Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:03:38.6712911Z getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:03:38.6713233Z mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths 2025-03-04T21:03:38.6713507Z ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul; getitem_12 = mul = None 2025-03-04T21:03:38.6713755Z 2025-03-04T21:03:38.6714236Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:03:38.6714769Z getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:03:38.6715076Z mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights 2025-03-04T21:03:38.6715359Z ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1; getitem_13 = mul_1 = None 2025-03-04T21:03:38.6715609Z 2025-03-04T21:03:38.6716023Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:03:38.6716555Z getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:03:38.6716880Z dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0; getitem_14 = None 2025-03-04T21:03:38.6717114Z 2025-03-04T21:03:38.6717500Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:03:38.6718013Z getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:03:38.6718324Z dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0; getitem_15 = None 2025-03-04T21:03:38.6718556Z 2025-03-04T21:03:38.6718943Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:03:38.6719450Z getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:03:38.6719764Z dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0; getitem_16 = None 2025-03-04T21:03:38.6719986Z 2025-03-04T21:03:38.6720366Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:03:38.6720906Z getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:03:38.6721240Z dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0; getitem_17 = None 2025-03-04T21:03:38.6721466Z 2025-03-04T21:03:38.6721908Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:03:38.6722452Z dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:03:38.6722706Z 2025-03-04T21:03:38.6723124Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:03:38.6723645Z dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:03:38.6723897Z 2025-03-04T21:03:38.6724324Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:03:38.6724869Z getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:03:38.6725188Z mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18; dx = getitem_18 = None 2025-03-04T21:03:38.6725519Z getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:03:38.6725873Z pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19; mul_2 = getitem_19 = None 2025-03-04T21:03:38.6726125Z 2025-03-04T21:03:38.6726550Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:03:38.6727093Z getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:03:38.6727416Z mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20; dy = getitem_20 = None 2025-03-04T21:03:38.6727734Z getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:03:38.6728067Z pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21; mul_3 = getitem_21 = None 2025-03-04T21:03:38.6728315Z 2025-03-04T21:03:38.6728721Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:03:38.6729221Z exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:03:38.6729543Z getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:03:38.6729880Z pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22; exp = getitem_22 = None 2025-03-04T21:03:38.6730123Z 2025-03-04T21:03:38.6730534Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:03:38.6731028Z exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:03:38.6731357Z getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:03:38.6731700Z pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23; exp_1 = getitem_23 = None 2025-03-04T21:03:38.6731942Z 2025-03-04T21:03:38.6732332Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:03:38.6732782Z mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:03:38.6733038Z x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:03:38.6733259Z 2025-03-04T21:03:38.6733645Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:03:38.6734089Z mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:03:38.6734366Z y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:03:38.6734589Z 2025-03-04T21:03:38.6734959Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:03:38.6735418Z mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:03:38.6735697Z x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:03:38.6735932Z 2025-03-04T21:03:38.6736306Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:03:38.6736757Z mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:03:38.6737030Z y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:03:38.6737261Z 2025-03-04T21:03:38.6737678Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:03:38.6738260Z pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:03:38.6738542Z 2025-03-04T21:03:38.6738947Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:03:38.6739485Z predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320)); pred_boxes = None 2025-03-04T21:03:38.6739761Z 2025-03-04T21:03:38.6740192Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:03:38.6740853Z split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]); predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None 2025-03-04T21:03:38.6741264Z boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0] 2025-03-04T21:03:38.6741542Z getitem_25: "f32[s1, 320][320, 1]cpu" = split[1]; getitem_25 = None 2025-03-04T21:03:38.6741847Z getitem_26: "f32[s2, 320][320, 1]cpu" = split[2]; getitem_26 = None 2025-03-04T21:03:38.6742142Z getitem_27: "f32[s3, 320][320, 1]cpu" = split[3]; split = getitem_27 = None 2025-03-04T21:03:38.6742375Z 2025-03-04T21:03:38.6742739Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:03:38.6743279Z size_4 = l_proposals_0_fields_proposal_boxes_tensor.size(); l_proposals_0_fields_proposal_boxes_tensor = None 2025-03-04T21:03:38.6743616Z getitem_28: "Sym(s0)" = size_4[0] 2025-03-04T21:03:38.6743849Z getitem_29 = size_4[1]; size_4 = getitem_29 = None 2025-03-04T21:03:38.6744206Z size_5 = l_proposals_1_fields_proposal_boxes_tensor.size(); l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:03:38.6744537Z getitem_30: "Sym(s1)" = size_5[0] 2025-03-04T21:03:38.6744764Z getitem_31 = size_5[1]; size_5 = getitem_31 = None 2025-03-04T21:03:38.6745112Z size_6 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:03:38.6745442Z getitem_32: "Sym(s2)" = size_6[0] 2025-03-04T21:03:38.6745665Z getitem_33 = size_6[1]; size_6 = getitem_33 = None 2025-03-04T21:03:38.6746007Z size_7 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:03:38.6746334Z getitem_34: "Sym(s3)" = size_7[0] 2025-03-04T21:03:38.6746552Z getitem_35 = size_7[1]; size_7 = getitem_35 = None 2025-03-04T21:03:38.6746760Z 2025-03-04T21:03:38.6747226Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:03:38.6747805Z probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1); l_predictions_0_ = None 2025-03-04T21:03:38.6748115Z 2025-03-04T21:03:38.6748542Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:03:38.6749185Z split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0); probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None 2025-03-04T21:03:38.6749583Z scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:03:38.6749852Z getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1]; getitem_37 = None 2025-03-04T21:03:38.6750131Z getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2]; getitem_38 = None 2025-03-04T21:03:38.6750420Z getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_39 = None 2025-03-04T21:03:38.6750675Z 2025-03-04T21:03:38.6751214Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:03:38.6751893Z isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:03:38.6752216Z all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:03:38.6752542Z isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:03:38.6752876Z all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:03:38.6753162Z valid_mask: "b8[s0][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:03:38.6753392Z 2025-03-04T21:03:38.6753826Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:03:38.6754467Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:03:38.6754709Z 2025-03-04T21:03:39.0609570Z 2025-03-04T21:03:39.0610119Z class GraphModule(torch.nn.Module): 2025-03-04T21:03:39.0610594Z def forward(self, s0: "Sym(s0)", L_scores_0_: "f32[s0, 81][81, 1]cpu", s1: "Sym(s0)", L_boxes_0_: "f32[s0, 320][320, 1]cpu"): 2025-03-04T21:03:39.0610910Z l_scores_0_ = L_scores_0_ 2025-03-04T21:03:39.0611108Z l_boxes_0_ = L_boxes_0_ 2025-03-04T21:03:39.0611295Z 2025-03-04T21:03:39.0611935Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:03:39.0612640Z isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(l_boxes_0_); l_boxes_0_ = None 2025-03-04T21:03:39.0612969Z all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:03:39.0613296Z isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(l_scores_0_); l_scores_0_ = None 2025-03-04T21:03:39.0613616Z all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:03:39.0613905Z valid_mask: "b8[s0][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:03:39.0614146Z 2025-03-04T21:03:39.0614599Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:03:39.0615124Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:03:39.0615662Z 2025-03-04T21:03:39.0616043Z 2025-03-04T21:03:39.0616154Z class GraphModule(torch.nn.Module): 2025-03-04T21:03:39.0616486Z def forward(self, s0: "Sym(s0)", L_scores_0_: "f32[s0, 81][81, 1]cpu", s1: "Sym(s0)", L_boxes_0_: "f32[s0, 320][320, 1]cpu"): 2025-03-04T21:03:39.0616806Z l_scores_0_ = L_scores_0_ 2025-03-04T21:03:39.0617007Z l_boxes_0_ = L_boxes_0_ 2025-03-04T21:03:39.0617187Z 2025-03-04T21:03:39.0617740Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:03:39.0618413Z isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(l_boxes_0_); l_boxes_0_ = None 2025-03-04T21:03:39.0620374Z all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:03:39.0620828Z isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(l_scores_0_); l_scores_0_ = None 2025-03-04T21:03:39.0621223Z all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:03:39.0621552Z valid_mask: "b8[s0][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:03:39.0622052Z 2025-03-04T21:03:39.0622671Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:03:39.0623278Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:03:39.0623563Z 2025-03-04T21:04:48.3406809Z Compilation time (from dynamo_timed): 89.805443026 2025-03-04T21:04:48.3410714Z pass 2025-03-04T21:04:48.3418921Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T21:04:48.3419794Z TIMING: entire_frame_compile:89.80544 gc:0.05089 _recursive_pre_grad_passes:0.04527 async_compile.wait:12.12108 backend_compile:33.76951 _recursive_joint_graph_passes:0.19351 inductor_compile:17.09304 _recursive_post_grad_passes:0.0543 code_gen:14.79809 total_wall_time:89.80544 2025-03-04T21:04:48.3421253Z STATS: call_* op count: 1308 | FakeTensorMode.__torch_dispatch__:20246 | FakeTensor.__torch_dispatch__:738 | ProxyTorchDispatchMode.__torch_dispatch__:2198 | attempt fast:284 | slow no contiguity match:46 | fast is_contiguous:228 | slow both tensors nontrivially broadcast:10 2025-03-04T21:04:48.3422061Z Dynamo produced 78 graphs covering 1308 ops with 62 graph breaks (8 unique) 2025-03-04T21:04:57.0396892Z 2025-03-04T21:05:09.7503568Z loading model: 0it [00:00, ?it/s] 2025-03-04T21:05:09.7510574Z loading model: 0it [00:12, ?it/s] 2025-03-04T21:05:09.7512876Z cpu eval detectron2_maskrcnn_r_50_c4 2025-03-04T21:05:17.1657027Z WARNING:common:fp64 golden ref were not generated for detectron2_maskrcnn_r_50_c4. Setting accuracy check to cosine 2025-03-04T21:05:17.1768392Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T21:05:37.6740954Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T21:05:59.6317102Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T21:06:08.3141528Z 2025-03-04T21:06:08.3144671Z class GraphModule(torch.nn.Module): 2025-03-04T21:06:08.3193398Z def forward(self, L_stack0_tensor: "f32[4, 3, 1156, 1199][4158132, 1386044, 1199, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_: "f32[512, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_: "f32[1024, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[1024, 1024, 3, 3][9216, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[1024][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"): 2025-03-04T21:06:08.3238044Z l_stack0_tensor = L_stack0_tensor 2025-03-04T21:06:08.3238589Z l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ 2025-03-04T21:06:08.3239373Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3240205Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3241045Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3241793Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3242489Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:06:08.3243171Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3243915Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3244646Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3245343Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3246039Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:06:08.3246753Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3247515Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3248256Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3248967Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3249665Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:06:08.3250371Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3251126Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3251838Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3252530Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3253198Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:06:08.3253909Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3254706Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3255486Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3256220Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3256899Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:06:08.3257587Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3258325Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3259049Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3259762Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3260422Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:06:08.3261114Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3261867Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3262588Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3263311Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3263972Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:06:08.3264667Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3265412Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3266133Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3266830Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3267485Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:06:08.3268180Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3268972Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3269750Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3270482Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3271163Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:06:08.3271910Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3272705Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3273449Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3274134Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3274789Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:06:08.3275472Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3276216Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3276951Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3277645Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3278302Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:06:08.3278982Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3279711Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3280412Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3281084Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3281731Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:06:08.3282418Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3283148Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3283849Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3284520Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3285158Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:06:08.3285828Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3286555Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3287285Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3287995Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3288657Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:06:08.3289356Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3290103Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3290849Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3291556Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3292214Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:06:08.3292891Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3293624Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3294328Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3295004Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3295647Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:06:08.3296354Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3297084Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3297812Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3298497Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3299147Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:06:08.3299830Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3300581Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3301300Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3302105Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3302767Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:06:08.3303458Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3304243Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3304970Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3305678Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3306344Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:06:08.3307043Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3307803Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3308528Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3309240Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3309933Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:06:08.3310635Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3311374Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3312173Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3312870Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3313532Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ 2025-03-04T21:06:08.3314222Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3314988Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3315709Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3316404Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3317059Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ 2025-03-04T21:06:08.3317745Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3318517Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3319239Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3319932Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3320592Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ 2025-03-04T21:06:08.3321290Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3322019Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3322723Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3323401Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3324072Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:06:08.3324758Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3325479Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3326181Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3326858Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3327502Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:06:08.3328194Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3328929Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3329653Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3330332Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3330984Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:06:08.3331684Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3332405Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3333111Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3333790Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3334452Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:06:08.3335161Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3335907Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3336648Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3337419Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3338087Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:06:08.3338766Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3339495Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3340204Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3340885Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3341539Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:06:08.3342208Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3342935Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3343637Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3344315Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3344974Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:06:08.3345645Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3346374Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3347091Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3347781Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3348434Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:06:08.3349125Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3349868Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3350603Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3351308Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3352039Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:06:08.3352733Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3353470Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3354190Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3354886Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3355571Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:06:08.3356272Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3357022Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3357749Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3358452Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3359138Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ 2025-03-04T21:06:08.3359829Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3360613Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3361389Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3362091Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3362750Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ 2025-03-04T21:06:08.3363441Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3364190Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3364936Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3365633Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3366295Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ 2025-03-04T21:06:08.3366990Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3367736Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3368461Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3369171Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3369828Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ 2025-03-04T21:06:08.3370521Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3371272Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3371990Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3372706Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3373348Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ 2025-03-04T21:06:08.3374013Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3374735Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3375435Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3376119Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3376777Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ 2025-03-04T21:06:08.3377461Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3378215Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3378925Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3379600Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3380249Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ 2025-03-04T21:06:08.3380931Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3381666Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3382389Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3383074Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3383705Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ 2025-03-04T21:06:08.3384371Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3385093Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3385790Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3386488Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3387145Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ 2025-03-04T21:06:08.3387834Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3388574Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3389293Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3389991Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3390714Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T21:06:08.3391450Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T21:06:08.3392242Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T21:06:08.3392994Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T21:06:08.3393792Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T21:06:08.3394565Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T21:06:08.3395322Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T21:06:08.3395796Z 2025-03-04T21:06:08.3396189Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.3397014Z x: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1); l_stack0_tensor = l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:08.3397616Z 2025-03-04T21:06:08.3397983Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.3399810Z x_1: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.3401393Z 2025-03-04T21:06:08.3401771Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x) 2025-03-04T21:06:08.3402346Z x_2: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.relu_(x_1); x_1 = None 2025-03-04T21:06:08.3402606Z 2025-03-04T21:06:08.3403065Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1) 2025-03-04T21:06:08.3403722Z x_3: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1); x_2 = None 2025-03-04T21:06:08.3404075Z 2025-03-04T21:06:08.3404419Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.3405164Z x_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:08.3405690Z 2025-03-04T21:06:08.3406082Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.3407925Z x_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_4 = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.3409534Z 2025-03-04T21:06:08.3409904Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.3410379Z out: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_5); x_5 = None 2025-03-04T21:06:08.3410676Z 2025-03-04T21:06:08.3411014Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.3411753Z x_6: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out = l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:08.3412291Z 2025-03-04T21:06:08.3412638Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.3414489Z x_7: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_6 = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.3416173Z 2025-03-04T21:06:08.3416555Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.3417044Z out_1: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_7); x_7 = None 2025-03-04T21:06:08.3417304Z 2025-03-04T21:06:08.3417642Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.3418385Z x_8: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_1 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:08.3418925Z 2025-03-04T21:06:08.3419267Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.3421111Z x_9: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_8 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.3422720Z 2025-03-04T21:06:08.3423053Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.3423784Z x_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); x_3 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:06:08.3424348Z 2025-03-04T21:06:08.3424689Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.3426605Z x_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_10 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.3428319Z 2025-03-04T21:06:08.3428687Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:08.3429171Z x_9 += x_11; out_2: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_9; x_9 = x_11 = None 2025-03-04T21:06:08.3429428Z 2025-03-04T21:06:08.3429798Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.3430289Z out_3: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_2); out_2 = None 2025-03-04T21:06:08.3430554Z 2025-03-04T21:06:08.3430886Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.3431673Z x_12: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:08.3432222Z 2025-03-04T21:06:08.3432575Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.3434466Z x_13: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_12 = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.3436138Z 2025-03-04T21:06:08.3436511Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.3437011Z out_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_13); x_13 = None 2025-03-04T21:06:08.3437273Z 2025-03-04T21:06:08.3437614Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.3438359Z x_14: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_4 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:08.3438924Z 2025-03-04T21:06:08.3439276Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.3441150Z x_15: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_14 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.3442819Z 2025-03-04T21:06:08.3443190Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.3443676Z out_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_15); x_15 = None 2025-03-04T21:06:08.3443933Z 2025-03-04T21:06:08.3444262Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.3445030Z x_16: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_5 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:08.3445581Z 2025-03-04T21:06:08.3445923Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.3447790Z x_17: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_16 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.3449460Z 2025-03-04T21:06:08.3449821Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:08.3450356Z x_17 += out_3; out_6: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_17; x_17 = out_3 = None 2025-03-04T21:06:08.3450637Z 2025-03-04T21:06:08.3451001Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.3451491Z out_7: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_6); out_6 = None 2025-03-04T21:06:08.3451767Z 2025-03-04T21:06:08.3452150Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.3452882Z x_18: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:08.3453430Z 2025-03-04T21:06:08.3453799Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.3455659Z x_19: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_18 = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.3457304Z 2025-03-04T21:06:08.3457669Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.3458138Z out_8: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_19); x_19 = None 2025-03-04T21:06:08.3458389Z 2025-03-04T21:06:08.3458717Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.3459445Z x_20: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_8 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:08.3459992Z 2025-03-04T21:06:08.3460330Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.3462191Z x_21: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_20 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.3463864Z 2025-03-04T21:06:08.3464242Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.3464723Z out_9: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_21); x_21 = None 2025-03-04T21:06:08.3464991Z 2025-03-04T21:06:08.3465329Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.3466108Z x_22: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_9 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:08.3466692Z 2025-03-04T21:06:08.3467062Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.3469122Z x_23: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_22 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.3471042Z 2025-03-04T21:06:08.3471701Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:08.3472550Z x_23 += out_7; out_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_23; x_23 = out_7 = None 2025-03-04T21:06:08.3472987Z 2025-03-04T21:06:08.3473532Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.3474286Z out_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_10); out_10 = None 2025-03-04T21:06:08.3474681Z 2025-03-04T21:06:08.3475109Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.3475880Z x_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:08.3476443Z 2025-03-04T21:06:08.3476802Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.3478807Z x_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_24 = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.3480718Z 2025-03-04T21:06:08.3481294Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.3481901Z out_12: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_25); x_25 = None 2025-03-04T21:06:08.3482203Z 2025-03-04T21:06:08.3482568Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.3483421Z x_26: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_12 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:08.3483982Z 2025-03-04T21:06:08.3484332Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.3486290Z x_27: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_26 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.3488272Z 2025-03-04T21:06:08.3488664Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.3489176Z out_13: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_27); x_27 = None 2025-03-04T21:06:08.3489603Z 2025-03-04T21:06:08.3490162Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.3491190Z x_28: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_13 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:08.3491779Z 2025-03-04T21:06:08.3492261Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.3494255Z x_29: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_28 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.3496043Z 2025-03-04T21:06:08.3496398Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.3497204Z x_30: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); out_11 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:06:08.3497830Z 2025-03-04T21:06:08.3498181Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.3500115Z x_31: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_30 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.3501839Z 2025-03-04T21:06:08.3502415Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:08.3502909Z x_29 += x_31; out_14: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_29; x_29 = x_31 = None 2025-03-04T21:06:08.3503177Z 2025-03-04T21:06:08.3503543Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.3504040Z out_15: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_14); out_14 = None 2025-03-04T21:06:08.3504316Z 2025-03-04T21:06:08.3504651Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.3505391Z x_32: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:08.3505932Z 2025-03-04T21:06:08.3506281Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.3508293Z x_33: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_32 = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.3510158Z 2025-03-04T21:06:08.3510578Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.3511127Z out_16: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_33); x_33 = None 2025-03-04T21:06:08.3511428Z 2025-03-04T21:06:08.3511970Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.3512833Z x_34: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_16 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:08.3513515Z 2025-03-04T21:06:08.3513923Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.3516104Z x_35: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_34 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.3518022Z 2025-03-04T21:06:08.3518453Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.3519006Z out_17: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_35); x_35 = None 2025-03-04T21:06:08.3519299Z 2025-03-04T21:06:08.3519650Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.3520393Z x_36: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_17 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:08.3520943Z 2025-03-04T21:06:08.3521297Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.3523193Z x_37: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_36 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.3524886Z 2025-03-04T21:06:08.3525253Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:08.3525751Z x_37 += out_15; out_18: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_37; x_37 = out_15 = None 2025-03-04T21:06:08.3526024Z 2025-03-04T21:06:08.3526389Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.3526887Z out_19: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_18); out_18 = None 2025-03-04T21:06:08.3527154Z 2025-03-04T21:06:08.3527494Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.3528250Z x_38: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:08.3528792Z 2025-03-04T21:06:08.3529142Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.3531025Z x_39: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_38 = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.3532700Z 2025-03-04T21:06:08.3533057Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.3533535Z out_20: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_39); x_39 = None 2025-03-04T21:06:08.3535963Z 2025-03-04T21:06:08.3536327Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.3537077Z x_40: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_20 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:08.3537637Z 2025-03-04T21:06:08.3537999Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.3539920Z x_41: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_40 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.3541589Z 2025-03-04T21:06:08.3541950Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.3542422Z out_21: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_41); x_41 = None 2025-03-04T21:06:08.3542673Z 2025-03-04T21:06:08.3543005Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.3543740Z x_42: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_21 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:08.3544293Z 2025-03-04T21:06:08.3544636Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.3546471Z x_43: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_42 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.3548146Z 2025-03-04T21:06:08.3548510Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:08.3549004Z x_43 += out_19; out_22: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_43; x_43 = out_19 = None 2025-03-04T21:06:08.3549271Z 2025-03-04T21:06:08.3549660Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.3550235Z out_23: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_22); out_22 = None 2025-03-04T21:06:08.3550504Z 2025-03-04T21:06:08.3550837Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.3551657Z x_44: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:08.3552216Z 2025-03-04T21:06:08.3552590Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.3554529Z x_45: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_44 = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.3556204Z 2025-03-04T21:06:08.3556582Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.3557081Z out_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_45); x_45 = None 2025-03-04T21:06:08.3557364Z 2025-03-04T21:06:08.3557712Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.3558468Z x_46: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_24 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:08.3559032Z 2025-03-04T21:06:08.3559388Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.3561343Z x_47: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_46 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.3562997Z 2025-03-04T21:06:08.3563353Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.3563821Z out_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_47); x_47 = None 2025-03-04T21:06:08.3564106Z 2025-03-04T21:06:08.3564432Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.3565160Z x_48: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_25 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:08.3565698Z 2025-03-04T21:06:08.3566042Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.3567875Z x_49: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_48 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.3569484Z 2025-03-04T21:06:08.3569852Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:08.3570339Z x_49 += out_23; out_26: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_49; x_49 = out_23 = None 2025-03-04T21:06:08.3570599Z 2025-03-04T21:06:08.3570965Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.3571477Z out_27: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_26); out_26 = None 2025-03-04T21:06:08.3571737Z 2025-03-04T21:06:08.3572065Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.3572772Z x_50: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:08.3573287Z 2025-03-04T21:06:08.3573643Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.3575484Z x_51: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_50 = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.3577129Z 2025-03-04T21:06:08.3577502Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.3577999Z out_28: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_51); x_51 = None 2025-03-04T21:06:08.3578250Z 2025-03-04T21:06:08.3578579Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.3579297Z x_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_28 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:08.3579831Z 2025-03-04T21:06:08.3580178Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.3582021Z x_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_52 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.3583615Z 2025-03-04T21:06:08.3583975Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.3584438Z out_29: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_53); x_53 = None 2025-03-04T21:06:08.3584700Z 2025-03-04T21:06:08.3585032Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.3585759Z x_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_29 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:08.3586296Z 2025-03-04T21:06:08.3586657Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.3588535Z x_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_54 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.3590179Z 2025-03-04T21:06:08.3590512Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.3591260Z x_56: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); out_27 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:06:08.3591909Z 2025-03-04T21:06:08.3592276Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.3594260Z x_57: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_56 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.3595957Z 2025-03-04T21:06:08.3596325Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:08.3596802Z x_55 += x_57; out_30: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_55; x_55 = x_57 = None 2025-03-04T21:06:08.3597057Z 2025-03-04T21:06:08.3597425Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.3597913Z out_31: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_30); out_30 = None 2025-03-04T21:06:08.3598170Z 2025-03-04T21:06:08.3598502Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.3599244Z x_58: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:08.3599778Z 2025-03-04T21:06:08.3600128Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.3602137Z x_59: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_58 = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.3603779Z 2025-03-04T21:06:08.3604149Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.3604625Z out_32: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_59); x_59 = None 2025-03-04T21:06:08.3604876Z 2025-03-04T21:06:08.3605212Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.3605985Z x_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_32 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:08.3606525Z 2025-03-04T21:06:08.3606869Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.3609137Z x_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_60 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.3610776Z 2025-03-04T21:06:08.3611145Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.3611622Z out_33: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_61); x_61 = None 2025-03-04T21:06:08.3611867Z 2025-03-04T21:06:08.3612193Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.3612912Z x_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_33 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:08.3613473Z 2025-03-04T21:06:08.3613818Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.3615694Z x_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_62 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.3617312Z 2025-03-04T21:06:08.3617666Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:08.3618133Z x_63 += out_31; out_34: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_63; x_63 = out_31 = None 2025-03-04T21:06:08.3618390Z 2025-03-04T21:06:08.3618744Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.3619211Z out_35: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_34); out_34 = None 2025-03-04T21:06:08.3619481Z 2025-03-04T21:06:08.3619802Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.3620527Z x_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:08.3621053Z 2025-03-04T21:06:08.3621394Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.3623249Z x_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_64 = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.3624844Z 2025-03-04T21:06:08.3625199Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.3625658Z out_36: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_65); x_65 = None 2025-03-04T21:06:08.3625902Z 2025-03-04T21:06:08.3626222Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.3626979Z x_66: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_36 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:08.3627511Z 2025-03-04T21:06:08.3627858Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.3629753Z x_67: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_66 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.3631469Z 2025-03-04T21:06:08.3631918Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.3632405Z out_37: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_67); x_67 = None 2025-03-04T21:06:08.3632660Z 2025-03-04T21:06:08.3633000Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.3633771Z x_68: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_37 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:08.3634319Z 2025-03-04T21:06:08.3634677Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.3636583Z x_69: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_68 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.3638239Z 2025-03-04T21:06:08.3638602Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:08.3639084Z x_69 += out_35; out_38: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_69; x_69 = out_35 = None 2025-03-04T21:06:08.3639346Z 2025-03-04T21:06:08.3639718Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.3640202Z out_39: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_38); out_38 = None 2025-03-04T21:06:08.3640487Z 2025-03-04T21:06:08.3640824Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.3641554Z x_70: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:08.3642093Z 2025-03-04T21:06:08.3642450Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.3644322Z x_71: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_70 = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.3645986Z 2025-03-04T21:06:08.3646356Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.3646835Z out_40: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_71); x_71 = None 2025-03-04T21:06:08.3647100Z 2025-03-04T21:06:08.3647430Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.3648155Z x_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_40 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:08.3648694Z 2025-03-04T21:06:08.3649038Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.3650911Z x_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_72 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.3652555Z 2025-03-04T21:06:08.3652915Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.3653376Z out_41: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_73); x_73 = None 2025-03-04T21:06:08.3653624Z 2025-03-04T21:06:08.3653946Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.3654685Z x_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_41 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:08.3655215Z 2025-03-04T21:06:08.3655555Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.3657433Z x_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_74 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.3659050Z 2025-03-04T21:06:08.3659412Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:08.3659911Z x_75 += out_39; out_42: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_75; x_75 = out_39 = None 2025-03-04T21:06:08.3660188Z 2025-03-04T21:06:08.3660575Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.3661121Z out_43: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_42); out_42 = None 2025-03-04T21:06:08.3661398Z 2025-03-04T21:06:08.3661751Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.3662489Z x_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:08.3663033Z 2025-03-04T21:06:08.3663419Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.3665288Z x_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_76 = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.3666957Z 2025-03-04T21:06:08.3667326Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.3667803Z out_44: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_77); x_77 = None 2025-03-04T21:06:08.3668093Z 2025-03-04T21:06:08.3668563Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.3669369Z x_78: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_44 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:08.3669912Z 2025-03-04T21:06:08.3670262Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.3672207Z x_79: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_78 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.3673849Z 2025-03-04T21:06:08.3674219Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.3674698Z out_45: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_79); x_79 = None 2025-03-04T21:06:08.3674977Z 2025-03-04T21:06:08.3675317Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.3676057Z x_80: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_45 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:08.3676603Z 2025-03-04T21:06:08.3676969Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.3678875Z x_81: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_80 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.3680619Z 2025-03-04T21:06:08.3680965Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:08.3681435Z x_81 += out_43; out_46: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_81; x_81 = out_43 = None 2025-03-04T21:06:08.3681690Z 2025-03-04T21:06:08.3682048Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.3682536Z out_47: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_46); out_46 = None 2025-03-04T21:06:08.3682785Z 2025-03-04T21:06:08.3683110Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.3683814Z x_82: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:08.3684331Z 2025-03-04T21:06:08.3684674Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.3686487Z x_83: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_82 = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.3688077Z 2025-03-04T21:06:08.3688436Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.3688920Z out_48: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_83); x_83 = None 2025-03-04T21:06:08.3689168Z 2025-03-04T21:06:08.3689496Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.3690211Z x_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_48 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:08.3690736Z 2025-03-04T21:06:08.3691080Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.3692907Z x_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_84 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.3694507Z 2025-03-04T21:06:08.3694871Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.3695337Z out_49: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_85); x_85 = None 2025-03-04T21:06:08.3695601Z 2025-03-04T21:06:08.3695929Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.3696654Z x_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_49 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:08.3697184Z 2025-03-04T21:06:08.3697527Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.3699369Z x_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_86 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.3701475Z 2025-03-04T21:06:08.3702178Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:08.3702884Z x_87 += out_47; out_50: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_87; x_87 = out_47 = None 2025-03-04T21:06:08.3703207Z 2025-03-04T21:06:08.3703583Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.3704071Z out_51: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_50); out_50 = None 2025-03-04T21:06:08.3704320Z 2025-03-04T21:06:08.3704830Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:06:08.3705464Z arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32) 2025-03-04T21:06:08.3705729Z 2025-03-04T21:06:08.3706139Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:06:08.3706703Z shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T21:06:08.3707088Z 2025-03-04T21:06:08.3707992Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:06:08.3709056Z arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32) 2025-03-04T21:06:08.3709449Z 2025-03-04T21:06:08.3709938Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:06:08.3710735Z shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T21:06:08.3711113Z 2025-03-04T21:06:08.3711958Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:06:08.3713052Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T21:06:08.3713555Z shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0] 2025-03-04T21:06:08.3713924Z shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T21:06:08.3714155Z 2025-03-04T21:06:08.3714667Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:06:08.3715178Z shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T21:06:08.3715418Z 2025-03-04T21:06:08.3715835Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:06:08.3716334Z shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T21:06:08.3716574Z 2025-03-04T21:06:08.3717085Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:06:08.3717752Z shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T21:06:08.3718225Z 2025-03-04T21:06:08.3718839Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:06:08.3719451Z view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T21:06:08.3720078Z view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T21:06:08.3720724Z add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T21:06:08.3721016Z x_88: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4); add = None 2025-03-04T21:06:08.3721253Z 2025-03-04T21:06:08.3721651Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:06:08.3722139Z tensor: "f32[82125, 4][4, 1]cpu" = x_88.to(torch.float32); x_88 = None 2025-03-04T21:06:08.3722388Z 2025-03-04T21:06:08.3722740Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.3723894Z x_89: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); out_51 = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T21:06:08.3724815Z 2025-03-04T21:06:08.3725186Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:06:08.3725716Z x_90: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_89, inplace = False); x_89 = None 2025-03-04T21:06:08.3726024Z 2025-03-04T21:06:08.3726506Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:06:08.3727841Z score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_90, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T21:06:08.3728864Z 2025-03-04T21:06:08.3729294Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:06:08.3730539Z x_91: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_90, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_90 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T21:06:08.3731491Z 2025-03-04T21:06:08.3731907Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T21:06:08.3732442Z permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T21:06:08.3732775Z logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T21:06:08.3733022Z 2025-03-04T21:06:08.3733516Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T21:06:08.3734152Z view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_91.view(4, -1, 4, 73, 75); x_91 = None 2025-03-04T21:06:08.3734524Z permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2); view_2 = None 2025-03-04T21:06:08.3734909Z pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2); permute_1 = None 2025-03-04T21:06:08.3735194Z 2025-03-04T21:06:08.3735683Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:06:08.3736348Z pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T21:06:08.3736672Z 2025-03-04T21:06:08.3737215Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:06:08.3737865Z unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T21:06:08.3738229Z expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T21:06:08.3738565Z anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T21:06:08.3738821Z 2025-03-04T21:06:08.3739279Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:06:08.3739885Z deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T21:06:08.3740169Z 2025-03-04T21:06:08.3740575Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:06:08.3741106Z boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T21:06:08.3741386Z 2025-03-04T21:06:08.3741779Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:06:08.3742271Z getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:06:08.3742571Z getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:06:08.3742886Z widths: "f32[328500][1]cpu" = getitem_2 - getitem_3; getitem_2 = getitem_3 = None 2025-03-04T21:06:08.3743140Z 2025-03-04T21:06:08.3743537Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:06:08.3744035Z getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:06:08.3744330Z getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:06:08.3744658Z heights: "f32[328500][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T21:06:08.3744912Z 2025-03-04T21:06:08.3745293Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:06:08.3745779Z getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:06:08.3746038Z mul: "f32[328500][1]cpu" = 0.5 * widths 2025-03-04T21:06:08.3746294Z ctr_x: "f32[328500][1]cpu" = getitem_6 + mul; getitem_6 = mul = None 2025-03-04T21:06:08.3746528Z 2025-03-04T21:06:08.3746926Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:06:08.3747466Z getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:06:08.3747751Z mul_1: "f32[328500][1]cpu" = 0.5 * heights 2025-03-04T21:06:08.3748017Z ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1; getitem_7 = mul_1 = None 2025-03-04T21:06:08.3748258Z 2025-03-04T21:06:08.3748692Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:06:08.3749204Z getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:06:08.3749527Z dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0; getitem_8 = None 2025-03-04T21:06:08.3749754Z 2025-03-04T21:06:08.3750158Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:06:08.3750657Z getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:06:08.3750978Z dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0; getitem_9 = None 2025-03-04T21:06:08.3751204Z 2025-03-04T21:06:08.3751670Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:06:08.3752213Z getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:06:08.3752544Z dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0; getitem_10 = None 2025-03-04T21:06:08.3752793Z 2025-03-04T21:06:08.3753190Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:06:08.3753737Z getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:06:08.3754101Z dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0; getitem_11 = None 2025-03-04T21:06:08.3754329Z 2025-03-04T21:06:08.3754759Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:06:08.3755289Z dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:06:08.3755546Z 2025-03-04T21:06:08.3755965Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:06:08.3756487Z dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:06:08.3756732Z 2025-03-04T21:06:08.3757168Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:06:08.3757713Z getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:06:08.3758061Z mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12; dx = getitem_12 = None 2025-03-04T21:06:08.3758392Z getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:06:08.3758743Z pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13; mul_2 = getitem_13 = None 2025-03-04T21:06:08.3758998Z 2025-03-04T21:06:08.3759444Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:06:08.3759991Z getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:06:08.3760333Z mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14; dy = getitem_14 = None 2025-03-04T21:06:08.3760671Z getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:06:08.3761022Z pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15; mul_3 = getitem_15 = None 2025-03-04T21:06:08.3761284Z 2025-03-04T21:06:08.3761717Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:06:08.3762221Z exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:06:08.3762546Z getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:06:08.3762902Z pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16; exp = getitem_16 = None 2025-03-04T21:06:08.3763144Z 2025-03-04T21:06:08.3763554Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:06:08.3764051Z exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:06:08.3764382Z getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:06:08.3764727Z pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17; exp_1 = getitem_17 = None 2025-03-04T21:06:08.3764970Z 2025-03-04T21:06:08.3765358Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:06:08.3765814Z mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T21:06:08.3766072Z x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:06:08.3766301Z 2025-03-04T21:06:08.3766689Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:06:08.3767153Z mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T21:06:08.3767408Z y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:06:08.3767640Z 2025-03-04T21:06:08.3768028Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:06:08.3768502Z mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:06:08.3768805Z x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:06:08.3769046Z 2025-03-04T21:06:08.3769426Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:06:08.3769888Z mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:06:08.3770171Z y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:06:08.3770408Z 2025-03-04T21:06:08.3770842Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:06:08.3771413Z pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:06:08.3771696Z 2025-03-04T21:06:08.3772095Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:06:08.3772638Z proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4)); pred_boxes = None 2025-03-04T21:06:08.3772945Z 2025-03-04T21:06:08.3773399Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:06:08.3773991Z proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T21:06:08.3774273Z 2025-03-04T21:06:08.3774827Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T21:06:08.3775483Z arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T21:06:08.3775727Z 2025-03-04T21:06:08.3776113Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:06:08.3776591Z batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T21:06:08.3776839Z 2025-03-04T21:06:08.3777354Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:06:08.3777938Z topk = logits_i.topk(6000, dim = 1); logits_i = None 2025-03-04T21:06:08.3778196Z topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0] 2025-03-04T21:06:08.3778456Z topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1]; topk = None 2025-03-04T21:06:08.3778676Z 2025-03-04T21:06:08.3779214Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:06:08.3779870Z getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T21:06:08.3780353Z topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)]; proposals_i_1 = getitem_20 = topk_idx = None 2025-03-04T21:06:08.3780694Z 2025-03-04T21:06:08.3781234Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:06:08.3781899Z full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:06:08.3782173Z 2025-03-04T21:06:08.3782544Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:06:08.3783039Z level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu')); full = level_ids = None 2025-03-04T21:06:08.3783302Z 2025-03-04T21:06:08.3783766Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T21:06:08.3784366Z getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0]; topk_proposals_i = None 2025-03-04T21:06:08.3784625Z 2025-03-04T21:06:08.3784997Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:06:08.3785487Z tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32); getitem_22 = None 2025-03-04T21:06:08.3785745Z 2025-03-04T21:06:08.3786217Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T21:06:08.3786806Z scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0]; topk_scores_i = None 2025-03-04T21:06:08.3787063Z 2025-03-04T21:06:08.3787633Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T21:06:08.3788307Z isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1); tensor_1 = None 2025-03-04T21:06:08.3788614Z all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:06:08.3788942Z isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T21:06:08.3789290Z valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T21:06:08.3789551Z 2025-03-04T21:06:08.3790037Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T21:06:08.3790596Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T21:06:08.3790837Z 2025-03-04T21:06:08.3791199Z 2025-03-04T21:06:08.3791300Z class GraphModule(torch.nn.Module): 2025-03-04T21:06:08.3833441Z def forward(self, L_stack0_tensor: "f32[4, 3, 1156, 1199][4158132, 1386044, 1199, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_: "f32[512, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_: "f32[1024, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[1024, 1024, 3, 3][9216, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[1024][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"): 2025-03-04T21:06:08.3875194Z l_stack0_tensor = L_stack0_tensor 2025-03-04T21:06:08.3875700Z l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ 2025-03-04T21:06:08.3876446Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3877246Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3878006Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3878669Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3879332Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:06:08.3880013Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3880746Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3881466Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3882149Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3882804Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:06:08.3883488Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3884217Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3884932Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3885636Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3886287Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:06:08.3886967Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3887725Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3888436Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3889124Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3889789Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:06:08.3890493Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3891249Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3891985Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3892713Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3893376Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:06:08.3894056Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3894786Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3895492Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3896190Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3896830Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:06:08.3897498Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3898227Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3898953Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3899649Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3900304Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:06:08.3900972Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3901705Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3902548Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3903238Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3903875Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:06:08.3904552Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3905276Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3906003Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3906679Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3907319Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:06:08.3907986Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3908716Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3909447Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3910142Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3910802Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:06:08.3911492Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3912279Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3913033Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3913729Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3914387Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:06:08.3915097Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3915852Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3916585Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3917285Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3917949Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:06:08.3918642Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3919386Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3920121Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3920818Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3921478Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:06:08.3922165Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3922921Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3923664Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3924374Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3925036Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:06:08.3925740Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3926506Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3927240Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3927939Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3928596Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:06:08.3929281Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3930010Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3930711Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3931387Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3932028Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:06:08.3932696Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3933431Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3934131Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3934812Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3935452Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:06:08.3936131Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3936869Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3937571Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3938245Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3938898Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:06:08.3939625Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3940355Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3941065Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3941757Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3942444Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:06:08.3943144Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3943879Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3944605Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3945297Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3945955Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:06:08.3946644Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3947407Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3948130Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3948821Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3949486Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ 2025-03-04T21:06:08.3950179Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3950939Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3951704Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3952406Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3953076Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ 2025-03-04T21:06:08.3953791Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3954543Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3955260Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3955972Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3956631Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ 2025-03-04T21:06:08.3957329Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3958076Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3958796Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3959515Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3960207Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:06:08.3960948Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3961737Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3962494Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3963224Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3963920Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:06:08.3964666Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3965447Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3966207Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3966937Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3967651Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:06:08.3968382Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3969166Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3969956Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3970665Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3971326Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:06:08.3972022Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3972764Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3973494Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3974203Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3974877Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:06:08.3975547Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3976277Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3976980Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3977663Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3978319Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:06:08.3978993Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3979717Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3980428Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3981115Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3981760Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:06:08.3982432Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3983157Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3983884Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3984568Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3985214Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:06:08.3985891Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3986622Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3987346Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3988044Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3988721Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:06:08.3989404Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3990154Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3990920Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3991658Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3992363Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:06:08.3993058Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3993797Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3994516Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3995245Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3995922Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ 2025-03-04T21:06:08.3996645Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.3997395Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.3998171Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:08.3998865Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:08.3999522Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ 2025-03-04T21:06:08.4000207Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.4000949Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.4001674Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:08.4002582Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:08.4003245Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ 2025-03-04T21:06:08.4003929Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.4004675Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.4005395Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:08.4006112Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:08.4006755Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ 2025-03-04T21:06:08.4007423Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.4008144Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.4008878Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:08.4009553Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:08.4010198Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ 2025-03-04T21:06:08.4010867Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.4011604Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.4012309Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:08.4012994Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:08.4013639Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ 2025-03-04T21:06:08.4014309Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.4015045Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.4015744Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:08.4016438Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:08.4017086Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ 2025-03-04T21:06:08.4017765Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.4018487Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.4019189Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:08.4019882Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:08.4020525Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ 2025-03-04T21:06:08.4021191Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.4021913Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.4022635Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:08.4023305Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:08.4023946Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ 2025-03-04T21:06:08.4024615Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.4025349Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.4026056Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:08.4026739Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:08.4027441Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T21:06:08.4028164Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T21:06:08.4028857Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T21:06:08.4029614Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T21:06:08.4030401Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T21:06:08.4031169Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T21:06:08.4031986Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T21:06:08.4032466Z 2025-03-04T21:06:08.4032834Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4033662Z x: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1); l_stack0_tensor = l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:08.4034264Z 2025-03-04T21:06:08.4034625Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4036436Z x_1: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4038022Z 2025-03-04T21:06:08.4038389Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x) 2025-03-04T21:06:08.4038859Z x_2: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.relu_(x_1); x_1 = None 2025-03-04T21:06:08.4039117Z 2025-03-04T21:06:08.4039582Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1) 2025-03-04T21:06:08.4040243Z x_3: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1); x_2 = None 2025-03-04T21:06:08.4040594Z 2025-03-04T21:06:08.4040933Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4041672Z x_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:08.4042207Z 2025-03-04T21:06:08.4042562Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4044429Z x_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_4 = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4046080Z 2025-03-04T21:06:08.4046447Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4046918Z out: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_5); x_5 = None 2025-03-04T21:06:08.4047166Z 2025-03-04T21:06:08.4047510Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4048243Z x_6: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out = l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:08.4048783Z 2025-03-04T21:06:08.4049128Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4050963Z x_7: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_6 = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4052580Z 2025-03-04T21:06:08.4052960Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4053429Z out_1: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_7); x_7 = None 2025-03-04T21:06:08.4053680Z 2025-03-04T21:06:08.4054006Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4054735Z x_8: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_1 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:08.4055273Z 2025-03-04T21:06:08.4055616Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4057446Z x_9: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_8 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4059072Z 2025-03-04T21:06:08.4059400Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4060126Z x_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); x_3 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:06:08.4060671Z 2025-03-04T21:06:08.4061025Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4063079Z x_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_10 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4064752Z 2025-03-04T21:06:08.4065110Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:08.4065580Z x_9 += x_11; out_2: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_9; x_9 = x_11 = None 2025-03-04T21:06:08.4065836Z 2025-03-04T21:06:08.4066203Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4066709Z out_3: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_2); out_2 = None 2025-03-04T21:06:08.4066976Z 2025-03-04T21:06:08.4067318Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4068044Z x_12: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:08.4068582Z 2025-03-04T21:06:08.4068929Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4070813Z x_13: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_12 = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4072531Z 2025-03-04T21:06:08.4072904Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4073389Z out_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_13); x_13 = None 2025-03-04T21:06:08.4073648Z 2025-03-04T21:06:08.4073988Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4074752Z x_14: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_4 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:08.4075293Z 2025-03-04T21:06:08.4075641Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4077508Z x_15: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_14 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4079170Z 2025-03-04T21:06:08.4079539Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4080020Z out_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_15); x_15 = None 2025-03-04T21:06:08.4080274Z 2025-03-04T21:06:08.4080619Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4081370Z x_16: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_5 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:08.4081911Z 2025-03-04T21:06:08.4082248Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4084060Z x_17: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_16 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4085678Z 2025-03-04T21:06:08.4086034Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:08.4086519Z x_17 += out_3; out_6: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_17; x_17 = out_3 = None 2025-03-04T21:06:08.4086782Z 2025-03-04T21:06:08.4087145Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4087626Z out_7: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_6); out_6 = None 2025-03-04T21:06:08.4087886Z 2025-03-04T21:06:08.4088226Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4088947Z x_18: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:08.4089483Z 2025-03-04T21:06:08.4089825Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4091677Z x_19: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_18 = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4093331Z 2025-03-04T21:06:08.4093693Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4094186Z out_8: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_19); x_19 = None 2025-03-04T21:06:08.4094438Z 2025-03-04T21:06:08.4094758Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4095492Z x_20: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_8 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:08.4096027Z 2025-03-04T21:06:08.4096366Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4098189Z x_21: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_20 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4099809Z 2025-03-04T21:06:08.4100168Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4100635Z out_9: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_21); x_21 = None 2025-03-04T21:06:08.4100881Z 2025-03-04T21:06:08.4101207Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4102126Z x_22: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_9 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:08.4102675Z 2025-03-04T21:06:08.4103018Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4104883Z x_23: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_22 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4106555Z 2025-03-04T21:06:08.4106914Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:08.4107398Z x_23 += out_7; out_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_23; x_23 = out_7 = None 2025-03-04T21:06:08.4107666Z 2025-03-04T21:06:08.4108055Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4108553Z out_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_10); out_10 = None 2025-03-04T21:06:08.4108824Z 2025-03-04T21:06:08.4109163Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4109989Z x_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:08.4110602Z 2025-03-04T21:06:08.4110994Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4113091Z x_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_24 = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4114804Z 2025-03-04T21:06:08.4115178Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4115666Z out_12: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_25); x_25 = None 2025-03-04T21:06:08.4115926Z 2025-03-04T21:06:08.4116282Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4117030Z x_26: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_12 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:08.4117578Z 2025-03-04T21:06:08.4117926Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4119785Z x_27: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_26 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4121438Z 2025-03-04T21:06:08.4121825Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4122305Z out_13: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_27); x_27 = None 2025-03-04T21:06:08.4122561Z 2025-03-04T21:06:08.4122886Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4123615Z x_28: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_13 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:08.4124154Z 2025-03-04T21:06:08.4124495Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4126315Z x_29: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_28 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4127928Z 2025-03-04T21:06:08.4128253Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4128986Z x_30: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); out_11 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:06:08.4129534Z 2025-03-04T21:06:08.4129899Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4131767Z x_31: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_30 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4133471Z 2025-03-04T21:06:08.4133833Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:08.4134321Z x_29 += x_31; out_14: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_29; x_29 = x_31 = None 2025-03-04T21:06:08.4134584Z 2025-03-04T21:06:08.4134958Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4135482Z out_15: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_14); out_14 = None 2025-03-04T21:06:08.4135746Z 2025-03-04T21:06:08.4136070Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4136783Z x_32: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:08.4137317Z 2025-03-04T21:06:08.4137655Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4139487Z x_33: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_32 = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4141126Z 2025-03-04T21:06:08.4141484Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4141957Z out_16: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_33); x_33 = None 2025-03-04T21:06:08.4142211Z 2025-03-04T21:06:08.4142535Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4143277Z x_34: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_16 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:08.4143817Z 2025-03-04T21:06:08.4144154Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4145989Z x_35: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_34 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4148206Z 2025-03-04T21:06:08.4148579Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4149067Z out_17: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_35); x_35 = None 2025-03-04T21:06:08.4149327Z 2025-03-04T21:06:08.4149683Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4150435Z x_36: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_17 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:08.4151002Z 2025-03-04T21:06:08.4151361Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4153312Z x_37: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_36 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4154987Z 2025-03-04T21:06:08.4155352Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:08.4155845Z x_37 += out_15; out_18: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_37; x_37 = out_15 = None 2025-03-04T21:06:08.4156117Z 2025-03-04T21:06:08.4156486Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4156980Z out_19: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_18); out_18 = None 2025-03-04T21:06:08.4157252Z 2025-03-04T21:06:08.4157586Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4158335Z x_38: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:08.4158871Z 2025-03-04T21:06:08.4159215Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4161085Z x_39: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_38 = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4162750Z 2025-03-04T21:06:08.4163161Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4163824Z out_20: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_39); x_39 = None 2025-03-04T21:06:08.4164088Z 2025-03-04T21:06:08.4164426Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4165174Z x_40: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_20 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:08.4165724Z 2025-03-04T21:06:08.4166070Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4167903Z x_41: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_40 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4169564Z 2025-03-04T21:06:08.4169926Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4170393Z out_21: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_41); x_41 = None 2025-03-04T21:06:08.4170640Z 2025-03-04T21:06:08.4170962Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4171701Z x_42: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_21 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:08.4172239Z 2025-03-04T21:06:08.4172569Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4174410Z x_43: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_42 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4176029Z 2025-03-04T21:06:08.4176382Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:08.4176872Z x_43 += out_19; out_22: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_43; x_43 = out_19 = None 2025-03-04T21:06:08.4177139Z 2025-03-04T21:06:08.4177517Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4178010Z out_23: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_22); out_22 = None 2025-03-04T21:06:08.4178270Z 2025-03-04T21:06:08.4178593Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4179307Z x_44: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:08.4179836Z 2025-03-04T21:06:08.4180174Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4181999Z x_45: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_44 = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4183628Z 2025-03-04T21:06:08.4183988Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4184467Z out_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_45); x_45 = None 2025-03-04T21:06:08.4184727Z 2025-03-04T21:06:08.4185072Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4185807Z x_46: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_24 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:08.4186359Z 2025-03-04T21:06:08.4186704Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4188572Z x_47: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_46 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4190242Z 2025-03-04T21:06:08.4190614Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4191117Z out_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_47); x_47 = None 2025-03-04T21:06:08.4191378Z 2025-03-04T21:06:08.4191768Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4192551Z x_48: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_25 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:08.4193102Z 2025-03-04T21:06:08.4193451Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4195322Z x_49: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_48 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4197001Z 2025-03-04T21:06:08.4197370Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:08.4197863Z x_49 += out_23; out_26: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_49; x_49 = out_23 = None 2025-03-04T21:06:08.4198142Z 2025-03-04T21:06:08.4198506Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4199032Z out_27: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_26); out_26 = None 2025-03-04T21:06:08.4199304Z 2025-03-04T21:06:08.4199637Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4200368Z x_50: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:08.4200910Z 2025-03-04T21:06:08.4201257Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4203246Z x_51: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_50 = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4204894Z 2025-03-04T21:06:08.4205266Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4205742Z out_28: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_51); x_51 = None 2025-03-04T21:06:08.4205991Z 2025-03-04T21:06:08.4206320Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4207036Z x_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_28 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:08.4207566Z 2025-03-04T21:06:08.4207909Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4209735Z x_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_52 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4209828Z 2025-03-04T21:06:08.4210114Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4210256Z out_29: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_53); x_53 = None 2025-03-04T21:06:08.4210317Z 2025-03-04T21:06:08.4210596Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4211014Z x_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_29 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:08.4211083Z 2025-03-04T21:06:08.4211344Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4212855Z x_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_54 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4212980Z 2025-03-04T21:06:08.4213231Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4213686Z x_56: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); out_27 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:06:08.4213747Z 2025-03-04T21:06:08.4214014Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4215560Z x_57: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_56 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4215643Z 2025-03-04T21:06:08.4215928Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:08.4216069Z x_55 += x_57; out_30: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_55; x_55 = x_57 = None 2025-03-04T21:06:08.4216138Z 2025-03-04T21:06:08.4216422Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4216573Z out_31: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_30); out_30 = None 2025-03-04T21:06:08.4216635Z 2025-03-04T21:06:08.4216895Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4217317Z x_58: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:08.4217385Z 2025-03-04T21:06:08.4217641Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4219289Z x_59: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_58 = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4219377Z 2025-03-04T21:06:08.4219672Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4219810Z out_32: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_59); x_59 = None 2025-03-04T21:06:08.4219882Z 2025-03-04T21:06:08.4220133Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4220549Z x_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_32 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:08.4220617Z 2025-03-04T21:06:08.4220873Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4222410Z x_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_60 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4222495Z 2025-03-04T21:06:08.4222785Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4222926Z out_33: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_61); x_61 = None 2025-03-04T21:06:08.4222987Z 2025-03-04T21:06:08.4223248Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4223696Z x_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_33 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:08.4223766Z 2025-03-04T21:06:08.4224033Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4225573Z x_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_62 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4225656Z 2025-03-04T21:06:08.4225940Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:08.4226093Z x_63 += out_31; out_34: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_63; x_63 = out_31 = None 2025-03-04T21:06:08.4226154Z 2025-03-04T21:06:08.4226463Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4226605Z out_35: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_34); out_34 = None 2025-03-04T21:06:08.4226675Z 2025-03-04T21:06:08.4226928Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4227349Z x_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:08.4227411Z 2025-03-04T21:06:08.4227684Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4229237Z x_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_64 = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4229323Z 2025-03-04T21:06:08.4229625Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4229762Z out_36: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_65); x_65 = None 2025-03-04T21:06:08.4229831Z 2025-03-04T21:06:08.4230082Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4230526Z x_66: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_36 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:08.4230587Z 2025-03-04T21:06:08.4230859Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4232439Z x_67: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_66 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4232525Z 2025-03-04T21:06:08.4232819Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4232950Z out_37: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_67); x_67 = None 2025-03-04T21:06:08.4233035Z 2025-03-04T21:06:08.4233286Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4233712Z x_68: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_37 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:08.4233773Z 2025-03-04T21:06:08.4234043Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4235572Z x_69: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_68 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4235648Z 2025-03-04T21:06:08.4235926Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:08.4236066Z x_69 += out_35; out_38: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_69; x_69 = out_35 = None 2025-03-04T21:06:08.4236133Z 2025-03-04T21:06:08.4236408Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4236551Z out_39: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_38); out_38 = None 2025-03-04T21:06:08.4236612Z 2025-03-04T21:06:08.4236876Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4237279Z x_70: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:08.4237349Z 2025-03-04T21:06:08.4237608Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4239107Z x_71: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_70 = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4239190Z 2025-03-04T21:06:08.4239489Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4239628Z out_40: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_71); x_71 = None 2025-03-04T21:06:08.4239689Z 2025-03-04T21:06:08.4239943Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4240348Z x_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_40 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:08.4240416Z 2025-03-04T21:06:08.4240681Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4242160Z x_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_72 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4242243Z 2025-03-04T21:06:08.4242521Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4242657Z out_41: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_73); x_73 = None 2025-03-04T21:06:08.4242718Z 2025-03-04T21:06:08.4242969Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4243394Z x_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_41 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:08.4243463Z 2025-03-04T21:06:08.4243718Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4245198Z x_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_74 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4245282Z 2025-03-04T21:06:08.4245553Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:08.4245720Z x_75 += out_39; out_42: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_75; x_75 = out_39 = None 2025-03-04T21:06:08.4245781Z 2025-03-04T21:06:08.4246061Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4246197Z out_43: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_42); out_42 = None 2025-03-04T21:06:08.4246265Z 2025-03-04T21:06:08.4246512Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4246921Z x_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:08.4246987Z 2025-03-04T21:06:08.4247245Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4248767Z x_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_76 = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4248843Z 2025-03-04T21:06:08.4249133Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4249263Z out_44: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_77); x_77 = None 2025-03-04T21:06:08.4249331Z 2025-03-04T21:06:08.4249597Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4250013Z x_78: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_44 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:08.4250080Z 2025-03-04T21:06:08.4250338Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4251839Z x_79: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_78 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4251916Z 2025-03-04T21:06:08.4252228Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4252357Z out_45: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_79); x_79 = None 2025-03-04T21:06:08.4252426Z 2025-03-04T21:06:08.4252672Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4253087Z x_80: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_45 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:08.4253155Z 2025-03-04T21:06:08.4253414Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4254909Z x_81: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_80 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4254985Z 2025-03-04T21:06:08.4255264Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:08.4255412Z x_81 += out_43; out_46: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_81; x_81 = out_43 = None 2025-03-04T21:06:08.4255473Z 2025-03-04T21:06:08.4255770Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4255922Z out_47: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_46); out_46 = None 2025-03-04T21:06:08.4255989Z 2025-03-04T21:06:08.4256234Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4256643Z x_82: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:08.4256701Z 2025-03-04T21:06:08.4256964Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4258473Z x_83: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_82 = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4258543Z 2025-03-04T21:06:08.4258827Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4258956Z out_48: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_83); x_83 = None 2025-03-04T21:06:08.4259024Z 2025-03-04T21:06:08.4259271Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4259698Z x_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_48 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:08.4259758Z 2025-03-04T21:06:08.4260031Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4261581Z x_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_84 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4261664Z 2025-03-04T21:06:08.4261955Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4262087Z out_49: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_85); x_85 = None 2025-03-04T21:06:08.4262155Z 2025-03-04T21:06:08.4262415Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4262844Z x_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_49 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:08.4262907Z 2025-03-04T21:06:08.4263179Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4264719Z x_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_86 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4264807Z 2025-03-04T21:06:08.4265112Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:08.4265259Z x_87 += out_47; out_50: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_87; x_87 = out_47 = None 2025-03-04T21:06:08.4265331Z 2025-03-04T21:06:08.4265614Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4265763Z out_51: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_50); out_50 = None 2025-03-04T21:06:08.4265825Z 2025-03-04T21:06:08.4266280Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:06:08.4266432Z arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32) 2025-03-04T21:06:08.4266501Z 2025-03-04T21:06:08.4266800Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:06:08.4266942Z shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T21:06:08.4267020Z 2025-03-04T21:06:08.4267485Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:06:08.4267633Z arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32) 2025-03-04T21:06:08.4267702Z 2025-03-04T21:06:08.4267997Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:06:08.4268142Z shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T21:06:08.4268205Z 2025-03-04T21:06:08.4268611Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:06:08.4268824Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T21:06:08.4268932Z shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0] 2025-03-04T21:06:08.4269055Z shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T21:06:08.4269126Z 2025-03-04T21:06:08.4269478Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:06:08.4269618Z shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T21:06:08.4269682Z 2025-03-04T21:06:08.4270035Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:06:08.4270169Z shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T21:06:08.4270238Z 2025-03-04T21:06:08.4270623Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:06:08.4270847Z shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T21:06:08.4270907Z 2025-03-04T21:06:08.4271339Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:06:08.4271484Z view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T21:06:08.4272003Z view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T21:06:08.4272141Z add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T21:06:08.4272253Z x_88: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4); add = None 2025-03-04T21:06:08.4272323Z 2025-03-04T21:06:08.4272623Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:06:08.4272752Z tensor: "f32[82125, 4][4, 1]cpu" = x_88.to(torch.float32); x_88 = None 2025-03-04T21:06:08.4272815Z 2025-03-04T21:06:08.4273077Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4273852Z x_89: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); out_51 = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T21:06:08.4273954Z 2025-03-04T21:06:08.4274227Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:06:08.4274420Z x_90: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_89, inplace = False); x_89 = None 2025-03-04T21:06:08.4274482Z 2025-03-04T21:06:08.4274874Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:06:08.4275763Z score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_90, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T21:06:08.4275827Z 2025-03-04T21:06:08.4276190Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:06:08.4277012Z x_91: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_90, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_90 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T21:06:08.4277109Z 2025-03-04T21:06:08.4277452Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T21:06:08.4277613Z permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T21:06:08.4277755Z logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T21:06:08.4277827Z 2025-03-04T21:06:08.4278263Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T21:06:08.4278429Z view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_91.view(4, -1, 4, 73, 75); x_91 = None 2025-03-04T21:06:08.4278610Z permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2); view_2 = None 2025-03-04T21:06:08.4278786Z pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2); permute_1 = None 2025-03-04T21:06:08.4278847Z 2025-03-04T21:06:08.4279243Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:06:08.4279448Z pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T21:06:08.4279510Z 2025-03-04T21:06:08.4279938Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:06:08.4280097Z unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T21:06:08.4280245Z expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T21:06:08.4280378Z anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T21:06:08.4280445Z 2025-03-04T21:06:08.4280809Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:06:08.4280982Z deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T21:06:08.4281041Z 2025-03-04T21:06:08.4281357Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:06:08.4281508Z boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T21:06:08.4281576Z 2025-03-04T21:06:08.4281889Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:06:08.4282020Z getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:06:08.4282140Z getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:06:08.4282287Z widths: "f32[328500][1]cpu" = getitem_2 - getitem_3; getitem_2 = getitem_3 = None 2025-03-04T21:06:08.4282349Z 2025-03-04T21:06:08.4282674Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:06:08.4282810Z getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:06:08.4282933Z getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:06:08.4283073Z heights: "f32[328500][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T21:06:08.4283140Z 2025-03-04T21:06:08.4283443Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:06:08.4283567Z getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:06:08.4283649Z mul: "f32[328500][1]cpu" = 0.5 * widths 2025-03-04T21:06:08.4283777Z ctr_x: "f32[328500][1]cpu" = getitem_6 + mul; getitem_6 = mul = None 2025-03-04T21:06:08.4283853Z 2025-03-04T21:06:08.4284174Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:06:08.4284317Z getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:06:08.4284413Z mul_1: "f32[328500][1]cpu" = 0.5 * heights 2025-03-04T21:06:08.4284538Z ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1; getitem_7 = mul_1 = None 2025-03-04T21:06:08.4284607Z 2025-03-04T21:06:08.4284962Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:06:08.4285127Z getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:06:08.4285236Z dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0; getitem_8 = None 2025-03-04T21:06:08.4285304Z 2025-03-04T21:06:08.4285600Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:06:08.4285767Z getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:06:08.4285875Z dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0; getitem_9 = None 2025-03-04T21:06:08.4285945Z 2025-03-04T21:06:08.4286241Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:06:08.4286388Z getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:06:08.4286500Z dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0; getitem_10 = None 2025-03-04T21:06:08.4286559Z 2025-03-04T21:06:08.4286860Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:06:08.4287038Z getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:06:08.4287150Z dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0; getitem_11 = None 2025-03-04T21:06:08.4287225Z 2025-03-04T21:06:08.4287561Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:06:08.4287695Z dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:06:08.4287763Z 2025-03-04T21:06:08.4288086Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:06:08.4288225Z dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:06:08.4288304Z 2025-03-04T21:06:08.4288651Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:06:08.4288787Z getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:06:08.4288914Z mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12; dx = getitem_12 = None 2025-03-04T21:06:08.4289064Z getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:06:08.4289205Z pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13; mul_2 = getitem_13 = None 2025-03-04T21:06:08.4289267Z 2025-03-04T21:06:08.4289635Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:06:08.4289767Z getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:06:08.4289891Z mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14; dy = getitem_14 = None 2025-03-04T21:06:08.4290034Z getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:06:08.4290173Z pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15; mul_3 = getitem_15 = None 2025-03-04T21:06:08.4290235Z 2025-03-04T21:06:08.4290565Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:06:08.4290676Z exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:06:08.4290838Z getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:06:08.4290963Z pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16; exp = getitem_16 = None 2025-03-04T21:06:08.4291033Z 2025-03-04T21:06:08.4291352Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:06:08.4291484Z exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:06:08.4291644Z getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:06:08.4291779Z pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17; exp_1 = getitem_17 = None 2025-03-04T21:06:08.4291837Z 2025-03-04T21:06:08.4292154Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:06:08.4292246Z mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T21:06:08.4292363Z x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:06:08.4292425Z 2025-03-04T21:06:08.4292732Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:06:08.4292833Z mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T21:06:08.4292949Z y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:06:08.4293008Z 2025-03-04T21:06:08.4293311Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:06:08.4293425Z mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:06:08.4293546Z x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:06:08.4293613Z 2025-03-04T21:06:08.4293911Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:06:08.4294036Z mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:06:08.4294157Z y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:06:08.4294223Z 2025-03-04T21:06:08.4294560Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:06:08.4294742Z pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:06:08.4294801Z 2025-03-04T21:06:08.4295132Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:06:08.4295303Z proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4)); pred_boxes = None 2025-03-04T21:06:08.4295370Z 2025-03-04T21:06:08.4295742Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:06:08.4295918Z proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T21:06:08.4295978Z 2025-03-04T21:06:08.4296457Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T21:06:08.4296585Z arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T21:06:08.4296653Z 2025-03-04T21:06:08.4296945Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:06:08.4297085Z batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T21:06:08.4297160Z 2025-03-04T21:06:08.4297611Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:06:08.4297721Z topk = logits_i.topk(6000, dim = 1); logits_i = None 2025-03-04T21:06:08.4297828Z topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0] 2025-03-04T21:06:08.4297938Z topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1]; topk = None 2025-03-04T21:06:08.4298005Z 2025-03-04T21:06:08.4298460Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:06:08.4298625Z getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T21:06:08.4298873Z topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)]; proposals_i_1 = getitem_20 = topk_idx = None 2025-03-04T21:06:08.4298943Z 2025-03-04T21:06:08.4299390Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:06:08.4299564Z full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:06:08.4299623Z 2025-03-04T21:06:08.4299917Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:06:08.4300067Z level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu')); full = level_ids = None 2025-03-04T21:06:08.4300141Z 2025-03-04T21:06:08.4300529Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T21:06:08.4300670Z getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0]; topk_proposals_i = None 2025-03-04T21:06:08.4300737Z 2025-03-04T21:06:08.4301026Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:06:08.4301172Z tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32); getitem_22 = None 2025-03-04T21:06:08.4301233Z 2025-03-04T21:06:08.4301625Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T21:06:08.4301757Z scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0]; topk_scores_i = None 2025-03-04T21:06:08.4301825Z 2025-03-04T21:06:08.4302449Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T21:06:08.4302593Z isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1); tensor_1 = None 2025-03-04T21:06:08.4302709Z all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:06:08.4302867Z isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T21:06:08.4302993Z valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T21:06:08.4303060Z 2025-03-04T21:06:08.4303425Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T21:06:08.4303584Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T21:06:08.4303645Z 2025-03-04T21:06:08.4303652Z 2025-03-04T21:06:08.4303746Z class GraphModule(torch.nn.Module): 2025-03-04T21:06:08.4345221Z def forward(self, L_stack0_tensor: "f32[4, 3, 1156, 1199][4158132, 1386044, 1199, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", 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L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_: "f32[1024, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_: 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L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[1024, 1024, 3, 3][9216, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[1024][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"): 2025-03-04T21:06:08.4345662Z l_stack0_tensor = L_stack0_tensor 2025-03-04T21:06:08.4345979Z l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ 2025-03-04T21:06:08.4346329Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.4346648Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.4346976Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:08.4347339Z l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:08.4347709Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:06:08.4348114Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.4348511Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.4348934Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:08.4349320Z l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:08.4349670Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:06:08.4350078Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.4350477Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.4350799Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:08.4351118Z l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:08.4351413Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:06:08.4351892Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.4352302Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.4352696Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:08.4353051Z l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:08.4353346Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:06:08.4353705Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.4354055Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.4354397Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:06:08.4354755Z l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:06:08.4355037Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:06:08.4355379Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.4355717Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.4356132Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:08.4356483Z l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:08.4356773Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:06:08.4357110Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.4357480Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.4357815Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:08.4358126Z l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:08.4358414Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:06:08.4358756Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.4359102Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.4359435Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:08.4359748Z l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:08.4360030Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:06:08.4360376Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.4360719Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.4361056Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:08.4361369Z l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:08.4361648Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:06:08.4361989Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.4362332Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.4362660Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:08.4362966Z l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:08.4363250Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:06:08.4363612Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.4363979Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.4364338Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:08.4364677Z l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:08.4364965Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:06:08.4365298Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.4365638Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.4365970Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:08.4366305Z l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:08.4366587Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:06:08.4366932Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.4367269Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.4367599Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:08.4367910Z l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:08.4368186Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:06:08.4368529Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.4368874Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.4369206Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:08.4369510Z l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:08.4369811Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:06:08.4370179Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.4370529Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.4370865Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:06:08.4371184Z l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:06:08.4371473Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:06:08.4371807Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.4372168Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.4372481Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:08.4372794Z l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:08.4373080Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:06:08.4373414Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.4373769Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.4374085Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:08.4374398Z l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:08.4374675Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:06:08.4375045Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.4375377Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.4375698Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:08.4376011Z l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:08.4376302Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:06:08.4376644Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.4376974Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.4377294Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:08.4377599Z l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:08.4377893Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:06:08.4378225Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.4378574Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.4378890Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:08.4379191Z l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:08.4379479Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:06:08.4379827Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.4380161Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.4380474Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:08.4380787Z l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:08.4381065Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ 2025-03-04T21:06:08.4381419Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.4381752Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.4382062Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:08.4382385Z l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:08.4382663Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ 2025-03-04T21:06:08.4383005Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.4383338Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.4383658Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:08.4383963Z l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:08.4384252Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ 2025-03-04T21:06:08.4384611Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.4384941Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.4385261Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:08.4385568Z l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:08.4385853Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:06:08.4386201Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.4386541Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.4386891Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:08.4387249Z l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:08.4387549Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:06:08.4387885Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.4388220Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.4388545Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:08.4388904Z l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:08.4389214Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:06:08.4389594Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.4389938Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.4390258Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:08.4390568Z l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:08.4390878Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:06:08.4391236Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.4391629Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.4391976Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:06:08.4392333Z l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:06:08.4392681Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:06:08.4393014Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.4393384Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.4393739Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:08.4394095Z l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:08.4394415Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:06:08.4394786Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.4395161Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.4395531Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:08.4395883Z l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:08.4396197Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:06:08.4396578Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.4396956Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.4397308Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:08.4397656Z l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:08.4397983Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:06:08.4398360Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.4398741Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.4399096Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:08.4399434Z l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:08.4399776Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:06:08.4400155Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.4400527Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.4400882Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:08.4401241Z l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:08.4401562Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:06:08.4402063Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.4402452Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.4402836Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:08.4403194Z l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:08.4403481Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ 2025-03-04T21:06:08.4403810Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.4404151Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.4404469Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:08.4404807Z l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:08.4405084Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ 2025-03-04T21:06:08.4405428Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.4405757Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.4406079Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:08.4406411Z l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:08.4406690Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ 2025-03-04T21:06:08.4407030Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.4407366Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.4407712Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:08.4408020Z l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:08.4408305Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ 2025-03-04T21:06:08.4408637Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.4408989Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.4409336Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:08.4409677Z l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:08.4409996Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ 2025-03-04T21:06:08.4410363Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.4410741Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.4411091Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:08.4411455Z l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:08.4411761Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ 2025-03-04T21:06:08.4412139Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.4412520Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.4412901Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:08.4413251Z l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:08.4413544Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ 2025-03-04T21:06:08.4413888Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.4414246Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.4414622Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:06:08.4414962Z l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:06:08.4415280Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ 2025-03-04T21:06:08.4415668Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.4416061Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.4416423Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:06:08.4416751Z l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:06:08.4417043Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ 2025-03-04T21:06:08.4417383Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:06:08.4417728Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:06:08.4418092Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:06:08.4418420Z l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:06:08.4418771Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T21:06:08.4419095Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T21:06:08.4419416Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T21:06:08.4419807Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T21:06:08.4420182Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T21:06:08.4420534Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T21:06:08.4420885Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T21:06:08.4420971Z 2025-03-04T21:06:08.4421262Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4421743Z x: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1); l_stack0_tensor = l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:08.4421807Z 2025-03-04T21:06:08.4422092Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4423589Z x_1: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4423669Z 2025-03-04T21:06:08.4423971Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x) 2025-03-04T21:06:08.4424123Z x_2: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.relu_(x_1); x_1 = None 2025-03-04T21:06:08.4424188Z 2025-03-04T21:06:08.4424579Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1) 2025-03-04T21:06:08.4424855Z x_3: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1); x_2 = None 2025-03-04T21:06:08.4424921Z 2025-03-04T21:06:08.4425196Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4425626Z x_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:08.4425698Z 2025-03-04T21:06:08.4425987Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4427641Z x_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_4 = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4427717Z 2025-03-04T21:06:08.4428035Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4428188Z out: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_5); x_5 = None 2025-03-04T21:06:08.4428255Z 2025-03-04T21:06:08.4428529Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4428986Z x_6: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out = l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:08.4429057Z 2025-03-04T21:06:08.4429362Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4430976Z x_7: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_6 = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4431049Z 2025-03-04T21:06:08.4431354Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4431507Z out_1: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_7); x_7 = None 2025-03-04T21:06:08.4431634Z 2025-03-04T21:06:08.4431918Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4432382Z x_8: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_1 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:08.4432453Z 2025-03-04T21:06:08.4432735Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4434381Z x_9: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_8 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4434451Z 2025-03-04T21:06:08.4434704Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4435165Z x_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); x_3 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:06:08.4435231Z 2025-03-04T21:06:08.4435505Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4437147Z x_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_10 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4437220Z 2025-03-04T21:06:08.4437505Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:08.4437646Z x_9 += x_11; out_2: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_9; x_9 = x_11 = None 2025-03-04T21:06:08.4437714Z 2025-03-04T21:06:08.4437996Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4438149Z out_3: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_2); out_2 = None 2025-03-04T21:06:08.4438211Z 2025-03-04T21:06:08.4438484Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4438911Z x_12: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:08.4438977Z 2025-03-04T21:06:08.4439239Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4440803Z x_13: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_12 = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4440876Z 2025-03-04T21:06:08.4441161Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4441309Z out_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_13); x_13 = None 2025-03-04T21:06:08.4441384Z 2025-03-04T21:06:08.4441642Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4442066Z x_14: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_4 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:08.4442136Z 2025-03-04T21:06:08.4442401Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4443946Z x_15: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_14 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4444019Z 2025-03-04T21:06:08.4444296Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4444436Z out_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_15); x_15 = None 2025-03-04T21:06:08.4444496Z 2025-03-04T21:06:08.4444750Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4445191Z x_16: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_5 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:08.4445261Z 2025-03-04T21:06:08.4445527Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4447106Z x_17: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_16 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4447178Z 2025-03-04T21:06:08.4447450Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:08.4447603Z x_17 += out_3; out_6: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_17; x_17 = out_3 = None 2025-03-04T21:06:08.4447662Z 2025-03-04T21:06:08.4447946Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4448103Z out_7: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_6); out_6 = None 2025-03-04T21:06:08.4448173Z 2025-03-04T21:06:08.4448415Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4448836Z x_18: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:08.4448895Z 2025-03-04T21:06:08.4449163Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4450692Z x_19: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_18 = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4450756Z 2025-03-04T21:06:08.4451042Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4451175Z out_8: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_19); x_19 = None 2025-03-04T21:06:08.4451243Z 2025-03-04T21:06:08.4451498Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4451918Z x_20: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_8 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:08.4451977Z 2025-03-04T21:06:08.4452245Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4453761Z x_21: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_20 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4453826Z 2025-03-04T21:06:08.4454113Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4454245Z out_9: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_21); x_21 = None 2025-03-04T21:06:08.4454328Z 2025-03-04T21:06:08.4454575Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4455011Z x_22: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_9 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:08.4455073Z 2025-03-04T21:06:08.4455349Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4456907Z x_23: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_22 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4456971Z 2025-03-04T21:06:08.4457259Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:08.4457412Z x_23 += out_7; out_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_23; x_23 = out_7 = None 2025-03-04T21:06:08.4457483Z 2025-03-04T21:06:08.4457775Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4457944Z out_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_10); out_10 = None 2025-03-04T21:06:08.4458005Z 2025-03-04T21:06:08.4458255Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4458678Z x_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:08.4458745Z 2025-03-04T21:06:08.4459019Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4460598Z x_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_24 = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4460669Z 2025-03-04T21:06:08.4460954Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4461119Z out_12: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_25); x_25 = None 2025-03-04T21:06:08.4461180Z 2025-03-04T21:06:08.4461439Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4461877Z x_26: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_12 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:08.4461939Z 2025-03-04T21:06:08.4462214Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4463779Z x_27: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_26 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4463849Z 2025-03-04T21:06:08.4464132Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4464278Z out_13: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_27); x_27 = None 2025-03-04T21:06:08.4464353Z 2025-03-04T21:06:08.4464610Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4465044Z x_28: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_13 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:08.4465104Z 2025-03-04T21:06:08.4465376Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4466925Z x_29: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_28 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4466996Z 2025-03-04T21:06:08.4467248Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4467694Z x_30: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); out_11 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:06:08.4467783Z 2025-03-04T21:06:08.4468050Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4469680Z x_31: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_30 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4469743Z 2025-03-04T21:06:08.4470031Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:08.4470186Z x_29 += x_31; out_14: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_29; x_29 = x_31 = None 2025-03-04T21:06:08.4470247Z 2025-03-04T21:06:08.4470538Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4470692Z out_15: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_14); out_14 = None 2025-03-04T21:06:08.4470761Z 2025-03-04T21:06:08.4471011Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4471458Z x_32: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:08.4471520Z 2025-03-04T21:06:08.4471862Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4473484Z x_33: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_32 = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4473558Z 2025-03-04T21:06:08.4473848Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4473998Z out_16: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_33); x_33 = None 2025-03-04T21:06:08.4474068Z 2025-03-04T21:06:08.4474333Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4474764Z x_34: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_16 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:08.4474824Z 2025-03-04T21:06:08.4475088Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4476591Z x_35: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_34 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4476664Z 2025-03-04T21:06:08.4476955Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4477093Z out_17: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_35); x_35 = None 2025-03-04T21:06:08.4477162Z 2025-03-04T21:06:08.4477408Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4477840Z x_36: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_17 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:08.4477929Z 2025-03-04T21:06:08.4478194Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4479733Z x_37: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_36 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4479803Z 2025-03-04T21:06:08.4480079Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:08.4480226Z x_37 += out_15; out_18: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_37; x_37 = out_15 = None 2025-03-04T21:06:08.4480294Z 2025-03-04T21:06:08.4480572Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4480742Z out_19: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_18); out_18 = None 2025-03-04T21:06:08.4480803Z 2025-03-04T21:06:08.4481053Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4481464Z x_38: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:08.4481532Z 2025-03-04T21:06:08.4481792Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4483309Z x_39: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_38 = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4483380Z 2025-03-04T21:06:08.4483675Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4483818Z out_20: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_39); x_39 = None 2025-03-04T21:06:08.4483882Z 2025-03-04T21:06:08.4484150Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4484616Z x_40: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_20 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:08.4484684Z 2025-03-04T21:06:08.4484943Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4486514Z x_41: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_40 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4486586Z 2025-03-04T21:06:08.4486861Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4487005Z out_21: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_41); x_41 = None 2025-03-04T21:06:08.4487081Z 2025-03-04T21:06:08.4487334Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4487758Z x_42: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_21 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:08.4487827Z 2025-03-04T21:06:08.4488086Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4489624Z x_43: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_42 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4489693Z 2025-03-04T21:06:08.4489964Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:08.4490117Z x_43 += out_19; out_22: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_43; x_43 = out_19 = None 2025-03-04T21:06:08.4490179Z 2025-03-04T21:06:08.4490457Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4490621Z out_23: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_22); out_22 = None 2025-03-04T21:06:08.4490689Z 2025-03-04T21:06:08.4490934Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4491359Z x_44: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:08.4491419Z 2025-03-04T21:06:08.4491688Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4493247Z x_45: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_44 = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4493311Z 2025-03-04T21:06:08.4493603Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4493755Z out_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_45); x_45 = None 2025-03-04T21:06:08.4493826Z 2025-03-04T21:06:08.4494075Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4494512Z x_46: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_24 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:08.4494572Z 2025-03-04T21:06:08.4494848Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4496401Z x_47: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_46 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4496466Z 2025-03-04T21:06:08.4496758Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4496898Z out_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_47); x_47 = None 2025-03-04T21:06:08.4496982Z 2025-03-04T21:06:08.4497230Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4497665Z x_48: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_25 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:08.4497726Z 2025-03-04T21:06:08.4497996Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4499570Z x_49: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_48 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4499634Z 2025-03-04T21:06:08.4499923Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:08.4500076Z x_49 += out_23; out_26: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_49; x_49 = out_23 = None 2025-03-04T21:06:08.4500161Z 2025-03-04T21:06:08.4500442Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4500600Z out_27: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_26); out_26 = None 2025-03-04T21:06:08.4500662Z 2025-03-04T21:06:08.4500916Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4501334Z x_50: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:08.4501404Z 2025-03-04T21:06:08.4501691Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4503367Z x_51: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_50 = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4503444Z 2025-03-04T21:06:08.4521368Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4522042Z out_28: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_51); x_51 = None 2025-03-04T21:06:08.4522130Z 2025-03-04T21:06:08.4522437Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4522903Z x_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_28 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:08.4522970Z 2025-03-04T21:06:08.4523268Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4524887Z x_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_52 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4524961Z 2025-03-04T21:06:08.4525262Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4525445Z out_29: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_53); x_53 = None 2025-03-04T21:06:08.4525516Z 2025-03-04T21:06:08.4525771Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4526206Z x_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_29 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:08.4526268Z 2025-03-04T21:06:08.4526540Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4528078Z x_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_54 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4528153Z 2025-03-04T21:06:08.4528414Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4528845Z x_56: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); out_27 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:06:08.4528928Z 2025-03-04T21:06:08.4529193Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4530765Z x_57: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_56 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4530836Z 2025-03-04T21:06:08.4531116Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:08.4531264Z x_55 += x_57; out_30: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_55; x_55 = x_57 = None 2025-03-04T21:06:08.4531325Z 2025-03-04T21:06:08.4531622Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4531762Z out_31: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_30); out_30 = None 2025-03-04T21:06:08.4531845Z 2025-03-04T21:06:08.4532099Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4532525Z x_58: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:08.4532589Z 2025-03-04T21:06:08.4532861Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4534397Z x_59: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_58 = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4534468Z 2025-03-04T21:06:08.4534759Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4534894Z out_32: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_59); x_59 = None 2025-03-04T21:06:08.4534963Z 2025-03-04T21:06:08.4535214Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4535657Z x_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_32 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:08.4535719Z 2025-03-04T21:06:08.4535995Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4537555Z x_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_60 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4537622Z 2025-03-04T21:06:08.4537916Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4538046Z out_33: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_61); x_61 = None 2025-03-04T21:06:08.4538114Z 2025-03-04T21:06:08.4538368Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4538810Z x_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_33 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:08.4538871Z 2025-03-04T21:06:08.4539146Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4540718Z x_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_62 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4540783Z 2025-03-04T21:06:08.4541063Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:08.4541208Z x_63 += out_31; out_34: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_63; x_63 = out_31 = None 2025-03-04T21:06:08.4541276Z 2025-03-04T21:06:08.4541552Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4541699Z out_35: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_34); out_34 = None 2025-03-04T21:06:08.4541777Z 2025-03-04T21:06:08.4542031Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4542441Z x_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:08.4542515Z 2025-03-04T21:06:08.4542784Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4544311Z x_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_64 = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4544381Z 2025-03-04T21:06:08.4544663Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4544799Z out_36: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_65); x_65 = None 2025-03-04T21:06:08.4544893Z 2025-03-04T21:06:08.4545146Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4545560Z x_66: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_36 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:08.4545628Z 2025-03-04T21:06:08.4545891Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4547421Z x_67: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_66 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4547492Z 2025-03-04T21:06:08.4547769Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4547907Z out_37: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_67); x_67 = None 2025-03-04T21:06:08.4547966Z 2025-03-04T21:06:08.4548217Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4548644Z x_68: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_37 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:08.4548711Z 2025-03-04T21:06:08.4548970Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4550490Z x_69: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_68 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4550561Z 2025-03-04T21:06:08.4550834Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:08.4550983Z x_69 += out_35; out_38: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_69; x_69 = out_35 = None 2025-03-04T21:06:08.4551044Z 2025-03-04T21:06:08.4551329Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4551484Z out_39: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_38); out_38 = None 2025-03-04T21:06:08.4551636Z 2025-03-04T21:06:08.4551902Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4552324Z x_70: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:08.4552387Z 2025-03-04T21:06:08.4552691Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4554190Z x_71: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_70 = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4554253Z 2025-03-04T21:06:08.4554540Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4554669Z out_40: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_71); x_71 = None 2025-03-04T21:06:08.4554753Z 2025-03-04T21:06:08.4554997Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4555414Z x_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_40 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:08.4555483Z 2025-03-04T21:06:08.4555739Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4557246Z x_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_72 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4557310Z 2025-03-04T21:06:08.4557597Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4557734Z out_41: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_73); x_73 = None 2025-03-04T21:06:08.4557808Z 2025-03-04T21:06:08.4558060Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4558470Z x_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_41 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:08.4558537Z 2025-03-04T21:06:08.4558795Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4560362Z x_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_74 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4560433Z 2025-03-04T21:06:08.4560712Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:08.4560863Z x_75 += out_39; out_42: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_75; x_75 = out_39 = None 2025-03-04T21:06:08.4560926Z 2025-03-04T21:06:08.4561213Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4561368Z out_43: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_42); out_42 = None 2025-03-04T21:06:08.4561435Z 2025-03-04T21:06:08.4561683Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4562105Z x_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:08.4562166Z 2025-03-04T21:06:08.4562440Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4563979Z x_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_76 = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4564048Z 2025-03-04T21:06:08.4564345Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4564492Z out_44: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_77); x_77 = None 2025-03-04T21:06:08.4564559Z 2025-03-04T21:06:08.4564808Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4565233Z x_78: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_44 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:08.4565293Z 2025-03-04T21:06:08.4565583Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4567104Z x_79: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_78 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4567175Z 2025-03-04T21:06:08.4567471Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4567604Z out_45: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_79); x_79 = None 2025-03-04T21:06:08.4567686Z 2025-03-04T21:06:08.4567935Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4568359Z x_80: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_45 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:08.4568421Z 2025-03-04T21:06:08.4568690Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4570231Z x_81: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_80 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4570302Z 2025-03-04T21:06:08.4570586Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:08.4570731Z x_81 += out_43; out_46: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_81; x_81 = out_43 = None 2025-03-04T21:06:08.4570814Z 2025-03-04T21:06:08.4571097Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4571244Z out_47: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_46); out_46 = None 2025-03-04T21:06:08.4571306Z 2025-03-04T21:06:08.4571561Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4571975Z x_82: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = None 2025-03-04T21:06:08.4572056Z 2025-03-04T21:06:08.4572322Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4573864Z x_83: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_82 = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4573934Z 2025-03-04T21:06:08.4574221Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4574373Z out_48: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_83); x_83 = None 2025-03-04T21:06:08.4574434Z 2025-03-04T21:06:08.4574692Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4575120Z x_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_48 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = None 2025-03-04T21:06:08.4575189Z 2025-03-04T21:06:08.4575445Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4576951Z x_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_84 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4577018Z 2025-03-04T21:06:08.4577300Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4577447Z out_49: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_85); x_85 = None 2025-03-04T21:06:08.4577506Z 2025-03-04T21:06:08.4577757Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4578172Z x_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_49 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = None 2025-03-04T21:06:08.4578239Z 2025-03-04T21:06:08.4578510Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:06:08.4580026Z x_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_86 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:06:08.4580095Z 2025-03-04T21:06:08.4580368Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:06:08.4580512Z x_87 += out_47; out_50: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_87; x_87 = out_47 = None 2025-03-04T21:06:08.4580584Z 2025-03-04T21:06:08.4580866Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:06:08.4581001Z out_51: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_50); out_50 = None 2025-03-04T21:06:08.4581067Z 2025-03-04T21:06:08.4581496Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:06:08.4581653Z arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32) 2025-03-04T21:06:08.4581716Z 2025-03-04T21:06:08.4582011Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:06:08.4582143Z shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T21:06:08.4582213Z 2025-03-04T21:06:08.4582649Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:06:08.4582802Z arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32) 2025-03-04T21:06:08.4582860Z 2025-03-04T21:06:08.4583153Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:06:08.4583285Z shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T21:06:08.4583367Z 2025-03-04T21:06:08.4583729Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:06:08.4583911Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T21:06:08.4584004Z shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0] 2025-03-04T21:06:08.4584129Z shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T21:06:08.4584190Z 2025-03-04T21:06:08.4584530Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:06:08.4584669Z shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T21:06:08.4584728Z 2025-03-04T21:06:08.4585066Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:06:08.4585185Z shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T21:06:08.4585251Z 2025-03-04T21:06:08.4585637Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:06:08.4585855Z shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T21:06:08.4585915Z 2025-03-04T21:06:08.4586345Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:06:08.4586470Z view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T21:06:08.4586906Z view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T21:06:08.4587042Z add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T21:06:08.4587171Z x_88: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4); add = None 2025-03-04T21:06:08.4587231Z 2025-03-04T21:06:08.4587530Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:06:08.4587650Z tensor: "f32[82125, 4][4, 1]cpu" = x_88.to(torch.float32); x_88 = None 2025-03-04T21:06:08.4587716Z 2025-03-04T21:06:08.4587959Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:08.4588736Z x_89: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); out_51 = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T21:06:08.4588799Z 2025-03-04T21:06:08.4589083Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:06:08.4589259Z x_90: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_89, inplace = False); x_89 = None 2025-03-04T21:06:08.4589327Z 2025-03-04T21:06:08.4589707Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:06:08.4590569Z score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_90, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T21:06:08.4590637Z 2025-03-04T21:06:08.4591006Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:06:08.4591962Z x_91: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_90, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_90 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T21:06:08.4592043Z 2025-03-04T21:06:08.4592381Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T21:06:08.4592537Z permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T21:06:08.4592675Z logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T21:06:08.4592746Z 2025-03-04T21:06:08.4593168Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T21:06:08.4593354Z view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_91.view(4, -1, 4, 73, 75); x_91 = None 2025-03-04T21:06:08.4593530Z permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2); view_2 = None 2025-03-04T21:06:08.4593713Z pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2); permute_1 = None 2025-03-04T21:06:08.4593774Z 2025-03-04T21:06:08.4594187Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:06:08.4594390Z pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T21:06:08.4594462Z 2025-03-04T21:06:08.4594900Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:06:08.4595071Z unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T21:06:08.4595217Z expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T21:06:08.4595361Z anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T21:06:08.4595421Z 2025-03-04T21:06:08.4595811Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:06:08.4595978Z deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T21:06:08.4596068Z 2025-03-04T21:06:08.4596379Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:06:08.4596524Z boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T21:06:08.4596585Z 2025-03-04T21:06:08.4596905Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:06:08.4597033Z getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:06:08.4597162Z getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:06:08.4597309Z widths: "f32[328500][1]cpu" = getitem_2 - getitem_3; getitem_2 = getitem_3 = None 2025-03-04T21:06:08.4597370Z 2025-03-04T21:06:08.4597711Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:06:08.4597835Z getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:06:08.4597958Z getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:06:08.4598101Z heights: "f32[328500][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T21:06:08.4598169Z 2025-03-04T21:06:08.4598481Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:06:08.4598605Z getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:06:08.4598688Z mul: "f32[328500][1]cpu" = 0.5 * widths 2025-03-04T21:06:08.4598813Z ctr_x: "f32[328500][1]cpu" = getitem_6 + mul; getitem_6 = mul = None 2025-03-04T21:06:08.4598875Z 2025-03-04T21:06:08.4599195Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:06:08.4599354Z getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:06:08.4599449Z mul_1: "f32[328500][1]cpu" = 0.5 * heights 2025-03-04T21:06:08.4599572Z ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1; getitem_7 = mul_1 = None 2025-03-04T21:06:08.4599640Z 2025-03-04T21:06:08.4599980Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:06:08.4600137Z getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:06:08.4600249Z dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0; getitem_8 = None 2025-03-04T21:06:08.4600317Z 2025-03-04T21:06:08.4600630Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:06:08.4600784Z getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:06:08.4600905Z dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0; getitem_9 = None 2025-03-04T21:06:08.4600971Z 2025-03-04T21:06:08.4601259Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:06:08.4601413Z getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:06:08.4601517Z dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0; getitem_10 = None 2025-03-04T21:06:08.4601583Z 2025-03-04T21:06:08.4601875Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:06:08.4602297Z getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:06:08.4602409Z dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0; getitem_11 = None 2025-03-04T21:06:08.4602476Z 2025-03-04T21:06:08.4602813Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:06:08.4602954Z dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:06:08.4603014Z 2025-03-04T21:06:08.4603349Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:06:08.4603509Z dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:06:08.4603582Z 2025-03-04T21:06:08.4603922Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:06:08.4604067Z getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:06:08.4604195Z mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12; dx = getitem_12 = None 2025-03-04T21:06:08.4604344Z getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:06:08.4604485Z pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13; mul_2 = getitem_13 = None 2025-03-04T21:06:08.4604549Z 2025-03-04T21:06:08.4604901Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:06:08.4605035Z getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:06:08.4605185Z mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14; dy = getitem_14 = None 2025-03-04T21:06:08.4605329Z getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:06:08.4605465Z pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15; mul_3 = getitem_15 = None 2025-03-04T21:06:08.4605525Z 2025-03-04T21:06:08.4605856Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:06:08.4605965Z exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:06:08.4606122Z getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:06:08.4606245Z pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16; exp = getitem_16 = None 2025-03-04T21:06:08.4606314Z 2025-03-04T21:06:08.4606636Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:06:08.4606772Z exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:06:08.4606930Z getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:06:08.4607064Z pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17; exp_1 = getitem_17 = None 2025-03-04T21:06:08.4607123Z 2025-03-04T21:06:08.4607441Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:06:08.4607534Z mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T21:06:08.4607655Z x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:06:08.4607737Z 2025-03-04T21:06:08.4608063Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:06:08.4608154Z mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T21:06:08.4608268Z y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:06:08.4608327Z 2025-03-04T21:06:08.4608631Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:06:08.4608740Z mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:06:08.4608868Z x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:06:08.4608927Z 2025-03-04T21:06:08.4609250Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:06:08.4609362Z mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:06:08.4609492Z y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:06:08.4609564Z 2025-03-04T21:06:08.4609907Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:06:08.4610078Z pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:06:08.4610144Z 2025-03-04T21:06:08.4610467Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:06:08.4610627Z proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4)); pred_boxes = None 2025-03-04T21:06:08.4610687Z 2025-03-04T21:06:08.4611076Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:06:08.4611240Z proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T21:06:08.4611307Z 2025-03-04T21:06:08.4611778Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T21:06:08.4611911Z arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T21:06:08.4611971Z 2025-03-04T21:06:08.4612263Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:06:08.4612403Z batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T21:06:08.4612464Z 2025-03-04T21:06:08.4612907Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:06:08.4613017Z topk = logits_i.topk(6000, dim = 1); logits_i = None 2025-03-04T21:06:08.4613121Z topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0] 2025-03-04T21:06:08.4613230Z topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1]; topk = None 2025-03-04T21:06:08.4613296Z 2025-03-04T21:06:08.4613749Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:06:08.4613930Z getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T21:06:08.4614167Z topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)]; proposals_i_1 = getitem_20 = topk_idx = None 2025-03-04T21:06:08.4614233Z 2025-03-04T21:06:08.4614694Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:06:08.4614859Z full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:06:08.4614917Z 2025-03-04T21:06:08.4615232Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:06:08.4615379Z level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu')); full = level_ids = None 2025-03-04T21:06:08.4615446Z 2025-03-04T21:06:08.4615820Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T21:06:08.4615968Z getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0]; topk_proposals_i = None 2025-03-04T21:06:08.4616026Z 2025-03-04T21:06:08.4616324Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:06:08.4616461Z tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32); getitem_22 = None 2025-03-04T21:06:08.4616529Z 2025-03-04T21:06:08.4616899Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T21:06:08.4617039Z scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0]; topk_scores_i = None 2025-03-04T21:06:08.4617115Z 2025-03-04T21:06:08.4617599Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T21:06:08.4617731Z isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1); tensor_1 = None 2025-03-04T21:06:08.4617855Z all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:06:08.4618003Z isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T21:06:08.4618139Z valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T21:06:08.4618200Z 2025-03-04T21:06:08.4618569Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T21:06:08.4618700Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T21:06:08.4618767Z 2025-03-04T21:06:11.8850372Z 2025-03-04T21:06:11.8855551Z class GraphModule(torch.nn.Module): 2025-03-04T21:06:11.8858622Z def forward(self, L_features_res4_: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_weight_: "f32[1024, 1024, 3, 3][9216, 9, 3, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_bias_: "f32[1024][1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"): 2025-03-04T21:06:11.8860669Z l_features_res4_ = L_features_res4_ 2025-03-04T21:06:11.8861140Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T21:06:11.8861775Z l_self_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T21:06:11.8868050Z l_self_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T21:06:11.8869643Z l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T21:06:11.8870655Z l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T21:06:11.8871281Z l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T21:06:11.8871880Z l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T21:06:11.8872273Z 2025-03-04T21:06:11.8872886Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:06:11.8873597Z arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32) 2025-03-04T21:06:11.8873882Z 2025-03-04T21:06:11.8874306Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:06:11.8874822Z shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T21:06:11.8875164Z 2025-03-04T21:06:11.8875727Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:06:11.8876406Z arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32) 2025-03-04T21:06:11.8876689Z 2025-03-04T21:06:11.8877095Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:06:11.8877616Z shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T21:06:11.8877887Z 2025-03-04T21:06:11.8878369Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:06:11.8879032Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T21:06:11.8879386Z shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0] 2025-03-04T21:06:11.8879686Z shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T21:06:11.8879922Z 2025-03-04T21:06:11.8880345Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:06:11.8880874Z shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T21:06:11.8881121Z 2025-03-04T21:06:11.8881543Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:06:11.8882069Z shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T21:06:11.8882329Z 2025-03-04T21:06:11.8882798Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:06:11.8883451Z shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T21:06:11.8883781Z 2025-03-04T21:06:11.8884274Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:06:11.8884867Z view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T21:06:11.8885387Z view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T21:06:11.8885880Z add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T21:06:11.8886167Z x: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4); add = None 2025-03-04T21:06:11.8886394Z 2025-03-04T21:06:11.8886788Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:06:11.8887263Z tensor: "f32[82125, 4][4, 1]cpu" = x.to(torch.float32); x = None 2025-03-04T21:06:11.8887503Z 2025-03-04T21:06:11.8887845Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:11.8888780Z x_1: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(l_features_res4_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_res4_ = l_self_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T21:06:11.8889530Z 2025-03-04T21:06:11.8889901Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:06:11.8890404Z x_2: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False); x_1 = None 2025-03-04T21:06:11.8890695Z 2025-03-04T21:06:11.8891156Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:06:11.8892245Z score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T21:06:11.8893003Z 2025-03-04T21:06:11.8893473Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:06:11.8894483Z x_3: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_2 = l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T21:06:11.8895189Z 2025-03-04T21:06:11.8895612Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T21:06:11.8896179Z permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T21:06:11.8896523Z logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T21:06:11.8896788Z 2025-03-04T21:06:11.8897278Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T21:06:11.8897893Z view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_3.view(4, -1, 4, 73, 75); x_3 = None 2025-03-04T21:06:11.8898341Z permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2); view_2 = None 2025-03-04T21:06:11.8898962Z pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2); permute_1 = None 2025-03-04T21:06:11.8899417Z 2025-03-04T21:06:11.8899915Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:06:11.8901002Z pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T21:06:11.8901548Z 2025-03-04T21:06:11.8902298Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:06:11.8903219Z unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T21:06:11.8903582Z expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T21:06:11.8903940Z anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T21:06:11.8904265Z 2025-03-04T21:06:11.8904738Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:06:11.8905465Z deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T21:06:11.8905760Z 2025-03-04T21:06:11.8906171Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:06:11.8906674Z boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T21:06:11.8906929Z 2025-03-04T21:06:11.8907330Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:06:11.8907833Z getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:06:11.8908150Z getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:06:11.8908513Z widths: "f32[328500][1]cpu" = getitem_2 - getitem_3; getitem_2 = getitem_3 = None 2025-03-04T21:06:11.8908783Z 2025-03-04T21:06:11.8909221Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:06:11.8909778Z getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:06:11.8910108Z getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:06:11.8910537Z heights: "f32[328500][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T21:06:11.8910832Z 2025-03-04T21:06:11.8911303Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:06:11.8911828Z getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:06:11.8912090Z mul: "f32[328500][1]cpu" = 0.5 * widths 2025-03-04T21:06:11.8912350Z ctr_x: "f32[328500][1]cpu" = getitem_6 + mul; getitem_6 = mul = None 2025-03-04T21:06:11.8912585Z 2025-03-04T21:06:11.8912981Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:06:11.8913499Z getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:06:11.8913786Z mul_1: "f32[328500][1]cpu" = 0.5 * heights 2025-03-04T21:06:11.8914087Z ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1; getitem_7 = mul_1 = None 2025-03-04T21:06:11.8914324Z 2025-03-04T21:06:11.8914743Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:06:11.8915241Z getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:06:11.8915555Z dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0; getitem_8 = None 2025-03-04T21:06:11.8915780Z 2025-03-04T21:06:11.8916160Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:06:11.8916653Z getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:06:11.8916966Z dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0; getitem_9 = None 2025-03-04T21:06:11.8917188Z 2025-03-04T21:06:11.8917570Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:06:11.8918081Z getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:06:11.8918387Z dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0; getitem_10 = None 2025-03-04T21:06:11.8918604Z 2025-03-04T21:06:11.8918978Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:06:11.8919498Z getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:06:11.8919831Z dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0; getitem_11 = None 2025-03-04T21:06:11.8920053Z 2025-03-04T21:06:11.8920464Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:06:11.8920983Z dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:06:11.8921234Z 2025-03-04T21:06:11.8921683Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:06:11.8922196Z dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:06:11.8922441Z 2025-03-04T21:06:11.8922860Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:06:11.8923388Z getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:06:11.8923699Z mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12; dx = getitem_12 = None 2025-03-04T21:06:11.8924059Z getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:06:11.8924405Z pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13; mul_2 = getitem_13 = None 2025-03-04T21:06:11.8924668Z 2025-03-04T21:06:11.8925093Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:06:11.8925619Z getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:06:11.8925924Z mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14; dy = getitem_14 = None 2025-03-04T21:06:11.8926238Z getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:06:11.8926584Z pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15; mul_3 = getitem_15 = None 2025-03-04T21:06:11.8926832Z 2025-03-04T21:06:11.8927239Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:06:11.8927730Z exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:06:11.8928045Z getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:06:11.8928375Z pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16; exp = getitem_16 = None 2025-03-04T21:06:11.8928613Z 2025-03-04T21:06:11.8929023Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:06:11.8929511Z exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:06:11.8929833Z getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:06:11.8930174Z pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17; exp_1 = getitem_17 = None 2025-03-04T21:06:11.8930436Z 2025-03-04T21:06:11.8930829Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:06:11.8931305Z mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T21:06:11.8931561Z x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:06:11.8931783Z 2025-03-04T21:06:11.8932163Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:06:11.8932607Z mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T21:06:11.8932852Z y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:06:11.8933067Z 2025-03-04T21:06:11.8933447Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:06:11.8933913Z mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:06:11.8934215Z x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:06:11.8934453Z 2025-03-04T21:06:11.8934833Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:06:11.8935292Z mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:06:11.8935574Z y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:06:11.8935821Z 2025-03-04T21:06:11.8936258Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:06:11.8936855Z pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:06:11.8937140Z 2025-03-04T21:06:11.8937544Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:06:11.8938071Z proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4)); pred_boxes = None 2025-03-04T21:06:11.8938336Z 2025-03-04T21:06:11.8938794Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:06:11.8939406Z proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T21:06:11.8939683Z 2025-03-04T21:06:11.8940238Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T21:06:11.8940914Z arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T21:06:11.8941156Z 2025-03-04T21:06:11.8941529Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:06:11.8942008Z batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T21:06:11.8942260Z 2025-03-04T21:06:11.8942777Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:06:11.8943373Z topk = logits_i.topk(6000, dim = 1); logits_i = None 2025-03-04T21:06:11.8943633Z topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0] 2025-03-04T21:06:11.8943914Z topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1]; topk = None 2025-03-04T21:06:11.8944136Z 2025-03-04T21:06:11.8944670Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:06:11.8945336Z getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T21:06:11.8945789Z topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)]; proposals_i_1 = getitem_20 = topk_idx = None 2025-03-04T21:06:11.8946139Z 2025-03-04T21:06:11.8946682Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:06:11.8947361Z full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:06:11.8947658Z 2025-03-04T21:06:11.8948038Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:06:11.8948552Z level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu')); full = level_ids = None 2025-03-04T21:06:11.8948824Z 2025-03-04T21:06:11.8949296Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T21:06:11.8949887Z getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0]; topk_proposals_i = None 2025-03-04T21:06:11.8950264Z 2025-03-04T21:06:11.8950700Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:06:11.8951262Z tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32); getitem_22 = None 2025-03-04T21:06:11.8951543Z 2025-03-04T21:06:11.8952006Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T21:06:11.8952579Z scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0]; topk_scores_i = None 2025-03-04T21:06:11.8952836Z 2025-03-04T21:06:11.8953431Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T21:06:11.8954111Z isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1); tensor_1 = None 2025-03-04T21:06:11.8954421Z all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:06:11.8954747Z isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T21:06:11.8955084Z valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T21:06:11.8955331Z 2025-03-04T21:06:11.8955788Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T21:06:11.8956327Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T21:06:11.8956553Z 2025-03-04T21:06:11.8956699Z 2025-03-04T21:06:11.8956788Z class GraphModule(torch.nn.Module): 2025-03-04T21:06:11.8958134Z def forward(self, L_features_res4_: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_weight_: "f32[1024, 1024, 3, 3][9216, 9, 3, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_bias_: "f32[1024][1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"): 2025-03-04T21:06:11.8959450Z l_features_res4_ = L_features_res4_ 2025-03-04T21:06:11.8959846Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T21:06:11.8960374Z l_self_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T21:06:11.8960854Z l_self_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T21:06:11.8961404Z l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T21:06:11.8962010Z l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T21:06:11.8962571Z l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T21:06:11.8963129Z l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T21:06:11.8963500Z 2025-03-04T21:06:11.8964043Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:06:11.8964713Z arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32) 2025-03-04T21:06:11.8964979Z 2025-03-04T21:06:11.8965368Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:06:11.8965862Z shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T21:06:11.8966112Z 2025-03-04T21:06:11.8966646Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:06:11.8967319Z arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32) 2025-03-04T21:06:11.8967583Z 2025-03-04T21:06:11.8967962Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:06:11.8968445Z shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T21:06:11.8968695Z 2025-03-04T21:06:11.8969152Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:06:11.8969738Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T21:06:11.8970055Z shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0] 2025-03-04T21:06:11.8970310Z shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T21:06:11.8970531Z 2025-03-04T21:06:11.8970936Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:06:11.8971466Z shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T21:06:11.8971699Z 2025-03-04T21:06:11.8972114Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:06:11.8972606Z shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T21:06:11.8972835Z 2025-03-04T21:06:11.8973285Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:06:11.8973919Z shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T21:06:11.8974233Z 2025-03-04T21:06:11.8974723Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:06:11.8975322Z view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T21:06:11.8975800Z view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T21:06:11.8976280Z add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T21:06:11.8976561Z x: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4); add = None 2025-03-04T21:06:11.8976783Z 2025-03-04T21:06:11.8977166Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:06:11.8977648Z tensor: "f32[82125, 4][4, 1]cpu" = x.to(torch.float32); x = None 2025-03-04T21:06:11.8977884Z 2025-03-04T21:06:11.8978225Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:06:11.8979143Z x_1: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(l_features_res4_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_res4_ = l_self_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T21:06:11.8979855Z 2025-03-04T21:06:11.8980211Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:06:11.8980737Z x_2: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False); x_1 = None 2025-03-04T21:06:11.8981036Z 2025-03-04T21:06:11.8981508Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:06:11.8982580Z score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T21:06:11.8983327Z 2025-03-04T21:06:11.8983779Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:06:11.8984794Z x_3: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_2 = l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T21:06:11.8985525Z 2025-03-04T21:06:11.8985950Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T21:06:11.8986493Z permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T21:06:11.8986834Z logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T21:06:11.8987089Z 2025-03-04T21:06:11.8987590Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T21:06:11.8988209Z view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_3.view(4, -1, 4, 73, 75); x_3 = None 2025-03-04T21:06:11.8988605Z permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2); view_2 = None 2025-03-04T21:06:11.8989005Z pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2); permute_1 = None 2025-03-04T21:06:11.8989300Z 2025-03-04T21:06:11.8989800Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:06:11.8990555Z pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T21:06:11.8990916Z 2025-03-04T21:06:11.8991462Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:06:11.8992114Z unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T21:06:11.8992461Z expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T21:06:11.8992793Z anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T21:06:11.8993046Z 2025-03-04T21:06:11.8993510Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:06:11.8994120Z deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T21:06:11.8994412Z 2025-03-04T21:06:11.8994816Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:06:11.8995333Z boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T21:06:11.8995601Z 2025-03-04T21:06:11.8996008Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:06:11.8996510Z getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:06:11.8996824Z getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:06:11.8997154Z widths: "f32[328500][1]cpu" = getitem_2 - getitem_3; getitem_2 = getitem_3 = None 2025-03-04T21:06:11.8997422Z 2025-03-04T21:06:11.8997834Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:06:11.8998354Z getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:06:11.8998643Z getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:06:11.8998957Z heights: "f32[328500][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T21:06:11.8999213Z 2025-03-04T21:06:11.8999604Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:06:11.9000079Z getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:06:11.9000334Z mul: "f32[328500][1]cpu" = 0.5 * widths 2025-03-04T21:06:11.9000579Z ctr_x: "f32[328500][1]cpu" = getitem_6 + mul; getitem_6 = mul = None 2025-03-04T21:06:11.9000814Z 2025-03-04T21:06:11.9001212Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:06:11.9001738Z getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:06:11.9002185Z mul_1: "f32[328500][1]cpu" = 0.5 * heights 2025-03-04T21:06:11.9002449Z ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1; getitem_7 = mul_1 = None 2025-03-04T21:06:11.9002691Z 2025-03-04T21:06:11.9003091Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:06:11.9003602Z getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:06:11.9003935Z dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0; getitem_8 = None 2025-03-04T21:06:11.9004170Z 2025-03-04T21:06:11.9004599Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:06:11.9005095Z getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:06:11.9005411Z dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0; getitem_9 = None 2025-03-04T21:06:11.9005634Z 2025-03-04T21:06:11.9006008Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:06:11.9006501Z getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:06:11.9006813Z dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0; getitem_10 = None 2025-03-04T21:06:11.9007037Z 2025-03-04T21:06:11.9007437Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:06:11.9007964Z getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:06:11.9008304Z dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0; getitem_11 = None 2025-03-04T21:06:11.9008531Z 2025-03-04T21:06:11.9008943Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:06:11.9009480Z dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:06:11.9009735Z 2025-03-04T21:06:11.9010153Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:06:11.9010675Z dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:06:11.9010925Z 2025-03-04T21:06:11.9011357Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:06:11.9011929Z getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:06:11.9012249Z mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12; dx = getitem_12 = None 2025-03-04T21:06:11.9012577Z getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:06:11.9012925Z pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13; mul_2 = getitem_13 = None 2025-03-04T21:06:11.9013181Z 2025-03-04T21:06:11.9013616Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:06:11.9014163Z getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:06:11.9014479Z mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14; dy = getitem_14 = None 2025-03-04T21:06:11.9014828Z getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:06:11.9015164Z pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15; mul_3 = getitem_15 = None 2025-03-04T21:06:11.9015419Z 2025-03-04T21:06:11.9015849Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:06:11.9016354Z exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:06:11.9016677Z getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:06:11.9017025Z pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16; exp = getitem_16 = None 2025-03-04T21:06:11.9017295Z 2025-03-04T21:06:11.9017719Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:06:11.9018229Z exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:06:11.9018559Z getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:06:11.9018911Z pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17; exp_1 = getitem_17 = None 2025-03-04T21:06:11.9019153Z 2025-03-04T21:06:11.9019552Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:06:11.9020054Z mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T21:06:11.9020313Z x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:06:11.9020548Z 2025-03-04T21:06:11.9020939Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:06:11.9021406Z mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T21:06:11.9021660Z y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:06:11.9021887Z 2025-03-04T21:06:11.9022276Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:06:11.9022746Z mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:06:11.9023040Z x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:06:11.9023283Z 2025-03-04T21:06:11.9023673Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:06:11.9024164Z mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:06:11.9024449Z y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:06:11.9024687Z 2025-03-04T21:06:11.9025122Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:06:11.9025702Z pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:06:11.9025990Z 2025-03-04T21:06:11.9026405Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:06:11.9026953Z proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4)); pred_boxes = None 2025-03-04T21:06:11.9027239Z 2025-03-04T21:06:11.9027735Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:06:11.9028360Z proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T21:06:11.9028655Z 2025-03-04T21:06:11.9029238Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T21:06:11.9029933Z arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T21:06:11.9030244Z 2025-03-04T21:06:11.9030648Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:06:11.9031201Z batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T21:06:11.9031457Z 2025-03-04T21:06:11.9031978Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:06:11.9032577Z topk = logits_i.topk(6000, dim = 1); logits_i = None 2025-03-04T21:06:11.9032845Z topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0] 2025-03-04T21:06:11.9033110Z topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1]; topk = None 2025-03-04T21:06:11.9033334Z 2025-03-04T21:06:11.9033902Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:06:11.9034580Z getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T21:06:11.9035044Z topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)]; proposals_i_1 = getitem_20 = topk_idx = None 2025-03-04T21:06:11.9035380Z 2025-03-04T21:06:11.9035914Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:06:11.9036583Z full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:06:11.9036859Z 2025-03-04T21:06:11.9037230Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:06:11.9037719Z level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu')); full = level_ids = None 2025-03-04T21:06:11.9038000Z 2025-03-04T21:06:11.9038466Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T21:06:11.9039058Z getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0]; topk_proposals_i = None 2025-03-04T21:06:11.9039315Z 2025-03-04T21:06:11.9039680Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:06:11.9040173Z tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32); getitem_22 = None 2025-03-04T21:06:11.9040418Z 2025-03-04T21:06:11.9040881Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T21:06:11.9041449Z scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0]; topk_scores_i = None 2025-03-04T21:06:11.9041699Z 2025-03-04T21:06:11.9043715Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T21:06:11.9044411Z isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1); tensor_1 = None 2025-03-04T21:06:11.9044718Z all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:06:11.9045051Z isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T21:06:11.9045385Z valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T21:06:11.9045634Z 2025-03-04T21:06:11.9046116Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T21:06:11.9046666Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T21:06:11.9046902Z 2025-03-04T21:06:12.2987146Z 2025-03-04T21:06:12.2992475Z class GraphModule(torch.nn.Module): 2025-03-04T21:06:12.2994576Z def forward(self, L_pred_anchor_deltas_0_: "f32[4, 82125, 4][328500, 4, 1]cpu", L_anchors_0_tensor: "f32[82125, 4][4, 1]cpu", L_pred_objectness_logits_0_: "f32[4, 82125][82125, 1]cpu"): 2025-03-04T21:06:12.2995261Z l_pred_anchor_deltas_0_ = L_pred_anchor_deltas_0_ 2025-03-04T21:06:12.3000228Z l_anchors_0_tensor = L_anchors_0_tensor 2025-03-04T21:06:12.3002693Z l_pred_objectness_logits_0_ = L_pred_objectness_logits_0_ 2025-03-04T21:06:12.3003427Z 2025-03-04T21:06:12.3008991Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:06:12.3010465Z pred_anchor_deltas_i: "f32[328500, 4][4, 1]cpu" = l_pred_anchor_deltas_0_.reshape(-1, 4); l_pred_anchor_deltas_0_ = None 2025-03-04T21:06:12.3010928Z 2025-03-04T21:06:12.3011481Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:06:12.3012228Z unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = l_anchors_0_tensor.unsqueeze(0); l_anchors_0_tensor = None 2025-03-04T21:06:12.3016477Z expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T21:06:12.3018716Z anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T21:06:12.3019118Z 2025-03-04T21:06:12.3024498Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:06:12.3026696Z deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.float(); pred_anchor_deltas_i = None 2025-03-04T21:06:12.3027077Z 2025-03-04T21:06:12.3027531Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:06:12.3028067Z boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T21:06:12.3028334Z 2025-03-04T21:06:12.3028748Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:06:12.3029259Z getitem: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:06:12.3029576Z getitem_1: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:06:12.3029910Z widths: "f32[328500][1]cpu" = getitem - getitem_1; getitem = getitem_1 = None 2025-03-04T21:06:12.3030305Z 2025-03-04T21:06:12.3030914Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:06:12.3031449Z getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:06:12.3031752Z getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:06:12.3032079Z heights: "f32[328500][1]cpu" = getitem_2 - getitem_3; getitem_2 = getitem_3 = None 2025-03-04T21:06:12.3032352Z 2025-03-04T21:06:12.3032764Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:06:12.3033319Z getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:06:12.3033587Z mul: "f32[328500][1]cpu" = 0.5 * widths 2025-03-04T21:06:12.3033847Z ctr_x: "f32[328500][1]cpu" = getitem_4 + mul; getitem_4 = mul = None 2025-03-04T21:06:12.3034085Z 2025-03-04T21:06:12.3034488Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:06:12.3035007Z getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:06:12.3035292Z mul_1: "f32[328500][1]cpu" = 0.5 * heights 2025-03-04T21:06:12.3035563Z ctr_y: "f32[328500][1]cpu" = getitem_5 + mul_1; getitem_5 = mul_1 = None 2025-03-04T21:06:12.3035807Z 2025-03-04T21:06:12.3036267Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:06:12.3036785Z getitem_6: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:06:12.3037112Z dx: "f32[328500, 1][1, 1]cpu" = getitem_6 / 1.0; getitem_6 = None 2025-03-04T21:06:12.3037343Z 2025-03-04T21:06:12.3037727Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:06:12.3038235Z getitem_7: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:06:12.3038553Z dy: "f32[328500, 1][1, 1]cpu" = getitem_7 / 1.0; getitem_7 = None 2025-03-04T21:06:12.3038783Z 2025-03-04T21:06:12.3039170Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:06:12.3039675Z getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:06:12.3040013Z dw: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0; getitem_8 = None 2025-03-04T21:06:12.3040247Z 2025-03-04T21:06:12.3040626Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:06:12.3041145Z getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:06:12.3041475Z dh: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0; getitem_9 = None 2025-03-04T21:06:12.3041693Z 2025-03-04T21:06:12.3042106Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:06:12.3042624Z dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:06:12.3042871Z 2025-03-04T21:06:12.3043282Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:06:12.3043811Z dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:06:12.3044059Z 2025-03-04T21:06:12.3044478Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:06:12.3045007Z getitem_10: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:06:12.3045317Z mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_10; dx = getitem_10 = None 2025-03-04T21:06:12.3045641Z getitem_11: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:06:12.3046005Z pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_11; mul_2 = getitem_11 = None 2025-03-04T21:06:12.3046280Z 2025-03-04T21:06:12.3046710Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:06:12.3047239Z getitem_12: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:06:12.3047550Z mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_12; dy = getitem_12 = None 2025-03-04T21:06:12.3047875Z getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:06:12.3048212Z pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_13; mul_3 = getitem_13 = None 2025-03-04T21:06:12.3048463Z 2025-03-04T21:06:12.3048888Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:06:12.3049383Z exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:06:12.3049704Z getitem_14: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:06:12.3050040Z pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_14; exp = getitem_14 = None 2025-03-04T21:06:12.3050284Z 2025-03-04T21:06:12.3050690Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:06:12.3051182Z exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:06:12.3051503Z getitem_15: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:06:12.3051844Z pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_15; exp_1 = getitem_15 = None 2025-03-04T21:06:12.3052088Z 2025-03-04T21:06:12.3052474Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:06:12.3052966Z mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T21:06:12.3053226Z x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:06:12.3053449Z 2025-03-04T21:06:12.3053835Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:06:12.3054281Z mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T21:06:12.3054532Z y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:06:12.3054759Z 2025-03-04T21:06:12.3055146Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:06:12.3055619Z mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:06:12.3055905Z x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:06:12.3056144Z 2025-03-04T21:06:12.3056543Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:06:12.3057009Z mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:06:12.3057286Z y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:06:12.3057524Z 2025-03-04T21:06:12.3057953Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:06:12.3058531Z pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:06:12.3058833Z 2025-03-04T21:06:12.3059249Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:06:12.3059786Z proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4)); pred_boxes = None 2025-03-04T21:06:12.3060056Z 2025-03-04T21:06:12.3060518Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:06:12.3061114Z proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T21:06:12.3061395Z 2025-03-04T21:06:12.3061969Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T21:06:12.3062638Z arange: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T21:06:12.3062877Z 2025-03-04T21:06:12.3063248Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:06:12.3063716Z batch_idx: "i64[4][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T21:06:12.3063958Z 2025-03-04T21:06:12.3064463Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:06:12.3065107Z topk = l_pred_objectness_logits_0_.topk(6000, dim = 1); l_pred_objectness_logits_0_ = None 2025-03-04T21:06:12.3065432Z topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0] 2025-03-04T21:06:12.3065694Z topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1]; topk = None 2025-03-04T21:06:12.3065938Z 2025-03-04T21:06:12.3066470Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:06:12.3067133Z getitem_18: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T21:06:12.3067568Z topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_18, topk_idx)]; proposals_i_1 = getitem_18 = topk_idx = None 2025-03-04T21:06:12.3067903Z 2025-03-04T21:06:12.3068440Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:06:12.3069121Z full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:06:12.3069402Z 2025-03-04T21:06:12.3069810Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:06:12.3070394Z level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu')); full = level_ids = None 2025-03-04T21:06:12.3070664Z 2025-03-04T21:06:12.3071135Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T21:06:12.3071714Z getitem_20: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0]; topk_proposals_i = None 2025-03-04T21:06:12.3071979Z 2025-03-04T21:06:12.3072362Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:06:12.3072886Z tensor: "f32[6000, 4][4, 1]cpu" = getitem_20.to(torch.float32); getitem_20 = None 2025-03-04T21:06:12.3073150Z 2025-03-04T21:06:12.3073620Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T21:06:12.3074196Z scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0]; topk_scores_i = None 2025-03-04T21:06:12.3074454Z 2025-03-04T21:06:12.3075021Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T21:06:12.3075708Z isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor); tensor = None 2025-03-04T21:06:12.3076021Z all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:06:12.3076354Z isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T21:06:12.3076696Z valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T21:06:12.3076941Z 2025-03-04T21:06:12.3077400Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T21:06:12.3077944Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T21:06:12.3078182Z 2025-03-04T21:06:26.2486643Z 2025-03-04T21:06:26.2487392Z class GraphModule(torch.nn.Module): 2025-03-04T21:06:26.2489121Z def forward(self, L_stack0_: "f32[3233, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1233 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1233 - s0, 4][4, 1]cpu"): 2025-03-04T21:06:26.2490945Z l_stack0_ = L_stack0_ 2025-03-04T21:06:26.2491352Z l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_ 2025-03-04T21:06:26.2491942Z l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_ 2025-03-04T21:06:26.2492524Z l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ 2025-03-04T21:06:26.2493103Z l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ 2025-03-04T21:06:26.2493658Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:06:26.2494066Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:06:26.2494457Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:06:26.2494858Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:06:26.2495157Z 2025-03-04T21:06:26.2495751Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/roi_heads.py:480 in torch_dynamo_resume_in_forward_at_477, code: predictions = self.box_predictor(box_features.mean(dim=[2, 3])) 2025-03-04T21:06:26.2496457Z mean: "f32[3233, 2048][2048, 1]cpu" = l_stack0_.mean(dim = [2, 3]); l_stack0_ = None 2025-03-04T21:06:26.2496726Z 2025-03-04T21:06:26.2497127Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x) 2025-03-04T21:06:26.2498113Z scores: "f32[3233, 81][81, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_); l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None 2025-03-04T21:06:26.2498884Z 2025-03-04T21:06:26.2499359Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x) 2025-03-04T21:06:26.2500416Z proposal_deltas: "f32[3233, 320][320, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_); mean = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None 2025-03-04T21:06:26.2501212Z 2025-03-04T21:06:26.2501602Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:06:26.2502278Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:06:26.2502540Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:06:26.2502780Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:06:26.2503068Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:06:26.2503349Z getitem_2: "Sym(1233 - s0)" = size_1[0] 2025-03-04T21:06:26.2503608Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:06:26.2503845Z 2025-03-04T21:06:26.2504356Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:06:26.2505389Z proposal_boxes: "f32[3233, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:06:26.2506143Z 2025-03-04T21:06:26.2506605Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:06:26.2507206Z deltas: "f32[3233, 320][320, 1]cpu" = proposal_deltas.float(); proposal_deltas = None 2025-03-04T21:06:26.2507494Z 2025-03-04T21:06:26.2507916Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:06:26.2508508Z boxes: "f32[3233, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:06:26.2508800Z 2025-03-04T21:06:26.2509222Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:06:26.2509745Z getitem_4: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:06:26.2510058Z getitem_5: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:06:26.2510390Z widths: "f32[3233][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T21:06:26.2510668Z 2025-03-04T21:06:26.2511097Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:06:26.2511657Z getitem_6: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:06:26.2511966Z getitem_7: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:06:26.2512297Z heights: "f32[3233][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T21:06:26.2512572Z 2025-03-04T21:06:26.2512995Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:06:26.2513515Z getitem_8: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:06:26.2513784Z mul: "f32[3233][1]cpu" = 0.5 * widths 2025-03-04T21:06:26.2514080Z ctr_x: "f32[3233][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T21:06:26.2514319Z 2025-03-04T21:06:26.2514724Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:06:26.2515234Z getitem_9: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:06:26.2515516Z mul_1: "f32[3233][1]cpu" = 0.5 * heights 2025-03-04T21:06:26.2515775Z ctr_y: "f32[3233][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T21:06:26.2516022Z 2025-03-04T21:06:26.2516453Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:06:26.2516955Z getitem_10: "f32[3233, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:06:26.2517274Z dx: "f32[3233, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T21:06:26.2517502Z 2025-03-04T21:06:26.2517882Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:06:26.2518430Z getitem_11: "f32[3233, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:06:26.2518755Z dy: "f32[3233, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T21:06:26.2518986Z 2025-03-04T21:06:26.2519368Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:06:26.2519867Z getitem_12: "f32[3233, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:06:26.2520183Z dw: "f32[3233, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T21:06:26.2520411Z 2025-03-04T21:06:26.2520799Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:06:26.2521338Z getitem_13: "f32[3233, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:06:26.2521702Z dh: "f32[3233, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T21:06:26.2521931Z 2025-03-04T21:06:26.2522486Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:06:26.2523032Z dw_1: "f32[3233, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:06:26.2523284Z 2025-03-04T21:06:26.2523692Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:06:26.2524211Z dh_1: "f32[3233, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:06:26.2524541Z 2025-03-04T21:06:26.2524968Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:06:26.2525490Z getitem_14: "f32[3233, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:06:26.2525794Z mul_2: "f32[3233, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T21:06:26.2526124Z getitem_15: "f32[3233, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:06:26.2526468Z pred_ctr_x: "f32[3233, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T21:06:26.2526715Z 2025-03-04T21:06:26.2527203Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:06:26.2527754Z getitem_16: "f32[3233, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:06:26.2528057Z mul_3: "f32[3233, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T21:06:26.2528371Z getitem_17: "f32[3233, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:06:26.2528700Z pred_ctr_y: "f32[3233, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T21:06:26.2528952Z 2025-03-04T21:06:26.2529377Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:06:26.2529869Z exp: "f32[3233, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:06:26.2530182Z getitem_18: "f32[3233, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:06:26.2530516Z pred_w: "f32[3233, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T21:06:26.2530759Z 2025-03-04T21:06:26.2531189Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:06:26.2531679Z exp_1: "f32[3233, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:06:26.2532000Z getitem_19: "f32[3233, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:06:26.2532341Z pred_h: "f32[3233, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T21:06:26.2532587Z 2025-03-04T21:06:26.2532986Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:06:26.2533449Z mul_6: "f32[3233, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:06:26.2533717Z x1: "f32[3233, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:06:26.2533946Z 2025-03-04T21:06:26.2534333Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:06:26.2534800Z mul_7: "f32[3233, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:06:26.2535064Z y1: "f32[3233, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:06:26.2535292Z 2025-03-04T21:06:26.2535686Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:06:26.2536158Z mul_8: "f32[3233, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:06:26.2536445Z x2: "f32[3233, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:06:26.2536686Z 2025-03-04T21:06:26.2537078Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:06:26.2537568Z mul_9: "f32[3233, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:06:26.2537853Z y2: "f32[3233, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:06:26.2538095Z 2025-03-04T21:06:26.2538530Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:06:26.2539111Z pred_boxes: "f32[3233, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:06:26.2539402Z 2025-03-04T21:06:26.2539842Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:06:26.2540408Z predict_boxes: "f32[3233, 320][320, 1]cpu" = pred_boxes.reshape((3233, 320)); pred_boxes = None 2025-03-04T21:06:26.2540699Z 2025-03-04T21:06:26.2541155Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:06:26.2541788Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T21:06:26.2542156Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T21:06:26.2542443Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T21:06:26.2542751Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T21:06:26.2543074Z getitem_23: "f32[1233 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T21:06:26.2543349Z 2025-03-04T21:06:26.2543730Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:06:26.2544414Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:06:26.2544783Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T21:06:26.2545034Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T21:06:26.2545421Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:06:26.2545801Z getitem_26: "Sym(1233 - s0)" = size_3[0] 2025-03-04T21:06:26.2546058Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T21:06:26.2546289Z 2025-03-04T21:06:26.2546729Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:06:26.2547289Z probs: "f32[3233, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1); scores = None 2025-03-04T21:06:26.2547577Z 2025-03-04T21:06:26.2548043Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:06:26.2548650Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T21:06:26.2549010Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:06:26.2549312Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T21:06:26.2549604Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T21:06:26.2549910Z getitem_31: "f32[1233 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T21:06:26.2550161Z 2025-03-04T21:06:26.2550701Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:06:26.2551425Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:06:26.2551757Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:06:26.2552084Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:06:26.2552414Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:06:26.2552699Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:06:26.2552932Z 2025-03-04T21:06:26.2553381Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:06:26.2553895Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:06:26.2554119Z 2025-03-04T21:06:26.2554250Z 2025-03-04T21:06:26.2554338Z class GraphModule(torch.nn.Module): 2025-03-04T21:06:26.2555664Z def forward(self, L_stack0_: "f32[3233, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1233 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1233 - s0, 4][4, 1]cpu"): 2025-03-04T21:06:26.2556962Z l_stack0_ = L_stack0_ 2025-03-04T21:06:26.2557339Z l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_ 2025-03-04T21:06:26.2557916Z l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_ 2025-03-04T21:06:26.2558459Z l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ 2025-03-04T21:06:26.2559008Z l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ 2025-03-04T21:06:26.2559472Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:06:26.2559858Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:06:26.2560243Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:06:26.2560626Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:06:26.2560909Z 2025-03-04T21:06:26.2561436Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/roi_heads.py:480 in torch_dynamo_resume_in_forward_at_477, code: predictions = self.box_predictor(box_features.mean(dim=[2, 3])) 2025-03-04T21:06:26.2562043Z mean: "f32[3233, 2048][2048, 1]cpu" = l_stack0_.mean(dim = [2, 3]); l_stack0_ = None 2025-03-04T21:06:26.2562304Z 2025-03-04T21:06:26.2562695Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x) 2025-03-04T21:06:26.2563662Z scores: "f32[3233, 81][81, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_); l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None 2025-03-04T21:06:26.2564403Z 2025-03-04T21:06:26.2564815Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x) 2025-03-04T21:06:26.2565849Z proposal_deltas: "f32[3233, 320][320, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_); mean = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None 2025-03-04T21:06:26.2566609Z 2025-03-04T21:06:26.2567005Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:06:26.2567466Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:06:26.2567718Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:06:26.2567948Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:06:26.2568218Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:06:26.2568476Z getitem_2: "Sym(1233 - s0)" = size_1[0] 2025-03-04T21:06:26.2568709Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:06:26.2568929Z 2025-03-04T21:06:26.2569299Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:06:26.2570240Z proposal_boxes: "f32[3233, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:06:26.2570965Z 2025-03-04T21:06:26.2571441Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:06:26.2572015Z deltas: "f32[3233, 320][320, 1]cpu" = proposal_deltas.float(); proposal_deltas = None 2025-03-04T21:06:26.2572277Z 2025-03-04T21:06:26.2572676Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:06:26.2573200Z boxes: "f32[3233, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:06:26.2573474Z 2025-03-04T21:06:26.2573877Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:06:26.2574374Z getitem_4: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:06:26.2574678Z getitem_5: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:06:26.2575113Z widths: "f32[3233][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T21:06:26.2575376Z 2025-03-04T21:06:26.2575788Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:06:26.2576310Z getitem_6: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:06:26.2576604Z getitem_7: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:06:26.2576919Z heights: "f32[3233][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T21:06:26.2577182Z 2025-03-04T21:06:26.2577589Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:06:26.2578107Z getitem_8: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:06:26.2578362Z mul: "f32[3233][1]cpu" = 0.5 * widths 2025-03-04T21:06:26.2578612Z ctr_x: "f32[3233][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T21:06:26.2578847Z 2025-03-04T21:06:26.2579241Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:06:26.2579744Z getitem_9: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:06:26.2580022Z mul_1: "f32[3233][1]cpu" = 0.5 * heights 2025-03-04T21:06:26.2580281Z ctr_y: "f32[3233][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T21:06:26.2580534Z 2025-03-04T21:06:26.2580943Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:06:26.2581460Z getitem_10: "f32[3233, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:06:26.2581781Z dx: "f32[3233, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T21:06:26.2582012Z 2025-03-04T21:06:26.2582399Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:06:26.2582906Z getitem_11: "f32[3233, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:06:26.2583224Z dy: "f32[3233, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T21:06:26.2583454Z 2025-03-04T21:06:26.2583852Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:06:26.2584467Z getitem_12: "f32[3233, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:06:26.2584814Z dw: "f32[3233, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T21:06:26.2585051Z 2025-03-04T21:06:26.2585451Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:06:26.2586004Z getitem_13: "f32[3233, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:06:26.2586362Z dh: "f32[3233, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T21:06:26.2586618Z 2025-03-04T21:06:26.2587055Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:06:26.2587598Z dw_1: "f32[3233, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:06:26.2587865Z 2025-03-04T21:06:26.2588312Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:06:26.2588846Z dh_1: "f32[3233, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:06:26.2589104Z 2025-03-04T21:06:26.2589536Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:06:26.2590090Z getitem_14: "f32[3233, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:06:26.2590410Z mul_2: "f32[3233, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T21:06:26.2590743Z getitem_15: "f32[3233, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:06:26.2591116Z pred_ctr_x: "f32[3233, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T21:06:26.2591380Z 2025-03-04T21:06:26.2591826Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:06:26.2592379Z getitem_16: "f32[3233, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:06:26.2592698Z mul_3: "f32[3233, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T21:06:26.2593030Z getitem_17: "f32[3233, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:06:26.2593377Z pred_ctr_y: "f32[3233, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T21:06:26.2593634Z 2025-03-04T21:06:26.2594081Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:06:26.2594602Z exp: "f32[3233, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:06:26.2594933Z getitem_18: "f32[3233, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:06:26.2595279Z pred_w: "f32[3233, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T21:06:26.2595533Z 2025-03-04T21:06:26.2595969Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:06:26.2596493Z exp_1: "f32[3233, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:06:26.2596823Z getitem_19: "f32[3233, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:06:26.2597174Z pred_h: "f32[3233, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T21:06:26.2597428Z 2025-03-04T21:06:26.2597854Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:06:26.2598310Z mul_6: "f32[3233, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:06:26.2598563Z x1: "f32[3233, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:06:26.2598789Z 2025-03-04T21:06:26.2599172Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:06:26.2599624Z mul_7: "f32[3233, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:06:26.2599876Z y1: "f32[3233, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:06:26.2600102Z 2025-03-04T21:06:26.2600487Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:06:26.2600954Z mul_8: "f32[3233, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:06:26.2601255Z x2: "f32[3233, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:06:26.2601500Z 2025-03-04T21:06:26.2601888Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:06:26.2602550Z mul_9: "f32[3233, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:06:26.2602836Z y2: "f32[3233, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:06:26.2603075Z 2025-03-04T21:06:26.2603502Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:06:26.2604132Z pred_boxes: "f32[3233, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:06:26.2604419Z 2025-03-04T21:06:26.2604828Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:06:26.2605370Z predict_boxes: "f32[3233, 320][320, 1]cpu" = pred_boxes.reshape((3233, 320)); pred_boxes = None 2025-03-04T21:06:26.2605647Z 2025-03-04T21:06:26.2606075Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:06:26.2606676Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T21:06:26.2607060Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T21:06:26.2607354Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T21:06:26.2607666Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T21:06:26.2607984Z getitem_23: "f32[1233 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T21:06:26.2608244Z 2025-03-04T21:06:26.2608623Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:06:26.2609177Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:06:26.2609531Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T21:06:26.2609764Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T21:06:26.2610120Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:06:26.2610463Z getitem_26: "Sym(1233 - s0)" = size_3[0] 2025-03-04T21:06:26.2610706Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T21:06:26.2610941Z 2025-03-04T21:06:26.2611350Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:06:26.2611884Z probs: "f32[3233, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1); scores = None 2025-03-04T21:06:26.2612159Z 2025-03-04T21:06:26.2612584Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:06:26.2613164Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T21:06:26.2613509Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:06:26.2613789Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T21:06:26.2614079Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T21:06:26.2614406Z getitem_31: "f32[1233 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T21:06:26.2614658Z 2025-03-04T21:06:26.2615196Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:06:26.2615870Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:06:26.2616200Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:06:26.2616528Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:06:26.2616881Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:06:26.2617166Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:06:26.2617400Z 2025-03-04T21:06:26.2617840Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:06:26.2618347Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:06:26.2618571Z 2025-03-04T21:06:26.2618697Z 2025-03-04T21:06:26.2618784Z class GraphModule(torch.nn.Module): 2025-03-04T21:06:26.2620169Z def forward(self, L_stack0_: "f32[3233, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1233 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1233 - s0, 4][4, 1]cpu"): 2025-03-04T21:06:26.2621511Z l_stack0_ = L_stack0_ 2025-03-04T21:06:26.2621893Z l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_ 2025-03-04T21:06:26.2622460Z l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_ 2025-03-04T21:06:26.2623029Z l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ 2025-03-04T21:06:26.2623589Z l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ 2025-03-04T21:06:26.2624087Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:06:26.2627355Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:06:26.2627778Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:06:26.2628179Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:06:26.2628475Z 2025-03-04T21:06:26.2629019Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/roi_heads.py:480 in torch_dynamo_resume_in_forward_at_477, code: predictions = self.box_predictor(box_features.mean(dim=[2, 3])) 2025-03-04T21:06:26.2629665Z mean: "f32[3233, 2048][2048, 1]cpu" = l_stack0_.mean(dim = [2, 3]); l_stack0_ = None 2025-03-04T21:06:26.2629938Z 2025-03-04T21:06:26.2630337Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x) 2025-03-04T21:06:26.2631428Z scores: "f32[3233, 81][81, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_); l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None 2025-03-04T21:06:26.2632155Z 2025-03-04T21:06:26.2632565Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x) 2025-03-04T21:06:26.2633604Z proposal_deltas: "f32[3233, 320][320, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_); mean = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None 2025-03-04T21:06:26.2634395Z 2025-03-04T21:06:26.2634784Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:06:26.2635256Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:06:26.2635516Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:06:26.2635755Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:06:26.2636038Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:06:26.2636296Z getitem_2: "Sym(1233 - s0)" = size_1[0] 2025-03-04T21:06:26.2636534Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:06:26.2636775Z 2025-03-04T21:06:26.2637149Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:06:26.2638104Z proposal_boxes: "f32[3233, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:06:26.2638823Z 2025-03-04T21:06:26.2639279Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:06:26.2639860Z deltas: "f32[3233, 320][320, 1]cpu" = proposal_deltas.float(); proposal_deltas = None 2025-03-04T21:06:26.2640131Z 2025-03-04T21:06:26.2640541Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:06:26.2641103Z boxes: "f32[3233, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:06:26.2641380Z 2025-03-04T21:06:26.2641782Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:06:26.2642280Z getitem_4: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:06:26.2642586Z getitem_5: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:06:26.2642900Z widths: "f32[3233][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T21:06:26.2643157Z 2025-03-04T21:06:26.2643559Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:06:26.2644050Z getitem_6: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:06:26.2644342Z getitem_7: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:06:26.2644678Z heights: "f32[3233][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T21:06:26.2644944Z 2025-03-04T21:06:26.2645353Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:06:26.2645847Z getitem_8: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:06:26.2646107Z mul: "f32[3233][1]cpu" = 0.5 * widths 2025-03-04T21:06:26.2646364Z ctr_x: "f32[3233][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T21:06:26.2646602Z 2025-03-04T21:06:26.2647015Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:06:26.2647555Z getitem_9: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:06:26.2647843Z mul_1: "f32[3233][1]cpu" = 0.5 * heights 2025-03-04T21:06:26.2648111Z ctr_y: "f32[3233][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T21:06:26.2648352Z 2025-03-04T21:06:26.2648764Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:06:26.2649285Z getitem_10: "f32[3233, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:06:26.2649611Z dx: "f32[3233, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T21:06:26.2649846Z 2025-03-04T21:06:26.2650255Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:06:26.2650777Z getitem_11: "f32[3233, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:06:26.2651107Z dy: "f32[3233, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T21:06:26.2651344Z 2025-03-04T21:06:26.2651740Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:06:26.2652260Z getitem_12: "f32[3233, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:06:26.2652584Z dw: "f32[3233, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T21:06:26.2652817Z 2025-03-04T21:06:26.2653213Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:06:26.2653772Z getitem_13: "f32[3233, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:06:26.2654126Z dh: "f32[3233, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T21:06:26.2654380Z 2025-03-04T21:06:26.2654815Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:06:26.2655363Z dw_1: "f32[3233, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:06:26.2655624Z 2025-03-04T21:06:26.2656062Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:06:26.2656618Z dh_1: "f32[3233, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:06:26.2656870Z 2025-03-04T21:06:26.2657327Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:06:26.2657899Z getitem_14: "f32[3233, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:06:26.2658241Z mul_2: "f32[3233, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T21:06:26.2658578Z getitem_15: "f32[3233, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:06:26.2658932Z pred_ctr_x: "f32[3233, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T21:06:26.2659195Z 2025-03-04T21:06:26.2659658Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:06:26.2660213Z getitem_16: "f32[3233, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:06:26.2660535Z mul_3: "f32[3233, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T21:06:26.2660885Z getitem_17: "f32[3233, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:06:26.2661236Z pred_ctr_y: "f32[3233, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T21:06:26.2661492Z 2025-03-04T21:06:26.2661921Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:06:26.2662437Z exp: "f32[3233, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:06:26.2662768Z getitem_18: "f32[3233, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:06:26.2663134Z pred_w: "f32[3233, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T21:06:26.2663389Z 2025-03-04T21:06:26.2663856Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:06:26.2664470Z exp_1: "f32[3233, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:06:26.2664825Z getitem_19: "f32[3233, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:06:26.2665218Z pred_h: "f32[3233, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T21:06:26.2665502Z 2025-03-04T21:06:26.2665914Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:06:26.2666389Z mul_6: "f32[3233, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:06:26.2666661Z x1: "f32[3233, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:06:26.2666907Z 2025-03-04T21:06:26.2667330Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:06:26.2667840Z mul_7: "f32[3233, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:06:26.2668109Z y1: "f32[3233, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:06:26.2668352Z 2025-03-04T21:06:26.2668764Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:06:26.2669260Z mul_8: "f32[3233, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:06:26.2669561Z x2: "f32[3233, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:06:26.2669815Z 2025-03-04T21:06:26.2670227Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:06:26.2670724Z mul_9: "f32[3233, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:06:26.2671024Z y2: "f32[3233, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:06:26.2671280Z 2025-03-04T21:06:26.2671758Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:06:26.2672396Z pred_boxes: "f32[3233, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:06:26.2672709Z 2025-03-04T21:06:26.2673148Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:06:26.2673738Z predict_boxes: "f32[3233, 320][320, 1]cpu" = pred_boxes.reshape((3233, 320)); pred_boxes = None 2025-03-04T21:06:26.2674047Z 2025-03-04T21:06:26.2674538Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:06:26.2675177Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T21:06:26.2675539Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T21:06:26.2675823Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T21:06:26.2676126Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T21:06:26.2676442Z getitem_23: "f32[1233 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T21:06:26.2676703Z 2025-03-04T21:06:26.2677114Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:06:26.2677670Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:06:26.2678011Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T21:06:26.2678245Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T21:06:26.2678603Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:06:26.2678944Z getitem_26: "Sym(1233 - s0)" = size_3[0] 2025-03-04T21:06:26.2679180Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T21:06:26.2679390Z 2025-03-04T21:06:26.2679795Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:06:26.2680338Z probs: "f32[3233, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1); scores = None 2025-03-04T21:06:26.2680620Z 2025-03-04T21:06:26.2681057Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:06:26.2681687Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T21:06:26.2682036Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:06:26.2682316Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T21:06:26.2682609Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T21:06:26.2682913Z getitem_31: "f32[1233 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T21:06:26.2683159Z 2025-03-04T21:06:26.2683692Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:06:26.2684368Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:06:26.2684698Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:06:26.2685053Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:06:26.2685396Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:06:26.2685695Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:06:26.2685923Z 2025-03-04T21:06:26.2686348Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:06:26.2686852Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:06:26.2687076Z 2025-03-04T21:06:26.2687227Z 2025-03-04T21:06:26.2687311Z class GraphModule(torch.nn.Module): 2025-03-04T21:06:26.2688636Z def forward(self, L_stack0_: "f32[3233, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1233 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1233 - s0, 4][4, 1]cpu"): 2025-03-04T21:06:26.2689939Z l_stack0_ = L_stack0_ 2025-03-04T21:06:26.2690334Z l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_ 2025-03-04T21:06:26.2690902Z l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_ 2025-03-04T21:06:26.2691466Z l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ 2025-03-04T21:06:26.2692025Z l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ 2025-03-04T21:06:26.2692506Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:06:26.2692915Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:06:26.2693314Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:06:26.2693710Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:06:26.2693996Z 2025-03-04T21:06:26.2694524Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/roi_heads.py:480 in torch_dynamo_resume_in_forward_at_477, code: predictions = self.box_predictor(box_features.mean(dim=[2, 3])) 2025-03-04T21:06:26.2695182Z mean: "f32[3233, 2048][2048, 1]cpu" = l_stack0_.mean(dim = [2, 3]); l_stack0_ = None 2025-03-04T21:06:26.2695438Z 2025-03-04T21:06:26.2695830Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x) 2025-03-04T21:06:26.2696798Z scores: "f32[3233, 81][81, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_); l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None 2025-03-04T21:06:26.2697520Z 2025-03-04T21:06:26.2697929Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x) 2025-03-04T21:06:26.2698964Z proposal_deltas: "f32[3233, 320][320, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_); mean = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None 2025-03-04T21:06:26.2699715Z 2025-03-04T21:06:26.2700086Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:06:26.2700553Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:06:26.2700809Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:06:26.2701071Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:06:26.2701344Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:06:26.2701592Z getitem_2: "Sym(1233 - s0)" = size_1[0] 2025-03-04T21:06:26.2701830Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:06:26.2702229Z 2025-03-04T21:06:26.2702608Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:06:26.2703563Z proposal_boxes: "f32[3233, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:06:26.2706812Z 2025-03-04T21:06:26.2707348Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:06:26.2707951Z deltas: "f32[3233, 320][320, 1]cpu" = proposal_deltas.float(); proposal_deltas = None 2025-03-04T21:06:26.2708226Z 2025-03-04T21:06:26.2708627Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:06:26.2709155Z boxes: "f32[3233, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:06:26.2709434Z 2025-03-04T21:06:26.2709843Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:06:26.2710342Z getitem_4: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:06:26.2710647Z getitem_5: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:06:26.2710961Z widths: "f32[3233][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T21:06:26.2711259Z 2025-03-04T21:06:26.2711673Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:06:26.2712181Z getitem_6: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:06:26.2712480Z getitem_7: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:06:26.2712801Z heights: "f32[3233][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T21:06:26.2713067Z 2025-03-04T21:06:26.2713477Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:06:26.2713973Z getitem_8: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:06:26.2714238Z mul: "f32[3233][1]cpu" = 0.5 * widths 2025-03-04T21:06:26.2714502Z ctr_x: "f32[3233][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T21:06:26.2714744Z 2025-03-04T21:06:26.2715181Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:06:26.2715701Z getitem_9: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:06:26.2715995Z mul_1: "f32[3233][1]cpu" = 0.5 * heights 2025-03-04T21:06:26.2716250Z ctr_y: "f32[3233][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T21:06:26.2716484Z 2025-03-04T21:06:26.2716887Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:06:26.2717444Z getitem_10: "f32[3233, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:06:26.2717775Z dx: "f32[3233, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T21:06:26.2718005Z 2025-03-04T21:06:26.2718396Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:06:26.2718910Z getitem_11: "f32[3233, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:06:26.2719224Z dy: "f32[3233, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T21:06:26.2719448Z 2025-03-04T21:06:26.2719824Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:06:26.2720339Z getitem_12: "f32[3233, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:06:26.2720653Z dw: "f32[3233, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T21:06:26.2720880Z 2025-03-04T21:06:26.2721272Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:06:26.2721811Z getitem_13: "f32[3233, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:06:26.2722157Z dh: "f32[3233, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T21:06:26.2722378Z 2025-03-04T21:06:26.2722780Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:06:26.2723305Z dw_1: "f32[3233, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:06:26.2723543Z 2025-03-04T21:06:26.2723948Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:06:26.2724474Z dh_1: "f32[3233, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:06:26.2724716Z 2025-03-04T21:06:26.2725133Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:06:26.2725655Z getitem_14: "f32[3233, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:06:26.2725959Z mul_2: "f32[3233, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T21:06:26.2726276Z getitem_15: "f32[3233, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:06:26.2726612Z pred_ctr_x: "f32[3233, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T21:06:26.2726869Z 2025-03-04T21:06:26.2727307Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:06:26.2727876Z getitem_16: "f32[3233, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:06:26.2728189Z mul_3: "f32[3233, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T21:06:26.2728516Z getitem_17: "f32[3233, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:06:26.2728854Z pred_ctr_y: "f32[3233, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T21:06:26.2729110Z 2025-03-04T21:06:26.2729533Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:06:26.2730073Z exp: "f32[3233, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:06:26.2730388Z getitem_18: "f32[3233, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:06:26.2730720Z pred_w: "f32[3233, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T21:06:26.2730960Z 2025-03-04T21:06:26.2731365Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:06:26.2731852Z exp_1: "f32[3233, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:06:26.2732168Z getitem_19: "f32[3233, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:06:26.2732506Z pred_h: "f32[3233, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T21:06:26.2732750Z 2025-03-04T21:06:26.2733170Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:06:26.2733626Z mul_6: "f32[3233, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:06:26.2733883Z x1: "f32[3233, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:06:26.2734110Z 2025-03-04T21:06:26.2734497Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:06:26.2734945Z mul_7: "f32[3233, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:06:26.2735197Z y1: "f32[3233, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:06:26.2735424Z 2025-03-04T21:06:26.2735810Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:06:26.2736273Z mul_8: "f32[3233, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:06:26.2736557Z x2: "f32[3233, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:06:26.2736814Z 2025-03-04T21:06:26.2737195Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:06:26.2737664Z mul_9: "f32[3233, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:06:26.2737945Z y2: "f32[3233, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:06:26.2738177Z 2025-03-04T21:06:26.2738605Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:06:26.2739183Z pred_boxes: "f32[3233, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:06:26.2739474Z 2025-03-04T21:06:26.2739890Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:06:26.2740463Z predict_boxes: "f32[3233, 320][320, 1]cpu" = pred_boxes.reshape((3233, 320)); pred_boxes = None 2025-03-04T21:06:26.2740749Z 2025-03-04T21:06:26.2741192Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:06:26.2741808Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T21:06:26.2742178Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T21:06:26.2742474Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T21:06:26.2742786Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T21:06:26.2743129Z getitem_23: "f32[1233 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T21:06:26.2743398Z 2025-03-04T21:06:26.2743783Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:06:26.2744441Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:06:26.2744806Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T21:06:26.2745054Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T21:06:26.2745428Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:06:26.2745794Z getitem_26: "Sym(1233 - s0)" = size_3[0] 2025-03-04T21:06:26.2746069Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T21:06:26.2746294Z 2025-03-04T21:06:26.2746729Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:06:26.2747292Z probs: "f32[3233, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1); scores = None 2025-03-04T21:06:26.2747579Z 2025-03-04T21:06:26.2748021Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:06:26.2748621Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T21:06:26.2748980Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:06:26.2749268Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T21:06:26.2749564Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T21:06:26.2749876Z getitem_31: "f32[1233 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T21:06:26.2750153Z 2025-03-04T21:06:26.2750709Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:06:26.2751410Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:06:26.2751751Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:06:26.2752092Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:06:26.2752431Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:06:26.2752724Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:06:26.2752968Z 2025-03-04T21:06:26.2753467Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:06:26.2754007Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:06:26.2754242Z 2025-03-04T21:06:27.7998310Z 2025-03-04T21:06:27.8001452Z class GraphModule(torch.nn.Module): 2025-03-04T21:06:27.8002837Z def forward(self, L_predictions_0_: "f32[3233, 81][81, 1]cpu", L_predictions_1_: "f32[3233, 320][320, 1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1233 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1233 - s0, 4][4, 1]cpu"): 2025-03-04T21:06:27.8003866Z l_predictions_0_ = L_predictions_0_ 2025-03-04T21:06:27.8004565Z l_predictions_1_ = L_predictions_1_ 2025-03-04T21:06:27.8004903Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:06:27.8005370Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:06:27.8005809Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:06:27.8006252Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:06:27.8006582Z 2025-03-04T21:06:27.8007068Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:06:27.8007601Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:06:27.8007947Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:06:27.8008193Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:06:27.8008476Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:06:27.8008754Z getitem_2: "Sym(1233 - s0)" = size_1[0] 2025-03-04T21:06:27.8009012Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:06:27.8009247Z 2025-03-04T21:06:27.8009652Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:06:27.8010728Z proposal_boxes: "f32[3233, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:06:27.8011478Z 2025-03-04T21:06:27.8012032Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:06:27.8012678Z deltas: "f32[3233, 320][320, 1]cpu" = l_predictions_1_.float(); l_predictions_1_ = None 2025-03-04T21:06:27.8012945Z 2025-03-04T21:06:27.8013353Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:06:27.8013885Z boxes: "f32[3233, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:06:27.8014161Z 2025-03-04T21:06:27.8014561Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:06:27.8015065Z getitem_4: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:06:27.8015376Z getitem_5: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:06:27.8015700Z widths: "f32[3233][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T21:06:27.8015960Z 2025-03-04T21:06:27.8016418Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:06:27.8016940Z getitem_6: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:06:27.8017243Z getitem_7: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:06:27.8017568Z heights: "f32[3233][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T21:06:27.8017835Z 2025-03-04T21:06:27.8018257Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:06:27.8018781Z getitem_8: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:06:27.8019074Z mul: "f32[3233][1]cpu" = 0.5 * widths 2025-03-04T21:06:27.8019337Z ctr_x: "f32[3233][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T21:06:27.8019585Z 2025-03-04T21:06:27.8020006Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:06:27.8020542Z getitem_9: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:06:27.8020837Z mul_1: "f32[3233][1]cpu" = 0.5 * heights 2025-03-04T21:06:27.8021113Z ctr_y: "f32[3233][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T21:06:27.8021360Z 2025-03-04T21:06:27.8021831Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:06:27.8022422Z getitem_10: "f32[3233, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:06:27.8022772Z dx: "f32[3233, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T21:06:27.8023028Z 2025-03-04T21:06:27.8023447Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:06:27.8024142Z getitem_11: "f32[3233, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:06:27.8024482Z dy: "f32[3233, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T21:06:27.8024727Z 2025-03-04T21:06:27.8025141Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:06:27.8025676Z getitem_12: "f32[3233, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:06:27.8025996Z dw: "f32[3233, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T21:06:27.8026227Z 2025-03-04T21:06:27.8026657Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:06:27.8027223Z getitem_13: "f32[3233, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:06:27.8027585Z dh: "f32[3233, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T21:06:27.8027821Z 2025-03-04T21:06:27.8028269Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:06:27.8028831Z dw_1: "f32[3233, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:06:27.8029099Z 2025-03-04T21:06:27.8029540Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:06:27.8030091Z dh_1: "f32[3233, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:06:27.8030352Z 2025-03-04T21:06:27.8030830Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:06:27.8031394Z getitem_14: "f32[3233, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:06:27.8031723Z mul_2: "f32[3233, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T21:06:27.8032065Z getitem_15: "f32[3233, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:06:27.8032425Z pred_ctr_x: "f32[3233, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T21:06:27.8032695Z 2025-03-04T21:06:27.8033180Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:06:27.8033753Z getitem_16: "f32[3233, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:06:27.8034079Z mul_3: "f32[3233, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T21:06:27.8034411Z getitem_17: "f32[3233, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:06:27.8034768Z pred_ctr_y: "f32[3233, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T21:06:27.8035032Z 2025-03-04T21:06:27.8035471Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:06:27.8036016Z exp: "f32[3233, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:06:27.8036399Z getitem_18: "f32[3233, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:06:27.8036750Z pred_w: "f32[3233, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T21:06:27.8037005Z 2025-03-04T21:06:27.8037435Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:06:27.8037934Z exp_1: "f32[3233, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:06:27.8038259Z getitem_19: "f32[3233, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:06:27.8038599Z pred_h: "f32[3233, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T21:06:27.8038846Z 2025-03-04T21:06:27.8039244Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:06:27.8039702Z mul_6: "f32[3233, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:06:27.8039982Z x1: "f32[3233, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:06:27.8040217Z 2025-03-04T21:06:27.8040616Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:06:27.8041079Z mul_7: "f32[3233, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:06:27.8041339Z y1: "f32[3233, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:06:27.8041570Z 2025-03-04T21:06:27.8041963Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:06:27.8042442Z mul_8: "f32[3233, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:06:27.8042735Z x2: "f32[3233, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:06:27.8042981Z 2025-03-04T21:06:27.8043394Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:06:27.8043866Z mul_9: "f32[3233, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:06:27.8044149Z y2: "f32[3233, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:06:27.8044390Z 2025-03-04T21:06:27.8044830Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:06:27.8045422Z pred_boxes: "f32[3233, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:06:27.8045716Z 2025-03-04T21:06:27.8046145Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:06:27.8046744Z predict_boxes: "f32[3233, 320][320, 1]cpu" = pred_boxes.reshape((3233, 320)); pred_boxes = None 2025-03-04T21:06:27.8047041Z 2025-03-04T21:06:27.8047500Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:06:27.8048129Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T21:06:27.8048496Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T21:06:27.8048784Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T21:06:27.8049105Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T21:06:27.8049421Z getitem_23: "f32[1233 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T21:06:27.8049672Z 2025-03-04T21:06:27.8050051Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:06:27.8050612Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:06:27.8050960Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T21:06:27.8051198Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T21:06:27.8051565Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:06:27.8051916Z getitem_26: "Sym(1233 - s0)" = size_3[0] 2025-03-04T21:06:27.8052158Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T21:06:27.8052376Z 2025-03-04T21:06:27.8052793Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:06:27.8053422Z probs: "f32[3233, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1); l_predictions_0_ = None 2025-03-04T21:06:27.8053741Z 2025-03-04T21:06:27.8054186Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:06:27.8054812Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T21:06:27.8055183Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:06:27.8055481Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T21:06:27.8055803Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T21:06:27.8056124Z getitem_31: "f32[1233 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T21:06:27.8056393Z 2025-03-04T21:06:27.8056997Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:06:27.8057734Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:06:27.8058089Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:06:27.8058443Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:06:27.8058795Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:06:27.8059101Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:06:27.8059369Z 2025-03-04T21:06:27.8059826Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:06:27.8060375Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:06:27.8060622Z 2025-03-04T21:06:27.8060752Z 2025-03-04T21:06:27.8060845Z class GraphModule(torch.nn.Module): 2025-03-04T21:06:27.8061755Z def forward(self, L_predictions_0_: "f32[3233, 81][81, 1]cpu", L_predictions_1_: "f32[3233, 320][320, 1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1233 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1233 - s0, 4][4, 1]cpu"): 2025-03-04T21:06:27.8062658Z l_predictions_0_ = L_predictions_0_ 2025-03-04T21:06:27.8062887Z l_predictions_1_ = L_predictions_1_ 2025-03-04T21:06:27.8063224Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:06:27.8063753Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:06:27.8064208Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:06:27.8064642Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:06:27.8064970Z 2025-03-04T21:06:27.8065378Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:06:27.8065865Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:06:27.8066131Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:06:27.8066377Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:06:27.8066667Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:06:27.8066940Z getitem_2: "Sym(1233 - s0)" = size_1[0] 2025-03-04T21:06:27.8067218Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:06:27.8067447Z 2025-03-04T21:06:27.8067844Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:06:27.8068865Z proposal_boxes: "f32[3233, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:06:27.8069637Z 2025-03-04T21:06:27.8070136Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:06:27.8070752Z deltas: "f32[3233, 320][320, 1]cpu" = l_predictions_1_.float(); l_predictions_1_ = None 2025-03-04T21:06:27.8071040Z 2025-03-04T21:06:27.8071479Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:06:27.8072028Z boxes: "f32[3233, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:06:27.8072315Z 2025-03-04T21:06:27.8072743Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:06:27.8073236Z getitem_4: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:06:27.8073538Z getitem_5: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:06:27.8073855Z widths: "f32[3233][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T21:06:27.8074137Z 2025-03-04T21:06:27.8074544Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:06:27.8075041Z getitem_6: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:06:27.8075333Z getitem_7: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:06:27.8075652Z heights: "f32[3233][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T21:06:27.8075920Z 2025-03-04T21:06:27.8076328Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:06:27.8076842Z getitem_8: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:06:27.8077117Z mul: "f32[3233][1]cpu" = 0.5 * widths 2025-03-04T21:06:27.8077378Z ctr_x: "f32[3233][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T21:06:27.8077620Z 2025-03-04T21:06:27.8078025Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:06:27.8078540Z getitem_9: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:06:27.8078820Z mul_1: "f32[3233][1]cpu" = 0.5 * heights 2025-03-04T21:06:27.8079082Z ctr_y: "f32[3233][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T21:06:27.8079326Z 2025-03-04T21:06:27.8079741Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:06:27.8080263Z getitem_10: "f32[3233, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:06:27.8080596Z dx: "f32[3233, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T21:06:27.8080848Z 2025-03-04T21:06:27.8081236Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:06:27.8081810Z getitem_11: "f32[3233, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:06:27.8082125Z dy: "f32[3233, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T21:06:27.8082357Z 2025-03-04T21:06:27.8082741Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:06:27.8083240Z getitem_12: "f32[3233, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:06:27.8083555Z dw: "f32[3233, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T21:06:27.8083782Z 2025-03-04T21:06:27.8084167Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:06:27.8085122Z getitem_13: "f32[3233, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:06:27.8085477Z dh: "f32[3233, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T21:06:27.8085700Z 2025-03-04T21:06:27.8086121Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:06:27.8086652Z dw_1: "f32[3233, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:06:27.8086906Z 2025-03-04T21:06:27.8087326Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:06:27.8087861Z dh_1: "f32[3233, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:06:27.8088113Z 2025-03-04T21:06:27.8088544Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:06:27.8089078Z getitem_14: "f32[3233, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:06:27.8089386Z mul_2: "f32[3233, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T21:06:27.8089707Z getitem_15: "f32[3233, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:06:27.8090044Z pred_ctr_x: "f32[3233, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T21:06:27.8090312Z 2025-03-04T21:06:27.8090753Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:06:27.8091300Z getitem_16: "f32[3233, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:06:27.8091612Z mul_3: "f32[3233, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T21:06:27.8091934Z getitem_17: "f32[3233, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:06:27.8092274Z pred_ctr_y: "f32[3233, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T21:06:27.8092524Z 2025-03-04T21:06:27.8092949Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:06:27.8093467Z exp: "f32[3233, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:06:27.8093794Z getitem_18: "f32[3233, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:06:27.8094145Z pred_w: "f32[3233, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T21:06:27.8094413Z 2025-03-04T21:06:27.8094843Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:06:27.8095357Z exp_1: "f32[3233, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:06:27.8095692Z getitem_19: "f32[3233, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:06:27.8096043Z pred_h: "f32[3233, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T21:06:27.8096295Z 2025-03-04T21:06:27.8096704Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:06:27.8097178Z mul_6: "f32[3233, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:06:27.8097445Z x1: "f32[3233, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:06:27.8097675Z 2025-03-04T21:06:27.8098099Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:06:27.8098580Z mul_7: "f32[3233, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:06:27.8098849Z y1: "f32[3233, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:06:27.8099092Z 2025-03-04T21:06:27.8099510Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:06:27.8100009Z mul_8: "f32[3233, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:06:27.8100314Z x2: "f32[3233, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:06:27.8100590Z 2025-03-04T21:06:27.8101016Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:06:27.8101508Z mul_9: "f32[3233, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:06:27.8101826Z y2: "f32[3233, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:06:27.8102233Z 2025-03-04T21:06:27.8102691Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:06:27.8103304Z pred_boxes: "f32[3233, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:06:27.8105325Z 2025-03-04T21:06:27.8105984Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:06:27.8107325Z predict_boxes: "f32[3233, 320][320, 1]cpu" = pred_boxes.reshape((3233, 320)); pred_boxes = None 2025-03-04T21:06:27.8107634Z 2025-03-04T21:06:27.8108110Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:06:27.8108748Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T21:06:27.8109124Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T21:06:27.8109420Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T21:06:27.8109734Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T21:06:27.8111264Z getitem_23: "f32[1233 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T21:06:27.8111538Z 2025-03-04T21:06:27.8111930Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:06:27.8112579Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:06:27.8112935Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T21:06:27.8113180Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T21:06:27.8113551Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:06:27.8113898Z getitem_26: "Sym(1233 - s0)" = size_3[0] 2025-03-04T21:06:27.8114147Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T21:06:27.8114367Z 2025-03-04T21:06:27.8114804Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:06:27.8115411Z probs: "f32[3233, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1); l_predictions_0_ = None 2025-03-04T21:06:27.8115734Z 2025-03-04T21:06:27.8116229Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:06:27.8116851Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T21:06:27.8117207Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:06:27.8117490Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T21:06:27.8117776Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T21:06:27.8118086Z getitem_31: "f32[1233 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T21:06:27.8118372Z 2025-03-04T21:06:27.8118921Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:06:27.8119620Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:06:27.8119961Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:06:27.8120298Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:06:27.8120639Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:06:27.8120931Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:06:27.8121187Z 2025-03-04T21:06:27.8121641Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:06:27.8122164Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:06:27.8122398Z 2025-03-04T21:06:27.8122524Z 2025-03-04T21:06:27.8122617Z class GraphModule(torch.nn.Module): 2025-03-04T21:06:27.8123426Z def forward(self, L_predictions_0_: "f32[3233, 81][81, 1]cpu", L_predictions_1_: "f32[3233, 320][320, 1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1233 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1233 - s0, 4][4, 1]cpu"): 2025-03-04T21:06:27.8124215Z l_predictions_0_ = L_predictions_0_ 2025-03-04T21:06:27.8124436Z l_predictions_1_ = L_predictions_1_ 2025-03-04T21:06:27.8124745Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:06:27.8125165Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:06:27.8125560Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:06:27.8125951Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:06:27.8126241Z 2025-03-04T21:06:27.8126614Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:06:27.8127076Z size = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:06:27.8127319Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:06:27.8127546Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:06:27.8127817Z size_1 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:06:27.8128074Z getitem_2: "Sym(1233 - s0)" = size_1[0] 2025-03-04T21:06:27.8128303Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:06:27.8128513Z 2025-03-04T21:06:27.8128896Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:06:27.8129838Z proposal_boxes: "f32[3233, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0); l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:06:27.8130561Z 2025-03-04T21:06:27.8131019Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:06:27.8131615Z deltas: "f32[3233, 320][320, 1]cpu" = l_predictions_1_.float(); l_predictions_1_ = None 2025-03-04T21:06:27.8131883Z 2025-03-04T21:06:27.8132279Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:06:27.8132801Z boxes: "f32[3233, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:06:27.8133076Z 2025-03-04T21:06:27.8133471Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:06:27.8133968Z getitem_4: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:06:27.8134264Z getitem_5: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:06:27.8134597Z widths: "f32[3233][1]cpu" = getitem_4 - getitem_5; getitem_4 = getitem_5 = None 2025-03-04T21:06:27.8134858Z 2025-03-04T21:06:27.8135262Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:06:27.8135755Z getitem_6: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:06:27.8136047Z getitem_7: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:06:27.8136355Z heights: "f32[3233][1]cpu" = getitem_6 - getitem_7; getitem_6 = getitem_7 = None 2025-03-04T21:06:27.8136612Z 2025-03-04T21:06:27.8137006Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:06:27.8137491Z getitem_8: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:06:27.8137744Z mul: "f32[3233][1]cpu" = 0.5 * widths 2025-03-04T21:06:27.8137994Z ctr_x: "f32[3233][1]cpu" = getitem_8 + mul; getitem_8 = mul = None 2025-03-04T21:06:27.8138228Z 2025-03-04T21:06:27.8138652Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:06:27.8139176Z getitem_9: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:06:27.8139462Z mul_1: "f32[3233][1]cpu" = 0.5 * heights 2025-03-04T21:06:27.8139723Z ctr_y: "f32[3233][1]cpu" = getitem_9 + mul_1; getitem_9 = mul_1 = None 2025-03-04T21:06:27.8139965Z 2025-03-04T21:06:27.8140380Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:06:27.8140915Z getitem_10: "f32[3233, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:06:27.8141234Z dx: "f32[3233, 80][80, 1]cpu" = getitem_10 / 10.0; getitem_10 = None 2025-03-04T21:06:27.8141463Z 2025-03-04T21:06:27.8141842Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:06:27.8142361Z getitem_11: "f32[3233, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:06:27.8142675Z dy: "f32[3233, 80][80, 1]cpu" = getitem_11 / 10.0; getitem_11 = None 2025-03-04T21:06:27.8142902Z 2025-03-04T21:06:27.8143280Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:06:27.8143875Z getitem_12: "f32[3233, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:06:27.8144211Z dw: "f32[3233, 80][80, 1]cpu" = getitem_12 / 5.0; getitem_12 = None 2025-03-04T21:06:27.8144451Z 2025-03-04T21:06:27.8144886Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:06:27.8145443Z getitem_13: "f32[3233, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:06:27.8145794Z dh: "f32[3233, 80][80, 1]cpu" = getitem_13 / 5.0; getitem_13 = None 2025-03-04T21:06:27.8146032Z 2025-03-04T21:06:27.8146477Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:06:27.8147033Z dw_1: "f32[3233, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:06:27.8147302Z 2025-03-04T21:06:27.8147748Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:06:27.8148297Z dh_1: "f32[3233, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:06:27.8148562Z 2025-03-04T21:06:27.8149014Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:06:27.8149577Z getitem_14: "f32[3233, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:06:27.8149905Z mul_2: "f32[3233, 80][80, 1]cpu" = dx * getitem_14; dx = getitem_14 = None 2025-03-04T21:06:27.8150250Z getitem_15: "f32[3233, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:06:27.8150613Z pred_ctr_x: "f32[3233, 80][80, 1]cpu" = mul_2 + getitem_15; mul_2 = getitem_15 = None 2025-03-04T21:06:27.8150878Z 2025-03-04T21:06:27.8151339Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:06:27.8151918Z getitem_16: "f32[3233, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:06:27.8152244Z mul_3: "f32[3233, 80][80, 1]cpu" = dy * getitem_16; dy = getitem_16 = None 2025-03-04T21:06:27.8152584Z getitem_17: "f32[3233, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:06:27.8152936Z pred_ctr_y: "f32[3233, 80][80, 1]cpu" = mul_3 + getitem_17; mul_3 = getitem_17 = None 2025-03-04T21:06:27.8153196Z 2025-03-04T21:06:27.8153635Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:06:27.8154159Z exp: "f32[3233, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:06:27.8154494Z getitem_18: "f32[3233, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:06:27.8154845Z pred_w: "f32[3233, 80][80, 1]cpu" = exp * getitem_18; exp = getitem_18 = None 2025-03-04T21:06:27.8155103Z 2025-03-04T21:06:27.8155558Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:06:27.8156087Z exp_1: "f32[3233, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:06:27.8156426Z getitem_19: "f32[3233, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:06:27.8156787Z pred_h: "f32[3233, 80][80, 1]cpu" = exp_1 * getitem_19; exp_1 = getitem_19 = None 2025-03-04T21:06:27.8157044Z 2025-03-04T21:06:27.8157465Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:06:27.8157946Z mul_6: "f32[3233, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:06:27.8158254Z x1: "f32[3233, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:06:27.8158511Z 2025-03-04T21:06:27.8158941Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:06:27.8159422Z mul_7: "f32[3233, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:06:27.8159689Z y1: "f32[3233, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:06:27.8159930Z 2025-03-04T21:06:27.8160335Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:06:27.8160831Z mul_8: "f32[3233, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:06:27.8161147Z x2: "f32[3233, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:06:27.8161395Z 2025-03-04T21:06:27.8161791Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:06:27.8162285Z mul_9: "f32[3233, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:06:27.8162572Z y2: "f32[3233, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:06:27.8162817Z 2025-03-04T21:06:27.8163254Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:06:27.8163851Z pred_boxes: "f32[3233, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:06:27.8164136Z 2025-03-04T21:06:27.8164566Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:06:27.8165137Z predict_boxes: "f32[3233, 320][320, 1]cpu" = pred_boxes.reshape((3233, 320)); pred_boxes = None 2025-03-04T21:06:27.8165444Z 2025-03-04T21:06:27.8165896Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:06:27.8167878Z split = predict_boxes.split([1000, 1000, getitem, getitem_2]); predict_boxes = getitem = getitem_2 = None 2025-03-04T21:06:27.8168262Z boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0] 2025-03-04T21:06:27.8168561Z getitem_21: "f32[1000, 320][320, 1]cpu" = split[1]; getitem_21 = None 2025-03-04T21:06:27.8168873Z getitem_22: "f32[s0, 320][320, 1]cpu" = split[2]; getitem_22 = None 2025-03-04T21:06:27.8169206Z getitem_23: "f32[1233 - s0, 320][320, 1]cpu" = split[3]; split = getitem_23 = None 2025-03-04T21:06:27.8169476Z 2025-03-04T21:06:27.8169867Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:06:27.8170488Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:06:27.8170846Z getitem_24: "Sym(s0)" = size_2[0] 2025-03-04T21:06:27.8171089Z getitem_25 = size_2[1]; size_2 = getitem_25 = None 2025-03-04T21:06:27.8171456Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:06:27.8171816Z getitem_26: "Sym(1233 - s0)" = size_3[0] 2025-03-04T21:06:27.8172060Z getitem_27 = size_3[1]; size_3 = getitem_27 = None 2025-03-04T21:06:27.8172294Z 2025-03-04T21:06:27.8172714Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:06:27.8173327Z probs: "f32[3233, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1); l_predictions_0_ = None 2025-03-04T21:06:27.8173648Z 2025-03-04T21:06:27.8174093Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:06:27.8174693Z split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0); probs = getitem_24 = getitem_26 = None 2025-03-04T21:06:27.8175043Z scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:06:27.8175328Z getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1]; getitem_29 = None 2025-03-04T21:06:27.8175628Z getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2]; getitem_30 = None 2025-03-04T21:06:27.8175970Z getitem_31: "f32[1233 - s0, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_31 = None 2025-03-04T21:06:27.8176235Z 2025-03-04T21:06:27.8176794Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:06:27.8177504Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:06:27.8177843Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:06:27.8178176Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:06:27.8178517Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:06:27.8178810Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:06:27.8179051Z 2025-03-04T21:06:27.8179502Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:06:27.8180058Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:06:27.8180291Z 2025-03-04T21:06:30.4221726Z 2025-03-04T21:06:30.4222536Z class GraphModule(torch.nn.Module): 2025-03-04T21:06:30.4223121Z def forward(self, L_scores_0_: "f32[1000, 81][81, 1]cpu", L_boxes_0_: "f32[1000, 320][320, 1]cpu"): 2025-03-04T21:06:30.4223443Z l_scores_0_ = L_scores_0_ 2025-03-04T21:06:30.4223657Z l_boxes_0_ = L_boxes_0_ 2025-03-04T21:06:30.4223853Z 2025-03-04T21:06:30.4224532Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:06:30.4225394Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(l_boxes_0_); l_boxes_0_ = None 2025-03-04T21:06:30.4225775Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:06:30.4226179Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(l_scores_0_); l_scores_0_ = None 2025-03-04T21:06:30.4226858Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:06:30.4227194Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:06:30.4227478Z 2025-03-04T21:06:30.4228009Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:06:30.4228598Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:06:30.4228895Z 2025-03-04T21:06:30.4228992Z 2025-03-04T21:06:30.4229097Z class GraphModule(torch.nn.Module): 2025-03-04T21:06:30.4229437Z def forward(self, L_scores_0_: "f32[1000, 81][81, 1]cpu", L_boxes_0_: "f32[1000, 320][320, 1]cpu"): 2025-03-04T21:06:30.4229814Z l_scores_0_ = L_scores_0_ 2025-03-04T21:06:30.4230027Z l_boxes_0_ = L_boxes_0_ 2025-03-04T21:06:30.4230217Z 2025-03-04T21:06:30.4230835Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:06:30.4231577Z isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(l_boxes_0_); l_boxes_0_ = None 2025-03-04T21:06:30.4231929Z all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:06:30.4232247Z isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(l_scores_0_); l_scores_0_ = None 2025-03-04T21:06:30.4232564Z all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:06:30.4232899Z valid_mask: "b8[1000][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:06:30.4233135Z 2025-03-04T21:06:30.4233578Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:06:30.4234100Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:06:30.4234329Z 2025-03-04T21:07:34.2010202Z Compilation time (from dynamo_timed): 75.737883695 2025-03-04T21:07:34.2014714Z pass 2025-03-04T21:07:34.2019492Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T21:07:34.2024959Z TIMING: entire_frame_compile:75.73788 gc:0.0406 _recursive_pre_grad_passes:0.03527 async_compile.wait:14.47892 backend_compile:27.81541 _recursive_joint_graph_passes:0.16645 inductor_compile:18.16981 _recursive_post_grad_passes:0.05249 code_gen:17.11946 total_wall_time:75.73788 2025-03-04T21:07:34.2026551Z STATS: call_* op count: 766 | FakeTensorMode.__torch_dispatch__:15706 | FakeTensor.__torch_dispatch__:939 | ProxyTorchDispatchMode.__torch_dispatch__:3032 | attempt fast:161 | slow no contiguity match:24 | fast is_contiguous:133 | slow both tensors nontrivially broadcast:4 2025-03-04T21:07:34.2027577Z Dynamo produced 67 graphs covering 766 ops with 56 graph breaks (8 unique) 2025-03-04T21:07:40.2609124Z 2025-03-04T21:07:46.4049113Z loading model: 0it [00:00, ?it/s] 2025-03-04T21:07:46.4050827Z loading model: 0it [00:06, ?it/s] 2025-03-04T21:07:46.4055356Z cpu eval detectron2_maskrcnn_r_50_fpn 2025-03-04T21:07:59.4537350Z WARNING:common:fp64 golden ref were not generated for detectron2_maskrcnn_r_50_fpn. Setting accuracy check to cosine 2025-03-04T21:07:59.4794641Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T21:08:07.6851832Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T21:08:15.5167383Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T21:08:25.2556386Z 2025-03-04T21:08:25.2556716Z class GraphModule(torch.nn.Module): 2025-03-04T21:08:25.2636896Z def forward(self, L_stack0_tensor: "f32[4, 3, 1184, 1216][4319232, 1439744, 1216, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_: "f32[512, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_: "f32[512, 128, 1, 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L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_lateral_convs_0_parameters_weight_: "f32[256, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_0_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_0_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_0_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_1_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_1_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_1_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_2_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_2_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_2_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_3_parameters_weight_: "f32[256, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_3_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_3_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_3_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[3, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[3][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[12, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[12][1]cpu"): 2025-03-04T21:08:25.2700342Z l_stack0_tensor = L_stack0_tensor 2025-03-04T21:08:25.2701075Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ 2025-03-04T21:08:25.2701947Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.2702796Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.2703603Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:25.2704391Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:25.2705183Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:08:25.2705992Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.2706854Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.2707715Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:25.2708548Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:25.2709381Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:08:25.2710210Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.2711097Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.2711968Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:25.2712821Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:25.2713616Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:08:25.2714440Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.2715338Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.2716420Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:25.2717374Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:25.2718204Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:08:25.2719026Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.2719922Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.2720783Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:08:25.2721627Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:08:25.2722430Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:08:25.2723254Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.2724131Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.2724964Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:25.2725775Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:25.2726579Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:08:25.2727376Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.2728224Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.2729056Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:25.2729873Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:25.2730646Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:08:25.2731448Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.2732299Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.2733163Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:25.2733985Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:25.2734775Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:08:25.2735619Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.2736502Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.2737357Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:25.2738188Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:25.2738981Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:08:25.2739804Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.2740694Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.2741545Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:25.2742372Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:25.2743202Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:08:25.2744037Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.2744957Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.2745843Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:25.2746720Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:25.2747664Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:08:25.2748564Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.2749742Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.2750683Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:25.2751630Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:25.2752541Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:08:25.2753484Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.2754526Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.2755522Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:25.2756578Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:25.2757564Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:08:25.2758599Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.2759627Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.2760600Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:25.2761520Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:25.2762475Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:08:25.2763469Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.2764526Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.2765542Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:08:25.2766525Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:08:25.2767472Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:08:25.2768372Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.2769302Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.2770265Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:25.2771131Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:25.2772021Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:08:25.2772908Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.2773840Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.2774761Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:25.2775620Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:25.2788539Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:08:25.2789395Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.2790314Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.2791309Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:25.2792143Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:25.2792953Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:08:25.2793839Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.2794752Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.2795641Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:25.2796579Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:25.2797432Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:08:25.2798253Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.2799168Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.2800032Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:25.2800891Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:25.2801704Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:08:25.2802724Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.2803718Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.2804578Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:25.2805445Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:25.2806239Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ 2025-03-04T21:08:25.2807066Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.2807971Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.2808835Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:25.2809668Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:25.2810461Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ 2025-03-04T21:08:25.2811288Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.2812164Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.2813044Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:25.2813868Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:25.2814656Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ 2025-03-04T21:08:25.2815470Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.2816366Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.2817227Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:25.2818068Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:25.2818835Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:08:25.2819651Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.2820505Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.2821364Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:25.2822176Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:25.2822953Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:08:25.2823755Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.2824605Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.2825436Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:25.2826236Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:25.2827021Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:08:25.2827823Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.2828683Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.2829512Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:25.2830354Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:25.2831156Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:08:25.2832005Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.2832930Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.2833832Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:08:25.2834717Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:08:25.2835567Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:08:25.2836461Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.2837374Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.2838263Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:25.2839084Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:25.2839871Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:08:25.2840723Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.2841594Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.2842449Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:25.2843276Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:25.2844082Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:08:25.2844905Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.2845785Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.2846641Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:25.2847482Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:25.2848282Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:08:25.2849108Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.2850003Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.2850866Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:25.2851691Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:25.2852484Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:08:25.2853300Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.2854179Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.2855023Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:25.2855827Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:25.2856607Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:08:25.2857405Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.2858274Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.2859103Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:25.2859903Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:25.2860667Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ 2025-03-04T21:08:25.2861486Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.2862363Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.2863219Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:25.2864056Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:25.2864825Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ 2025-03-04T21:08:25.2865623Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.2866470Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.2867320Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:25.2868174Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:25.2868946Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ 2025-03-04T21:08:25.2869767Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.2870644Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.2871510Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:25.2872331Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:25.2873117Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ 2025-03-04T21:08:25.2873927Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.2874828Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.2875703Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:25.2876614Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:25.2877448Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ 2025-03-04T21:08:25.2878282Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.2879158Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.2880020Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:25.2880847Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:25.2881639Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ 2025-03-04T21:08:25.2882481Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.2883355Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.2884210Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:25.2885034Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:25.2885840Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ 2025-03-04T21:08:25.2886640Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.2887490Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.2888315Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:25.2889139Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:25.2889924Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ 2025-03-04T21:08:25.2890737Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.2891668Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.2892572Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:25.2893448Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:25.2894279Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ 2025-03-04T21:08:25.2895116Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.2896002Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.2896870Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:25.2897697Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:25.2898482Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:08:25.2899310Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.2900214Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.2901062Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:25.2902025Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:25.2902857Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:08:25.2903689Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.2904566Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.2905436Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:25.2906263Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:25.2907057Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:08:25.2907873Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.2908748Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.2909599Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:25.2910452Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:25.2911267Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:08:25.2912151Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.2913084Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.2914030Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:08:25.2914909Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:08:25.2915731Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:08:25.2916615Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.2917543Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.2918407Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:25.2919248Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:25.2920079Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:08:25.2920922Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.2921815Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.2922690Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:25.2923534Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:25.2924341Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:08:25.2925201Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.2926110Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.2926965Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:25.2927793Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:25.2928581Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:08:25.2929397Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.2930269Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.2931122Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:25.2931959Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:25.2932752Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:08:25.2933549Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.2934424Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.2935257Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:25.2936064Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:25.2936852Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:08:25.2937659Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.2938538Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.2939384Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:25.2940206Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:25.2940868Z l_self_modules_backbone_lateral_convs_0_parameters_weight_ = L_self_modules_backbone_lateral_convs_0_parameters_weight_ 2025-03-04T21:08:25.2941383Z l_self_modules_backbone_lateral_convs_0_parameters_bias_ = L_self_modules_backbone_lateral_convs_0_parameters_bias_ 2025-03-04T21:08:25.2941903Z l_self_modules_backbone_output_convs_0_parameters_weight_ = L_self_modules_backbone_output_convs_0_parameters_weight_ 2025-03-04T21:08:25.2942403Z l_self_modules_backbone_output_convs_0_parameters_bias_ = L_self_modules_backbone_output_convs_0_parameters_bias_ 2025-03-04T21:08:25.2942906Z l_self_modules_backbone_lateral_convs_1_parameters_weight_ = L_self_modules_backbone_lateral_convs_1_parameters_weight_ 2025-03-04T21:08:25.2943393Z l_self_modules_backbone_lateral_convs_1_parameters_bias_ = L_self_modules_backbone_lateral_convs_1_parameters_bias_ 2025-03-04T21:08:25.2943875Z l_self_modules_backbone_output_convs_1_parameters_weight_ = L_self_modules_backbone_output_convs_1_parameters_weight_ 2025-03-04T21:08:25.2944356Z l_self_modules_backbone_output_convs_1_parameters_bias_ = L_self_modules_backbone_output_convs_1_parameters_bias_ 2025-03-04T21:08:25.2944863Z l_self_modules_backbone_lateral_convs_2_parameters_weight_ = L_self_modules_backbone_lateral_convs_2_parameters_weight_ 2025-03-04T21:08:25.2945356Z l_self_modules_backbone_lateral_convs_2_parameters_bias_ = L_self_modules_backbone_lateral_convs_2_parameters_bias_ 2025-03-04T21:08:25.2945857Z l_self_modules_backbone_output_convs_2_parameters_weight_ = L_self_modules_backbone_output_convs_2_parameters_weight_ 2025-03-04T21:08:25.2946334Z l_self_modules_backbone_output_convs_2_parameters_bias_ = L_self_modules_backbone_output_convs_2_parameters_bias_ 2025-03-04T21:08:25.2946827Z l_self_modules_backbone_lateral_convs_3_parameters_weight_ = L_self_modules_backbone_lateral_convs_3_parameters_weight_ 2025-03-04T21:08:25.2947361Z l_self_modules_backbone_lateral_convs_3_parameters_bias_ = L_self_modules_backbone_lateral_convs_3_parameters_bias_ 2025-03-04T21:08:25.2947856Z l_self_modules_backbone_output_convs_3_parameters_weight_ = L_self_modules_backbone_output_convs_3_parameters_weight_ 2025-03-04T21:08:25.2948349Z l_self_modules_backbone_output_convs_3_parameters_bias_ = L_self_modules_backbone_output_convs_3_parameters_bias_ 2025-03-04T21:08:25.2948974Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T21:08:25.2949741Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ 2025-03-04T21:08:25.2950499Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ 2025-03-04T21:08:25.2951256Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ 2025-03-04T21:08:25.2952034Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ 2025-03-04T21:08:25.2952777Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T21:08:25.2953469Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T21:08:25.2954229Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T21:08:25.2955043Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T21:08:25.2955862Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T21:08:25.2956708Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T21:08:25.2957196Z 2025-03-04T21:08:25.2957611Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.2958505Z x: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1); l_stack0_tensor = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:25.2959186Z 2025-03-04T21:08:25.2959550Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.2961727Z x_1: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.2963577Z 2025-03-04T21:08:25.2963953Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x) 2025-03-04T21:08:25.2964430Z x_2: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.relu_(x_1); x_1 = None 2025-03-04T21:08:25.2964689Z 2025-03-04T21:08:25.2965146Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1) 2025-03-04T21:08:25.2965806Z x_3: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1); x_2 = None 2025-03-04T21:08:25.2966153Z 2025-03-04T21:08:25.2966534Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.2967342Z x_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:25.2967945Z 2025-03-04T21:08:25.2968301Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.2970456Z x_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.2972367Z 2025-03-04T21:08:25.2972743Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.2973246Z out: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_5); x_5 = None 2025-03-04T21:08:25.2973502Z 2025-03-04T21:08:25.2973841Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.2974652Z x_6: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:25.2975259Z 2025-03-04T21:08:25.2975609Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.2977759Z x_7: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_6 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.2979731Z 2025-03-04T21:08:25.2980099Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.2981466Z out_1: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_7); x_7 = None 2025-03-04T21:08:25.2981726Z 2025-03-04T21:08:25.2982069Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.2982880Z x_8: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_1 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:25.2983497Z 2025-03-04T21:08:25.2983838Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.2985941Z x_9: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.2987823Z 2025-03-04T21:08:25.2988142Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.2988923Z x_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); x_3 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:08:25.2989518Z 2025-03-04T21:08:25.2989859Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.2992045Z x_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_10 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.2994162Z 2025-03-04T21:08:25.2994524Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:25.2994998Z x_9 += x_11; out_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_9; x_9 = x_11 = None 2025-03-04T21:08:25.2995266Z 2025-03-04T21:08:25.2995627Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.2996126Z out_3: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_2); out_2 = None 2025-03-04T21:08:25.2996462Z 2025-03-04T21:08:25.2996812Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.2997658Z x_12: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:25.2998276Z 2025-03-04T21:08:25.2998622Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.3001371Z x_13: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_12 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.3004495Z 2025-03-04T21:08:25.3004902Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.3005388Z out_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_13); x_13 = None 2025-03-04T21:08:25.3005640Z 2025-03-04T21:08:25.3006503Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.3009488Z x_14: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:25.3010363Z 2025-03-04T21:08:25.3010926Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.3015559Z x_15: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_14 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.3019989Z 2025-03-04T21:08:25.3020441Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.3021172Z out_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_15); x_15 = None 2025-03-04T21:08:25.3021653Z 2025-03-04T21:08:25.3022022Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.3022881Z x_16: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_5 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:25.3023529Z 2025-03-04T21:08:25.3023935Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.3026215Z x_17: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_16 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.3028218Z 2025-03-04T21:08:25.3028598Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:25.3029087Z x_17 += out_3; out_6: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_17; x_17 = out_3 = None 2025-03-04T21:08:25.3029353Z 2025-03-04T21:08:25.3029741Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.3030245Z out_7: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_6); out_6 = None 2025-03-04T21:08:25.3030513Z 2025-03-04T21:08:25.3030852Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.3031707Z x_18: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:25.3032352Z 2025-03-04T21:08:25.3032725Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.3035019Z x_19: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_18 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.3037348Z 2025-03-04T21:08:25.3037763Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.3038245Z out_8: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_19); x_19 = None 2025-03-04T21:08:25.3038506Z 2025-03-04T21:08:25.3038861Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.3039677Z x_20: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:25.3040290Z 2025-03-04T21:08:25.3040655Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.3042964Z x_21: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_20 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.3045085Z 2025-03-04T21:08:25.3045475Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.3045964Z out_9: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_21); x_21 = None 2025-03-04T21:08:25.3046223Z 2025-03-04T21:08:25.3046572Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.3047424Z x_22: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_9 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:25.3048072Z 2025-03-04T21:08:25.3048444Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.3050700Z x_23: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_22 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.3052751Z 2025-03-04T21:08:25.3053138Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:25.3053669Z x_23 += out_7; out_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_23; x_23 = out_7 = None 2025-03-04T21:08:25.3053958Z 2025-03-04T21:08:25.3054352Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.3054883Z out_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_10); out_10 = None 2025-03-04T21:08:25.3055167Z 2025-03-04T21:08:25.3055530Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.3056346Z x_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:25.3056970Z 2025-03-04T21:08:25.3057324Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.3059477Z x_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.3061409Z 2025-03-04T21:08:25.3061799Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.3062285Z out_12: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_25); x_25 = None 2025-03-04T21:08:25.3062543Z 2025-03-04T21:08:25.3062877Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.3063691Z x_26: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_12 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:25.3064335Z 2025-03-04T21:08:25.3064689Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.3066856Z x_27: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_26 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.3068812Z 2025-03-04T21:08:25.3069170Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.3069649Z out_13: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_27); x_27 = None 2025-03-04T21:08:25.3069910Z 2025-03-04T21:08:25.3070270Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.3071092Z x_28: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_13 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:25.3071710Z 2025-03-04T21:08:25.3072060Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.3074228Z x_29: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_28 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.3076171Z 2025-03-04T21:08:25.3076618Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.3077511Z x_30: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:08:25.3078160Z 2025-03-04T21:08:25.3078520Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.3080752Z x_31: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_30 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.3082881Z 2025-03-04T21:08:25.3083248Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:25.3083731Z x_29 += x_31; out_14: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_29; x_29 = x_31 = None 2025-03-04T21:08:25.3083990Z 2025-03-04T21:08:25.3084356Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.3084856Z out_15: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_14); out_14 = None 2025-03-04T21:08:25.3085121Z 2025-03-04T21:08:25.3085451Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.3086245Z x_32: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:25.3086846Z 2025-03-04T21:08:25.3087192Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.3089347Z x_33: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_32 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.3091258Z 2025-03-04T21:08:25.3091622Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.3092104Z out_16: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_33); x_33 = None 2025-03-04T21:08:25.3092365Z 2025-03-04T21:08:25.3092697Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.3093495Z x_34: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_16 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:25.3094110Z 2025-03-04T21:08:25.3094447Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.3096568Z x_35: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_34 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.3098446Z 2025-03-04T21:08:25.3098803Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.3099280Z out_17: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_35); x_35 = None 2025-03-04T21:08:25.3099532Z 2025-03-04T21:08:25.3099857Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.3100653Z x_36: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_17 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:25.3101290Z 2025-03-04T21:08:25.3101642Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.3103946Z x_37: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_36 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.3105861Z 2025-03-04T21:08:25.3106213Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:25.3106695Z x_37 += out_15; out_18: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_37; x_37 = out_15 = None 2025-03-04T21:08:25.3106961Z 2025-03-04T21:08:25.3107317Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.3107797Z out_19: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_18); out_18 = None 2025-03-04T21:08:25.3108061Z 2025-03-04T21:08:25.3108391Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.3109197Z x_38: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:25.3109786Z 2025-03-04T21:08:25.3110127Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.3112229Z x_39: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_38 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.3114155Z 2025-03-04T21:08:25.3114524Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.3115015Z out_20: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_39); x_39 = None 2025-03-04T21:08:25.3115303Z 2025-03-04T21:08:25.3115640Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.3116496Z x_40: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_20 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:25.3117143Z 2025-03-04T21:08:25.3117515Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.3119699Z x_41: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_40 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.3121650Z 2025-03-04T21:08:25.3122022Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.3122508Z out_21: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_41); x_41 = None 2025-03-04T21:08:25.3122771Z 2025-03-04T21:08:25.3123109Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.3123939Z x_42: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_21 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:25.3124556Z 2025-03-04T21:08:25.3124906Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.3127047Z x_43: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_42 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.3128989Z 2025-03-04T21:08:25.3129365Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:25.3129858Z x_43 += out_19; out_22: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_43; x_43 = out_19 = None 2025-03-04T21:08:25.3130120Z 2025-03-04T21:08:25.3130476Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.3130951Z out_23: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_22); out_22 = None 2025-03-04T21:08:25.3131211Z 2025-03-04T21:08:25.3131535Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.3132318Z x_44: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:25.3132910Z 2025-03-04T21:08:25.3133256Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.3135412Z x_45: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_44 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.3137310Z 2025-03-04T21:08:25.3137683Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.3138156Z out_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_45); x_45 = None 2025-03-04T21:08:25.3138409Z 2025-03-04T21:08:25.3138745Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.3139558Z x_46: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:25.3140194Z 2025-03-04T21:08:25.3140553Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.3142650Z x_47: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_46 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.3144505Z 2025-03-04T21:08:25.3144863Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.3145328Z out_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_47); x_47 = None 2025-03-04T21:08:25.3145580Z 2025-03-04T21:08:25.3145915Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.3146697Z x_48: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_25 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:25.3147314Z 2025-03-04T21:08:25.3147653Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.3149748Z x_49: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_48 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.3151680Z 2025-03-04T21:08:25.3152044Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:25.3152532Z x_49 += out_23; out_26: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_49; x_49 = out_23 = None 2025-03-04T21:08:25.3152802Z 2025-03-04T21:08:25.3153169Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.3153681Z out_27: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_26); out_26 = None 2025-03-04T21:08:25.3153950Z 2025-03-04T21:08:25.3154285Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.3155110Z x_50: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:25.3155742Z 2025-03-04T21:08:25.3156139Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.3158482Z x_51: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_50 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.3160409Z 2025-03-04T21:08:25.3160780Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.3161276Z out_28: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_51); x_51 = None 2025-03-04T21:08:25.3161532Z 2025-03-04T21:08:25.3161869Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.3162669Z x_52: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_28 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:25.3163277Z 2025-03-04T21:08:25.3163629Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.3165782Z x_53: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_52 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.3167708Z 2025-03-04T21:08:25.3168079Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.3168562Z out_29: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_53); x_53 = None 2025-03-04T21:08:25.3168808Z 2025-03-04T21:08:25.3169137Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.3169937Z x_54: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_29 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:25.3170524Z 2025-03-04T21:08:25.3170884Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.3173010Z x_55: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_54 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.3174946Z 2025-03-04T21:08:25.3175296Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.3176106Z x_56: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:08:25.3176704Z 2025-03-04T21:08:25.3177052Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.3179262Z x_57: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_56 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.3181333Z 2025-03-04T21:08:25.3181700Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:25.3182215Z x_55 += x_57; out_30: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_55; x_55 = x_57 = None 2025-03-04T21:08:25.3182468Z 2025-03-04T21:08:25.3182837Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.3183322Z out_31: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_30); out_30 = None 2025-03-04T21:08:25.3183572Z 2025-03-04T21:08:25.3183893Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.3184680Z x_58: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:25.3185255Z 2025-03-04T21:08:25.3185601Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.3187702Z x_59: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_58 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.3189634Z 2025-03-04T21:08:25.3190018Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.3190518Z out_32: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_59); x_59 = None 2025-03-04T21:08:25.3190782Z 2025-03-04T21:08:25.3191131Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.3192008Z x_60: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_32 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:25.3192685Z 2025-03-04T21:08:25.3193095Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.3195485Z x_61: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_60 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.3197758Z 2025-03-04T21:08:25.3198156Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.3198634Z out_33: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_61); x_61 = None 2025-03-04T21:08:25.3198890Z 2025-03-04T21:08:25.3199227Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.3200052Z x_62: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_33 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:25.3200663Z 2025-03-04T21:08:25.3201032Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.3203354Z x_63: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_62 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.3205301Z 2025-03-04T21:08:25.3205665Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:25.3206146Z x_63 += out_31; out_34: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_63; x_63 = out_31 = None 2025-03-04T21:08:25.3206409Z 2025-03-04T21:08:25.3206777Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.3207260Z out_35: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_34); out_34 = None 2025-03-04T21:08:25.3207520Z 2025-03-04T21:08:25.3207880Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.3208663Z x_64: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:25.3209256Z 2025-03-04T21:08:25.3209605Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.3211722Z x_65: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_64 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.3213630Z 2025-03-04T21:08:25.3214016Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.3214486Z out_36: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_65); x_65 = None 2025-03-04T21:08:25.3214740Z 2025-03-04T21:08:25.3215070Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.3215863Z x_66: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_36 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:25.3216444Z 2025-03-04T21:08:25.3216783Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.3218841Z x_67: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_66 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.3220724Z 2025-03-04T21:08:25.3221091Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.3221591Z out_37: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_67); x_67 = None 2025-03-04T21:08:25.3221839Z 2025-03-04T21:08:25.3222171Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.3222958Z x_68: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_37 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:25.3223554Z 2025-03-04T21:08:25.3223900Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.3226000Z x_69: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_68 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.3227882Z 2025-03-04T21:08:25.3228246Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:25.3228719Z x_69 += out_35; out_38: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_69; x_69 = out_35 = None 2025-03-04T21:08:25.3228977Z 2025-03-04T21:08:25.3229335Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.3229813Z out_39: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_38); out_38 = None 2025-03-04T21:08:25.3230069Z 2025-03-04T21:08:25.3230403Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.3231237Z x_70: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:25.3231889Z 2025-03-04T21:08:25.3232250Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.3234592Z x_71: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_70 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.3236678Z 2025-03-04T21:08:25.3237095Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.3237601Z out_40: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_71); x_71 = None 2025-03-04T21:08:25.3237855Z 2025-03-04T21:08:25.3238194Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.3239004Z x_72: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_40 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:25.3239614Z 2025-03-04T21:08:25.3239972Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.3242125Z x_73: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_72 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.3244024Z 2025-03-04T21:08:25.3244390Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.3244859Z out_41: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_73); x_73 = None 2025-03-04T21:08:25.3245109Z 2025-03-04T21:08:25.3245439Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.3246246Z x_74: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_41 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:25.3246842Z 2025-03-04T21:08:25.3247187Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.3249317Z x_75: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_74 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.3251201Z 2025-03-04T21:08:25.3251564Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:25.3252043Z x_75 += out_39; out_42: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_75; x_75 = out_39 = None 2025-03-04T21:08:25.3252316Z 2025-03-04T21:08:25.3252681Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.3253162Z out_43: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_42); out_42 = None 2025-03-04T21:08:25.3253419Z 2025-03-04T21:08:25.3253754Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.3254544Z x_76: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:25.3255138Z 2025-03-04T21:08:25.3255504Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.3257603Z x_77: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_76 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.3259525Z 2025-03-04T21:08:25.3259893Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.3260362Z out_44: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_77); x_77 = None 2025-03-04T21:08:25.3260611Z 2025-03-04T21:08:25.3260941Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.3261744Z x_78: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_44 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:25.3262347Z 2025-03-04T21:08:25.3262715Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.3264877Z x_79: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_78 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.3266801Z 2025-03-04T21:08:25.3267174Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.3267649Z out_45: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_79); x_79 = None 2025-03-04T21:08:25.3267900Z 2025-03-04T21:08:25.3268229Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.3269039Z x_80: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_45 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:25.3269648Z 2025-03-04T21:08:25.3270004Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.3272166Z x_81: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_80 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.3274197Z 2025-03-04T21:08:25.3274592Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:25.3275128Z x_81 += out_43; out_46: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_81; x_81 = out_43 = None 2025-03-04T21:08:25.3275399Z 2025-03-04T21:08:25.3275785Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.3276341Z out_47: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_46); out_46 = None 2025-03-04T21:08:25.3276622Z 2025-03-04T21:08:25.3276971Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.3277837Z x_82: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:25.3278477Z 2025-03-04T21:08:25.3278835Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.3280980Z x_83: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_82 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.3282876Z 2025-03-04T21:08:25.3283240Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.3283724Z out_48: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_83); x_83 = None 2025-03-04T21:08:25.3283976Z 2025-03-04T21:08:25.3284311Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.3285108Z x_84: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_48 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:25.3285710Z 2025-03-04T21:08:25.3286062Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.3288194Z x_85: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_84 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.3290187Z 2025-03-04T21:08:25.3290577Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.3291067Z out_49: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_85); x_85 = None 2025-03-04T21:08:25.3291331Z 2025-03-04T21:08:25.3291692Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.3292533Z x_86: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_49 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:25.3293168Z 2025-03-04T21:08:25.3293531Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.3295780Z x_87: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_86 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.3297823Z 2025-03-04T21:08:25.3298224Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:25.3298733Z x_87 += out_47; out_50: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_87; x_87 = out_47 = None 2025-03-04T21:08:25.3299008Z 2025-03-04T21:08:25.3299393Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.3299903Z out_51: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_50); out_50 = None 2025-03-04T21:08:25.3300167Z 2025-03-04T21:08:25.3300518Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.3301359Z x_88: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:25.3302174Z 2025-03-04T21:08:25.3302582Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.3304990Z x_89: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_88 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.3307004Z 2025-03-04T21:08:25.3307390Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.3307864Z out_52: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_89); x_89 = None 2025-03-04T21:08:25.3308113Z 2025-03-04T21:08:25.3308446Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.3309240Z x_90: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_52 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:25.3309880Z 2025-03-04T21:08:25.3310254Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.3312620Z x_91: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_90 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.3314776Z 2025-03-04T21:08:25.3315184Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.3315710Z out_53: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_91); x_91 = None 2025-03-04T21:08:25.3315987Z 2025-03-04T21:08:25.3316401Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.3317317Z x_92: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_53 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:25.3318029Z 2025-03-04T21:08:25.3318423Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.3320574Z x_93: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_92 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.3322490Z 2025-03-04T21:08:25.3322827Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.3323630Z x_94: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:08:25.3324254Z 2025-03-04T21:08:25.3324603Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.3326818Z x_95: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_94 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.3328855Z 2025-03-04T21:08:25.3329211Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:25.3329670Z x_93 += x_95; out_54: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_93; x_93 = x_95 = None 2025-03-04T21:08:25.3329918Z 2025-03-04T21:08:25.3330276Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.3330748Z out_55: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_54); out_54 = None 2025-03-04T21:08:25.3331004Z 2025-03-04T21:08:25.3331337Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.3332143Z x_96: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:25.3332737Z 2025-03-04T21:08:25.3333083Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.3335221Z x_97: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_96 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.3337119Z 2025-03-04T21:08:25.3337493Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.3337969Z out_56: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_97); x_97 = None 2025-03-04T21:08:25.3338235Z 2025-03-04T21:08:25.3338569Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.3339378Z x_98: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_56 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:25.3339989Z 2025-03-04T21:08:25.3342733Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.3345012Z x_99: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_98 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.3346916Z 2025-03-04T21:08:25.3347290Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.3347770Z out_57: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_99); x_99 = None 2025-03-04T21:08:25.3348049Z 2025-03-04T21:08:25.3348384Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.3349192Z x_100: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_57 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:25.3349804Z 2025-03-04T21:08:25.3350153Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.3352361Z x_101: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_100 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.3354380Z 2025-03-04T21:08:25.3354766Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:25.3355302Z x_101 += out_55; out_58: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_101; x_101 = out_55 = None 2025-03-04T21:08:25.3355584Z 2025-03-04T21:08:25.3355971Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.3356534Z out_59: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_58); out_58 = None 2025-03-04T21:08:25.3356819Z 2025-03-04T21:08:25.3357191Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.3358092Z x_102: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:25.3358755Z 2025-03-04T21:08:25.3359150Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.3361565Z x_103: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_102 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.3363746Z 2025-03-04T21:08:25.3364158Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.3364695Z out_60: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_103); x_103 = None 2025-03-04T21:08:25.3364978Z 2025-03-04T21:08:25.3365350Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.3366184Z x_104: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_60 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:25.3366776Z 2025-03-04T21:08:25.3367134Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.3369194Z x_105: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_104 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.3371077Z 2025-03-04T21:08:25.3371440Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.3371914Z out_61: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_105); x_105 = None 2025-03-04T21:08:25.3372195Z 2025-03-04T21:08:25.3372533Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.3373349Z x_106: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_61 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:25.3373954Z 2025-03-04T21:08:25.3374298Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.3376392Z x_107: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_106 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.3378268Z 2025-03-04T21:08:25.3378631Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:25.3379115Z x_107 += out_59; out_62: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_107; x_107 = out_59 = None 2025-03-04T21:08:25.3379381Z 2025-03-04T21:08:25.3379753Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.3380240Z out_63: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_62); out_62 = None 2025-03-04T21:08:25.3380489Z 2025-03-04T21:08:25.3380826Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.3381688Z x_108: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(out_63, l_self_modules_backbone_lateral_convs_0_parameters_weight_, l_self_modules_backbone_lateral_convs_0_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_63 = l_self_modules_backbone_lateral_convs_0_parameters_weight_ = l_self_modules_backbone_lateral_convs_0_parameters_bias_ = None 2025-03-04T21:08:25.3382363Z 2025-03-04T21:08:25.3382693Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.3383571Z x_109: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_108, l_self_modules_backbone_output_convs_0_parameters_weight_, l_self_modules_backbone_output_convs_0_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_self_modules_backbone_output_convs_0_parameters_weight_ = l_self_modules_backbone_output_convs_0_parameters_bias_ = None 2025-03-04T21:08:25.3384226Z 2025-03-04T21:08:25.3384714Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest") 2025-03-04T21:08:25.3385447Z top_down_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.interpolate(x_108, scale_factor = 2.0, mode = 'nearest'); x_108 = None 2025-03-04T21:08:25.3385832Z 2025-03-04T21:08:25.3386167Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.3387053Z x_110: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_lateral_convs_1_parameters_weight_, l_self_modules_backbone_lateral_convs_1_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_51 = l_self_modules_backbone_lateral_convs_1_parameters_weight_ = l_self_modules_backbone_lateral_convs_1_parameters_bias_ = None 2025-03-04T21:08:25.3387743Z 2025-03-04T21:08:25.3388176Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features 2025-03-04T21:08:25.3388778Z prev_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = x_110 + top_down_features; x_110 = top_down_features = None 2025-03-04T21:08:25.3389088Z 2025-03-04T21:08:25.3389419Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.3390307Z x_111: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(prev_features, l_self_modules_backbone_output_convs_1_parameters_weight_, l_self_modules_backbone_output_convs_1_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_self_modules_backbone_output_convs_1_parameters_weight_ = l_self_modules_backbone_output_convs_1_parameters_bias_ = None 2025-03-04T21:08:25.3391007Z 2025-03-04T21:08:25.3391492Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest") 2025-03-04T21:08:25.3392265Z top_down_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.interpolate(prev_features, scale_factor = 2.0, mode = 'nearest'); prev_features = None 2025-03-04T21:08:25.3392693Z 2025-03-04T21:08:25.3393028Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.3393927Z x_112: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_lateral_convs_2_parameters_weight_, l_self_modules_backbone_lateral_convs_2_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_27 = l_self_modules_backbone_lateral_convs_2_parameters_weight_ = l_self_modules_backbone_lateral_convs_2_parameters_bias_ = None 2025-03-04T21:08:25.3394630Z 2025-03-04T21:08:25.3395089Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features 2025-03-04T21:08:25.3395734Z prev_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = x_112 + top_down_features_1; x_112 = top_down_features_1 = None 2025-03-04T21:08:25.3396077Z 2025-03-04T21:08:25.3396485Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.3397469Z x_113: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(prev_features_1, l_self_modules_backbone_output_convs_2_parameters_weight_, l_self_modules_backbone_output_convs_2_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_self_modules_backbone_output_convs_2_parameters_weight_ = l_self_modules_backbone_output_convs_2_parameters_bias_ = None 2025-03-04T21:08:25.3398174Z 2025-03-04T21:08:25.3398659Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest") 2025-03-04T21:08:25.3399458Z top_down_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.interpolate(prev_features_1, scale_factor = 2.0, mode = 'nearest'); prev_features_1 = None 2025-03-04T21:08:25.3399901Z 2025-03-04T21:08:25.3400234Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.3401126Z x_114: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_lateral_convs_3_parameters_weight_, l_self_modules_backbone_lateral_convs_3_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_11 = l_self_modules_backbone_lateral_convs_3_parameters_weight_ = l_self_modules_backbone_lateral_convs_3_parameters_bias_ = None 2025-03-04T21:08:25.3401824Z 2025-03-04T21:08:25.3402357Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features 2025-03-04T21:08:25.3402971Z prev_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_114 + top_down_features_2; x_114 = top_down_features_2 = None 2025-03-04T21:08:25.3403306Z 2025-03-04T21:08:25.3403644Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.3404615Z x_115: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(prev_features_2, l_self_modules_backbone_output_convs_3_parameters_weight_, l_self_modules_backbone_output_convs_3_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); prev_features_2 = l_self_modules_backbone_output_convs_3_parameters_weight_ = l_self_modules_backbone_output_convs_3_parameters_bias_ = None 2025-03-04T21:08:25.3405350Z 2025-03-04T21:08:25.3405797Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:200 in forward, code: return [F.max_pool2d(x, kernel_size=1, stride=2, padding=0)] 2025-03-04T21:08:25.3406426Z res: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.max_pool2d(x_109, kernel_size = 1, stride = 2, padding = 0) 2025-03-04T21:08:25.3406749Z 2025-03-04T21:08:25.3407271Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:08:25.3407979Z arange: "f32[304][1]cpu" = torch.arange(0.0, 1216, step = 4, dtype = torch.float32) 2025-03-04T21:08:25.3408252Z 2025-03-04T21:08:25.3408630Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:25.3409118Z shifts_x: "f32[304][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T21:08:25.3409369Z 2025-03-04T21:08:25.3409883Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:08:25.3410500Z arange_1: "f32[296][1]cpu" = torch.arange(0.0, 1184, step = 4, dtype = torch.float32) 2025-03-04T21:08:25.3410779Z 2025-03-04T21:08:25.3411144Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:25.3411615Z shifts_y: "f32[296][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T21:08:25.3411866Z 2025-03-04T21:08:25.3412305Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:08:25.3412885Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T21:08:25.3413235Z shift_y: "f32[296, 304][1, 0]cpu" = meshgrid[0] 2025-03-04T21:08:25.3413495Z shift_x: "f32[296, 304][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T21:08:25.3413716Z 2025-03-04T21:08:25.3414122Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:08:25.3414617Z shift_x_1: "f32[89984][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T21:08:25.3414855Z 2025-03-04T21:08:25.3415254Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:08:25.3415746Z shift_y_1: "f32[89984][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T21:08:25.3415978Z 2025-03-04T21:08:25.3416431Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:08:25.3417068Z shifts: "f32[89984, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T21:08:25.3417383Z 2025-03-04T21:08:25.3417878Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:08:25.3418479Z view: "f32[89984, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T21:08:25.3419086Z view_1: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T21:08:25.3419687Z add_3: "f32[89984, 3, 4][12, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T21:08:25.3419986Z x_116: "f32[269952, 4][4, 1]cpu" = add_3.reshape(-1, 4); add_3 = None 2025-03-04T21:08:25.3420221Z 2025-03-04T21:08:25.3420749Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:08:25.3421383Z arange_2: "f32[152][1]cpu" = torch.arange(0.0, 1216, step = 8, dtype = torch.float32) 2025-03-04T21:08:25.3421650Z 2025-03-04T21:08:25.3422031Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:25.3422520Z shifts_x_1: "f32[152][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T21:08:25.3422776Z 2025-03-04T21:08:25.3423298Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:08:25.3423931Z arange_3: "f32[148][1]cpu" = torch.arange(0.0, 1184, step = 8, dtype = torch.float32) 2025-03-04T21:08:25.3424212Z 2025-03-04T21:08:25.3424601Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:25.3425101Z shifts_y_1: "f32[148][1]cpu" = arange_3.to(device(type='cpu')); arange_3 = None 2025-03-04T21:08:25.3425365Z 2025-03-04T21:08:25.3425840Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:08:25.3426492Z meshgrid_1 = torch.functional.meshgrid(shifts_y_1, shifts_x_1); shifts_y_1 = shifts_x_1 = None 2025-03-04T21:08:25.3426843Z shift_y_2: "f32[148, 152][1, 0]cpu" = meshgrid_1[0] 2025-03-04T21:08:25.3427119Z shift_x_2: "f32[148, 152][0, 1]cpu" = meshgrid_1[1]; meshgrid_1 = None 2025-03-04T21:08:25.3427355Z 2025-03-04T21:08:25.3427768Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:08:25.3428281Z shift_x_3: "f32[22496][1]cpu" = shift_x_2.reshape(-1); shift_x_2 = None 2025-03-04T21:08:25.3428521Z 2025-03-04T21:08:25.3428927Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:08:25.3429431Z shift_y_3: "f32[22496][1]cpu" = shift_y_2.reshape(-1); shift_y_2 = None 2025-03-04T21:08:25.3429671Z 2025-03-04T21:08:25.3430135Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:08:25.3430781Z shifts_1: "f32[22496, 4][4, 1]cpu" = torch.stack((shift_x_3, shift_y_3, shift_x_3, shift_y_3), dim = 1); shift_x_3 = shift_y_3 = None 2025-03-04T21:08:25.3431122Z 2025-03-04T21:08:25.3431618Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:08:25.3432219Z view_2: "f32[22496, 1, 4][4, 4, 1]cpu" = shifts_1.view(-1, 1, 4); shifts_1 = None 2025-03-04T21:08:25.3432830Z view_3: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = None 2025-03-04T21:08:25.3433864Z add_4: "f32[22496, 3, 4][12, 4, 1]cpu" = view_2 + view_3; view_2 = view_3 = None 2025-03-04T21:08:25.3434675Z x_117: "f32[67488, 4][4, 1]cpu" = add_4.reshape(-1, 4); add_4 = None 2025-03-04T21:08:25.3434934Z 2025-03-04T21:08:25.3435780Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:08:25.3437074Z arange_4: "f32[76][1]cpu" = torch.arange(0.0, 1216, step = 16, dtype = torch.float32) 2025-03-04T21:08:25.3437356Z 2025-03-04T21:08:25.3438016Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:25.3438779Z shifts_x_2: "f32[76][1]cpu" = arange_4.to(device(type='cpu')); arange_4 = None 2025-03-04T21:08:25.3439229Z 2025-03-04T21:08:25.3439969Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:08:25.3440831Z arange_5: "f32[74][1]cpu" = torch.arange(0.0, 1184, step = 16, dtype = torch.float32) 2025-03-04T21:08:25.3441104Z 2025-03-04T21:08:25.3441680Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:25.3442374Z shifts_y_2: "f32[74][1]cpu" = arange_5.to(device(type='cpu')); arange_5 = None 2025-03-04T21:08:25.3442629Z 2025-03-04T21:08:25.3443286Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:08:25.3444176Z meshgrid_2 = torch.functional.meshgrid(shifts_y_2, shifts_x_2); shifts_y_2 = shifts_x_2 = None 2025-03-04T21:08:25.3444735Z shift_y_4: "f32[74, 76][1, 0]cpu" = meshgrid_2[0] 2025-03-04T21:08:25.3445021Z shift_x_4: "f32[74, 76][0, 1]cpu" = meshgrid_2[1]; meshgrid_2 = None 2025-03-04T21:08:25.3445462Z 2025-03-04T21:08:25.3445902Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:08:25.3446612Z shift_x_5: "f32[5624][1]cpu" = shift_x_4.reshape(-1); shift_x_4 = None 2025-03-04T21:08:25.3446857Z 2025-03-04T21:08:25.3447268Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:08:25.3447773Z shift_y_5: "f32[5624][1]cpu" = shift_y_4.reshape(-1); shift_y_4 = None 2025-03-04T21:08:25.3448009Z 2025-03-04T21:08:25.3448661Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:08:25.3450165Z shifts_2: "f32[5624, 4][4, 1]cpu" = torch.stack((shift_x_5, shift_y_5, shift_x_5, shift_y_5), dim = 1); shift_x_5 = shift_y_5 = None 2025-03-04T21:08:25.3450529Z 2025-03-04T21:08:25.3451232Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:08:25.3452064Z view_4: "f32[5624, 1, 4][4, 4, 1]cpu" = shifts_2.view(-1, 1, 4); shifts_2 = None 2025-03-04T21:08:25.3453051Z view_5: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = None 2025-03-04T21:08:25.3453662Z add_5: "f32[5624, 3, 4][12, 4, 1]cpu" = view_4 + view_5; view_4 = view_5 = None 2025-03-04T21:08:25.3453960Z x_118: "f32[16872, 4][4, 1]cpu" = add_5.reshape(-1, 4); add_5 = None 2025-03-04T21:08:25.3454201Z 2025-03-04T21:08:25.3454762Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:08:25.3455417Z arange_6: "f32[38][1]cpu" = torch.arange(0.0, 1216, step = 32, dtype = torch.float32) 2025-03-04T21:08:25.3455692Z 2025-03-04T21:08:25.3456085Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:25.3456586Z shifts_x_3: "f32[38][1]cpu" = arange_6.to(device(type='cpu')); arange_6 = None 2025-03-04T21:08:25.3456837Z 2025-03-04T21:08:25.3457367Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:08:25.3458037Z arange_7: "f32[37][1]cpu" = torch.arange(0.0, 1184, step = 32, dtype = torch.float32) 2025-03-04T21:08:25.3458288Z 2025-03-04T21:08:25.3458665Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:25.3459146Z shifts_y_3: "f32[37][1]cpu" = arange_7.to(device(type='cpu')); arange_7 = None 2025-03-04T21:08:25.3459398Z 2025-03-04T21:08:25.3459881Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:08:25.3460521Z meshgrid_3 = torch.functional.meshgrid(shifts_y_3, shifts_x_3); shifts_y_3 = shifts_x_3 = None 2025-03-04T21:08:25.3460875Z shift_y_6: "f32[37, 38][1, 0]cpu" = meshgrid_3[0] 2025-03-04T21:08:25.3461155Z shift_x_6: "f32[37, 38][0, 1]cpu" = meshgrid_3[1]; meshgrid_3 = None 2025-03-04T21:08:25.3461412Z 2025-03-04T21:08:25.3461826Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:08:25.3462354Z shift_x_7: "f32[1406][1]cpu" = shift_x_6.reshape(-1); shift_x_6 = None 2025-03-04T21:08:25.3462599Z 2025-03-04T21:08:25.3463022Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:08:25.3463535Z shift_y_7: "f32[1406][1]cpu" = shift_y_6.reshape(-1); shift_y_6 = None 2025-03-04T21:08:25.3463775Z 2025-03-04T21:08:25.3464250Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:08:25.3464913Z shifts_3: "f32[1406, 4][4, 1]cpu" = torch.stack((shift_x_7, shift_y_7, shift_x_7, shift_y_7), dim = 1); shift_x_7 = shift_y_7 = None 2025-03-04T21:08:25.3465260Z 2025-03-04T21:08:25.3465770Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:08:25.3466379Z view_6: "f32[1406, 1, 4][4, 4, 1]cpu" = shifts_3.view(-1, 1, 4); shifts_3 = None 2025-03-04T21:08:25.3466994Z view_7: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = None 2025-03-04T21:08:25.3467595Z add_6: "f32[1406, 3, 4][12, 4, 1]cpu" = view_6 + view_7; view_6 = view_7 = None 2025-03-04T21:08:25.3467891Z x_119: "f32[4218, 4][4, 1]cpu" = add_6.reshape(-1, 4); add_6 = None 2025-03-04T21:08:25.3468127Z 2025-03-04T21:08:25.3468673Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:08:25.3469320Z arange_8: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32) 2025-03-04T21:08:25.3469590Z 2025-03-04T21:08:25.3469981Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:25.3470479Z shifts_x_4: "f32[19][1]cpu" = arange_8.to(device(type='cpu')); arange_8 = None 2025-03-04T21:08:25.3470746Z 2025-03-04T21:08:25.3471275Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:08:25.3471935Z arange_9: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32) 2025-03-04T21:08:25.3472203Z 2025-03-04T21:08:25.3472592Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:25.3473088Z shifts_y_4: "f32[19][1]cpu" = arange_9.to(device(type='cpu')); arange_9 = None 2025-03-04T21:08:25.3473348Z 2025-03-04T21:08:25.3473830Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:08:25.3474468Z meshgrid_4 = torch.functional.meshgrid(shifts_y_4, shifts_x_4); shifts_y_4 = shifts_x_4 = None 2025-03-04T21:08:25.3474823Z shift_y_8: "f32[19, 19][1, 0]cpu" = meshgrid_4[0] 2025-03-04T21:08:25.3475095Z shift_x_8: "f32[19, 19][0, 1]cpu" = meshgrid_4[1]; meshgrid_4 = None 2025-03-04T21:08:25.3475336Z 2025-03-04T21:08:25.3475759Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:08:25.3476329Z shift_x_9: "f32[361][1]cpu" = shift_x_8.reshape(-1); shift_x_8 = None 2025-03-04T21:08:25.3476599Z 2025-03-04T21:08:25.3477018Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:08:25.3477544Z shift_y_9: "f32[361][1]cpu" = shift_y_8.reshape(-1); shift_y_8 = None 2025-03-04T21:08:25.3477803Z 2025-03-04T21:08:25.3478276Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:08:25.3478963Z shifts_4: "f32[361, 4][4, 1]cpu" = torch.stack((shift_x_9, shift_y_9, shift_x_9, shift_y_9), dim = 1); shift_x_9 = shift_y_9 = None 2025-03-04T21:08:25.3479297Z 2025-03-04T21:08:25.3479803Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:08:25.3480412Z view_8: "f32[361, 1, 4][4, 4, 1]cpu" = shifts_4.view(-1, 1, 4); shifts_4 = None 2025-03-04T21:08:25.3481015Z view_9: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = None 2025-03-04T21:08:25.3481610Z add_7: "f32[361, 3, 4][12, 4, 1]cpu" = view_8 + view_9; view_8 = view_9 = None 2025-03-04T21:08:25.3481899Z x_120: "f32[1083, 4][4, 1]cpu" = add_7.reshape(-1, 4); add_7 = None 2025-03-04T21:08:25.3482129Z 2025-03-04T21:08:25.3482530Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:08:25.3483014Z tensor: "f32[269952, 4][4, 1]cpu" = x_116.to(torch.float32); x_116 = None 2025-03-04T21:08:25.3483323Z tensor_1: "f32[67488, 4][4, 1]cpu" = x_117.to(torch.float32); x_117 = None 2025-03-04T21:08:25.3483619Z tensor_2: "f32[16872, 4][4, 1]cpu" = x_118.to(torch.float32); x_118 = None 2025-03-04T21:08:25.3483923Z tensor_3: "f32[4218, 4][4, 1]cpu" = x_119.to(torch.float32); x_119 = None 2025-03-04T21:08:25.3484219Z tensor_4: "f32[1083, 4][4, 1]cpu" = x_120.to(torch.float32); x_120 = None 2025-03-04T21:08:25.3484457Z 2025-03-04T21:08:25.3484822Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.3485358Z x_121: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_115, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_115 = None 2025-03-04T21:08:25.3485421Z 2025-03-04T21:08:25.3485705Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:08:25.3485918Z x_122: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.relu(x_121, inplace = False); x_121 = None 2025-03-04T21:08:25.3485990Z 2025-03-04T21:08:25.3486383Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:08:25.3486907Z score: "f32[4, 3, 296, 304][269952, 89984, 304, 1]cpu" = torch.conv2d(x_122, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:08:25.3486976Z 2025-03-04T21:08:25.3487338Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:08:25.3487872Z x_131: "f32[4, 12, 296, 304][1079808, 89984, 304, 1]cpu" = torch.conv2d(x_122, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_122 = None 2025-03-04T21:08:25.3487935Z 2025-03-04T21:08:25.3488196Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.3488704Z x_123: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(x_113, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_113 = None 2025-03-04T21:08:25.3488773Z 2025-03-04T21:08:25.3489045Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:08:25.3489243Z x_124: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.relu(x_123, inplace = False); x_123 = None 2025-03-04T21:08:25.3489306Z 2025-03-04T21:08:25.3489687Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:08:25.3490225Z score_1: "f32[4, 3, 148, 152][67488, 22496, 152, 1]cpu" = torch.conv2d(x_124, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:08:25.3490286Z 2025-03-04T21:08:25.3490652Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:08:25.3491175Z x_132: "f32[4, 12, 148, 152][269952, 22496, 152, 1]cpu" = torch.conv2d(x_124, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_124 = None 2025-03-04T21:08:25.3491259Z 2025-03-04T21:08:25.3491510Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.3492002Z x_125: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(x_111, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_111 = None 2025-03-04T21:08:25.3492066Z 2025-03-04T21:08:25.3492350Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:08:25.3492551Z x_126: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.relu(x_125, inplace = False); x_125 = None 2025-03-04T21:08:25.3492622Z 2025-03-04T21:08:25.3493010Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:08:25.3493550Z score_2: "f32[4, 3, 74, 76][16872, 5624, 76, 1]cpu" = torch.conv2d(x_126, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:08:25.3493619Z 2025-03-04T21:08:25.3493999Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:08:25.3494546Z x_133: "f32[4, 12, 74, 76][67488, 5624, 76, 1]cpu" = torch.conv2d(x_126, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_126 = None 2025-03-04T21:08:25.3494611Z 2025-03-04T21:08:25.3494888Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.3495399Z x_127: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_109, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_109 = None 2025-03-04T21:08:25.3495466Z 2025-03-04T21:08:25.3495739Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:08:25.3495927Z x_128: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.nn.functional.relu(x_127, inplace = False); x_127 = None 2025-03-04T21:08:25.3495988Z 2025-03-04T21:08:25.3496384Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:08:25.3496933Z score_3: "f32[4, 3, 37, 38][4218, 1406, 38, 1]cpu" = torch.conv2d(x_128, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:08:25.3497027Z 2025-03-04T21:08:25.3497397Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:08:25.3497933Z x_134: "f32[4, 12, 37, 38][16872, 1406, 38, 1]cpu" = torch.conv2d(x_128, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_128 = None 2025-03-04T21:08:25.3498020Z 2025-03-04T21:08:25.3498291Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.3499112Z x_129: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.conv2d(res, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); res = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T21:08:25.3499177Z 2025-03-04T21:08:25.3499490Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:08:25.3499671Z x_130: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.relu(x_129, inplace = False); x_129 = None 2025-03-04T21:08:25.3499741Z 2025-03-04T21:08:25.3500138Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:08:25.3501041Z score_4: "f32[4, 3, 19, 19][1083, 361, 19, 1]cpu" = torch.conv2d(x_130, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T21:08:25.3501110Z 2025-03-04T21:08:25.3501483Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:08:25.3502495Z x_135: "f32[4, 12, 19, 19][4332, 361, 19, 1]cpu" = torch.conv2d(x_130, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_130 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T21:08:25.3502607Z 2025-03-04T21:08:25.3502981Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T21:08:25.3503159Z permute: "f32[4, 296, 304, 3][269952, 304, 1, 89984]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T21:08:25.3503319Z logits_i: "f32[4, 269952][269952, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T21:08:25.3503492Z permute_1: "f32[4, 148, 152, 3][67488, 152, 1, 22496]cpu" = score_1.permute(0, 2, 3, 1); score_1 = None 2025-03-04T21:08:25.3503677Z logits_i_1: "f32[4, 67488][67488, 1]cpu" = permute_1.flatten(1); permute_1 = None 2025-03-04T21:08:25.3503838Z permute_2: "f32[4, 74, 76, 3][16872, 76, 1, 5624]cpu" = score_2.permute(0, 2, 3, 1); score_2 = None 2025-03-04T21:08:25.3503988Z logits_i_2: "f32[4, 16872][16872, 1]cpu" = permute_2.flatten(1); permute_2 = None 2025-03-04T21:08:25.3504138Z permute_3: "f32[4, 37, 38, 3][4218, 38, 1, 1406]cpu" = score_3.permute(0, 2, 3, 1); score_3 = None 2025-03-04T21:08:25.3504289Z logits_i_3: "f32[4, 4218][4218, 1]cpu" = permute_3.flatten(1); permute_3 = None 2025-03-04T21:08:25.3504442Z permute_4: "f32[4, 19, 19, 3][1083, 19, 1, 361]cpu" = score_4.permute(0, 2, 3, 1); score_4 = None 2025-03-04T21:08:25.3504589Z logits_i_4: "f32[4, 1083][1083, 1]cpu" = permute_4.flatten(1); permute_4 = None 2025-03-04T21:08:25.3504682Z 2025-03-04T21:08:25.3505149Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T21:08:25.3505340Z view_10: "f32[4, 3, 4, 296, 304][1079808, 359936, 89984, 304, 1]cpu" = x_131.view(4, -1, 4, 296, 304); x_131 = None 2025-03-04T21:08:25.3505535Z permute_5: "f32[4, 296, 304, 3, 4][1079808, 304, 1, 359936, 89984]cpu" = view_10.permute(0, 3, 4, 1, 2); view_10 = None 2025-03-04T21:08:25.3505766Z pred_anchor_deltas_i: "f32[4, 269952, 4][1079808, 4, 1]cpu" = permute_5.flatten(1, -2); permute_5 = None 2025-03-04T21:08:25.3505929Z view_11: "f32[4, 3, 4, 148, 152][269952, 89984, 22496, 152, 1]cpu" = x_132.view(4, -1, 4, 148, 152); x_132 = None 2025-03-04T21:08:25.3506108Z permute_6: "f32[4, 148, 152, 3, 4][269952, 152, 1, 89984, 22496]cpu" = view_11.permute(0, 3, 4, 1, 2); view_11 = None 2025-03-04T21:08:25.3506282Z pred_anchor_deltas_i_2: "f32[4, 67488, 4][269952, 4, 1]cpu" = permute_6.flatten(1, -2); permute_6 = None 2025-03-04T21:08:25.3506436Z view_12: "f32[4, 3, 4, 74, 76][67488, 22496, 5624, 76, 1]cpu" = x_133.view(4, -1, 4, 74, 76); x_133 = None 2025-03-04T21:08:25.3506601Z permute_7: "f32[4, 74, 76, 3, 4][67488, 76, 1, 22496, 5624]cpu" = view_12.permute(0, 3, 4, 1, 2); view_12 = None 2025-03-04T21:08:25.3506774Z pred_anchor_deltas_i_4: "f32[4, 16872, 4][67488, 4, 1]cpu" = permute_7.flatten(1, -2); permute_7 = None 2025-03-04T21:08:25.3506914Z view_13: "f32[4, 3, 4, 37, 38][16872, 5624, 1406, 38, 1]cpu" = x_134.view(4, -1, 4, 37, 38); x_134 = None 2025-03-04T21:08:25.3507077Z permute_8: "f32[4, 37, 38, 3, 4][16872, 38, 1, 5624, 1406]cpu" = view_13.permute(0, 3, 4, 1, 2); view_13 = None 2025-03-04T21:08:25.3507240Z pred_anchor_deltas_i_6: "f32[4, 4218, 4][16872, 4, 1]cpu" = permute_8.flatten(1, -2); permute_8 = None 2025-03-04T21:08:25.3507403Z view_14: "f32[4, 3, 4, 19, 19][4332, 1444, 361, 19, 1]cpu" = x_135.view(4, -1, 4, 19, 19); x_135 = None 2025-03-04T21:08:25.3507562Z permute_9: "f32[4, 19, 19, 3, 4][4332, 19, 1, 1444, 361]cpu" = view_14.permute(0, 3, 4, 1, 2); view_14 = None 2025-03-04T21:08:25.3507732Z pred_anchor_deltas_i_8: "f32[4, 1083, 4][4332, 4, 1]cpu" = permute_9.flatten(1, -2); permute_9 = None 2025-03-04T21:08:25.3507794Z 2025-03-04T21:08:25.3508215Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:08:25.3508419Z pred_anchor_deltas_i_1: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T21:08:25.3508489Z 2025-03-04T21:08:25.3508928Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:08:25.3509109Z unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T21:08:25.3509266Z expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T21:08:25.3509404Z anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T21:08:25.3509474Z 2025-03-04T21:08:25.3509856Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:08:25.3510030Z deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T21:08:25.3510092Z 2025-03-04T21:08:25.3510431Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:08:25.3510573Z boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T21:08:25.3510642Z 2025-03-04T21:08:25.3510956Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:08:25.3511091Z getitem_10: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:08:25.3511216Z getitem_11: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:08:25.3511939Z widths: "f32[1079808][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T21:08:25.3512006Z 2025-03-04T21:08:25.3512335Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:08:25.3512460Z getitem_12: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:08:25.3512589Z getitem_13: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:08:25.3512741Z heights: "f32[1079808][1]cpu" = getitem_12 - getitem_13; getitem_12 = getitem_13 = None 2025-03-04T21:08:25.3512810Z 2025-03-04T21:08:25.3513125Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:08:25.3513254Z getitem_14: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:08:25.3513340Z mul: "f32[1079808][1]cpu" = 0.5 * widths 2025-03-04T21:08:25.3513475Z ctr_x: "f32[1079808][1]cpu" = getitem_14 + mul; getitem_14 = mul = None 2025-03-04T21:08:25.3513536Z 2025-03-04T21:08:25.3513862Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:08:25.3514029Z getitem_15: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:08:25.3514129Z mul_1: "f32[1079808][1]cpu" = 0.5 * heights 2025-03-04T21:08:25.3514263Z ctr_y: "f32[1079808][1]cpu" = getitem_15 + mul_1; getitem_15 = mul_1 = None 2025-03-04T21:08:25.3514335Z 2025-03-04T21:08:25.3514696Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:08:25.3514870Z getitem_16: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:08:25.3514989Z dx: "f32[1079808, 1][1, 1]cpu" = getitem_16 / 1.0; getitem_16 = None 2025-03-04T21:08:25.3515062Z 2025-03-04T21:08:25.3515381Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:08:25.3515551Z getitem_17: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:08:25.3515685Z dy: "f32[1079808, 1][1, 1]cpu" = getitem_17 / 1.0; getitem_17 = None 2025-03-04T21:08:25.3515757Z 2025-03-04T21:08:25.3516075Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:08:25.3516241Z getitem_18: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:08:25.3516439Z dw: "f32[1079808, 1][1, 1]cpu" = getitem_18 / 1.0; getitem_18 = None 2025-03-04T21:08:25.3516527Z 2025-03-04T21:08:25.3516846Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:08:25.3517056Z getitem_19: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:08:25.3517176Z dh: "f32[1079808, 1][1, 1]cpu" = getitem_19 / 1.0; getitem_19 = None 2025-03-04T21:08:25.3517240Z 2025-03-04T21:08:25.3517598Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:08:25.3517744Z dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:08:25.3517816Z 2025-03-04T21:08:25.3518183Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:08:25.3518322Z dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:08:25.3518383Z 2025-03-04T21:08:25.3518741Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:08:25.3518881Z getitem_20: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:08:25.3519009Z mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_20; dx = getitem_20 = None 2025-03-04T21:08:25.3519161Z getitem_21: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:08:25.3519304Z pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_21; mul_2 = getitem_21 = None 2025-03-04T21:08:25.3519366Z 2025-03-04T21:08:25.3519725Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:08:25.3519862Z getitem_22: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:08:25.3520006Z mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_22; dy = getitem_22 = None 2025-03-04T21:08:25.3520157Z getitem_23: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:08:25.3520296Z pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_23; mul_3 = getitem_23 = None 2025-03-04T21:08:25.3520357Z 2025-03-04T21:08:25.3520707Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:08:25.3520821Z exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:08:25.3520980Z getitem_24: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:08:25.3521106Z pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_24; exp = getitem_24 = None 2025-03-04T21:08:25.3521173Z 2025-03-04T21:08:25.3521496Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:08:25.3521629Z exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:08:25.3521791Z getitem_25: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:08:25.3521931Z pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_25; exp_1 = getitem_25 = None 2025-03-04T21:08:25.3521991Z 2025-03-04T21:08:25.3522308Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:08:25.3522399Z mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T21:08:25.3522521Z x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:08:25.3522595Z 2025-03-04T21:08:25.3522908Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:08:25.3522997Z mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T21:08:25.3523113Z y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:08:25.3523174Z 2025-03-04T21:08:25.3523480Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:08:25.3523605Z mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:08:25.3523738Z x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:08:25.3523797Z 2025-03-04T21:08:25.3524099Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:08:25.3524206Z mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:08:25.3524335Z y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:08:25.3524394Z 2025-03-04T21:08:25.3524748Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:08:25.3524935Z pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:08:25.3524996Z 2025-03-04T21:08:25.3525334Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:08:25.3525491Z proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4)); pred_boxes = None 2025-03-04T21:08:25.3525579Z 2025-03-04T21:08:25.3525965Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:08:25.3526145Z proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T21:08:25.3526205Z 2025-03-04T21:08:25.3526619Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:08:25.3526822Z pred_anchor_deltas_i_3: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_2.reshape(-1, 4); pred_anchor_deltas_i_2 = None 2025-03-04T21:08:25.3526890Z 2025-03-04T21:08:25.3527313Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:08:25.3527488Z unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = tensor_1.unsqueeze(0); tensor_1 = None 2025-03-04T21:08:25.3527633Z expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1); unsqueeze_1 = None 2025-03-04T21:08:25.3527773Z anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4); expand_1 = None 2025-03-04T21:08:25.3527833Z 2025-03-04T21:08:25.3528209Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:08:25.3528375Z deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float(); pred_anchor_deltas_i_3 = None 2025-03-04T21:08:25.3528443Z 2025-03-04T21:08:25.3528767Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:08:25.3528921Z boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32); anchors_i_1 = None 2025-03-04T21:08:25.3528985Z 2025-03-04T21:08:25.3529308Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:08:25.3529438Z getitem_26: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)] 2025-03-04T21:08:25.3529571Z getitem_27: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T21:08:25.3529755Z widths_1: "f32[269952][1]cpu" = getitem_26 - getitem_27; getitem_26 = getitem_27 = None 2025-03-04T21:08:25.3529826Z 2025-03-04T21:08:25.3530142Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:08:25.3530273Z getitem_28: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)] 2025-03-04T21:08:25.3530394Z getitem_29: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)] 2025-03-04T21:08:25.3530553Z heights_1: "f32[269952][1]cpu" = getitem_28 - getitem_29; getitem_28 = getitem_29 = None 2025-03-04T21:08:25.3530614Z 2025-03-04T21:08:25.3530934Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:08:25.3531054Z getitem_30: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T21:08:25.3531151Z mul_10: "f32[269952][1]cpu" = 0.5 * widths_1 2025-03-04T21:08:25.3531279Z ctr_x_1: "f32[269952][1]cpu" = getitem_30 + mul_10; getitem_30 = mul_10 = None 2025-03-04T21:08:25.3531346Z 2025-03-04T21:08:25.3531658Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:08:25.3531827Z getitem_31: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]; boxes_1 = None 2025-03-04T21:08:25.3531916Z mul_11: "f32[269952][1]cpu" = 0.5 * heights_1 2025-03-04T21:08:25.3532049Z ctr_y_1: "f32[269952][1]cpu" = getitem_31 + mul_11; getitem_31 = mul_11 = None 2025-03-04T21:08:25.3532108Z 2025-03-04T21:08:25.3532418Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:08:25.3532579Z getitem_32: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:08:25.3532692Z dx_1: "f32[269952, 1][1, 1]cpu" = getitem_32 / 1.0; getitem_32 = None 2025-03-04T21:08:25.3532769Z 2025-03-04T21:08:25.3533062Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:08:25.3533228Z getitem_33: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:08:25.3533338Z dy_1: "f32[269952, 1][1, 1]cpu" = getitem_33 / 1.0; getitem_33 = None 2025-03-04T21:08:25.3533404Z 2025-03-04T21:08:25.3533694Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:08:25.3533843Z getitem_34: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:08:25.3533950Z dw_2: "f32[269952, 1][1, 1]cpu" = getitem_34 / 1.0; getitem_34 = None 2025-03-04T21:08:25.3534018Z 2025-03-04T21:08:25.3534311Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:08:25.3534519Z getitem_35: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))]; deltas_1 = None 2025-03-04T21:08:25.3534627Z dh_2: "f32[269952, 1][1, 1]cpu" = getitem_35 / 1.0; getitem_35 = None 2025-03-04T21:08:25.3534693Z 2025-03-04T21:08:25.3535022Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:08:25.3535169Z dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356); dw_2 = None 2025-03-04T21:08:25.3535230Z 2025-03-04T21:08:25.3535588Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:08:25.3535723Z dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356); dh_2 = None 2025-03-04T21:08:25.3535794Z 2025-03-04T21:08:25.3536143Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:08:25.3536287Z getitem_36: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)] 2025-03-04T21:08:25.3536411Z mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_36; dx_1 = getitem_36 = None 2025-03-04T21:08:25.3536572Z getitem_37: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)]; ctr_x_1 = None 2025-03-04T21:08:25.3536716Z pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_37; mul_12 = getitem_37 = None 2025-03-04T21:08:25.3536783Z 2025-03-04T21:08:25.3537127Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:08:25.3537275Z getitem_38: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)] 2025-03-04T21:08:25.3537412Z mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_38; dy_1 = getitem_38 = None 2025-03-04T21:08:25.3537572Z getitem_39: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)]; ctr_y_1 = None 2025-03-04T21:08:25.3537717Z pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_39; mul_13 = getitem_39 = None 2025-03-04T21:08:25.3537779Z 2025-03-04T21:08:25.3538125Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:08:25.3538242Z exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3); dw_3 = None 2025-03-04T21:08:25.3538411Z getitem_40: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]; widths_1 = None 2025-03-04T21:08:25.3538550Z pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_40; exp_2 = getitem_40 = None 2025-03-04T21:08:25.3538622Z 2025-03-04T21:08:25.3538973Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:08:25.3539093Z exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3); dh_3 = None 2025-03-04T21:08:25.3539257Z getitem_41: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]; heights_1 = None 2025-03-04T21:08:25.3539401Z pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_41; exp_3 = getitem_41 = None 2025-03-04T21:08:25.3539463Z 2025-03-04T21:08:25.3539780Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:08:25.3539874Z mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1 2025-03-04T21:08:25.3540010Z x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16; mul_16 = None 2025-03-04T21:08:25.3540072Z 2025-03-04T21:08:25.3540384Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:08:25.3540472Z mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1 2025-03-04T21:08:25.3540590Z y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17; mul_17 = None 2025-03-04T21:08:25.3540651Z 2025-03-04T21:08:25.3540963Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:08:25.3541092Z mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1; pred_w_1 = None 2025-03-04T21:08:25.3541232Z x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18; pred_ctr_x_1 = mul_18 = None 2025-03-04T21:08:25.3541294Z 2025-03-04T21:08:25.3541607Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:08:25.3541722Z mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1; pred_h_1 = None 2025-03-04T21:08:25.3541859Z y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19; pred_ctr_y_1 = mul_19 = None 2025-03-04T21:08:25.3541921Z 2025-03-04T21:08:25.3542273Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:08:25.3542466Z pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1); x1_1 = y1_1 = x2_1 = y2_1 = None 2025-03-04T21:08:25.3542535Z 2025-03-04T21:08:25.3542866Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:08:25.3543036Z proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4)); pred_boxes_1 = None 2025-03-04T21:08:25.3543113Z 2025-03-04T21:08:25.3543503Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:08:25.3543675Z proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4); proposals_i_1 = None 2025-03-04T21:08:25.3543743Z 2025-03-04T21:08:25.3544140Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:08:25.3544352Z pred_anchor_deltas_i_5: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_4.reshape(-1, 4); pred_anchor_deltas_i_4 = None 2025-03-04T21:08:25.3544415Z 2025-03-04T21:08:25.3544865Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:08:25.3545022Z unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = tensor_2.unsqueeze(0); tensor_2 = None 2025-03-04T21:08:25.3545167Z expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1); unsqueeze_2 = None 2025-03-04T21:08:25.3545306Z anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4); expand_2 = None 2025-03-04T21:08:25.3545367Z 2025-03-04T21:08:25.3545741Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:08:25.3545905Z deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_5.float(); pred_anchor_deltas_i_5 = None 2025-03-04T21:08:25.3545985Z 2025-03-04T21:08:25.3546297Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:08:25.3546443Z boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32); anchors_i_2 = None 2025-03-04T21:08:25.3546502Z 2025-03-04T21:08:25.3546820Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:08:25.3546945Z getitem_42: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)] 2025-03-04T21:08:25.3547088Z getitem_43: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T21:08:25.3547237Z widths_2: "f32[67488][1]cpu" = getitem_42 - getitem_43; getitem_42 = getitem_43 = None 2025-03-04T21:08:25.3547304Z 2025-03-04T21:08:25.3547619Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:08:25.3547747Z getitem_44: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)] 2025-03-04T21:08:25.3547864Z getitem_45: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)] 2025-03-04T21:08:25.3548015Z heights_2: "f32[67488][1]cpu" = getitem_44 - getitem_45; getitem_44 = getitem_45 = None 2025-03-04T21:08:25.3548076Z 2025-03-04T21:08:25.3548389Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:08:25.3548508Z getitem_46: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T21:08:25.3548601Z mul_20: "f32[67488][1]cpu" = 0.5 * widths_2 2025-03-04T21:08:25.3548729Z ctr_x_2: "f32[67488][1]cpu" = getitem_46 + mul_20; getitem_46 = mul_20 = None 2025-03-04T21:08:25.3548797Z 2025-03-04T21:08:25.3549152Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:08:25.3549302Z getitem_47: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]; boxes_2 = None 2025-03-04T21:08:25.3549393Z mul_21: "f32[67488][1]cpu" = 0.5 * heights_2 2025-03-04T21:08:25.3549526Z ctr_y_2: "f32[67488][1]cpu" = getitem_47 + mul_21; getitem_47 = mul_21 = None 2025-03-04T21:08:25.3549588Z 2025-03-04T21:08:25.3549900Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:08:25.3550049Z getitem_48: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:08:25.3550169Z dx_2: "f32[67488, 1][1, 1]cpu" = getitem_48 / 1.0; getitem_48 = None 2025-03-04T21:08:25.3550232Z 2025-03-04T21:08:25.3550540Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:08:25.3550703Z getitem_49: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:08:25.3550823Z dy_2: "f32[67488, 1][1, 1]cpu" = getitem_49 / 1.0; getitem_49 = None 2025-03-04T21:08:25.3550884Z 2025-03-04T21:08:25.3551193Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:08:25.3551345Z getitem_50: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:08:25.3551461Z dw_4: "f32[67488, 1][1, 1]cpu" = getitem_50 / 1.0; getitem_50 = None 2025-03-04T21:08:25.3551542Z 2025-03-04T21:08:25.3551863Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:08:25.3552059Z getitem_51: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))]; deltas_2 = None 2025-03-04T21:08:25.3552169Z dh_4: "f32[67488, 1][1, 1]cpu" = getitem_51 / 1.0; getitem_51 = None 2025-03-04T21:08:25.3552238Z 2025-03-04T21:08:25.3552584Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:08:25.3552747Z dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356); dw_4 = None 2025-03-04T21:08:25.3552810Z 2025-03-04T21:08:25.3553159Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:08:25.3553298Z dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356); dh_4 = None 2025-03-04T21:08:25.3553371Z 2025-03-04T21:08:25.3553729Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:08:25.3553869Z getitem_52: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)] 2025-03-04T21:08:25.3553996Z mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_52; dx_2 = getitem_52 = None 2025-03-04T21:08:25.3554156Z getitem_53: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)]; ctr_x_2 = None 2025-03-04T21:08:25.3554299Z pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_53; mul_22 = getitem_53 = None 2025-03-04T21:08:25.3554369Z 2025-03-04T21:08:25.3554734Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:08:25.3554897Z getitem_54: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)] 2025-03-04T21:08:25.3555026Z mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_54; dy_2 = getitem_54 = None 2025-03-04T21:08:25.3555191Z getitem_55: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)]; ctr_y_2 = None 2025-03-04T21:08:25.3555333Z pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_55; mul_23 = getitem_55 = None 2025-03-04T21:08:25.3555405Z 2025-03-04T21:08:25.3555756Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:08:25.3555882Z exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5); dw_5 = None 2025-03-04T21:08:25.3556050Z getitem_56: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]; widths_2 = None 2025-03-04T21:08:25.3556200Z pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_56; exp_4 = getitem_56 = None 2025-03-04T21:08:25.3556269Z 2025-03-04T21:08:25.3556743Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:08:25.3556864Z exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5); dh_5 = None 2025-03-04T21:08:25.3557045Z getitem_57: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]; heights_2 = None 2025-03-04T21:08:25.3557182Z pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_57; exp_5 = getitem_57 = None 2025-03-04T21:08:25.3557253Z 2025-03-04T21:08:25.3557592Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:08:25.3557716Z mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2 2025-03-04T21:08:25.3557835Z x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26; mul_26 = None 2025-03-04T21:08:25.3557905Z 2025-03-04T21:08:25.3558227Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:08:25.3558327Z mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2 2025-03-04T21:08:25.3558441Z y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27; mul_27 = None 2025-03-04T21:08:25.3558509Z 2025-03-04T21:08:25.3558839Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:08:25.3558965Z mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2; pred_w_2 = None 2025-03-04T21:08:25.3559099Z x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28; pred_ctr_x_2 = mul_28 = None 2025-03-04T21:08:25.3559170Z 2025-03-04T21:08:25.3559491Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:08:25.3559615Z mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2; pred_h_2 = None 2025-03-04T21:08:25.3559749Z y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29; pred_ctr_y_2 = mul_29 = None 2025-03-04T21:08:25.3559812Z 2025-03-04T21:08:25.3560180Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:08:25.3560375Z pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1); x1_2 = y1_2 = x2_2 = y2_2 = None 2025-03-04T21:08:25.3560443Z 2025-03-04T21:08:25.3560792Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:08:25.3560976Z proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4)); pred_boxes_2 = None 2025-03-04T21:08:25.3561039Z 2025-03-04T21:08:25.3561436Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:08:25.3561610Z proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4); proposals_i_2 = None 2025-03-04T21:08:25.3561678Z 2025-03-04T21:08:25.3562088Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:08:25.3562301Z pred_anchor_deltas_i_7: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_6.reshape(-1, 4); pred_anchor_deltas_i_6 = None 2025-03-04T21:08:25.3562368Z 2025-03-04T21:08:25.3562832Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:08:25.3562981Z unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = tensor_3.unsqueeze(0); tensor_3 = None 2025-03-04T21:08:25.3563137Z expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1); unsqueeze_3 = None 2025-03-04T21:08:25.3563272Z anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4); expand_3 = None 2025-03-04T21:08:25.3563342Z 2025-03-04T21:08:25.3563719Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:08:25.3563911Z deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_7.float(); pred_anchor_deltas_i_7 = None 2025-03-04T21:08:25.3563974Z 2025-03-04T21:08:25.3564302Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:08:25.3564446Z boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32); anchors_i_3 = None 2025-03-04T21:08:25.3564516Z 2025-03-04T21:08:25.3564837Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:08:25.3564987Z getitem_58: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)] 2025-03-04T21:08:25.3565114Z getitem_59: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T21:08:25.3565272Z widths_3: "f32[16872][1]cpu" = getitem_58 - getitem_59; getitem_58 = getitem_59 = None 2025-03-04T21:08:25.3565335Z 2025-03-04T21:08:25.3565674Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:08:25.3565799Z getitem_60: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)] 2025-03-04T21:08:25.3565926Z getitem_61: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)] 2025-03-04T21:08:25.3566085Z heights_3: "f32[16872][1]cpu" = getitem_60 - getitem_61; getitem_60 = getitem_61 = None 2025-03-04T21:08:25.3566154Z 2025-03-04T21:08:25.3566476Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:08:25.3566618Z getitem_62: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T21:08:25.3566708Z mul_30: "f32[16872][1]cpu" = 0.5 * widths_3 2025-03-04T21:08:25.3566839Z ctr_x_3: "f32[16872][1]cpu" = getitem_62 + mul_30; getitem_62 = mul_30 = None 2025-03-04T21:08:25.3566921Z 2025-03-04T21:08:25.3567233Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:08:25.3567381Z getitem_63: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]; boxes_3 = None 2025-03-04T21:08:25.3567469Z mul_31: "f32[16872][1]cpu" = 0.5 * heights_3 2025-03-04T21:08:25.3567600Z ctr_y_3: "f32[16872][1]cpu" = getitem_63 + mul_31; getitem_63 = mul_31 = None 2025-03-04T21:08:25.3567660Z 2025-03-04T21:08:25.3567971Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:08:25.3568119Z getitem_64: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:08:25.3568234Z dx_3: "f32[16872, 1][1, 1]cpu" = getitem_64 / 1.0; getitem_64 = None 2025-03-04T21:08:25.3568295Z 2025-03-04T21:08:25.3568603Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:08:25.3568748Z getitem_65: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:08:25.3568859Z dy_3: "f32[16872, 1][1, 1]cpu" = getitem_65 / 1.0; getitem_65 = None 2025-03-04T21:08:25.3568919Z 2025-03-04T21:08:25.3569215Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:08:25.3569356Z getitem_66: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:08:25.3569466Z dw_6: "f32[16872, 1][1, 1]cpu" = getitem_66 / 1.0; getitem_66 = None 2025-03-04T21:08:25.3569538Z 2025-03-04T21:08:25.3569848Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:08:25.3570030Z getitem_67: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))]; deltas_3 = None 2025-03-04T21:08:25.3570143Z dh_6: "f32[16872, 1][1, 1]cpu" = getitem_67 / 1.0; getitem_67 = None 2025-03-04T21:08:25.3570203Z 2025-03-04T21:08:25.3570549Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:08:25.3570702Z dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356); dw_6 = None 2025-03-04T21:08:25.3570773Z 2025-03-04T21:08:25.3571100Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:08:25.3571244Z dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356); dh_6 = None 2025-03-04T21:08:25.3571306Z 2025-03-04T21:08:25.3571663Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:08:25.3571791Z getitem_68: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)] 2025-03-04T21:08:25.3571918Z mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_68; dx_3 = getitem_68 = None 2025-03-04T21:08:25.3572066Z getitem_69: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)]; ctr_x_3 = None 2025-03-04T21:08:25.3572209Z pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_69; mul_32 = getitem_69 = None 2025-03-04T21:08:25.3572277Z 2025-03-04T21:08:25.3572615Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:08:25.3572774Z getitem_70: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)] 2025-03-04T21:08:25.3572890Z mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_70; dy_3 = getitem_70 = None 2025-03-04T21:08:25.3573039Z getitem_71: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)]; ctr_y_3 = None 2025-03-04T21:08:25.3573169Z pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_71; mul_33 = getitem_71 = None 2025-03-04T21:08:25.3573236Z 2025-03-04T21:08:25.3573558Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:08:25.3573673Z exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7); dw_7 = None 2025-03-04T21:08:25.3573828Z getitem_72: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]; widths_3 = None 2025-03-04T21:08:25.3573961Z pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_72; exp_6 = getitem_72 = None 2025-03-04T21:08:25.3574037Z 2025-03-04T21:08:25.3574378Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:08:25.3574486Z exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7); dh_7 = None 2025-03-04T21:08:25.3574652Z getitem_73: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]; heights_3 = None 2025-03-04T21:08:25.3574782Z pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_73; exp_7 = getitem_73 = None 2025-03-04T21:08:25.3574850Z 2025-03-04T21:08:25.3575160Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:08:25.3575275Z mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3 2025-03-04T21:08:25.3575386Z x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36; mul_36 = None 2025-03-04T21:08:25.3575454Z 2025-03-04T21:08:25.3575757Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:08:25.3575853Z mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3 2025-03-04T21:08:25.3575964Z y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37; mul_37 = None 2025-03-04T21:08:25.3576031Z 2025-03-04T21:08:25.3576350Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:08:25.3576468Z mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3; pred_w_3 = None 2025-03-04T21:08:25.3576598Z x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38; pred_ctr_x_3 = mul_38 = None 2025-03-04T21:08:25.3576667Z 2025-03-04T21:08:25.3576970Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:08:25.3577094Z mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3; pred_h_3 = None 2025-03-04T21:08:25.3577218Z y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39; pred_ctr_y_3 = mul_39 = None 2025-03-04T21:08:25.3577284Z 2025-03-04T21:08:25.3577622Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:08:25.3577809Z pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1); x1_3 = y1_3 = x2_3 = y2_3 = None 2025-03-04T21:08:25.3577870Z 2025-03-04T21:08:25.3578200Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:08:25.3578383Z proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4)); pred_boxes_3 = None 2025-03-04T21:08:25.3578449Z 2025-03-04T21:08:25.3578825Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:08:25.3579002Z proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4); proposals_i_3 = None 2025-03-04T21:08:25.3579064Z 2025-03-04T21:08:25.3579472Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:08:25.3579682Z pred_anchor_deltas_i_9: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_8.reshape(-1, 4); pred_anchor_deltas_i_8 = None 2025-03-04T21:08:25.3579745Z 2025-03-04T21:08:25.3580204Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:08:25.3580348Z unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = tensor_4.unsqueeze(0); tensor_4 = None 2025-03-04T21:08:25.3580499Z expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1); unsqueeze_4 = None 2025-03-04T21:08:25.3580632Z anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4); expand_4 = None 2025-03-04T21:08:25.3580701Z 2025-03-04T21:08:25.3581073Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:08:25.3581269Z deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_9.float(); pred_anchor_deltas_i_9 = None 2025-03-04T21:08:25.3581331Z 2025-03-04T21:08:25.3581639Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:08:25.3581776Z boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32); anchors_i_4 = None 2025-03-04T21:08:25.3581844Z 2025-03-04T21:08:25.3582169Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:08:25.3582301Z getitem_74: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)] 2025-03-04T21:08:25.3582420Z getitem_75: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T21:08:25.3582572Z widths_4: "f32[4332][1]cpu" = getitem_74 - getitem_75; getitem_74 = getitem_75 = None 2025-03-04T21:08:25.3582634Z 2025-03-04T21:08:25.3582961Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:08:25.3583079Z getitem_76: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)] 2025-03-04T21:08:25.3583200Z getitem_77: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)] 2025-03-04T21:08:25.3583344Z heights_4: "f32[4332][1]cpu" = getitem_76 - getitem_77; getitem_76 = getitem_77 = None 2025-03-04T21:08:25.3583412Z 2025-03-04T21:08:25.3583725Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:08:25.3583850Z getitem_78: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T21:08:25.3583936Z mul_40: "f32[4332][1]cpu" = 0.5 * widths_4 2025-03-04T21:08:25.3584070Z ctr_x_4: "f32[4332][1]cpu" = getitem_78 + mul_40; getitem_78 = mul_40 = None 2025-03-04T21:08:25.3584145Z 2025-03-04T21:08:25.3584462Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:08:25.3584606Z getitem_79: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]; boxes_4 = None 2025-03-04T21:08:25.3584700Z mul_41: "f32[4332][1]cpu" = 0.5 * heights_4 2025-03-04T21:08:25.3584824Z ctr_y_4: "f32[4332][1]cpu" = getitem_79 + mul_41; getitem_79 = mul_41 = None 2025-03-04T21:08:25.3584893Z 2025-03-04T21:08:25.3585195Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:08:25.3585353Z getitem_80: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:08:25.3585462Z dx_4: "f32[4332, 1][1, 1]cpu" = getitem_80 / 1.0; getitem_80 = None 2025-03-04T21:08:25.3585532Z 2025-03-04T21:08:25.3585858Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:08:25.3586017Z getitem_81: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:08:25.3586123Z dy_4: "f32[4332, 1][1, 1]cpu" = getitem_81 / 1.0; getitem_81 = None 2025-03-04T21:08:25.3586191Z 2025-03-04T21:08:25.3586488Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:08:25.3586639Z getitem_82: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:08:25.3586766Z dw_8: "f32[4332, 1][1, 1]cpu" = getitem_82 / 1.0; getitem_82 = None 2025-03-04T21:08:25.3586829Z 2025-03-04T21:08:25.3587136Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:08:25.3587314Z getitem_83: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))]; deltas_4 = None 2025-03-04T21:08:25.3587426Z dh_8: "f32[4332, 1][1, 1]cpu" = getitem_83 / 1.0; getitem_83 = None 2025-03-04T21:08:25.3587489Z 2025-03-04T21:08:25.3587842Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:08:25.3587977Z dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356); dw_8 = None 2025-03-04T21:08:25.3588045Z 2025-03-04T21:08:25.3588376Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:08:25.3588518Z dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356); dh_8 = None 2025-03-04T21:08:25.3588579Z 2025-03-04T21:08:25.3588929Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:08:25.3589060Z getitem_84: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)] 2025-03-04T21:08:25.3589184Z mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_84; dx_4 = getitem_84 = None 2025-03-04T21:08:25.3589333Z getitem_85: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)]; ctr_x_4 = None 2025-03-04T21:08:25.3589475Z pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_85; mul_42 = getitem_85 = None 2025-03-04T21:08:25.3589537Z 2025-03-04T21:08:25.3589888Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:08:25.3590037Z getitem_86: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)] 2025-03-04T21:08:25.3590163Z mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_86; dy_4 = getitem_86 = None 2025-03-04T21:08:25.3590311Z getitem_87: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)]; ctr_y_4 = None 2025-03-04T21:08:25.3590452Z pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_87; mul_43 = getitem_87 = None 2025-03-04T21:08:25.3590512Z 2025-03-04T21:08:25.3590854Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:08:25.3590965Z exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9); dw_9 = None 2025-03-04T21:08:25.3591130Z getitem_88: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]; widths_4 = None 2025-03-04T21:08:25.3591275Z pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_88; exp_8 = getitem_88 = None 2025-03-04T21:08:25.3591344Z 2025-03-04T21:08:25.3591675Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:08:25.3591791Z exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9); dh_9 = None 2025-03-04T21:08:25.3591956Z getitem_89: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]; heights_4 = None 2025-03-04T21:08:25.3592089Z pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_89; exp_9 = getitem_89 = None 2025-03-04T21:08:25.3592149Z 2025-03-04T21:08:25.3592464Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:08:25.3592573Z mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4 2025-03-04T21:08:25.3592691Z x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46; mul_46 = None 2025-03-04T21:08:25.3592753Z 2025-03-04T21:08:25.3593071Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:08:25.3593162Z mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4 2025-03-04T21:08:25.3593278Z y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47; mul_47 = None 2025-03-04T21:08:25.3593340Z 2025-03-04T21:08:25.3593672Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:08:25.3593793Z mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4; pred_w_4 = None 2025-03-04T21:08:25.3593922Z x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48; pred_ctr_x_4 = mul_48 = None 2025-03-04T21:08:25.3593984Z 2025-03-04T21:08:25.3594294Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:08:25.3594410Z mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4; pred_h_4 = None 2025-03-04T21:08:25.3594536Z y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49; pred_ctr_y_4 = mul_49 = None 2025-03-04T21:08:25.3594606Z 2025-03-04T21:08:25.3594960Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:08:25.3595156Z pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1); x1_4 = y1_4 = x2_4 = y2_4 = None 2025-03-04T21:08:25.3595221Z 2025-03-04T21:08:25.3595565Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:08:25.3595745Z proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4)); pred_boxes_4 = None 2025-03-04T21:08:25.3595815Z 2025-03-04T21:08:25.3596204Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:08:25.3596480Z proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4); proposals_i_4 = None 2025-03-04T21:08:25.3596550Z 2025-03-04T21:08:25.3597084Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T21:08:25.3597228Z arange_10: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T21:08:25.3597301Z 2025-03-04T21:08:25.3597634Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:25.3597783Z batch_idx: "i64[4][1]cpu" = arange_10.to(device(type='cpu')); arange_10 = None 2025-03-04T21:08:25.3597845Z 2025-03-04T21:08:25.3598296Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:08:25.3598406Z topk = logits_i.topk(1000, dim = 1); logits_i = None 2025-03-04T21:08:25.3598518Z topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0] 2025-03-04T21:08:25.3598653Z topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1]; topk = None 2025-03-04T21:08:25.3598722Z 2025-03-04T21:08:25.3599192Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:08:25.3599328Z getitem_92: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:08:25.3599559Z topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_92, topk_idx)]; proposals_i_5 = getitem_92 = topk_idx = None 2025-03-04T21:08:25.3599629Z 2025-03-04T21:08:25.3600116Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:08:25.3600285Z full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:08:25.3600347Z 2025-03-04T21:08:25.3600646Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:25.3600767Z to_21: "i64[1000][1]cpu" = full.to(device(type='cpu')); full = None 2025-03-04T21:08:25.3600828Z 2025-03-04T21:08:25.3601261Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:08:25.3601373Z topk_1 = logits_i_1.topk(1000, dim = 1); logits_i_1 = None 2025-03-04T21:08:25.3601483Z topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0] 2025-03-04T21:08:25.3601593Z topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1]; topk_1 = None 2025-03-04T21:08:25.3601662Z 2025-03-04T21:08:25.3602240Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:08:25.3602418Z getitem_96: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:08:25.3602652Z topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_96, topk_idx_1)]; proposals_i_6 = getitem_96 = topk_idx_1 = None 2025-03-04T21:08:25.3602722Z 2025-03-04T21:08:25.3603180Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:08:25.3603345Z full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:08:25.3603407Z 2025-03-04T21:08:25.3603705Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:25.3603855Z to_22: "i64[1000][1]cpu" = full_1.to(device(type='cpu')); full_1 = None 2025-03-04T21:08:25.3603920Z 2025-03-04T21:08:25.3604350Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:08:25.3604468Z topk_2 = logits_i_2.topk(1000, dim = 1); logits_i_2 = None 2025-03-04T21:08:25.3604573Z topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0] 2025-03-04T21:08:25.3604692Z topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1]; topk_2 = None 2025-03-04T21:08:25.3604755Z 2025-03-04T21:08:25.3605227Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:08:25.3605377Z getitem_100: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:08:25.3605613Z topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_100, topk_idx_2)]; proposals_i_7 = getitem_100 = topk_idx_2 = None 2025-03-04T21:08:25.3605675Z 2025-03-04T21:08:25.3606156Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:08:25.3606314Z full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:08:25.3606380Z 2025-03-04T21:08:25.3606665Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:25.3606792Z to_23: "i64[1000][1]cpu" = full_2.to(device(type='cpu')); full_2 = None 2025-03-04T21:08:25.3606851Z 2025-03-04T21:08:25.3607281Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:08:25.3607392Z topk_3 = logits_i_3.topk(1000, dim = 1); logits_i_3 = None 2025-03-04T21:08:25.3607491Z topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0] 2025-03-04T21:08:25.3607608Z topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1]; topk_3 = None 2025-03-04T21:08:25.3607669Z 2025-03-04T21:08:25.3608120Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:08:25.3608264Z getitem_104: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:08:25.3608499Z topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_104, topk_idx_3)]; proposals_i_8 = getitem_104 = topk_idx_3 = None 2025-03-04T21:08:25.3608559Z 2025-03-04T21:08:25.3609003Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:08:25.3609159Z full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:08:25.3609227Z 2025-03-04T21:08:25.3609512Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:25.3609636Z to_24: "i64[1000][1]cpu" = full_3.to(device(type='cpu')); full_3 = None 2025-03-04T21:08:25.3609698Z 2025-03-04T21:08:25.3610135Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:08:25.3610241Z topk_4 = logits_i_4.topk(1000, dim = 1); logits_i_4 = None 2025-03-04T21:08:25.3610347Z topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0] 2025-03-04T21:08:25.3610455Z topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1]; topk_4 = None 2025-03-04T21:08:25.3610522Z 2025-03-04T21:08:25.3610969Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:08:25.3611150Z getitem_108: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T21:08:25.3611376Z topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_108, topk_idx_4)]; proposals_i_9 = getitem_108 = topk_idx_4 = None 2025-03-04T21:08:25.3611443Z 2025-03-04T21:08:25.3611880Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:08:25.3612040Z full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:08:25.3612114Z 2025-03-04T21:08:25.3612407Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:25.3612524Z to_25: "i64[1000][1]cpu" = full_4.to(device(type='cpu')); full_4 = None 2025-03-04T21:08:25.3612593Z 2025-03-04T21:08:25.3612865Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:08:25.3613242Z topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1); topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None 2025-03-04T21:08:25.3613308Z 2025-03-04T21:08:25.3613580Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:08:25.3614047Z topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1); topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None 2025-03-04T21:08:25.3614109Z 2025-03-04T21:08:25.3614407Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:08:25.3614605Z level_ids: "i64[5000][1]cpu" = torch.cat([to_21, to_22, to_23, to_24, to_25], 0); to_21 = to_22 = to_23 = to_24 = to_25 = level_ids = None 2025-03-04T21:08:25.3614671Z 2025-03-04T21:08:25.3615057Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T21:08:25.3615202Z getitem_110: "f32[5000, 4][4, 1]cpu" = topk_proposals[0]; topk_proposals = None 2025-03-04T21:08:25.3615262Z 2025-03-04T21:08:25.3615573Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:08:25.3615715Z tensor_5: "f32[5000, 4][4, 1]cpu" = getitem_110.to(torch.float32); getitem_110 = None 2025-03-04T21:08:25.3615782Z 2025-03-04T21:08:25.3616163Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T21:08:25.3616301Z scores_per_img: "f32[5000][1]cpu" = topk_scores[0]; topk_scores = None 2025-03-04T21:08:25.3616361Z 2025-03-04T21:08:25.3616842Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T21:08:25.3616974Z isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor_5); tensor_5 = None 2025-03-04T21:08:25.3617098Z all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:08:25.3617264Z isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T21:08:25.3617402Z valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T21:08:25.3617464Z 2025-03-04T21:08:25.3617841Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T21:08:25.3617955Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T21:08:25.3618025Z 2025-03-04T21:08:25.3618561Z 2025-03-04T21:08:25.3618660Z class GraphModule(torch.nn.Module): 2025-03-04T21:08:25.3684315Z def forward(self, L_stack0_tensor: "f32[4, 3, 1184, 1216][4319232, 1439744, 1216, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: 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3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_: "f32[2048, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_lateral_convs_0_parameters_weight_: "f32[256, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_0_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_0_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_0_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_1_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_1_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_1_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_2_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_2_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_2_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_3_parameters_weight_: "f32[256, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_3_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_3_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_3_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[3, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[3][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[12, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[12][1]cpu"): 2025-03-04T21:08:25.3748768Z l_stack0_tensor = L_stack0_tensor 2025-03-04T21:08:25.3749353Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ 2025-03-04T21:08:25.3750307Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.3751273Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.3752126Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:25.3752942Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:25.3753750Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:08:25.3754650Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.3755584Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.3756543Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:25.3757438Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:25.3758260Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:08:25.3759121Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.3760003Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.3760835Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:25.3761636Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:25.3762431Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:08:25.3763257Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.3764152Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.3765039Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:25.3765897Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:25.3766709Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:08:25.3767565Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.3768468Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.3769352Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:08:25.3770209Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:08:25.3771000Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:08:25.3771813Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.3772671Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.3773537Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:25.3774368Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:25.3775206Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:08:25.3776078Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.3776992Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.3777879Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:25.3778772Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:25.3779619Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:08:25.3780490Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.3781423Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.3782330Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:25.3783205Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:25.3784061Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:08:25.3784926Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.3785848Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.3786757Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:25.3787624Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:25.3788413Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:08:25.3789232Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.3790105Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.3790987Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:25.3791859Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:25.3792719Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:08:25.3793584Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.3794535Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.3795494Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:25.3796468Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:25.3797362Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:08:25.3798222Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.3799124Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.3799971Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:25.3800794Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:25.3801586Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:08:25.3802523Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.3803404Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.3804253Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:25.3805079Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:25.3805900Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:08:25.3806721Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.3807610Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.3808468Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:25.3809297Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:25.3810105Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:08:25.3810939Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.3811816Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.3812704Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:08:25.3813537Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:08:25.3814320Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:08:25.3815146Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.3816035Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.3816891Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:25.3817702Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:25.3818476Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:08:25.3819299Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.3820174Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.3821025Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:25.3821827Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:25.3822598Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:08:25.3823394Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.3824253Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.3825096Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:25.3825920Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:25.3826708Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:08:25.3827537Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.3828397Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.3829246Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:25.3830085Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:25.3830878Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:08:25.3831708Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.3832586Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.3833455Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:25.3834273Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:25.3835101Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:08:25.3835969Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.3836992Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.3837945Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:25.3838817Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:25.3839609Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ 2025-03-04T21:08:25.3840441Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.3841330Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.3842185Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:25.3843016Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:25.3843815Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ 2025-03-04T21:08:25.3844629Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.3845497Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.3846361Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:25.3847190Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:25.3847965Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ 2025-03-04T21:08:25.3848766Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.3849641Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.3850473Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:25.3851277Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:25.3852041Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:08:25.3852840Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.3853695Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.3854568Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:25.3855392Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:25.3856190Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:08:25.3857014Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.3857891Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.3858720Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:25.3859521Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:25.3860292Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:08:25.3861107Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.3861961Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.3862809Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:25.3863613Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:25.3864424Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:08:25.3865284Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.3866164Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.3867018Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:08:25.3867849Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:08:25.3868668Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:08:25.3869490Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.3870372Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.3871228Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:25.3872072Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:25.3872855Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:08:25.3873676Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.3874590Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.3875498Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:25.3876424Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:25.3877285Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:08:25.3878135Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.3879018Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.3879870Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:25.3880697Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:25.3881486Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:08:25.3882329Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.3883207Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.3884056Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:25.3884877Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:25.3885685Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:08:25.3886506Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.3887386Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.3888250Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:25.3889092Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:25.3889889Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:08:25.3890726Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.3891595Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.3892440Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:25.3893260Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:25.3894042Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ 2025-03-04T21:08:25.3894861Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.3895730Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.3896600Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:25.3897421Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:25.3898205Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ 2025-03-04T21:08:25.3899020Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.3899914Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.3900757Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:25.3901557Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:25.3902414Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ 2025-03-04T21:08:25.3903293Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.3904168Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.3905007Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:25.3905808Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:25.3906579Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ 2025-03-04T21:08:25.3907383Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.3908264Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.3909111Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:25.3909943Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:25.3910717Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ 2025-03-04T21:08:25.3911537Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.3912428Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.3913365Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:25.3914251Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:25.3915115Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ 2025-03-04T21:08:25.3915974Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.3916971Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.3917884Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:25.3918681Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:25.3919512Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ 2025-03-04T21:08:25.3920384Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.3921308Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.3922208Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:25.3923077Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:25.3923904Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ 2025-03-04T21:08:25.3924793Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.3925712Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.3926608Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:25.3927469Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:25.3928317Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ 2025-03-04T21:08:25.3929132Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.3930005Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.3930836Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:25.3931657Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:25.3932422Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:08:25.3933227Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.3934081Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.3934930Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:25.3935754Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:25.3936546Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:08:25.3937352Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.3938225Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.3939087Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:25.3939905Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:25.3940702Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:08:25.3941495Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.3942359Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.3943189Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:25.3944040Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:25.3944912Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:08:25.3945771Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.3946680Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.3947573Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:08:25.3948448Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:08:25.3949252Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:08:25.3950077Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.3950946Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.3951796Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:25.3952660Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:25.3953491Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:08:25.3954354Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.3955286Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.3956209Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:25.3957137Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:25.3957976Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:08:25.3958837Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.3959742Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.3960610Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:25.3961464Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:25.3962231Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:08:25.3963036Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.3963893Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.3964725Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:25.3965525Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:25.3966285Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:08:25.3967105Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.3967956Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.3968780Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:25.3969582Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:25.3970363Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:08:25.3971156Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.3972008Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.3972836Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:25.3973669Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:25.3974319Z l_self_modules_backbone_lateral_convs_0_parameters_weight_ = L_self_modules_backbone_lateral_convs_0_parameters_weight_ 2025-03-04T21:08:25.3974818Z l_self_modules_backbone_lateral_convs_0_parameters_bias_ = L_self_modules_backbone_lateral_convs_0_parameters_bias_ 2025-03-04T21:08:25.3975329Z l_self_modules_backbone_output_convs_0_parameters_weight_ = L_self_modules_backbone_output_convs_0_parameters_weight_ 2025-03-04T21:08:25.3975813Z l_self_modules_backbone_output_convs_0_parameters_bias_ = L_self_modules_backbone_output_convs_0_parameters_bias_ 2025-03-04T21:08:25.3976297Z l_self_modules_backbone_lateral_convs_1_parameters_weight_ = L_self_modules_backbone_lateral_convs_1_parameters_weight_ 2025-03-04T21:08:25.3976783Z l_self_modules_backbone_lateral_convs_1_parameters_bias_ = L_self_modules_backbone_lateral_convs_1_parameters_bias_ 2025-03-04T21:08:25.3977262Z l_self_modules_backbone_output_convs_1_parameters_weight_ = L_self_modules_backbone_output_convs_1_parameters_weight_ 2025-03-04T21:08:25.3977740Z l_self_modules_backbone_output_convs_1_parameters_bias_ = L_self_modules_backbone_output_convs_1_parameters_bias_ 2025-03-04T21:08:25.3978219Z l_self_modules_backbone_lateral_convs_2_parameters_weight_ = L_self_modules_backbone_lateral_convs_2_parameters_weight_ 2025-03-04T21:08:25.3978695Z l_self_modules_backbone_lateral_convs_2_parameters_bias_ = L_self_modules_backbone_lateral_convs_2_parameters_bias_ 2025-03-04T21:08:25.3979175Z l_self_modules_backbone_output_convs_2_parameters_weight_ = L_self_modules_backbone_output_convs_2_parameters_weight_ 2025-03-04T21:08:25.3979669Z l_self_modules_backbone_output_convs_2_parameters_bias_ = L_self_modules_backbone_output_convs_2_parameters_bias_ 2025-03-04T21:08:25.3980148Z l_self_modules_backbone_lateral_convs_3_parameters_weight_ = L_self_modules_backbone_lateral_convs_3_parameters_weight_ 2025-03-04T21:08:25.3980631Z l_self_modules_backbone_lateral_convs_3_parameters_bias_ = L_self_modules_backbone_lateral_convs_3_parameters_bias_ 2025-03-04T21:08:25.3981107Z l_self_modules_backbone_output_convs_3_parameters_weight_ = L_self_modules_backbone_output_convs_3_parameters_weight_ 2025-03-04T21:08:25.3981581Z l_self_modules_backbone_output_convs_3_parameters_bias_ = L_self_modules_backbone_output_convs_3_parameters_bias_ 2025-03-04T21:08:25.3982191Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T21:08:25.3982931Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ 2025-03-04T21:08:25.3983691Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ 2025-03-04T21:08:25.3984428Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ 2025-03-04T21:08:25.3985164Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ 2025-03-04T21:08:25.3985877Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T21:08:25.3986580Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T21:08:25.3987303Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T21:08:25.3988086Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T21:08:25.3988858Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T21:08:25.3989595Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T21:08:25.3990059Z 2025-03-04T21:08:25.3990429Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.3991286Z x: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1); l_stack0_tensor = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:25.3991926Z 2025-03-04T21:08:25.3992287Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.3994340Z x_1: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.3996217Z 2025-03-04T21:08:25.3996647Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x) 2025-03-04T21:08:25.3997132Z x_2: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.relu_(x_1); x_1 = None 2025-03-04T21:08:25.3997388Z 2025-03-04T21:08:25.3997867Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1) 2025-03-04T21:08:25.3998525Z x_3: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1); x_2 = None 2025-03-04T21:08:25.3998883Z 2025-03-04T21:08:25.3999230Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4000043Z x_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:25.4000662Z 2025-03-04T21:08:25.4001018Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4003298Z x_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4005218Z 2025-03-04T21:08:25.4005587Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4006069Z out: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_5); x_5 = None 2025-03-04T21:08:25.4006330Z 2025-03-04T21:08:25.4006673Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4007484Z x_6: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:25.4008133Z 2025-03-04T21:08:25.4008477Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4010635Z x_7: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_6 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4012536Z 2025-03-04T21:08:25.4012897Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4013364Z out_1: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_7); x_7 = None 2025-03-04T21:08:25.4013612Z 2025-03-04T21:08:25.4013939Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4014788Z x_8: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_1 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:25.4015393Z 2025-03-04T21:08:25.4015737Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4017846Z x_9: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4019738Z 2025-03-04T21:08:25.4020073Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4020878Z x_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); x_3 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:08:25.4021514Z 2025-03-04T21:08:25.4021868Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4024102Z x_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_10 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4026203Z 2025-03-04T21:08:25.4026567Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:25.4027044Z x_9 += x_11; out_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_9; x_9 = x_11 = None 2025-03-04T21:08:25.4027299Z 2025-03-04T21:08:25.4027672Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4028167Z out_3: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_2); out_2 = None 2025-03-04T21:08:25.4028446Z 2025-03-04T21:08:25.4028775Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4029568Z x_12: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:25.4030177Z 2025-03-04T21:08:25.4030542Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4032697Z x_13: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_12 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4034630Z 2025-03-04T21:08:25.4034998Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4035479Z out_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_13); x_13 = None 2025-03-04T21:08:25.4035750Z 2025-03-04T21:08:25.4036092Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4037004Z x_14: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:25.4037655Z 2025-03-04T21:08:25.4038007Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4040174Z x_15: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_14 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4042092Z 2025-03-04T21:08:25.4042466Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4042968Z out_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_15); x_15 = None 2025-03-04T21:08:25.4043226Z 2025-03-04T21:08:25.4043561Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4044392Z x_16: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_5 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:25.4044991Z 2025-03-04T21:08:25.4045340Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4047483Z x_17: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_16 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4049367Z 2025-03-04T21:08:25.4049723Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:25.4050221Z x_17 += out_3; out_6: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_17; x_17 = out_3 = None 2025-03-04T21:08:25.4050481Z 2025-03-04T21:08:25.4050834Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4051315Z out_7: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_6); out_6 = None 2025-03-04T21:08:25.4051575Z 2025-03-04T21:08:25.4051908Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4052702Z x_18: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:25.4053311Z 2025-03-04T21:08:25.4053667Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4055799Z x_19: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_18 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4057752Z 2025-03-04T21:08:25.4058101Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4058581Z out_8: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_19); x_19 = None 2025-03-04T21:08:25.4058830Z 2025-03-04T21:08:25.4059157Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4059971Z x_20: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:25.4060594Z 2025-03-04T21:08:25.4060928Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4063025Z x_21: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_20 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4064955Z 2025-03-04T21:08:25.4065321Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4065798Z out_9: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_21); x_21 = None 2025-03-04T21:08:25.4066049Z 2025-03-04T21:08:25.4066381Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4067202Z x_22: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_9 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:25.4067817Z 2025-03-04T21:08:25.4068160Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4070285Z x_23: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_22 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4072211Z 2025-03-04T21:08:25.4072601Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:25.4073102Z x_23 += out_7; out_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_23; x_23 = out_7 = None 2025-03-04T21:08:25.4073387Z 2025-03-04T21:08:25.4073771Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4074276Z out_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_10); out_10 = None 2025-03-04T21:08:25.4074563Z 2025-03-04T21:08:25.4074908Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4075773Z x_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:25.4076499Z 2025-03-04T21:08:25.4076890Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4079183Z x_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4081143Z 2025-03-04T21:08:25.4081533Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4082026Z out_12: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_25); x_25 = None 2025-03-04T21:08:25.4082284Z 2025-03-04T21:08:25.4082616Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4083425Z x_26: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_12 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:25.4084052Z 2025-03-04T21:08:25.4084400Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4086636Z x_27: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_26 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4088548Z 2025-03-04T21:08:25.4088926Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4089406Z out_13: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_27); x_27 = None 2025-03-04T21:08:25.4089664Z 2025-03-04T21:08:25.4089998Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4090806Z x_28: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_13 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:25.4091416Z 2025-03-04T21:08:25.4091785Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4093934Z x_29: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_28 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4095904Z 2025-03-04T21:08:25.4096289Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4097102Z x_30: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:08:25.4097718Z 2025-03-04T21:08:25.4098067Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4100296Z x_31: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_30 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4102464Z 2025-03-04T21:08:25.4102838Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:25.4103327Z x_29 += x_31; out_14: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_29; x_29 = x_31 = None 2025-03-04T21:08:25.4103590Z 2025-03-04T21:08:25.4103975Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4104460Z out_15: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_14); out_14 = None 2025-03-04T21:08:25.4104727Z 2025-03-04T21:08:25.4105057Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4105847Z x_32: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:25.4106473Z 2025-03-04T21:08:25.4106813Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4108897Z x_33: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_32 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4110761Z 2025-03-04T21:08:25.4111127Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4111618Z out_16: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_33); x_33 = None 2025-03-04T21:08:25.4111877Z 2025-03-04T21:08:25.4112210Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4113085Z x_34: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_16 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:25.4113735Z 2025-03-04T21:08:25.4114094Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4116401Z x_35: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_34 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4118380Z 2025-03-04T21:08:25.4118754Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4119241Z out_17: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_35); x_35 = None 2025-03-04T21:08:25.4119500Z 2025-03-04T21:08:25.4119832Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4120672Z x_36: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_17 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:25.4121285Z 2025-03-04T21:08:25.4121638Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4123822Z x_37: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_36 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4125747Z 2025-03-04T21:08:25.4126116Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:25.4126608Z x_37 += out_15; out_18: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_37; x_37 = out_15 = None 2025-03-04T21:08:25.4126889Z 2025-03-04T21:08:25.4127260Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4127754Z out_19: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_18); out_18 = None 2025-03-04T21:08:25.4128024Z 2025-03-04T21:08:25.4128359Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4129176Z x_38: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:25.4129785Z 2025-03-04T21:08:25.4130124Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4132203Z x_39: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_38 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4134086Z 2025-03-04T21:08:25.4134453Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4134933Z out_20: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_39); x_39 = None 2025-03-04T21:08:25.4135190Z 2025-03-04T21:08:25.4135532Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4136312Z x_40: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_20 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:25.4136912Z 2025-03-04T21:08:25.4137252Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4139321Z x_41: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_40 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4141173Z 2025-03-04T21:08:25.4141528Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4141994Z out_21: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_41); x_41 = None 2025-03-04T21:08:25.4142241Z 2025-03-04T21:08:25.4142576Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4143362Z x_42: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_21 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:25.4143964Z 2025-03-04T21:08:25.4144303Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4146404Z x_43: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_42 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4148285Z 2025-03-04T21:08:25.4148638Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:25.4149112Z x_43 += out_19; out_22: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_43; x_43 = out_19 = None 2025-03-04T21:08:25.4149379Z 2025-03-04T21:08:25.4149738Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4150225Z out_23: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_22); out_22 = None 2025-03-04T21:08:25.4150491Z 2025-03-04T21:08:25.4150824Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4151637Z x_44: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:25.4152243Z 2025-03-04T21:08:25.4152521Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4154449Z x_45: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_44 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4154543Z 2025-03-04T21:08:25.4154835Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4154979Z out_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_45); x_45 = None 2025-03-04T21:08:25.4155048Z 2025-03-04T21:08:25.4155298Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4155805Z x_46: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:25.4155866Z 2025-03-04T21:08:25.4156138Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4158002Z x_47: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_46 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4158099Z 2025-03-04T21:08:25.4158392Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4158532Z out_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_47); x_47 = None 2025-03-04T21:08:25.4158612Z 2025-03-04T21:08:25.4158863Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4159365Z x_48: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_25 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:25.4159435Z 2025-03-04T21:08:25.4159698Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4161541Z x_49: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_48 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4161617Z 2025-03-04T21:08:25.4161898Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:25.4162058Z x_49 += out_23; out_26: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_49; x_49 = out_23 = None 2025-03-04T21:08:25.4162119Z 2025-03-04T21:08:25.4162405Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4162552Z out_27: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_26); out_26 = None 2025-03-04T21:08:25.4162618Z 2025-03-04T21:08:25.4162867Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4163361Z x_50: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:25.4163440Z 2025-03-04T21:08:25.4163721Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4165557Z x_51: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_50 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4165622Z 2025-03-04T21:08:25.4165914Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4166049Z out_28: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_51); x_51 = None 2025-03-04T21:08:25.4166118Z 2025-03-04T21:08:25.4166368Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4166883Z x_52: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_28 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:25.4166945Z 2025-03-04T21:08:25.4167219Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4169044Z x_53: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_52 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4169112Z 2025-03-04T21:08:25.4169406Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4169548Z out_29: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_53); x_53 = None 2025-03-04T21:08:25.4169617Z 2025-03-04T21:08:25.4169864Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4170350Z x_54: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_29 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:25.4170425Z 2025-03-04T21:08:25.4170688Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4172469Z x_55: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_54 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4172532Z 2025-03-04T21:08:25.4172786Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4173265Z x_56: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:08:25.4173345Z 2025-03-04T21:08:25.4173618Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4175507Z x_57: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_56 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4175578Z 2025-03-04T21:08:25.4175859Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:25.4175994Z x_55 += x_57; out_30: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_55; x_55 = x_57 = None 2025-03-04T21:08:25.4176061Z 2025-03-04T21:08:25.4176343Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4176491Z out_31: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_30); out_30 = None 2025-03-04T21:08:25.4176572Z 2025-03-04T21:08:25.4176834Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4177297Z x_58: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:25.4177362Z 2025-03-04T21:08:25.4177618Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4179395Z x_59: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_58 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4179466Z 2025-03-04T21:08:25.4179742Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4179892Z out_32: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_59); x_59 = None 2025-03-04T21:08:25.4179955Z 2025-03-04T21:08:25.4180204Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4180677Z x_60: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_32 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:25.4180744Z 2025-03-04T21:08:25.4181025Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4182789Z x_61: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_60 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4182862Z 2025-03-04T21:08:25.4183140Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4183297Z out_33: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_61); x_61 = None 2025-03-04T21:08:25.4183358Z 2025-03-04T21:08:25.4183608Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4184082Z x_62: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_33 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:25.4184150Z 2025-03-04T21:08:25.4184408Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4186196Z x_63: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_62 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4186278Z 2025-03-04T21:08:25.4186549Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:25.4186700Z x_63 += out_31; out_34: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_63; x_63 = out_31 = None 2025-03-04T21:08:25.4186762Z 2025-03-04T21:08:25.4187046Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4187185Z out_35: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_34); out_34 = None 2025-03-04T21:08:25.4187255Z 2025-03-04T21:08:25.4187514Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4187985Z x_64: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:25.4188052Z 2025-03-04T21:08:25.4188313Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4190060Z x_65: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_64 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4190143Z 2025-03-04T21:08:25.4190421Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4190556Z out_36: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_65); x_65 = None 2025-03-04T21:08:25.4190616Z 2025-03-04T21:08:25.4190865Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4191363Z x_66: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_36 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:25.4191431Z 2025-03-04T21:08:25.4191695Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4193519Z x_67: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_66 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4193605Z 2025-03-04T21:08:25.4193891Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4194041Z out_37: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_67); x_67 = None 2025-03-04T21:08:25.4194103Z 2025-03-04T21:08:25.4194359Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4194851Z x_68: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_37 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:25.4194920Z 2025-03-04T21:08:25.4195184Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4197084Z x_69: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_68 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4197179Z 2025-03-04T21:08:25.4197461Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:25.4197614Z x_69 += out_35; out_38: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_69; x_69 = out_35 = None 2025-03-04T21:08:25.4197678Z 2025-03-04T21:08:25.4197968Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4198125Z out_39: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_38); out_38 = None 2025-03-04T21:08:25.4198197Z 2025-03-04T21:08:25.4198442Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4198930Z x_70: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:25.4198992Z 2025-03-04T21:08:25.4199266Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4201123Z x_71: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_70 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4201190Z 2025-03-04T21:08:25.4201482Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4201614Z out_40: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_71); x_71 = None 2025-03-04T21:08:25.4201683Z 2025-03-04T21:08:25.4202020Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4202525Z x_72: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_40 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:25.4202596Z 2025-03-04T21:08:25.4202865Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4204702Z x_73: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_72 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4204776Z 2025-03-04T21:08:25.4205092Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4205240Z out_41: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_73); x_73 = None 2025-03-04T21:08:25.4205301Z 2025-03-04T21:08:25.4205559Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4206048Z x_74: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_41 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:25.4206149Z 2025-03-04T21:08:25.4206414Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4208253Z x_75: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_74 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4208326Z 2025-03-04T21:08:25.4208607Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:25.4208757Z x_75 += out_39; out_42: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_75; x_75 = out_39 = None 2025-03-04T21:08:25.4208818Z 2025-03-04T21:08:25.4209107Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4209244Z out_43: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_42); out_42 = None 2025-03-04T21:08:25.4209312Z 2025-03-04T21:08:25.4209561Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4210061Z x_76: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:25.4210123Z 2025-03-04T21:08:25.4210395Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4212209Z x_77: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_76 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4212274Z 2025-03-04T21:08:25.4212567Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4212697Z out_44: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_77); x_77 = None 2025-03-04T21:08:25.4212780Z 2025-03-04T21:08:25.4213030Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4213527Z x_78: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_44 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:25.4213589Z 2025-03-04T21:08:25.4213858Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4215683Z x_79: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_78 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4215746Z 2025-03-04T21:08:25.4216027Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4216152Z out_45: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_79); x_79 = None 2025-03-04T21:08:25.4216235Z 2025-03-04T21:08:25.4216477Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4216957Z x_80: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_45 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:25.4217020Z 2025-03-04T21:08:25.4217276Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4219092Z x_81: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_80 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4219163Z 2025-03-04T21:08:25.4219443Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:25.4219603Z x_81 += out_43; out_46: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_81; x_81 = out_43 = None 2025-03-04T21:08:25.4219666Z 2025-03-04T21:08:25.4219956Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4220091Z out_47: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_46); out_46 = None 2025-03-04T21:08:25.4220156Z 2025-03-04T21:08:25.4220395Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4220884Z x_82: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:25.4220945Z 2025-03-04T21:08:25.4221223Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4223189Z x_83: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_82 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4223276Z 2025-03-04T21:08:25.4223565Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4223694Z out_48: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_83); x_83 = None 2025-03-04T21:08:25.4223762Z 2025-03-04T21:08:25.4224013Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4224533Z x_84: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_48 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:25.4224595Z 2025-03-04T21:08:25.4224872Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4226657Z x_85: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_84 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4226735Z 2025-03-04T21:08:25.4227037Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4227171Z out_49: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_85); x_85 = None 2025-03-04T21:08:25.4227242Z 2025-03-04T21:08:25.4227525Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4228028Z x_86: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_49 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:25.4228092Z 2025-03-04T21:08:25.4228364Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4230208Z x_87: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_86 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4230293Z 2025-03-04T21:08:25.4230594Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:25.4230743Z x_87 += out_47; out_50: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_87; x_87 = out_47 = None 2025-03-04T21:08:25.4230817Z 2025-03-04T21:08:25.4231112Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4231267Z out_51: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_50); out_50 = None 2025-03-04T21:08:25.4231334Z 2025-03-04T21:08:25.4231626Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4232138Z x_88: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:25.4232202Z 2025-03-04T21:08:25.4232489Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4234409Z x_89: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_88 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4234502Z 2025-03-04T21:08:25.4234807Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4234950Z out_52: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_89); x_89 = None 2025-03-04T21:08:25.4235020Z 2025-03-04T21:08:25.4235285Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4235804Z x_90: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_52 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:25.4235869Z 2025-03-04T21:08:25.4236155Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4238111Z x_91: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_90 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4238211Z 2025-03-04T21:08:25.4238519Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4238674Z out_53: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_91); x_91 = None 2025-03-04T21:08:25.4238746Z 2025-03-04T21:08:25.4239009Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4239538Z x_92: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_53 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:25.4239603Z 2025-03-04T21:08:25.4239893Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4241827Z x_93: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_92 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4241896Z 2025-03-04T21:08:25.4242167Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4242697Z x_94: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:08:25.4242768Z 2025-03-04T21:08:25.4243031Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4244975Z x_95: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_94 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4245094Z 2025-03-04T21:08:25.4245386Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:25.4245540Z x_93 += x_95; out_54: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_93; x_93 = x_95 = None 2025-03-04T21:08:25.4245606Z 2025-03-04T21:08:25.4245927Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4246075Z out_55: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_54); out_54 = None 2025-03-04T21:08:25.4246157Z 2025-03-04T21:08:25.4246408Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4246895Z x_96: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:25.4246981Z 2025-03-04T21:08:25.4247247Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4249073Z x_97: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_96 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4249149Z 2025-03-04T21:08:25.4249441Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4249585Z out_56: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_97); x_97 = None 2025-03-04T21:08:25.4249648Z 2025-03-04T21:08:25.4249948Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4250431Z x_98: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_56 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:25.4250518Z 2025-03-04T21:08:25.4250781Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4252594Z x_99: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_98 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4252666Z 2025-03-04T21:08:25.4252948Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4253081Z out_57: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_99); x_99 = None 2025-03-04T21:08:25.4253143Z 2025-03-04T21:08:25.4253400Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4253892Z x_100: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_57 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:25.4253977Z 2025-03-04T21:08:25.4254245Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4256080Z x_101: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_100 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4256151Z 2025-03-04T21:08:25.4256430Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:25.4256591Z x_101 += out_55; out_58: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_101; x_101 = out_55 = None 2025-03-04T21:08:25.4256651Z 2025-03-04T21:08:25.4256942Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4257080Z out_59: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_58); out_58 = None 2025-03-04T21:08:25.4257172Z 2025-03-04T21:08:25.4257422Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4257902Z x_102: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:25.4257963Z 2025-03-04T21:08:25.4258236Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4260053Z x_103: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_102 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4260118Z 2025-03-04T21:08:25.4260413Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4260565Z out_60: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_103); x_103 = None 2025-03-04T21:08:25.4260634Z 2025-03-04T21:08:25.4260887Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4261391Z x_104: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_60 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:25.4261461Z 2025-03-04T21:08:25.4261737Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4263556Z x_105: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_104 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4263626Z 2025-03-04T21:08:25.4263911Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4264070Z out_61: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_105); x_105 = None 2025-03-04T21:08:25.4264131Z 2025-03-04T21:08:25.4264392Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4264889Z x_106: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_61 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:25.4264959Z 2025-03-04T21:08:25.4265224Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4267058Z x_107: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_106 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4267145Z 2025-03-04T21:08:25.4267423Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:25.4267577Z x_107 += out_59; out_62: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_107; x_107 = out_59 = None 2025-03-04T21:08:25.4267637Z 2025-03-04T21:08:25.4267924Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4268077Z out_63: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_62); out_62 = None 2025-03-04T21:08:25.4268145Z 2025-03-04T21:08:25.4268392Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4268968Z x_108: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(out_63, l_self_modules_backbone_lateral_convs_0_parameters_weight_, l_self_modules_backbone_lateral_convs_0_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_63 = l_self_modules_backbone_lateral_convs_0_parameters_weight_ = l_self_modules_backbone_lateral_convs_0_parameters_bias_ = None 2025-03-04T21:08:25.4269029Z 2025-03-04T21:08:25.4269287Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4269829Z x_109: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_108, l_self_modules_backbone_output_convs_0_parameters_weight_, l_self_modules_backbone_output_convs_0_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_self_modules_backbone_output_convs_0_parameters_weight_ = l_self_modules_backbone_output_convs_0_parameters_bias_ = None 2025-03-04T21:08:25.4269898Z 2025-03-04T21:08:25.4270307Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest") 2025-03-04T21:08:25.4270600Z top_down_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.interpolate(x_108, scale_factor = 2.0, mode = 'nearest'); x_108 = None 2025-03-04T21:08:25.4270668Z 2025-03-04T21:08:25.4270914Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4271487Z x_110: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_lateral_convs_1_parameters_weight_, l_self_modules_backbone_lateral_convs_1_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_51 = l_self_modules_backbone_lateral_convs_1_parameters_weight_ = l_self_modules_backbone_lateral_convs_1_parameters_bias_ = None 2025-03-04T21:08:25.4271550Z 2025-03-04T21:08:25.4271904Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features 2025-03-04T21:08:25.4272110Z prev_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = x_110 + top_down_features; x_110 = top_down_features = None 2025-03-04T21:08:25.4272180Z 2025-03-04T21:08:25.4272431Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4273004Z x_111: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(prev_features, l_self_modules_backbone_output_convs_1_parameters_weight_, l_self_modules_backbone_output_convs_1_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_self_modules_backbone_output_convs_1_parameters_weight_ = l_self_modules_backbone_output_convs_1_parameters_bias_ = None 2025-03-04T21:08:25.4273068Z 2025-03-04T21:08:25.4273513Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest") 2025-03-04T21:08:25.4273856Z top_down_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.interpolate(prev_features, scale_factor = 2.0, mode = 'nearest'); prev_features = None 2025-03-04T21:08:25.4273928Z 2025-03-04T21:08:25.4274199Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4274828Z x_112: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_lateral_convs_2_parameters_weight_, l_self_modules_backbone_lateral_convs_2_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_27 = l_self_modules_backbone_lateral_convs_2_parameters_weight_ = l_self_modules_backbone_lateral_convs_2_parameters_bias_ = None 2025-03-04T21:08:25.4274901Z 2025-03-04T21:08:25.4275269Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features 2025-03-04T21:08:25.4275497Z prev_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = x_112 + top_down_features_1; x_112 = top_down_features_1 = None 2025-03-04T21:08:25.4275561Z 2025-03-04T21:08:25.4275832Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4276505Z x_113: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(prev_features_1, l_self_modules_backbone_output_convs_2_parameters_weight_, l_self_modules_backbone_output_convs_2_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_self_modules_backbone_output_convs_2_parameters_weight_ = l_self_modules_backbone_output_convs_2_parameters_bias_ = None 2025-03-04T21:08:25.4276583Z 2025-03-04T21:08:25.4277007Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest") 2025-03-04T21:08:25.4277388Z top_down_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.interpolate(prev_features_1, scale_factor = 2.0, mode = 'nearest'); prev_features_1 = None 2025-03-04T21:08:25.4277460Z 2025-03-04T21:08:25.4277717Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4278310Z x_114: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_lateral_convs_3_parameters_weight_, l_self_modules_backbone_lateral_convs_3_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_11 = l_self_modules_backbone_lateral_convs_3_parameters_weight_ = l_self_modules_backbone_lateral_convs_3_parameters_bias_ = None 2025-03-04T21:08:25.4278370Z 2025-03-04T21:08:25.4278737Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features 2025-03-04T21:08:25.4278944Z prev_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_114 + top_down_features_2; x_114 = top_down_features_2 = None 2025-03-04T21:08:25.4279012Z 2025-03-04T21:08:25.4279255Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4279866Z x_115: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(prev_features_2, l_self_modules_backbone_output_convs_3_parameters_weight_, l_self_modules_backbone_output_convs_3_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); prev_features_2 = l_self_modules_backbone_output_convs_3_parameters_weight_ = l_self_modules_backbone_output_convs_3_parameters_bias_ = None 2025-03-04T21:08:25.4279945Z 2025-03-04T21:08:25.4280307Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:200 in forward, code: return [F.max_pool2d(x, kernel_size=1, stride=2, padding=0)] 2025-03-04T21:08:25.4280513Z res: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.max_pool2d(x_109, kernel_size = 1, stride = 2, padding = 0) 2025-03-04T21:08:25.4280579Z 2025-03-04T21:08:25.4281029Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:08:25.4281185Z arange: "f32[304][1]cpu" = torch.arange(0.0, 1216, step = 4, dtype = torch.float32) 2025-03-04T21:08:25.4281245Z 2025-03-04T21:08:25.4281541Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:25.4281676Z shifts_x: "f32[304][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T21:08:25.4281741Z 2025-03-04T21:08:25.4282163Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:08:25.4282315Z arange_1: "f32[296][1]cpu" = torch.arange(0.0, 1184, step = 4, dtype = torch.float32) 2025-03-04T21:08:25.4282382Z 2025-03-04T21:08:25.4282666Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:25.4282807Z shifts_y: "f32[296][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T21:08:25.4282868Z 2025-03-04T21:08:25.4283241Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:08:25.4283436Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T21:08:25.4283538Z shift_y: "f32[296, 304][1, 0]cpu" = meshgrid[0] 2025-03-04T21:08:25.4283654Z shift_x: "f32[296, 304][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T21:08:25.4283720Z 2025-03-04T21:08:25.4284042Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:08:25.4284172Z shift_x_1: "f32[89984][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T21:08:25.4284232Z 2025-03-04T21:08:25.4284557Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:08:25.4284674Z shift_y_1: "f32[89984][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T21:08:25.4284740Z 2025-03-04T21:08:25.4285130Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:08:25.4285348Z shifts: "f32[89984, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T21:08:25.4285406Z 2025-03-04T21:08:25.4285821Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:08:25.4285943Z view: "f32[89984, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T21:08:25.4286378Z view_1: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T21:08:25.4286497Z add_3: "f32[89984, 3, 4][12, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T21:08:25.4286619Z x_116: "f32[269952, 4][4, 1]cpu" = add_3.reshape(-1, 4); add_3 = None 2025-03-04T21:08:25.4286679Z 2025-03-04T21:08:25.4287122Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:08:25.4287264Z arange_2: "f32[152][1]cpu" = torch.arange(0.0, 1216, step = 8, dtype = torch.float32) 2025-03-04T21:08:25.4287331Z 2025-03-04T21:08:25.4287622Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:25.4287766Z shifts_x_1: "f32[152][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T21:08:25.4287828Z 2025-03-04T21:08:25.4288255Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:08:25.4288401Z arange_3: "f32[148][1]cpu" = torch.arange(0.0, 1184, step = 8, dtype = torch.float32) 2025-03-04T21:08:25.4288461Z 2025-03-04T21:08:25.4288752Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:25.4288887Z shifts_y_1: "f32[148][1]cpu" = arange_3.to(device(type='cpu')); arange_3 = None 2025-03-04T21:08:25.4288956Z 2025-03-04T21:08:25.4289319Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:08:25.4289535Z meshgrid_1 = torch.functional.meshgrid(shifts_y_1, shifts_x_1); shifts_y_1 = shifts_x_1 = None 2025-03-04T21:08:25.4289632Z shift_y_2: "f32[148, 152][1, 0]cpu" = meshgrid_1[0] 2025-03-04T21:08:25.4289755Z shift_x_2: "f32[148, 152][0, 1]cpu" = meshgrid_1[1]; meshgrid_1 = None 2025-03-04T21:08:25.4289816Z 2025-03-04T21:08:25.4290138Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:08:25.4290260Z shift_x_3: "f32[22496][1]cpu" = shift_x_2.reshape(-1); shift_x_2 = None 2025-03-04T21:08:25.4290325Z 2025-03-04T21:08:25.4290639Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:08:25.4290769Z shift_y_3: "f32[22496][1]cpu" = shift_y_2.reshape(-1); shift_y_2 = None 2025-03-04T21:08:25.4290841Z 2025-03-04T21:08:25.4291221Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:08:25.4291429Z shifts_1: "f32[22496, 4][4, 1]cpu" = torch.stack((shift_x_3, shift_y_3, shift_x_3, shift_y_3), dim = 1); shift_x_3 = shift_y_3 = None 2025-03-04T21:08:25.4291497Z 2025-03-04T21:08:25.4291905Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:08:25.4292056Z view_2: "f32[22496, 1, 4][4, 4, 1]cpu" = shifts_1.view(-1, 1, 4); shifts_1 = None 2025-03-04T21:08:25.4292468Z view_3: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = None 2025-03-04T21:08:25.4292596Z add_4: "f32[22496, 3, 4][12, 4, 1]cpu" = view_2 + view_3; view_2 = view_3 = None 2025-03-04T21:08:25.4292705Z x_117: "f32[67488, 4][4, 1]cpu" = add_4.reshape(-1, 4); add_4 = None 2025-03-04T21:08:25.4292770Z 2025-03-04T21:08:25.4293206Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:08:25.4293358Z arange_4: "f32[76][1]cpu" = torch.arange(0.0, 1216, step = 16, dtype = torch.float32) 2025-03-04T21:08:25.4293419Z 2025-03-04T21:08:25.4293727Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:25.4293861Z shifts_x_2: "f32[76][1]cpu" = arange_4.to(device(type='cpu')); arange_4 = None 2025-03-04T21:08:25.4293929Z 2025-03-04T21:08:25.4294345Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:08:25.4294490Z arange_5: "f32[74][1]cpu" = torch.arange(0.0, 1184, step = 16, dtype = torch.float32) 2025-03-04T21:08:25.4294554Z 2025-03-04T21:08:25.4294841Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:25.4294974Z shifts_y_2: "f32[74][1]cpu" = arange_5.to(device(type='cpu')); arange_5 = None 2025-03-04T21:08:25.4295034Z 2025-03-04T21:08:25.4295424Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:08:25.4295609Z meshgrid_2 = torch.functional.meshgrid(shifts_y_2, shifts_x_2); shifts_y_2 = shifts_x_2 = None 2025-03-04T21:08:25.4295708Z shift_y_4: "f32[74, 76][1, 0]cpu" = meshgrid_2[0] 2025-03-04T21:08:25.4295821Z shift_x_4: "f32[74, 76][0, 1]cpu" = meshgrid_2[1]; meshgrid_2 = None 2025-03-04T21:08:25.4295886Z 2025-03-04T21:08:25.4296204Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:08:25.4296326Z shift_x_5: "f32[5624][1]cpu" = shift_x_4.reshape(-1); shift_x_4 = None 2025-03-04T21:08:25.4296388Z 2025-03-04T21:08:25.4296708Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:08:25.4296848Z shift_y_5: "f32[5624][1]cpu" = shift_y_4.reshape(-1); shift_y_4 = None 2025-03-04T21:08:25.4296914Z 2025-03-04T21:08:25.4297283Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:08:25.4297494Z shifts_2: "f32[5624, 4][4, 1]cpu" = torch.stack((shift_x_5, shift_y_5, shift_x_5, shift_y_5), dim = 1); shift_x_5 = shift_y_5 = None 2025-03-04T21:08:25.4297554Z 2025-03-04T21:08:25.4297963Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:08:25.4298101Z view_4: "f32[5624, 1, 4][4, 4, 1]cpu" = shifts_2.view(-1, 1, 4); shifts_2 = None 2025-03-04T21:08:25.4298520Z view_5: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = None 2025-03-04T21:08:25.4298636Z add_5: "f32[5624, 3, 4][12, 4, 1]cpu" = view_4 + view_5; view_4 = view_5 = None 2025-03-04T21:08:25.4298752Z x_118: "f32[16872, 4][4, 1]cpu" = add_5.reshape(-1, 4); add_5 = None 2025-03-04T21:08:25.4298812Z 2025-03-04T21:08:25.4299261Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:08:25.4299399Z arange_6: "f32[38][1]cpu" = torch.arange(0.0, 1216, step = 32, dtype = torch.float32) 2025-03-04T21:08:25.4299467Z 2025-03-04T21:08:25.4299755Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:25.4299895Z shifts_x_3: "f32[38][1]cpu" = arange_6.to(device(type='cpu')); arange_6 = None 2025-03-04T21:08:25.4299955Z 2025-03-04T21:08:25.4300384Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:08:25.4300532Z arange_7: "f32[37][1]cpu" = torch.arange(0.0, 1184, step = 32, dtype = torch.float32) 2025-03-04T21:08:25.4300593Z 2025-03-04T21:08:25.4300884Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:25.4301017Z shifts_y_3: "f32[37][1]cpu" = arange_7.to(device(type='cpu')); arange_7 = None 2025-03-04T21:08:25.4301100Z 2025-03-04T21:08:25.4301461Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:08:25.4301654Z meshgrid_3 = torch.functional.meshgrid(shifts_y_3, shifts_x_3); shifts_y_3 = shifts_x_3 = None 2025-03-04T21:08:25.4301748Z shift_y_6: "f32[37, 38][1, 0]cpu" = meshgrid_3[0] 2025-03-04T21:08:25.4301869Z shift_x_6: "f32[37, 38][0, 1]cpu" = meshgrid_3[1]; meshgrid_3 = None 2025-03-04T21:08:25.4302029Z 2025-03-04T21:08:25.4302374Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:08:25.4302496Z shift_x_7: "f32[1406][1]cpu" = shift_x_6.reshape(-1); shift_x_6 = None 2025-03-04T21:08:25.4302568Z 2025-03-04T21:08:25.4302909Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:08:25.4303070Z shift_y_7: "f32[1406][1]cpu" = shift_y_6.reshape(-1); shift_y_6 = None 2025-03-04T21:08:25.4303132Z 2025-03-04T21:08:25.4303510Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:08:25.4303714Z shifts_3: "f32[1406, 4][4, 1]cpu" = torch.stack((shift_x_7, shift_y_7, shift_x_7, shift_y_7), dim = 1); shift_x_7 = shift_y_7 = None 2025-03-04T21:08:25.4303779Z 2025-03-04T21:08:25.4304179Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:08:25.4304338Z view_6: "f32[1406, 1, 4][4, 4, 1]cpu" = shifts_3.view(-1, 1, 4); shifts_3 = None 2025-03-04T21:08:25.4304747Z view_7: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = None 2025-03-04T21:08:25.4304871Z add_6: "f32[1406, 3, 4][12, 4, 1]cpu" = view_6 + view_7; view_6 = view_7 = None 2025-03-04T21:08:25.4304977Z x_119: "f32[4218, 4][4, 1]cpu" = add_6.reshape(-1, 4); add_6 = None 2025-03-04T21:08:25.4305046Z 2025-03-04T21:08:25.4305487Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:08:25.4305636Z arange_8: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32) 2025-03-04T21:08:25.4305698Z 2025-03-04T21:08:25.4305992Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:25.4306121Z shifts_x_4: "f32[19][1]cpu" = arange_8.to(device(type='cpu')); arange_8 = None 2025-03-04T21:08:25.4306189Z 2025-03-04T21:08:25.4306603Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:08:25.4306746Z arange_9: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32) 2025-03-04T21:08:25.4306813Z 2025-03-04T21:08:25.4307098Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:25.4307232Z shifts_y_4: "f32[19][1]cpu" = arange_9.to(device(type='cpu')); arange_9 = None 2025-03-04T21:08:25.4307310Z 2025-03-04T21:08:25.4307677Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:08:25.4307863Z meshgrid_4 = torch.functional.meshgrid(shifts_y_4, shifts_x_4); shifts_y_4 = shifts_x_4 = None 2025-03-04T21:08:25.4307963Z shift_y_8: "f32[19, 19][1, 0]cpu" = meshgrid_4[0] 2025-03-04T21:08:25.4308076Z shift_x_8: "f32[19, 19][0, 1]cpu" = meshgrid_4[1]; meshgrid_4 = None 2025-03-04T21:08:25.4308144Z 2025-03-04T21:08:25.4308462Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:08:25.4308588Z shift_x_9: "f32[361][1]cpu" = shift_x_8.reshape(-1); shift_x_8 = None 2025-03-04T21:08:25.4308650Z 2025-03-04T21:08:25.4308993Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:08:25.4309112Z shift_y_9: "f32[361][1]cpu" = shift_y_8.reshape(-1); shift_y_8 = None 2025-03-04T21:08:25.4309180Z 2025-03-04T21:08:25.4309555Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:08:25.4309763Z shifts_4: "f32[361, 4][4, 1]cpu" = torch.stack((shift_x_9, shift_y_9, shift_x_9, shift_y_9), dim = 1); shift_x_9 = shift_y_9 = None 2025-03-04T21:08:25.4309821Z 2025-03-04T21:08:25.4310228Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:08:25.4310365Z view_8: "f32[361, 1, 4][4, 4, 1]cpu" = shifts_4.view(-1, 1, 4); shifts_4 = None 2025-03-04T21:08:25.4310779Z view_9: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = None 2025-03-04T21:08:25.4310894Z add_7: "f32[361, 3, 4][12, 4, 1]cpu" = view_8 + view_9; view_8 = view_9 = None 2025-03-04T21:08:25.4311009Z x_120: "f32[1083, 4][4, 1]cpu" = add_7.reshape(-1, 4); add_7 = None 2025-03-04T21:08:25.4311088Z 2025-03-04T21:08:25.4311392Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:08:25.4311520Z tensor: "f32[269952, 4][4, 1]cpu" = x_116.to(torch.float32); x_116 = None 2025-03-04T21:08:25.4311654Z tensor_1: "f32[67488, 4][4, 1]cpu" = x_117.to(torch.float32); x_117 = None 2025-03-04T21:08:25.4311777Z tensor_2: "f32[16872, 4][4, 1]cpu" = x_118.to(torch.float32); x_118 = None 2025-03-04T21:08:25.4311903Z tensor_3: "f32[4218, 4][4, 1]cpu" = x_119.to(torch.float32); x_119 = None 2025-03-04T21:08:25.4312015Z tensor_4: "f32[1083, 4][4, 1]cpu" = x_120.to(torch.float32); x_120 = None 2025-03-04T21:08:25.4312083Z 2025-03-04T21:08:25.4312335Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4312860Z x_121: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_115, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_115 = None 2025-03-04T21:08:25.4312930Z 2025-03-04T21:08:25.4313214Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:08:25.4313443Z x_122: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.relu(x_121, inplace = False); x_121 = None 2025-03-04T21:08:25.4313504Z 2025-03-04T21:08:25.4313908Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:08:25.4314447Z score: "f32[4, 3, 296, 304][269952, 89984, 304, 1]cpu" = torch.conv2d(x_122, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:08:25.4314516Z 2025-03-04T21:08:25.4314878Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:08:25.4315427Z x_131: "f32[4, 12, 296, 304][1079808, 89984, 304, 1]cpu" = torch.conv2d(x_122, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_122 = None 2025-03-04T21:08:25.4315490Z 2025-03-04T21:08:25.4315752Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4316260Z x_123: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(x_113, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_113 = None 2025-03-04T21:08:25.4316421Z 2025-03-04T21:08:25.4316720Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:08:25.4316935Z x_124: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.relu(x_123, inplace = False); x_123 = None 2025-03-04T21:08:25.4317008Z 2025-03-04T21:08:25.4317405Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:08:25.4317983Z score_1: "f32[4, 3, 148, 152][67488, 22496, 152, 1]cpu" = torch.conv2d(x_124, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:08:25.4318051Z 2025-03-04T21:08:25.4318438Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:08:25.4318958Z x_132: "f32[4, 12, 148, 152][269952, 22496, 152, 1]cpu" = torch.conv2d(x_124, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_124 = None 2025-03-04T21:08:25.4319028Z 2025-03-04T21:08:25.4319276Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4319754Z x_125: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(x_111, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_111 = None 2025-03-04T21:08:25.4319836Z 2025-03-04T21:08:25.4320120Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:08:25.4320303Z x_126: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.relu(x_125, inplace = False); x_125 = None 2025-03-04T21:08:25.4320374Z 2025-03-04T21:08:25.4320748Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:08:25.4321271Z score_2: "f32[4, 3, 74, 76][16872, 5624, 76, 1]cpu" = torch.conv2d(x_126, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:08:25.4321340Z 2025-03-04T21:08:25.4321689Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:08:25.4322219Z x_133: "f32[4, 12, 74, 76][67488, 5624, 76, 1]cpu" = torch.conv2d(x_126, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_126 = None 2025-03-04T21:08:25.4322280Z 2025-03-04T21:08:25.4322532Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4323000Z x_127: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_109, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_109 = None 2025-03-04T21:08:25.4323086Z 2025-03-04T21:08:25.4323370Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:08:25.4323556Z x_128: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.nn.functional.relu(x_127, inplace = False); x_127 = None 2025-03-04T21:08:25.4323619Z 2025-03-04T21:08:25.4324000Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:08:25.4324522Z score_3: "f32[4, 3, 37, 38][4218, 1406, 38, 1]cpu" = torch.conv2d(x_128, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:08:25.4324593Z 2025-03-04T21:08:25.4324958Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:08:25.4325462Z x_134: "f32[4, 12, 37, 38][16872, 1406, 38, 1]cpu" = torch.conv2d(x_128, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_128 = None 2025-03-04T21:08:25.4325531Z 2025-03-04T21:08:25.4325781Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4326588Z x_129: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.conv2d(res, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); res = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T21:08:25.4326670Z 2025-03-04T21:08:25.4326947Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:08:25.4327117Z x_130: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.relu(x_129, inplace = False); x_129 = None 2025-03-04T21:08:25.4327186Z 2025-03-04T21:08:25.4327555Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:08:25.4328450Z score_4: "f32[4, 3, 19, 19][1083, 361, 19, 1]cpu" = torch.conv2d(x_130, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T21:08:25.4328523Z 2025-03-04T21:08:25.4328885Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:08:25.4329712Z x_135: "f32[4, 12, 19, 19][4332, 361, 19, 1]cpu" = torch.conv2d(x_130, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_130 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T21:08:25.4329790Z 2025-03-04T21:08:25.4330141Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T21:08:25.4330306Z permute: "f32[4, 296, 304, 3][269952, 304, 1, 89984]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T21:08:25.4330454Z logits_i: "f32[4, 269952][269952, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T21:08:25.4350287Z permute_1: "f32[4, 148, 152, 3][67488, 152, 1, 22496]cpu" = score_1.permute(0, 2, 3, 1); score_1 = None 2025-03-04T21:08:25.4350575Z logits_i_1: "f32[4, 67488][67488, 1]cpu" = permute_1.flatten(1); permute_1 = None 2025-03-04T21:08:25.4350749Z permute_2: "f32[4, 74, 76, 3][16872, 76, 1, 5624]cpu" = score_2.permute(0, 2, 3, 1); score_2 = None 2025-03-04T21:08:25.4350896Z logits_i_2: "f32[4, 16872][16872, 1]cpu" = permute_2.flatten(1); permute_2 = None 2025-03-04T21:08:25.4351060Z permute_3: "f32[4, 37, 38, 3][4218, 38, 1, 1406]cpu" = score_3.permute(0, 2, 3, 1); score_3 = None 2025-03-04T21:08:25.4351197Z logits_i_3: "f32[4, 4218][4218, 1]cpu" = permute_3.flatten(1); permute_3 = None 2025-03-04T21:08:25.4351350Z permute_4: "f32[4, 19, 19, 3][1083, 19, 1, 361]cpu" = score_4.permute(0, 2, 3, 1); score_4 = None 2025-03-04T21:08:25.4351479Z logits_i_4: "f32[4, 1083][1083, 1]cpu" = permute_4.flatten(1); permute_4 = None 2025-03-04T21:08:25.4351553Z 2025-03-04T21:08:25.4352023Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T21:08:25.4352209Z view_10: "f32[4, 3, 4, 296, 304][1079808, 359936, 89984, 304, 1]cpu" = x_131.view(4, -1, 4, 296, 304); x_131 = None 2025-03-04T21:08:25.4352436Z permute_5: "f32[4, 296, 304, 3, 4][1079808, 304, 1, 359936, 89984]cpu" = view_10.permute(0, 3, 4, 1, 2); view_10 = None 2025-03-04T21:08:25.4352622Z pred_anchor_deltas_i: "f32[4, 269952, 4][1079808, 4, 1]cpu" = permute_5.flatten(1, -2); permute_5 = None 2025-03-04T21:08:25.4352796Z view_11: "f32[4, 3, 4, 148, 152][269952, 89984, 22496, 152, 1]cpu" = x_132.view(4, -1, 4, 148, 152); x_132 = None 2025-03-04T21:08:25.4352975Z permute_6: "f32[4, 148, 152, 3, 4][269952, 152, 1, 89984, 22496]cpu" = view_11.permute(0, 3, 4, 1, 2); view_11 = None 2025-03-04T21:08:25.4353169Z pred_anchor_deltas_i_2: "f32[4, 67488, 4][269952, 4, 1]cpu" = permute_6.flatten(1, -2); permute_6 = None 2025-03-04T21:08:25.4353318Z view_12: "f32[4, 3, 4, 74, 76][67488, 22496, 5624, 76, 1]cpu" = x_133.view(4, -1, 4, 74, 76); x_133 = None 2025-03-04T21:08:25.4353488Z permute_7: "f32[4, 74, 76, 3, 4][67488, 76, 1, 22496, 5624]cpu" = view_12.permute(0, 3, 4, 1, 2); view_12 = None 2025-03-04T21:08:25.4353658Z pred_anchor_deltas_i_4: "f32[4, 16872, 4][67488, 4, 1]cpu" = permute_7.flatten(1, -2); permute_7 = None 2025-03-04T21:08:25.4353837Z view_13: "f32[4, 3, 4, 37, 38][16872, 5624, 1406, 38, 1]cpu" = x_134.view(4, -1, 4, 37, 38); x_134 = None 2025-03-04T21:08:25.4354000Z permute_8: "f32[4, 37, 38, 3, 4][16872, 38, 1, 5624, 1406]cpu" = view_13.permute(0, 3, 4, 1, 2); view_13 = None 2025-03-04T21:08:25.4354178Z pred_anchor_deltas_i_6: "f32[4, 4218, 4][16872, 4, 1]cpu" = permute_8.flatten(1, -2); permute_8 = None 2025-03-04T21:08:25.4354322Z view_14: "f32[4, 3, 4, 19, 19][4332, 1444, 361, 19, 1]cpu" = x_135.view(4, -1, 4, 19, 19); x_135 = None 2025-03-04T21:08:25.4354491Z permute_9: "f32[4, 19, 19, 3, 4][4332, 19, 1, 1444, 361]cpu" = view_14.permute(0, 3, 4, 1, 2); view_14 = None 2025-03-04T21:08:25.4354658Z pred_anchor_deltas_i_8: "f32[4, 1083, 4][4332, 4, 1]cpu" = permute_9.flatten(1, -2); permute_9 = None 2025-03-04T21:08:25.4354763Z 2025-03-04T21:08:25.4355196Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:08:25.4355411Z pred_anchor_deltas_i_1: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T21:08:25.4355475Z 2025-03-04T21:08:25.4355949Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:08:25.4356110Z unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T21:08:25.4356269Z expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T21:08:25.4356507Z anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T21:08:25.4356583Z 2025-03-04T21:08:25.4356983Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:08:25.4357158Z deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T21:08:25.4357230Z 2025-03-04T21:08:25.4357557Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:08:25.4357711Z boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T21:08:25.4357777Z 2025-03-04T21:08:25.4358110Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:08:25.4358271Z getitem_10: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:08:25.4358411Z getitem_11: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:08:25.4358567Z widths: "f32[1079808][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T21:08:25.4358631Z 2025-03-04T21:08:25.4358969Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:08:25.4359097Z getitem_12: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:08:25.4359226Z getitem_13: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:08:25.4359381Z heights: "f32[1079808][1]cpu" = getitem_12 - getitem_13; getitem_12 = getitem_13 = None 2025-03-04T21:08:25.4359455Z 2025-03-04T21:08:25.4359775Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:08:25.4359933Z getitem_14: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:08:25.4360023Z mul: "f32[1079808][1]cpu" = 0.5 * widths 2025-03-04T21:08:25.4360161Z ctr_x: "f32[1079808][1]cpu" = getitem_14 + mul; getitem_14 = mul = None 2025-03-04T21:08:25.4360224Z 2025-03-04T21:08:25.4360550Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:08:25.4360701Z getitem_15: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:08:25.4360801Z mul_1: "f32[1079808][1]cpu" = 0.5 * heights 2025-03-04T21:08:25.4360933Z ctr_y: "f32[1079808][1]cpu" = getitem_15 + mul_1; getitem_15 = mul_1 = None 2025-03-04T21:08:25.4361022Z 2025-03-04T21:08:25.4361371Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:08:25.4361539Z getitem_16: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:08:25.4361655Z dx: "f32[1079808, 1][1, 1]cpu" = getitem_16 / 1.0; getitem_16 = None 2025-03-04T21:08:25.4361725Z 2025-03-04T21:08:25.4362039Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:08:25.4362246Z getitem_17: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:08:25.4362364Z dy: "f32[1079808, 1][1, 1]cpu" = getitem_17 / 1.0; getitem_17 = None 2025-03-04T21:08:25.4362427Z 2025-03-04T21:08:25.4362740Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:08:25.4362893Z getitem_18: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:08:25.4363010Z dw: "f32[1079808, 1][1, 1]cpu" = getitem_18 / 1.0; getitem_18 = None 2025-03-04T21:08:25.4363075Z 2025-03-04T21:08:25.4363396Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:08:25.4363593Z getitem_19: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:08:25.4363707Z dh: "f32[1079808, 1][1, 1]cpu" = getitem_19 / 1.0; getitem_19 = None 2025-03-04T21:08:25.4363766Z 2025-03-04T21:08:25.4364107Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:08:25.4364262Z dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:08:25.4364329Z 2025-03-04T21:08:25.4364652Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:08:25.4364788Z dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:08:25.4364849Z 2025-03-04T21:08:25.4365193Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:08:25.4365324Z getitem_20: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:08:25.4365452Z mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_20; dx = getitem_20 = None 2025-03-04T21:08:25.4365601Z getitem_21: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:08:25.4365757Z pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_21; mul_2 = getitem_21 = None 2025-03-04T21:08:25.4365816Z 2025-03-04T21:08:25.4366166Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:08:25.4366298Z getitem_22: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:08:25.4366424Z mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_22; dy = getitem_22 = None 2025-03-04T21:08:25.4366569Z getitem_23: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:08:25.4366703Z pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_23; mul_3 = getitem_23 = None 2025-03-04T21:08:25.4366778Z 2025-03-04T21:08:25.4367111Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:08:25.4367224Z exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:08:25.4367384Z getitem_24: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:08:25.4367510Z pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_24; exp = getitem_24 = None 2025-03-04T21:08:25.4367575Z 2025-03-04T21:08:25.4367923Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:08:25.4368034Z exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:08:25.4368199Z getitem_25: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:08:25.4368330Z pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_25; exp_1 = getitem_25 = None 2025-03-04T21:08:25.4368395Z 2025-03-04T21:08:25.4368702Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:08:25.4368801Z mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T21:08:25.4368914Z x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:08:25.4368980Z 2025-03-04T21:08:25.4369280Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:08:25.4369374Z mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T21:08:25.4369481Z y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:08:25.4369547Z 2025-03-04T21:08:25.4369843Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:08:25.4369973Z mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:08:25.4370095Z x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:08:25.4370161Z 2025-03-04T21:08:25.4370452Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:08:25.4370566Z mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:08:25.4370687Z y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:08:25.4370755Z 2025-03-04T21:08:25.4371089Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:08:25.4371296Z pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:08:25.4371359Z 2025-03-04T21:08:25.4371691Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:08:25.4371848Z proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4)); pred_boxes = None 2025-03-04T21:08:25.4371915Z 2025-03-04T21:08:25.4372289Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:08:25.4372465Z proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T21:08:25.4372541Z 2025-03-04T21:08:25.4372937Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:08:25.4373142Z pred_anchor_deltas_i_3: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_2.reshape(-1, 4); pred_anchor_deltas_i_2 = None 2025-03-04T21:08:25.4373213Z 2025-03-04T21:08:25.4373641Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:08:25.4373825Z unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = tensor_1.unsqueeze(0); tensor_1 = None 2025-03-04T21:08:25.4373970Z expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1); unsqueeze_1 = None 2025-03-04T21:08:25.4374114Z anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4); expand_1 = None 2025-03-04T21:08:25.4374176Z 2025-03-04T21:08:25.4374552Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:08:25.4374715Z deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float(); pred_anchor_deltas_i_3 = None 2025-03-04T21:08:25.4374781Z 2025-03-04T21:08:25.4375082Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:08:25.4375231Z boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32); anchors_i_1 = None 2025-03-04T21:08:25.4375298Z 2025-03-04T21:08:25.4375621Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:08:25.4375769Z getitem_26: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)] 2025-03-04T21:08:25.4375894Z getitem_27: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T21:08:25.4376045Z widths_1: "f32[269952][1]cpu" = getitem_26 - getitem_27; getitem_26 = getitem_27 = None 2025-03-04T21:08:25.4376106Z 2025-03-04T21:08:25.4376423Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:08:25.4376543Z getitem_28: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)] 2025-03-04T21:08:25.4376665Z getitem_29: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)] 2025-03-04T21:08:25.4376811Z heights_1: "f32[269952][1]cpu" = getitem_28 - getitem_29; getitem_28 = getitem_29 = None 2025-03-04T21:08:25.4376877Z 2025-03-04T21:08:25.4377179Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:08:25.4377317Z getitem_30: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T21:08:25.4377406Z mul_10: "f32[269952][1]cpu" = 0.5 * widths_1 2025-03-04T21:08:25.4377540Z ctr_x_1: "f32[269952][1]cpu" = getitem_30 + mul_10; getitem_30 = mul_10 = None 2025-03-04T21:08:25.4377600Z 2025-03-04T21:08:25.4377909Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:08:25.4378052Z getitem_31: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]; boxes_1 = None 2025-03-04T21:08:25.4378147Z mul_11: "f32[269952][1]cpu" = 0.5 * heights_1 2025-03-04T21:08:25.4378286Z ctr_y_1: "f32[269952][1]cpu" = getitem_31 + mul_11; getitem_31 = mul_11 = None 2025-03-04T21:08:25.4378354Z 2025-03-04T21:08:25.4378653Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:08:25.4378806Z getitem_32: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:08:25.4378919Z dx_1: "f32[269952, 1][1, 1]cpu" = getitem_32 / 1.0; getitem_32 = None 2025-03-04T21:08:25.4378986Z 2025-03-04T21:08:25.4379277Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:08:25.4379443Z getitem_33: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:08:25.4379551Z dy_1: "f32[269952, 1][1, 1]cpu" = getitem_33 / 1.0; getitem_33 = None 2025-03-04T21:08:25.4379618Z 2025-03-04T21:08:25.4379905Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:08:25.4380057Z getitem_34: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:08:25.4380167Z dw_2: "f32[269952, 1][1, 1]cpu" = getitem_34 / 1.0; getitem_34 = None 2025-03-04T21:08:25.4380226Z 2025-03-04T21:08:25.4380526Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:08:25.4380704Z getitem_35: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))]; deltas_1 = None 2025-03-04T21:08:25.4380814Z dh_2: "f32[269952, 1][1, 1]cpu" = getitem_35 / 1.0; getitem_35 = None 2025-03-04T21:08:25.4380872Z 2025-03-04T21:08:25.4381202Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:08:25.4381365Z dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356); dw_2 = None 2025-03-04T21:08:25.4381431Z 2025-03-04T21:08:25.4381755Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:08:25.4381893Z dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356); dh_2 = None 2025-03-04T21:08:25.4381951Z 2025-03-04T21:08:25.4382294Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:08:25.4382425Z getitem_36: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)] 2025-03-04T21:08:25.4382553Z mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_36; dx_1 = getitem_36 = None 2025-03-04T21:08:25.4382704Z getitem_37: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)]; ctr_x_1 = None 2025-03-04T21:08:25.4382860Z pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_37; mul_12 = getitem_37 = None 2025-03-04T21:08:25.4382921Z 2025-03-04T21:08:25.4383264Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:08:25.4383395Z getitem_38: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)] 2025-03-04T21:08:25.4383519Z mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_38; dy_1 = getitem_38 = None 2025-03-04T21:08:25.4383665Z getitem_39: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)]; ctr_y_1 = None 2025-03-04T21:08:25.4383822Z pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_39; mul_13 = getitem_39 = None 2025-03-04T21:08:25.4383881Z 2025-03-04T21:08:25.4384212Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:08:25.4384322Z exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3); dw_3 = None 2025-03-04T21:08:25.4384484Z getitem_40: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]; widths_1 = None 2025-03-04T21:08:25.4384612Z pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_40; exp_2 = getitem_40 = None 2025-03-04T21:08:25.4384678Z 2025-03-04T21:08:25.4385010Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:08:25.4385125Z exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3); dh_3 = None 2025-03-04T21:08:25.4385287Z getitem_41: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]; heights_1 = None 2025-03-04T21:08:25.4385422Z pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_41; exp_3 = getitem_41 = None 2025-03-04T21:08:25.4385482Z 2025-03-04T21:08:25.4385789Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:08:25.4385880Z mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1 2025-03-04T21:08:25.4385997Z x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16; mul_16 = None 2025-03-04T21:08:25.4386056Z 2025-03-04T21:08:25.4386359Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:08:25.4386445Z mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1 2025-03-04T21:08:25.4386559Z y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17; mul_17 = None 2025-03-04T21:08:25.4386633Z 2025-03-04T21:08:25.4386936Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:08:25.4387049Z mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1; pred_w_1 = None 2025-03-04T21:08:25.4387184Z x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18; pred_ctr_x_1 = mul_18 = None 2025-03-04T21:08:25.4387243Z 2025-03-04T21:08:25.4387543Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:08:25.4387657Z mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1; pred_h_1 = None 2025-03-04T21:08:25.4387783Z y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19; pred_ctr_y_1 = mul_19 = None 2025-03-04T21:08:25.4387849Z 2025-03-04T21:08:25.4388184Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:08:25.4388394Z pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1); x1_1 = y1_1 = x2_1 = y2_1 = None 2025-03-04T21:08:25.4388453Z 2025-03-04T21:08:25.4388778Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:08:25.4388935Z proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4)); pred_boxes_1 = None 2025-03-04T21:08:25.4389001Z 2025-03-04T21:08:25.4389374Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:08:25.4389562Z proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4); proposals_i_1 = None 2025-03-04T21:08:25.4389621Z 2025-03-04T21:08:25.4390017Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:08:25.4390219Z pred_anchor_deltas_i_5: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_4.reshape(-1, 4); pred_anchor_deltas_i_4 = None 2025-03-04T21:08:25.4390287Z 2025-03-04T21:08:25.4390724Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:08:25.4390876Z unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = tensor_2.unsqueeze(0); tensor_2 = None 2025-03-04T21:08:25.4391021Z expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1); unsqueeze_2 = None 2025-03-04T21:08:25.4391160Z anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4); expand_2 = None 2025-03-04T21:08:25.4391221Z 2025-03-04T21:08:25.4391594Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:08:25.4391755Z deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_5.float(); pred_anchor_deltas_i_5 = None 2025-03-04T21:08:25.4391823Z 2025-03-04T21:08:25.4392140Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:08:25.4392290Z boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32); anchors_i_2 = None 2025-03-04T21:08:25.4392350Z 2025-03-04T21:08:25.4392676Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:08:25.4392833Z getitem_42: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)] 2025-03-04T21:08:25.4392962Z getitem_43: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T21:08:25.4393108Z widths_2: "f32[67488][1]cpu" = getitem_42 - getitem_43; getitem_42 = getitem_43 = None 2025-03-04T21:08:25.4393175Z 2025-03-04T21:08:25.4393547Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:08:25.4393680Z getitem_44: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)] 2025-03-04T21:08:25.4393800Z getitem_45: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)] 2025-03-04T21:08:25.4393958Z heights_2: "f32[67488][1]cpu" = getitem_44 - getitem_45; getitem_44 = getitem_45 = None 2025-03-04T21:08:25.4394024Z 2025-03-04T21:08:25.4394380Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:08:25.4394512Z getitem_46: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T21:08:25.4394603Z mul_20: "f32[67488][1]cpu" = 0.5 * widths_2 2025-03-04T21:08:25.4394744Z ctr_x_2: "f32[67488][1]cpu" = getitem_46 + mul_20; getitem_46 = mul_20 = None 2025-03-04T21:08:25.4394810Z 2025-03-04T21:08:25.4395148Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:08:25.4395300Z getitem_47: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]; boxes_2 = None 2025-03-04T21:08:25.4395417Z mul_21: "f32[67488][1]cpu" = 0.5 * heights_2 2025-03-04T21:08:25.4395561Z ctr_y_2: "f32[67488][1]cpu" = getitem_47 + mul_21; getitem_47 = mul_21 = None 2025-03-04T21:08:25.4395632Z 2025-03-04T21:08:25.4395944Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:08:25.4396114Z getitem_48: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:08:25.4396224Z dx_2: "f32[67488, 1][1, 1]cpu" = getitem_48 / 1.0; getitem_48 = None 2025-03-04T21:08:25.4396372Z 2025-03-04T21:08:25.4396704Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:08:25.4396863Z getitem_49: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:08:25.4396973Z dy_2: "f32[67488, 1][1, 1]cpu" = getitem_49 / 1.0; getitem_49 = None 2025-03-04T21:08:25.4397044Z 2025-03-04T21:08:25.4397346Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:08:25.4397542Z getitem_50: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:08:25.4397650Z dw_4: "f32[67488, 1][1, 1]cpu" = getitem_50 / 1.0; getitem_50 = None 2025-03-04T21:08:25.4397719Z 2025-03-04T21:08:25.4398028Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:08:25.4398222Z getitem_51: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))]; deltas_2 = None 2025-03-04T21:08:25.4398328Z dh_4: "f32[67488, 1][1, 1]cpu" = getitem_51 / 1.0; getitem_51 = None 2025-03-04T21:08:25.4398399Z 2025-03-04T21:08:25.4398733Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:08:25.4398899Z dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356); dw_4 = None 2025-03-04T21:08:25.4398958Z 2025-03-04T21:08:25.4399302Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:08:25.4399433Z dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356); dh_4 = None 2025-03-04T21:08:25.4399500Z 2025-03-04T21:08:25.4399850Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:08:25.4399989Z getitem_52: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)] 2025-03-04T21:08:25.4400112Z mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_52; dx_2 = getitem_52 = None 2025-03-04T21:08:25.4400288Z getitem_53: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)]; ctr_x_2 = None 2025-03-04T21:08:25.4400431Z pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_53; mul_22 = getitem_53 = None 2025-03-04T21:08:25.4400491Z 2025-03-04T21:08:25.4400845Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:08:25.4400978Z getitem_54: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)] 2025-03-04T21:08:25.4401103Z mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_54; dy_2 = getitem_54 = None 2025-03-04T21:08:25.4401249Z getitem_55: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)]; ctr_y_2 = None 2025-03-04T21:08:25.4401404Z pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_55; mul_23 = getitem_55 = None 2025-03-04T21:08:25.4401465Z 2025-03-04T21:08:25.4401802Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:08:25.4402115Z exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5); dw_5 = None 2025-03-04T21:08:25.4402286Z getitem_56: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]; widths_2 = None 2025-03-04T21:08:25.4402470Z pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_56; exp_4 = getitem_56 = None 2025-03-04T21:08:25.4402541Z 2025-03-04T21:08:25.4402873Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:08:25.4402991Z exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5); dh_5 = None 2025-03-04T21:08:25.4403155Z getitem_57: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]; heights_2 = None 2025-03-04T21:08:25.4403292Z pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_57; exp_5 = getitem_57 = None 2025-03-04T21:08:25.4403353Z 2025-03-04T21:08:25.4403668Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:08:25.4403762Z mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2 2025-03-04T21:08:25.4403881Z x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26; mul_26 = None 2025-03-04T21:08:25.4403942Z 2025-03-04T21:08:25.4404253Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:08:25.4404347Z mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2 2025-03-04T21:08:25.4404490Z y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27; mul_27 = None 2025-03-04T21:08:25.4404553Z 2025-03-04T21:08:25.4404864Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:08:25.4404978Z mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2; pred_w_2 = None 2025-03-04T21:08:25.4405113Z x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28; pred_ctr_x_2 = mul_28 = None 2025-03-04T21:08:25.4405174Z 2025-03-04T21:08:25.4405486Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:08:25.4405597Z mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2; pred_h_2 = None 2025-03-04T21:08:25.4405731Z y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29; pred_ctr_y_2 = mul_29 = None 2025-03-04T21:08:25.4405793Z 2025-03-04T21:08:25.4406167Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:08:25.4406354Z pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1); x1_2 = y1_2 = x2_2 = y2_2 = None 2025-03-04T21:08:25.4406423Z 2025-03-04T21:08:25.4406758Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:08:25.4406925Z proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4)); pred_boxes_2 = None 2025-03-04T21:08:25.4406985Z 2025-03-04T21:08:25.4407375Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:08:25.4407566Z proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4); proposals_i_2 = None 2025-03-04T21:08:25.4407636Z 2025-03-04T21:08:25.4408037Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:08:25.4408247Z pred_anchor_deltas_i_7: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_6.reshape(-1, 4); pred_anchor_deltas_i_6 = None 2025-03-04T21:08:25.4408314Z 2025-03-04T21:08:25.4408767Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:08:25.4408921Z unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = tensor_3.unsqueeze(0); tensor_3 = None 2025-03-04T21:08:25.4409065Z expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1); unsqueeze_3 = None 2025-03-04T21:08:25.4409204Z anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4); expand_3 = None 2025-03-04T21:08:25.4409266Z 2025-03-04T21:08:25.4409639Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:08:25.4409802Z deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_7.float(); pred_anchor_deltas_i_7 = None 2025-03-04T21:08:25.4409869Z 2025-03-04T21:08:25.4410177Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:08:25.4410320Z boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32); anchors_i_3 = None 2025-03-04T21:08:25.4410381Z 2025-03-04T21:08:25.4410719Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:08:25.4410846Z getitem_58: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)] 2025-03-04T21:08:25.4410972Z getitem_59: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T21:08:25.4411113Z widths_3: "f32[16872][1]cpu" = getitem_58 - getitem_59; getitem_58 = getitem_59 = None 2025-03-04T21:08:25.4411182Z 2025-03-04T21:08:25.4411499Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:08:25.4411625Z getitem_60: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)] 2025-03-04T21:08:25.4411743Z getitem_61: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)] 2025-03-04T21:08:25.4411896Z heights_3: "f32[16872][1]cpu" = getitem_60 - getitem_61; getitem_60 = getitem_61 = None 2025-03-04T21:08:25.4411959Z 2025-03-04T21:08:25.4412289Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:08:25.4412406Z getitem_62: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T21:08:25.4412495Z mul_30: "f32[16872][1]cpu" = 0.5 * widths_3 2025-03-04T21:08:25.4412618Z ctr_x_3: "f32[16872][1]cpu" = getitem_62 + mul_30; getitem_62 = mul_30 = None 2025-03-04T21:08:25.4412684Z 2025-03-04T21:08:25.4412990Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:08:25.4413136Z getitem_63: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]; boxes_3 = None 2025-03-04T21:08:25.4413238Z mul_31: "f32[16872][1]cpu" = 0.5 * heights_3 2025-03-04T21:08:25.4413373Z ctr_y_3: "f32[16872][1]cpu" = getitem_63 + mul_31; getitem_63 = mul_31 = None 2025-03-04T21:08:25.4413437Z 2025-03-04T21:08:25.4413741Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:08:25.4413890Z getitem_64: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:08:25.4414006Z dx_3: "f32[16872, 1][1, 1]cpu" = getitem_64 / 1.0; getitem_64 = None 2025-03-04T21:08:25.4414068Z 2025-03-04T21:08:25.4414389Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:08:25.4414532Z getitem_65: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:08:25.4414643Z dy_3: "f32[16872, 1][1, 1]cpu" = getitem_65 / 1.0; getitem_65 = None 2025-03-04T21:08:25.4414701Z 2025-03-04T21:08:25.4414993Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:08:25.4415142Z getitem_66: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:08:25.4415245Z dw_6: "f32[16872, 1][1, 1]cpu" = getitem_66 / 1.0; getitem_66 = None 2025-03-04T21:08:25.4415309Z 2025-03-04T21:08:25.4415603Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:08:25.4415784Z getitem_67: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))]; deltas_3 = None 2025-03-04T21:08:25.4415888Z dh_6: "f32[16872, 1][1, 1]cpu" = getitem_67 / 1.0; getitem_67 = None 2025-03-04T21:08:25.4415970Z 2025-03-04T21:08:25.4416294Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:08:25.4416434Z dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356); dw_6 = None 2025-03-04T21:08:25.4416493Z 2025-03-04T21:08:25.4416817Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:08:25.4416947Z dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356); dh_6 = None 2025-03-04T21:08:25.4417014Z 2025-03-04T21:08:25.4417345Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:08:25.4417485Z getitem_68: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)] 2025-03-04T21:08:25.4417603Z mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_68; dx_3 = getitem_68 = None 2025-03-04T21:08:25.4417774Z getitem_69: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)]; ctr_x_3 = None 2025-03-04T21:08:25.4417909Z pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_69; mul_32 = getitem_69 = None 2025-03-04T21:08:25.4417976Z 2025-03-04T21:08:25.4418320Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:08:25.4418457Z getitem_70: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)] 2025-03-04T21:08:25.4418573Z mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_70; dy_3 = getitem_70 = None 2025-03-04T21:08:25.4418755Z getitem_71: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)]; ctr_y_3 = None 2025-03-04T21:08:25.4418892Z pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_71; mul_33 = getitem_71 = None 2025-03-04T21:08:25.4418959Z 2025-03-04T21:08:25.4419278Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:08:25.4419392Z exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7); dw_7 = None 2025-03-04T21:08:25.4419545Z getitem_72: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]; widths_3 = None 2025-03-04T21:08:25.4419692Z pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_72; exp_6 = getitem_72 = None 2025-03-04T21:08:25.4419754Z 2025-03-04T21:08:25.4420084Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:08:25.4420193Z exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7); dh_7 = None 2025-03-04T21:08:25.4420360Z getitem_73: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]; heights_3 = None 2025-03-04T21:08:25.4420485Z pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_73; exp_7 = getitem_73 = None 2025-03-04T21:08:25.4420551Z 2025-03-04T21:08:25.4420850Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:08:25.4420949Z mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3 2025-03-04T21:08:25.4421064Z x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36; mul_36 = None 2025-03-04T21:08:25.4421123Z 2025-03-04T21:08:25.4421430Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:08:25.4421534Z mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3 2025-03-04T21:08:25.4421647Z y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37; mul_37 = None 2025-03-04T21:08:25.4421708Z 2025-03-04T21:08:25.4422012Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:08:25.4422121Z mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3; pred_w_3 = None 2025-03-04T21:08:25.4422251Z x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38; pred_ctr_x_3 = mul_38 = None 2025-03-04T21:08:25.4422311Z 2025-03-04T21:08:25.4422613Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:08:25.4422720Z mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3; pred_h_3 = None 2025-03-04T21:08:25.4422855Z y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39; pred_ctr_y_3 = mul_39 = None 2025-03-04T21:08:25.4422916Z 2025-03-04T21:08:25.4423268Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:08:25.4423450Z pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1); x1_3 = y1_3 = x2_3 = y2_3 = None 2025-03-04T21:08:25.4423517Z 2025-03-04T21:08:25.4423840Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:08:25.4424001Z proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4)); pred_boxes_3 = None 2025-03-04T21:08:25.4424060Z 2025-03-04T21:08:25.4424455Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:08:25.4424620Z proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4); proposals_i_3 = None 2025-03-04T21:08:25.4424684Z 2025-03-04T21:08:25.4425069Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:08:25.4425272Z pred_anchor_deltas_i_9: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_8.reshape(-1, 4); pred_anchor_deltas_i_8 = None 2025-03-04T21:08:25.4425346Z 2025-03-04T21:08:25.4425775Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:08:25.4425918Z unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = tensor_4.unsqueeze(0); tensor_4 = None 2025-03-04T21:08:25.4426069Z expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1); unsqueeze_4 = None 2025-03-04T21:08:25.4426196Z anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4); expand_4 = None 2025-03-04T21:08:25.4426264Z 2025-03-04T21:08:25.4426624Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:08:25.4426791Z deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_9.float(); pred_anchor_deltas_i_9 = None 2025-03-04T21:08:25.4426850Z 2025-03-04T21:08:25.4427158Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:08:25.4427296Z boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32); anchors_i_4 = None 2025-03-04T21:08:25.4427378Z 2025-03-04T21:08:25.4427685Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:08:25.4427815Z getitem_74: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)] 2025-03-04T21:08:25.4427932Z getitem_75: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T21:08:25.4428080Z widths_4: "f32[4332][1]cpu" = getitem_74 - getitem_75; getitem_74 = getitem_75 = None 2025-03-04T21:08:25.4428140Z 2025-03-04T21:08:25.4428458Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:08:25.4428579Z getitem_76: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)] 2025-03-04T21:08:25.4428693Z getitem_77: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)] 2025-03-04T21:08:25.4428838Z heights_4: "f32[4332][1]cpu" = getitem_76 - getitem_77; getitem_76 = getitem_77 = None 2025-03-04T21:08:25.4428911Z 2025-03-04T21:08:25.4429219Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:08:25.4429331Z getitem_78: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T21:08:25.4429419Z mul_40: "f32[4332][1]cpu" = 0.5 * widths_4 2025-03-04T21:08:25.4429543Z ctr_x_4: "f32[4332][1]cpu" = getitem_78 + mul_40; getitem_78 = mul_40 = None 2025-03-04T21:08:25.4429610Z 2025-03-04T21:08:25.4429914Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:08:25.4430074Z getitem_79: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]; boxes_4 = None 2025-03-04T21:08:25.4430159Z mul_41: "f32[4332][1]cpu" = 0.5 * heights_4 2025-03-04T21:08:25.4430286Z ctr_y_4: "f32[4332][1]cpu" = getitem_79 + mul_41; getitem_79 = mul_41 = None 2025-03-04T21:08:25.4430344Z 2025-03-04T21:08:25.4430641Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:08:25.4430787Z getitem_80: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:08:25.4430911Z dx_4: "f32[4332, 1][1, 1]cpu" = getitem_80 / 1.0; getitem_80 = None 2025-03-04T21:08:25.4430971Z 2025-03-04T21:08:25.4431271Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:08:25.4431415Z getitem_81: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:08:25.4431528Z dy_4: "f32[4332, 1][1, 1]cpu" = getitem_81 / 1.0; getitem_81 = None 2025-03-04T21:08:25.4431589Z 2025-03-04T21:08:25.4431891Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:08:25.4432035Z getitem_82: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:08:25.4432146Z dw_8: "f32[4332, 1][1, 1]cpu" = getitem_82 / 1.0; getitem_82 = None 2025-03-04T21:08:25.4432207Z 2025-03-04T21:08:25.4432513Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:08:25.4432687Z getitem_83: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))]; deltas_4 = None 2025-03-04T21:08:25.4432798Z dh_8: "f32[4332, 1][1, 1]cpu" = getitem_83 / 1.0; getitem_83 = None 2025-03-04T21:08:25.4432875Z 2025-03-04T21:08:25.4433220Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:08:25.4433355Z dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356); dw_8 = None 2025-03-04T21:08:25.4433423Z 2025-03-04T21:08:25.4433750Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:08:25.4433888Z dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356); dh_8 = None 2025-03-04T21:08:25.4433948Z 2025-03-04T21:08:25.4434300Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:08:25.4434443Z getitem_84: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)] 2025-03-04T21:08:25.4434579Z mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_84; dx_4 = getitem_84 = None 2025-03-04T21:08:25.4434736Z getitem_85: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)]; ctr_x_4 = None 2025-03-04T21:08:25.4434872Z pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_85; mul_42 = getitem_85 = None 2025-03-04T21:08:25.4434939Z 2025-03-04T21:08:25.4435285Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:08:25.4435422Z getitem_86: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)] 2025-03-04T21:08:25.4435545Z mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_86; dy_4 = getitem_86 = None 2025-03-04T21:08:25.4435720Z getitem_87: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)]; ctr_y_4 = None 2025-03-04T21:08:25.4435854Z pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_87; mul_43 = getitem_87 = None 2025-03-04T21:08:25.4435921Z 2025-03-04T21:08:25.4436249Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:08:25.4436433Z exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9); dw_9 = None 2025-03-04T21:08:25.4436613Z getitem_88: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]; widths_4 = None 2025-03-04T21:08:25.4436751Z pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_88; exp_8 = getitem_88 = None 2025-03-04T21:08:25.4436813Z 2025-03-04T21:08:25.4437150Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:08:25.4437263Z exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9); dh_9 = None 2025-03-04T21:08:25.4437432Z getitem_89: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]; heights_4 = None 2025-03-04T21:08:25.4437559Z pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_89; exp_9 = getitem_89 = None 2025-03-04T21:08:25.4437627Z 2025-03-04T21:08:25.4437935Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:08:25.4438036Z mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4 2025-03-04T21:08:25.4438147Z x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46; mul_46 = None 2025-03-04T21:08:25.4438217Z 2025-03-04T21:08:25.4438524Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:08:25.4438639Z mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4 2025-03-04T21:08:25.4438752Z y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47; mul_47 = None 2025-03-04T21:08:25.4438819Z 2025-03-04T21:08:25.4439119Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:08:25.4439238Z mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4; pred_w_4 = None 2025-03-04T21:08:25.4439366Z x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48; pred_ctr_x_4 = mul_48 = None 2025-03-04T21:08:25.4439436Z 2025-03-04T21:08:25.4439741Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:08:25.4439861Z mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4; pred_h_4 = None 2025-03-04T21:08:25.4439987Z y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49; pred_ctr_y_4 = mul_49 = None 2025-03-04T21:08:25.4440055Z 2025-03-04T21:08:25.4440416Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:08:25.4440603Z pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1); x1_4 = y1_4 = x2_4 = y2_4 = None 2025-03-04T21:08:25.4440664Z 2025-03-04T21:08:25.4441005Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:08:25.4441161Z proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4)); pred_boxes_4 = None 2025-03-04T21:08:25.4441246Z 2025-03-04T21:08:25.4441628Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:08:25.4441806Z proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4); proposals_i_4 = None 2025-03-04T21:08:25.4441867Z 2025-03-04T21:08:25.4442359Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T21:08:25.4442518Z arange_10: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T21:08:25.4442579Z 2025-03-04T21:08:25.4442879Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:25.4443019Z batch_idx: "i64[4][1]cpu" = arange_10.to(device(type='cpu')); arange_10 = None 2025-03-04T21:08:25.4443087Z 2025-03-04T21:08:25.4443522Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:08:25.4443638Z topk = logits_i.topk(1000, dim = 1); logits_i = None 2025-03-04T21:08:25.4443737Z topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0] 2025-03-04T21:08:25.4443855Z topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1]; topk = None 2025-03-04T21:08:25.4443916Z 2025-03-04T21:08:25.4444382Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:08:25.4444512Z getitem_92: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:08:25.4444748Z topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_92, topk_idx)]; proposals_i_5 = getitem_92 = topk_idx = None 2025-03-04T21:08:25.4444826Z 2025-03-04T21:08:25.4445297Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:08:25.4445462Z full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:08:25.4445529Z 2025-03-04T21:08:25.4445828Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:25.4445953Z to_21: "i64[1000][1]cpu" = full.to(device(type='cpu')); full = None 2025-03-04T21:08:25.4446014Z 2025-03-04T21:08:25.4446455Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:08:25.4446585Z topk_1 = logits_i_1.topk(1000, dim = 1); logits_i_1 = None 2025-03-04T21:08:25.4446697Z topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0] 2025-03-04T21:08:25.4446809Z topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1]; topk_1 = None 2025-03-04T21:08:25.4446879Z 2025-03-04T21:08:25.4447343Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:08:25.4447480Z getitem_96: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:08:25.4447727Z topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_96, topk_idx_1)]; proposals_i_6 = getitem_96 = topk_idx_1 = None 2025-03-04T21:08:25.4447809Z 2025-03-04T21:08:25.4448260Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:08:25.4448427Z full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:08:25.4448496Z 2025-03-04T21:08:25.4448796Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:25.4448924Z to_22: "i64[1000][1]cpu" = full_1.to(device(type='cpu')); full_1 = None 2025-03-04T21:08:25.4448983Z 2025-03-04T21:08:25.4449414Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:08:25.4449526Z topk_2 = logits_i_2.topk(1000, dim = 1); logits_i_2 = None 2025-03-04T21:08:25.4449633Z topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0] 2025-03-04T21:08:25.4449742Z topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1]; topk_2 = None 2025-03-04T21:08:25.4449810Z 2025-03-04T21:08:25.4450258Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:08:25.4450393Z getitem_100: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:08:25.4450624Z topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_100, topk_idx_2)]; proposals_i_7 = getitem_100 = topk_idx_2 = None 2025-03-04T21:08:25.4450693Z 2025-03-04T21:08:25.4451150Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:08:25.4451309Z full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:08:25.4451368Z 2025-03-04T21:08:25.4451658Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:25.4451775Z to_23: "i64[1000][1]cpu" = full_2.to(device(type='cpu')); full_2 = None 2025-03-04T21:08:25.4451841Z 2025-03-04T21:08:25.4452258Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:08:25.4452375Z topk_3 = logits_i_3.topk(1000, dim = 1); logits_i_3 = None 2025-03-04T21:08:25.4452486Z topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0] 2025-03-04T21:08:25.4452603Z topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1]; topk_3 = None 2025-03-04T21:08:25.4452664Z 2025-03-04T21:08:25.4453111Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:08:25.4453239Z getitem_104: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:08:25.4453472Z topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_104, topk_idx_3)]; proposals_i_8 = getitem_104 = topk_idx_3 = None 2025-03-04T21:08:25.4453580Z 2025-03-04T21:08:25.4454031Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:08:25.4454190Z full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:08:25.4454255Z 2025-03-04T21:08:25.4454549Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:25.4454677Z to_24: "i64[1000][1]cpu" = full_3.to(device(type='cpu')); full_3 = None 2025-03-04T21:08:25.4454758Z 2025-03-04T21:08:25.4455192Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:08:25.4455307Z topk_4 = logits_i_4.topk(1000, dim = 1); logits_i_4 = None 2025-03-04T21:08:25.4455407Z topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0] 2025-03-04T21:08:25.4455524Z topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1]; topk_4 = None 2025-03-04T21:08:25.4455586Z 2025-03-04T21:08:25.4456033Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:08:25.4456191Z getitem_108: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T21:08:25.4456423Z topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_108, topk_idx_4)]; proposals_i_9 = getitem_108 = topk_idx_4 = None 2025-03-04T21:08:25.4456483Z 2025-03-04T21:08:25.4456929Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:08:25.4457097Z full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:08:25.4457162Z 2025-03-04T21:08:25.4457443Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:25.4457564Z to_25: "i64[1000][1]cpu" = full_4.to(device(type='cpu')); full_4 = None 2025-03-04T21:08:25.4457623Z 2025-03-04T21:08:25.4457896Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:08:25.4458260Z topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1); topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None 2025-03-04T21:08:25.4458331Z 2025-03-04T21:08:25.4458623Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:08:25.4459084Z topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1); topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None 2025-03-04T21:08:25.4459142Z 2025-03-04T21:08:25.4459419Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:08:25.4459618Z level_ids: "i64[5000][1]cpu" = torch.cat([to_21, to_22, to_23, to_24, to_25], 0); to_21 = to_22 = to_23 = to_24 = to_25 = level_ids = None 2025-03-04T21:08:25.4459693Z 2025-03-04T21:08:25.4460076Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T21:08:25.4460210Z getitem_110: "f32[5000, 4][4, 1]cpu" = topk_proposals[0]; topk_proposals = None 2025-03-04T21:08:25.4460278Z 2025-03-04T21:08:25.4460566Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:08:25.4460714Z tensor_5: "f32[5000, 4][4, 1]cpu" = getitem_110.to(torch.float32); getitem_110 = None 2025-03-04T21:08:25.4460787Z 2025-03-04T21:08:25.4461159Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T21:08:25.4461287Z scores_per_img: "f32[5000][1]cpu" = topk_scores[0]; topk_scores = None 2025-03-04T21:08:25.4461354Z 2025-03-04T21:08:25.4461825Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T21:08:25.4461963Z isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor_5); tensor_5 = None 2025-03-04T21:08:25.4462075Z all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:08:25.4462230Z isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T21:08:25.4462355Z valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T21:08:25.4462422Z 2025-03-04T21:08:25.4462779Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T21:08:25.4462914Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T21:08:25.4462977Z 2025-03-04T21:08:25.4462987Z 2025-03-04T21:08:25.4463084Z class GraphModule(torch.nn.Module): 2025-03-04T21:08:25.4526384Z def forward(self, L_stack0_tensor: "f32[4, 3, 1184, 1216][4319232, 1439744, 1216, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: 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L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_: "f32[512, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_: "f32[2048, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_lateral_convs_0_parameters_weight_: "f32[256, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_0_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_0_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_0_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_1_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_1_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_1_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_2_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_2_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_2_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_3_parameters_weight_: "f32[256, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_3_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_3_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_3_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[3, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[3][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[12, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[12][1]cpu"): 2025-03-04T21:08:25.4526902Z l_stack0_tensor = L_stack0_tensor 2025-03-04T21:08:25.4527342Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ 2025-03-04T21:08:25.4527820Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.4528271Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.4528663Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:25.4529035Z l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:25.4529390Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:08:25.4529802Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.4530202Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.4530604Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:25.4530978Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:25.4531334Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:08:25.4531753Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.4532156Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.4532546Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:25.4532919Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:25.4533275Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:08:25.4533677Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.4534099Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.4534483Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:25.4534853Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:25.4535218Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:08:25.4535649Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.4536071Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.4536467Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:08:25.4536864Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:08:25.4537234Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:08:25.4537633Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.4538048Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.4538428Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:25.4538809Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:25.4539149Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:08:25.4539557Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.4539958Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.4540335Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:25.4540726Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:25.4541066Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:08:25.4541476Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.4541875Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.4542273Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:25.4542650Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:25.4543075Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:08:25.4543557Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.4544016Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.4544475Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:25.4544917Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:25.4545344Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:08:25.4545814Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.4546256Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.4546686Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:25.4547102Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:25.4547509Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:08:25.4547952Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.4548410Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.4548853Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:25.4549296Z l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:25.4549705Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:08:25.4550166Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.4550631Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.4551094Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:25.4551530Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:25.4552006Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:08:25.4552503Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.4553012Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.4553510Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:25.4553999Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:25.4554449Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:08:25.4554967Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.4555506Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.4555989Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:25.4556505Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:25.4556975Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:08:25.4557538Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.4557994Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.4558437Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:08:25.4558888Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:08:25.4559280Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:08:25.4559740Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.4560207Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.4560647Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:25.4561080Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:25.4561459Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:08:25.4561914Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.4562367Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.4563210Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:25.4563633Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:25.4564030Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:08:25.4564492Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.4565012Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.4565451Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:25.4565885Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:25.4566286Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:08:25.4566764Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.4567225Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.4567678Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:25.4568102Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:25.4568501Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:08:25.4568961Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.4569416Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.4569850Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:25.4570280Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:25.4570699Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:08:25.4571155Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.4571615Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.4572040Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:25.4572487Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:25.4572879Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ 2025-03-04T21:08:25.4573356Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.4573813Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.4574264Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:25.4574694Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:25.4575100Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ 2025-03-04T21:08:25.4575567Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.4576021Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.4576460Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:25.4576888Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:25.4577282Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ 2025-03-04T21:08:25.4577746Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.4578200Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.4578630Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:25.4579048Z l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:25.4579445Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:08:25.4579910Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.4580353Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.4580781Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:25.4581205Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:25.4581593Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:08:25.4582036Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.4582498Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.4582928Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:25.4583342Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:25.4583727Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:08:25.4584175Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.4584618Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.4585053Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:25.4585472Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:25.4585875Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:08:25.4586332Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.4586808Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.4587248Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:08:25.4587684Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:08:25.4588074Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:08:25.4588498Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.4588902Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.4589309Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:25.4589733Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:25.4590115Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:08:25.4590576Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.4591022Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.4591459Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:25.4591883Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:25.4592287Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:08:25.4592732Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.4593172Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.4593606Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:25.4594056Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:25.4594460Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:08:25.4594919Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.4595386Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.4595835Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:25.4596263Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:25.4596747Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:08:25.4597213Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.4597668Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.4598062Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:25.4598434Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:25.4598779Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:08:25.4599193Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.4599593Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.4599969Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:25.4600347Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:25.4600705Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ 2025-03-04T21:08:25.4601112Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.4601515Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.4601975Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:25.4602392Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:25.4602736Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ 2025-03-04T21:08:25.4603166Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.4603585Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.4603977Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:25.4604357Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:25.4604708Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ 2025-03-04T21:08:25.4605137Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.4605536Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.4605954Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:25.4606340Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:25.4606691Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ 2025-03-04T21:08:25.4607104Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.4607528Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.4607922Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:25.4608304Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:25.4608660Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ 2025-03-04T21:08:25.4609097Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.4609499Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.4609906Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:25.4610285Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:25.4610647Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ 2025-03-04T21:08:25.4611056Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.4611468Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.4611860Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:25.4612254Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:25.4612609Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ 2025-03-04T21:08:25.4613015Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.4613419Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.4613818Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:25.4614204Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:25.4614556Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ 2025-03-04T21:08:25.4614963Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.4615392Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.4615784Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:25.4616174Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:25.4616548Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ 2025-03-04T21:08:25.4616969Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.4617383Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.4617772Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:25.4618163Z l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:25.4618516Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ 2025-03-04T21:08:25.4618951Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.4619357Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.4619752Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:25.4620137Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:25.4620505Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ 2025-03-04T21:08:25.4620920Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.4621324Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.4621718Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:25.4622121Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:25.4622472Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ 2025-03-04T21:08:25.4622894Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.4623286Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.4623675Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:25.4624044Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:25.4624409Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ 2025-03-04T21:08:25.4624820Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.4625236Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.4625650Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ 2025-03-04T21:08:25.4626032Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ 2025-03-04T21:08:25.4626382Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ 2025-03-04T21:08:25.4626778Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.4627185Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.4627560Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:25.4627935Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:25.4628297Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ 2025-03-04T21:08:25.4628699Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.4629099Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.4629488Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:25.4629869Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:25.4630214Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ 2025-03-04T21:08:25.4630620Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.4631021Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.4631398Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:25.4631793Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:25.4632131Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ 2025-03-04T21:08:25.4632537Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.4632938Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.4633332Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ 2025-03-04T21:08:25.4633709Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ 2025-03-04T21:08:25.4634050Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ 2025-03-04T21:08:25.4634454Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.4634867Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.4635257Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ 2025-03-04T21:08:25.4635654Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ 2025-03-04T21:08:25.4636004Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ 2025-03-04T21:08:25.4636482Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ 2025-03-04T21:08:25.4636914Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ 2025-03-04T21:08:25.4637348Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ 2025-03-04T21:08:25.4637717Z l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ 2025-03-04T21:08:25.4637953Z l_self_modules_backbone_lateral_convs_0_parameters_weight_ = L_self_modules_backbone_lateral_convs_0_parameters_weight_ 2025-03-04T21:08:25.4638194Z l_self_modules_backbone_lateral_convs_0_parameters_bias_ = L_self_modules_backbone_lateral_convs_0_parameters_bias_ 2025-03-04T21:08:25.4638425Z l_self_modules_backbone_output_convs_0_parameters_weight_ = L_self_modules_backbone_output_convs_0_parameters_weight_ 2025-03-04T21:08:25.4638646Z l_self_modules_backbone_output_convs_0_parameters_bias_ = L_self_modules_backbone_output_convs_0_parameters_bias_ 2025-03-04T21:08:25.4638884Z l_self_modules_backbone_lateral_convs_1_parameters_weight_ = L_self_modules_backbone_lateral_convs_1_parameters_weight_ 2025-03-04T21:08:25.4639108Z l_self_modules_backbone_lateral_convs_1_parameters_bias_ = L_self_modules_backbone_lateral_convs_1_parameters_bias_ 2025-03-04T21:08:25.4639327Z l_self_modules_backbone_output_convs_1_parameters_weight_ = L_self_modules_backbone_output_convs_1_parameters_weight_ 2025-03-04T21:08:25.4639549Z l_self_modules_backbone_output_convs_1_parameters_bias_ = L_self_modules_backbone_output_convs_1_parameters_bias_ 2025-03-04T21:08:25.4639788Z l_self_modules_backbone_lateral_convs_2_parameters_weight_ = L_self_modules_backbone_lateral_convs_2_parameters_weight_ 2025-03-04T21:08:25.4640010Z l_self_modules_backbone_lateral_convs_2_parameters_bias_ = L_self_modules_backbone_lateral_convs_2_parameters_bias_ 2025-03-04T21:08:25.4640240Z l_self_modules_backbone_output_convs_2_parameters_weight_ = L_self_modules_backbone_output_convs_2_parameters_weight_ 2025-03-04T21:08:25.4640457Z l_self_modules_backbone_output_convs_2_parameters_bias_ = L_self_modules_backbone_output_convs_2_parameters_bias_ 2025-03-04T21:08:25.4640678Z l_self_modules_backbone_lateral_convs_3_parameters_weight_ = L_self_modules_backbone_lateral_convs_3_parameters_weight_ 2025-03-04T21:08:25.4640918Z l_self_modules_backbone_lateral_convs_3_parameters_bias_ = L_self_modules_backbone_lateral_convs_3_parameters_bias_ 2025-03-04T21:08:25.4641136Z l_self_modules_backbone_output_convs_3_parameters_weight_ = L_self_modules_backbone_output_convs_3_parameters_weight_ 2025-03-04T21:08:25.4641351Z l_self_modules_backbone_output_convs_3_parameters_bias_ = L_self_modules_backbone_output_convs_3_parameters_bias_ 2025-03-04T21:08:25.4641707Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T21:08:25.4642082Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ 2025-03-04T21:08:25.4642442Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ 2025-03-04T21:08:25.4642793Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ 2025-03-04T21:08:25.4643151Z l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ 2025-03-04T21:08:25.4643477Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T21:08:25.4643800Z l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T21:08:25.4644174Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T21:08:25.4644558Z l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T21:08:25.4644908Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T21:08:25.4645254Z l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T21:08:25.4645323Z 2025-03-04T21:08:25.4645605Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4646170Z x: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1); l_stack0_tensor = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:25.4646235Z 2025-03-04T21:08:25.4646517Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4648288Z x_1: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4648374Z 2025-03-04T21:08:25.4648668Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x) 2025-03-04T21:08:25.4648824Z x_2: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.relu_(x_1); x_1 = None 2025-03-04T21:08:25.4648893Z 2025-03-04T21:08:25.4649255Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1) 2025-03-04T21:08:25.4649497Z x_3: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1); x_2 = None 2025-03-04T21:08:25.4649561Z 2025-03-04T21:08:25.4649823Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4650324Z x_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:25.4650395Z 2025-03-04T21:08:25.4650664Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4652460Z x_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4652543Z 2025-03-04T21:08:25.4652822Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4652976Z out: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_5); x_5 = None 2025-03-04T21:08:25.4653037Z 2025-03-04T21:08:25.4653290Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4653777Z x_6: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:25.4653848Z 2025-03-04T21:08:25.4654112Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4655918Z x_7: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_6 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4655991Z 2025-03-04T21:08:25.4656274Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4656414Z out_1: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_7); x_7 = None 2025-03-04T21:08:25.4656474Z 2025-03-04T21:08:25.4656726Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4657217Z x_8: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_1 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:25.4657285Z 2025-03-04T21:08:25.4657555Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4659346Z x_9: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4659416Z 2025-03-04T21:08:25.4659672Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4660169Z x_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); x_3 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:08:25.4660236Z 2025-03-04T21:08:25.4660495Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4662343Z x_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_10 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4662430Z 2025-03-04T21:08:25.4662710Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:25.4662865Z x_9 += x_11; out_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_9; x_9 = x_11 = None 2025-03-04T21:08:25.4662926Z 2025-03-04T21:08:25.4663225Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4663370Z out_3: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_2); out_2 = None 2025-03-04T21:08:25.4663436Z 2025-03-04T21:08:25.4663682Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4664169Z x_12: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:25.4664245Z 2025-03-04T21:08:25.4664507Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4666374Z x_13: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_12 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4666436Z 2025-03-04T21:08:25.4666719Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4666856Z out_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_13); x_13 = None 2025-03-04T21:08:25.4666927Z 2025-03-04T21:08:25.4667167Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4667654Z x_14: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:25.4667730Z 2025-03-04T21:08:25.4667998Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4669765Z x_15: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_14 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4669828Z 2025-03-04T21:08:25.4670115Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4670254Z out_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_15); x_15 = None 2025-03-04T21:08:25.4670322Z 2025-03-04T21:08:25.4670570Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4671078Z x_16: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_5 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:25.4671191Z 2025-03-04T21:08:25.4671459Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4673294Z x_17: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_16 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4673357Z 2025-03-04T21:08:25.4673644Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:25.4673802Z x_17 += out_3; out_6: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_17; x_17 = out_3 = None 2025-03-04T21:08:25.4673862Z 2025-03-04T21:08:25.4674165Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4674312Z out_7: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_6); out_6 = None 2025-03-04T21:08:25.4674381Z 2025-03-04T21:08:25.4674628Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4675146Z x_18: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:25.4675208Z 2025-03-04T21:08:25.4675487Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4677376Z x_19: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_18 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4677451Z 2025-03-04T21:08:25.4677740Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4677896Z out_8: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_19); x_19 = None 2025-03-04T21:08:25.4677965Z 2025-03-04T21:08:25.4678224Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4678733Z x_20: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:25.4678795Z 2025-03-04T21:08:25.4679067Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4680900Z x_21: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_20 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4680978Z 2025-03-04T21:08:25.4681273Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4681409Z out_9: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_21); x_21 = None 2025-03-04T21:08:25.4681477Z 2025-03-04T21:08:25.4681741Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4682288Z x_22: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_9 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:25.4682351Z 2025-03-04T21:08:25.4682633Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4684442Z x_23: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_22 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4684520Z 2025-03-04T21:08:25.4684817Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:25.4684968Z x_23 += out_7; out_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_23; x_23 = out_7 = None 2025-03-04T21:08:25.4685038Z 2025-03-04T21:08:25.4685339Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4685496Z out_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_10); out_10 = None 2025-03-04T21:08:25.4685558Z 2025-03-04T21:08:25.4685832Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4686370Z x_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:25.4686439Z 2025-03-04T21:08:25.4686720Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4688529Z x_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4688615Z 2025-03-04T21:08:25.4688932Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4689075Z out_12: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_25); x_25 = None 2025-03-04T21:08:25.4689155Z 2025-03-04T21:08:25.4689399Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4689888Z x_26: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_12 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:25.4689948Z 2025-03-04T21:08:25.4690210Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4691958Z x_27: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_26 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4692050Z 2025-03-04T21:08:25.4692334Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4692472Z out_13: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_27); x_27 = None 2025-03-04T21:08:25.4692540Z 2025-03-04T21:08:25.4692797Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4693302Z x_28: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_13 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:25.4693362Z 2025-03-04T21:08:25.4693638Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4695502Z x_29: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_28 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4695581Z 2025-03-04T21:08:25.4695836Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4696336Z x_30: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:08:25.4696407Z 2025-03-04T21:08:25.4696671Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4698527Z x_31: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_30 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4698611Z 2025-03-04T21:08:25.4698885Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:25.4699034Z x_29 += x_31; out_14: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_29; x_29 = x_31 = None 2025-03-04T21:08:25.4699094Z 2025-03-04T21:08:25.4699378Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4699544Z out_15: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_14); out_14 = None 2025-03-04T21:08:25.4699613Z 2025-03-04T21:08:25.4699868Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4700369Z x_32: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:25.4700436Z 2025-03-04T21:08:25.4700706Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4702696Z x_33: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_32 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4702773Z 2025-03-04T21:08:25.4703063Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4703207Z out_16: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_33); x_33 = None 2025-03-04T21:08:25.4703268Z 2025-03-04T21:08:25.4703518Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4704001Z x_34: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_16 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:25.4704072Z 2025-03-04T21:08:25.4704328Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4706109Z x_35: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_34 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4706179Z 2025-03-04T21:08:25.4706480Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4706623Z out_17: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_35); x_35 = None 2025-03-04T21:08:25.4706682Z 2025-03-04T21:08:25.4706928Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4707412Z x_36: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_17 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:25.4707499Z 2025-03-04T21:08:25.4707755Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4709533Z x_37: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_36 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4709604Z 2025-03-04T21:08:25.4709883Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:25.4710039Z x_37 += out_15; out_18: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_37; x_37 = out_15 = None 2025-03-04T21:08:25.4710098Z 2025-03-04T21:08:25.4710379Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4710526Z out_19: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_18); out_18 = None 2025-03-04T21:08:25.4710594Z 2025-03-04T21:08:25.4710842Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4711336Z x_38: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:25.4711396Z 2025-03-04T21:08:25.4711659Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4713474Z x_39: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_38 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4713541Z 2025-03-04T21:08:25.4713831Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4713984Z out_20: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_39); x_39 = None 2025-03-04T21:08:25.4714055Z 2025-03-04T21:08:25.4714305Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4714807Z x_40: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_20 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:25.4714876Z 2025-03-04T21:08:25.4715163Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4717045Z x_41: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_40 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4717125Z 2025-03-04T21:08:25.4717423Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4717571Z out_21: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_41); x_41 = None 2025-03-04T21:08:25.4717654Z 2025-03-04T21:08:25.4717908Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4718412Z x_42: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_21 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:25.4718482Z 2025-03-04T21:08:25.4718746Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4720604Z x_43: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_42 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4720691Z 2025-03-04T21:08:25.4720974Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:25.4721140Z x_43 += out_19; out_22: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_43; x_43 = out_19 = None 2025-03-04T21:08:25.4721203Z 2025-03-04T21:08:25.4721498Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4721643Z out_23: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_22); out_22 = None 2025-03-04T21:08:25.4721710Z 2025-03-04T21:08:25.4721973Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4722472Z x_44: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:25.4722534Z 2025-03-04T21:08:25.4722808Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4724650Z x_45: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_44 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4724729Z 2025-03-04T21:08:25.4725023Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4725161Z out_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_45); x_45 = None 2025-03-04T21:08:25.4725230Z 2025-03-04T21:08:25.4725480Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4725979Z x_46: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:25.4726057Z 2025-03-04T21:08:25.4726329Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4728124Z x_47: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_46 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4728201Z 2025-03-04T21:08:25.4728491Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4728640Z out_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_47); x_47 = None 2025-03-04T21:08:25.4728708Z 2025-03-04T21:08:25.4728948Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4729440Z x_48: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_25 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:25.4729508Z 2025-03-04T21:08:25.4729763Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4731518Z x_49: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_48 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4731602Z 2025-03-04T21:08:25.4731877Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:25.4732029Z x_49 += out_23; out_26: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_49; x_49 = out_23 = None 2025-03-04T21:08:25.4732089Z 2025-03-04T21:08:25.4732369Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4732525Z out_27: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_26); out_26 = None 2025-03-04T21:08:25.4732593Z 2025-03-04T21:08:25.4732839Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4733319Z x_50: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:25.4733377Z 2025-03-04T21:08:25.4733643Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4735430Z x_51: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_50 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4735500Z 2025-03-04T21:08:25.4735787Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4735917Z out_28: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_51); x_51 = None 2025-03-04T21:08:25.4735982Z 2025-03-04T21:08:25.4736223Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4736706Z x_52: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_28 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:25.4736764Z 2025-03-04T21:08:25.4737032Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4738823Z x_53: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_52 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4738884Z 2025-03-04T21:08:25.4739186Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4739314Z out_29: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_53); x_53 = None 2025-03-04T21:08:25.4739379Z 2025-03-04T21:08:25.4739622Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4740102Z x_54: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_29 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:25.4740183Z 2025-03-04T21:08:25.4740445Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4742218Z x_55: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_54 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4742280Z 2025-03-04T21:08:25.4742533Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4743014Z x_56: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:08:25.4743082Z 2025-03-04T21:08:25.4743346Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4745165Z x_57: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_56 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4745249Z 2025-03-04T21:08:25.4745531Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:25.4745683Z x_55 += x_57; out_30: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_55; x_55 = x_57 = None 2025-03-04T21:08:25.4745752Z 2025-03-04T21:08:25.4746029Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4746170Z out_31: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_30); out_30 = None 2025-03-04T21:08:25.4746230Z 2025-03-04T21:08:25.4746482Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4746946Z x_58: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:25.4747029Z 2025-03-04T21:08:25.4747289Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4749063Z x_59: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_58 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4749135Z 2025-03-04T21:08:25.4749412Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4749546Z out_32: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_59); x_59 = None 2025-03-04T21:08:25.4749605Z 2025-03-04T21:08:25.4749851Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4750326Z x_60: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_32 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:25.4750410Z 2025-03-04T21:08:25.4750670Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4752437Z x_61: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_60 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4752508Z 2025-03-04T21:08:25.4752781Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4752918Z out_33: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_61); x_61 = None 2025-03-04T21:08:25.4752977Z 2025-03-04T21:08:25.4753227Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4753721Z x_62: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_33 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:25.4753789Z 2025-03-04T21:08:25.4754049Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4755873Z x_63: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_62 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4755944Z 2025-03-04T21:08:25.4756230Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:25.4756433Z x_63 += out_31; out_34: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_63; x_63 = out_31 = None 2025-03-04T21:08:25.4756501Z 2025-03-04T21:08:25.4756796Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4756959Z out_35: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_34); out_34 = None 2025-03-04T21:08:25.4757028Z 2025-03-04T21:08:25.4757282Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4757809Z x_64: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:25.4757890Z 2025-03-04T21:08:25.4758198Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4760029Z x_65: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_64 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4760129Z 2025-03-04T21:08:25.4760458Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4760608Z out_36: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_65); x_65 = None 2025-03-04T21:08:25.4760670Z 2025-03-04T21:08:25.4760971Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4761503Z x_66: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_36 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:25.4761583Z 2025-03-04T21:08:25.4761893Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4763704Z x_67: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_66 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4763801Z 2025-03-04T21:08:25.4764110Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4764245Z out_37: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_67); x_67 = None 2025-03-04T21:08:25.4764306Z 2025-03-04T21:08:25.4764577Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4765095Z x_68: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_37 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:25.4765163Z 2025-03-04T21:08:25.4765456Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4767291Z x_69: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_68 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4767378Z 2025-03-04T21:08:25.4767667Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:25.4767817Z x_69 += out_35; out_38: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_69; x_69 = out_35 = None 2025-03-04T21:08:25.4767877Z 2025-03-04T21:08:25.4768198Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4768339Z out_39: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_38); out_38 = None 2025-03-04T21:08:25.4768406Z 2025-03-04T21:08:25.4768665Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4769151Z x_70: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:25.4769212Z 2025-03-04T21:08:25.4769483Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4771275Z x_71: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_70 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4771351Z 2025-03-04T21:08:25.4771666Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4771796Z out_40: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_71); x_71 = None 2025-03-04T21:08:25.4771866Z 2025-03-04T21:08:25.4772120Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4772656Z x_72: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_40 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:25.4772724Z 2025-03-04T21:08:25.4773009Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4774817Z x_73: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_72 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4774977Z 2025-03-04T21:08:25.4775259Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4775404Z out_41: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_73); x_73 = None 2025-03-04T21:08:25.4775466Z 2025-03-04T21:08:25.4775714Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4776188Z x_74: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_41 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:25.4776256Z 2025-03-04T21:08:25.4776514Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4778274Z x_75: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_74 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4778357Z 2025-03-04T21:08:25.4778633Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:25.4778781Z x_75 += out_39; out_42: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_75; x_75 = out_39 = None 2025-03-04T21:08:25.4778841Z 2025-03-04T21:08:25.4779138Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4779274Z out_43: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_42); out_42 = None 2025-03-04T21:08:25.4779340Z 2025-03-04T21:08:25.4779583Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4780056Z x_76: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:25.4780133Z 2025-03-04T21:08:25.4780396Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4782167Z x_77: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_76 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4782233Z 2025-03-04T21:08:25.4782515Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4782639Z out_44: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_77); x_77 = None 2025-03-04T21:08:25.4782707Z 2025-03-04T21:08:25.4782950Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4783429Z x_78: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_44 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:25.4783502Z 2025-03-04T21:08:25.4783767Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4785550Z x_79: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_78 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4785613Z 2025-03-04T21:08:25.4785898Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4786022Z out_45: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_79); x_79 = None 2025-03-04T21:08:25.4786089Z 2025-03-04T21:08:25.4786336Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4786823Z x_80: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_45 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:25.4786905Z 2025-03-04T21:08:25.4787163Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4788945Z x_81: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_80 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4789007Z 2025-03-04T21:08:25.4789286Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:25.4789432Z x_81 += out_43; out_46: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_81; x_81 = out_43 = None 2025-03-04T21:08:25.4789491Z 2025-03-04T21:08:25.4789772Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4789910Z out_47: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_46); out_46 = None 2025-03-04T21:08:25.4789998Z 2025-03-04T21:08:25.4790243Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4790719Z x_82: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:25.4790779Z 2025-03-04T21:08:25.4791047Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4792810Z x_83: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_82 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4792880Z 2025-03-04T21:08:25.4793159Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4793303Z out_48: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_83); x_83 = None 2025-03-04T21:08:25.4793369Z 2025-03-04T21:08:25.4793611Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4794084Z x_84: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_48 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:25.4794157Z 2025-03-04T21:08:25.4794418Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4796198Z x_85: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_84 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4796260Z 2025-03-04T21:08:25.4796598Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4796752Z out_49: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_85); x_85 = None 2025-03-04T21:08:25.4796818Z 2025-03-04T21:08:25.4797068Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4797571Z x_86: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_49 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:25.4797630Z 2025-03-04T21:08:25.4797898Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4799742Z x_87: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_86 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4799820Z 2025-03-04T21:08:25.4800108Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:25.4800252Z x_87 += out_47; out_50: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_87; x_87 = out_47 = None 2025-03-04T21:08:25.4800320Z 2025-03-04T21:08:25.4800601Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4800748Z out_51: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_50); out_50 = None 2025-03-04T21:08:25.4800825Z 2025-03-04T21:08:25.4801084Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4801563Z x_88: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:25.4801635Z 2025-03-04T21:08:25.4801987Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4803787Z x_89: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_88 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4803902Z 2025-03-04T21:08:25.4804193Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4804327Z out_52: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_89); x_89 = None 2025-03-04T21:08:25.4804395Z 2025-03-04T21:08:25.4804657Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4805177Z x_90: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_52 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:25.4805241Z 2025-03-04T21:08:25.4805512Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4807293Z x_91: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_90 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4807385Z 2025-03-04T21:08:25.4807698Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4807828Z out_53: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_91); x_91 = None 2025-03-04T21:08:25.4807895Z 2025-03-04T21:08:25.4808142Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4808637Z x_92: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_53 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:25.4808698Z 2025-03-04T21:08:25.4808970Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4810791Z x_93: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_92 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4810868Z 2025-03-04T21:08:25.4811127Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4811618Z x_94: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = None 2025-03-04T21:08:25.4811689Z 2025-03-04T21:08:25.4811969Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4813842Z x_95: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_94 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4813926Z 2025-03-04T21:08:25.4814198Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:25.4814360Z x_93 += x_95; out_54: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_93; x_93 = x_95 = None 2025-03-04T21:08:25.4814420Z 2025-03-04T21:08:25.4814706Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4814844Z out_55: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_54); out_54 = None 2025-03-04T21:08:25.4814911Z 2025-03-04T21:08:25.4815157Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4815631Z x_96: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:25.4815698Z 2025-03-04T21:08:25.4815957Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4817722Z x_97: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_96 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4817798Z 2025-03-04T21:08:25.4818083Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4818211Z out_56: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_97); x_97 = None 2025-03-04T21:08:25.4818276Z 2025-03-04T21:08:25.4818533Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4819006Z x_98: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_56 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:25.4819074Z 2025-03-04T21:08:25.4819331Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4821090Z x_99: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_98 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4821178Z 2025-03-04T21:08:25.4821457Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4821592Z out_57: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_99); x_99 = None 2025-03-04T21:08:25.4821651Z 2025-03-04T21:08:25.4821901Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4822381Z x_100: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_57 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:25.4822449Z 2025-03-04T21:08:25.4822706Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4824521Z x_101: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_100 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4824602Z 2025-03-04T21:08:25.4824873Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:25.4825041Z x_101 += out_55; out_58: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_101; x_101 = out_55 = None 2025-03-04T21:08:25.4825101Z 2025-03-04T21:08:25.4825381Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4825516Z out_59: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_58); out_58 = None 2025-03-04T21:08:25.4825581Z 2025-03-04T21:08:25.4825823Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4826294Z x_102: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = None 2025-03-04T21:08:25.4826371Z 2025-03-04T21:08:25.4826635Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4828395Z x_103: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_102 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4828458Z 2025-03-04T21:08:25.4828739Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4828869Z out_60: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_103); x_103 = None 2025-03-04T21:08:25.4828935Z 2025-03-04T21:08:25.4829175Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4829655Z x_104: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1); out_60 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = None 2025-03-04T21:08:25.4829730Z 2025-03-04T21:08:25.4829994Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4831779Z x_105: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_104 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4831843Z 2025-03-04T21:08:25.4832134Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4832266Z out_61: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_105); x_105 = None 2025-03-04T21:08:25.4832333Z 2025-03-04T21:08:25.4832579Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4833093Z x_106: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1); out_61 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = None 2025-03-04T21:08:25.4833162Z 2025-03-04T21:08:25.4833430Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm( 2025-03-04T21:08:25.4835248Z x_107: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05); x_106 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = None 2025-03-04T21:08:25.4835321Z 2025-03-04T21:08:25.4835610Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut 2025-03-04T21:08:25.4835766Z x_107 += out_59; out_62: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_107; x_107 = out_59 = None 2025-03-04T21:08:25.4835828Z 2025-03-04T21:08:25.4836118Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out) 2025-03-04T21:08:25.4836325Z out_63: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_62); out_62 = None 2025-03-04T21:08:25.4836404Z 2025-03-04T21:08:25.4836675Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4837289Z x_108: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(out_63, l_self_modules_backbone_lateral_convs_0_parameters_weight_, l_self_modules_backbone_lateral_convs_0_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_63 = l_self_modules_backbone_lateral_convs_0_parameters_weight_ = l_self_modules_backbone_lateral_convs_0_parameters_bias_ = None 2025-03-04T21:08:25.4837353Z 2025-03-04T21:08:25.4837621Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4838221Z x_109: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_108, l_self_modules_backbone_output_convs_0_parameters_weight_, l_self_modules_backbone_output_convs_0_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_self_modules_backbone_output_convs_0_parameters_weight_ = l_self_modules_backbone_output_convs_0_parameters_bias_ = None 2025-03-04T21:08:25.4838292Z 2025-03-04T21:08:25.4838708Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest") 2025-03-04T21:08:25.4838983Z top_down_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.interpolate(x_108, scale_factor = 2.0, mode = 'nearest'); x_108 = None 2025-03-04T21:08:25.4839076Z 2025-03-04T21:08:25.4839336Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4839922Z x_110: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_lateral_convs_1_parameters_weight_, l_self_modules_backbone_lateral_convs_1_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_51 = l_self_modules_backbone_lateral_convs_1_parameters_weight_ = l_self_modules_backbone_lateral_convs_1_parameters_bias_ = None 2025-03-04T21:08:25.4839985Z 2025-03-04T21:08:25.4840371Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features 2025-03-04T21:08:25.4840567Z prev_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = x_110 + top_down_features; x_110 = top_down_features = None 2025-03-04T21:08:25.4840640Z 2025-03-04T21:08:25.4840889Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4841478Z x_111: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(prev_features, l_self_modules_backbone_output_convs_1_parameters_weight_, l_self_modules_backbone_output_convs_1_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_self_modules_backbone_output_convs_1_parameters_weight_ = l_self_modules_backbone_output_convs_1_parameters_bias_ = None 2025-03-04T21:08:25.4841539Z 2025-03-04T21:08:25.4841947Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest") 2025-03-04T21:08:25.4842268Z top_down_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.interpolate(prev_features, scale_factor = 2.0, mode = 'nearest'); prev_features = None 2025-03-04T21:08:25.4842336Z 2025-03-04T21:08:25.4842596Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4843200Z x_112: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_lateral_convs_2_parameters_weight_, l_self_modules_backbone_lateral_convs_2_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_27 = l_self_modules_backbone_lateral_convs_2_parameters_weight_ = l_self_modules_backbone_lateral_convs_2_parameters_bias_ = None 2025-03-04T21:08:25.4843267Z 2025-03-04T21:08:25.4843608Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features 2025-03-04T21:08:25.4843822Z prev_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = x_112 + top_down_features_1; x_112 = top_down_features_1 = None 2025-03-04T21:08:25.4843886Z 2025-03-04T21:08:25.4844139Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4844728Z x_113: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(prev_features_1, l_self_modules_backbone_output_convs_2_parameters_weight_, l_self_modules_backbone_output_convs_2_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_self_modules_backbone_output_convs_2_parameters_weight_ = l_self_modules_backbone_output_convs_2_parameters_bias_ = None 2025-03-04T21:08:25.4844798Z 2025-03-04T21:08:25.4845203Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest") 2025-03-04T21:08:25.4845538Z top_down_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.interpolate(prev_features_1, scale_factor = 2.0, mode = 'nearest'); prev_features_1 = None 2025-03-04T21:08:25.4845644Z 2025-03-04T21:08:25.4845905Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4846492Z x_114: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_lateral_convs_3_parameters_weight_, l_self_modules_backbone_lateral_convs_3_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); out_11 = l_self_modules_backbone_lateral_convs_3_parameters_weight_ = l_self_modules_backbone_lateral_convs_3_parameters_bias_ = None 2025-03-04T21:08:25.4846553Z 2025-03-04T21:08:25.4846921Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features 2025-03-04T21:08:25.4847130Z prev_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_114 + top_down_features_2; x_114 = top_down_features_2 = None 2025-03-04T21:08:25.4847197Z 2025-03-04T21:08:25.4847450Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4848093Z x_115: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(prev_features_2, l_self_modules_backbone_output_convs_3_parameters_weight_, l_self_modules_backbone_output_convs_3_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); prev_features_2 = l_self_modules_backbone_output_convs_3_parameters_weight_ = l_self_modules_backbone_output_convs_3_parameters_bias_ = None 2025-03-04T21:08:25.4848153Z 2025-03-04T21:08:25.4848527Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:200 in forward, code: return [F.max_pool2d(x, kernel_size=1, stride=2, padding=0)] 2025-03-04T21:08:25.4848739Z res: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.max_pool2d(x_109, kernel_size = 1, stride = 2, padding = 0) 2025-03-04T21:08:25.4848825Z 2025-03-04T21:08:25.4849263Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:08:25.4849421Z arange: "f32[304][1]cpu" = torch.arange(0.0, 1216, step = 4, dtype = torch.float32) 2025-03-04T21:08:25.4849482Z 2025-03-04T21:08:25.4849785Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:25.4849921Z shifts_x: "f32[304][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T21:08:25.4849990Z 2025-03-04T21:08:25.4850418Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:08:25.4850576Z arange_1: "f32[296][1]cpu" = torch.arange(0.0, 1184, step = 4, dtype = torch.float32) 2025-03-04T21:08:25.4850635Z 2025-03-04T21:08:25.4850948Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:25.4851094Z shifts_y: "f32[296][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T21:08:25.4851154Z 2025-03-04T21:08:25.4851539Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:08:25.4851716Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T21:08:25.4851817Z shift_y: "f32[296, 304][1, 0]cpu" = meshgrid[0] 2025-03-04T21:08:25.4851951Z shift_x: "f32[296, 304][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T21:08:25.4852020Z 2025-03-04T21:08:25.4852350Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:08:25.4852482Z shift_x_1: "f32[89984][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T21:08:25.4852544Z 2025-03-04T21:08:25.4852874Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:08:25.4853011Z shift_y_1: "f32[89984][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T21:08:25.4853078Z 2025-03-04T21:08:25.4853454Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:08:25.4853682Z shifts: "f32[89984, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T21:08:25.4853742Z 2025-03-04T21:08:25.4854148Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:08:25.4854271Z view: "f32[89984, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T21:08:25.4854695Z view_1: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T21:08:25.4854813Z add_3: "f32[89984, 3, 4][12, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T21:08:25.4854935Z x_116: "f32[269952, 4][4, 1]cpu" = add_3.reshape(-1, 4); add_3 = None 2025-03-04T21:08:25.4855011Z 2025-03-04T21:08:25.4855449Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:08:25.4855598Z arange_2: "f32[152][1]cpu" = torch.arange(0.0, 1216, step = 8, dtype = torch.float32) 2025-03-04T21:08:25.4855667Z 2025-03-04T21:08:25.4855961Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:25.4856110Z shifts_x_1: "f32[152][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T21:08:25.4856170Z 2025-03-04T21:08:25.4856617Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:08:25.4856759Z arange_3: "f32[148][1]cpu" = torch.arange(0.0, 1184, step = 8, dtype = torch.float32) 2025-03-04T21:08:25.4856838Z 2025-03-04T21:08:25.4857125Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:25.4857263Z shifts_y_1: "f32[148][1]cpu" = arange_3.to(device(type='cpu')); arange_3 = None 2025-03-04T21:08:25.4857328Z 2025-03-04T21:08:25.4857692Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:08:25.4857889Z meshgrid_1 = torch.functional.meshgrid(shifts_y_1, shifts_x_1); shifts_y_1 = shifts_x_1 = None 2025-03-04T21:08:25.4858001Z shift_y_2: "f32[148, 152][1, 0]cpu" = meshgrid_1[0] 2025-03-04T21:08:25.4858130Z shift_x_2: "f32[148, 152][0, 1]cpu" = meshgrid_1[1]; meshgrid_1 = None 2025-03-04T21:08:25.4858191Z 2025-03-04T21:08:25.4858525Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:08:25.4858649Z shift_x_3: "f32[22496][1]cpu" = shift_x_2.reshape(-1); shift_x_2 = None 2025-03-04T21:08:25.4858714Z 2025-03-04T21:08:25.4859035Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:08:25.4859178Z shift_y_3: "f32[22496][1]cpu" = shift_y_2.reshape(-1); shift_y_2 = None 2025-03-04T21:08:25.4859239Z 2025-03-04T21:08:25.4859626Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:08:25.4859839Z shifts_1: "f32[22496, 4][4, 1]cpu" = torch.stack((shift_x_3, shift_y_3, shift_x_3, shift_y_3), dim = 1); shift_x_3 = shift_y_3 = None 2025-03-04T21:08:25.4859904Z 2025-03-04T21:08:25.4860305Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:08:25.4860434Z view_2: "f32[22496, 1, 4][4, 4, 1]cpu" = shifts_1.view(-1, 1, 4); shifts_1 = None 2025-03-04T21:08:25.4860840Z view_3: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = None 2025-03-04T21:08:25.4860970Z add_4: "f32[22496, 3, 4][12, 4, 1]cpu" = view_2 + view_3; view_2 = view_3 = None 2025-03-04T21:08:25.4861082Z x_117: "f32[67488, 4][4, 1]cpu" = add_4.reshape(-1, 4); add_4 = None 2025-03-04T21:08:25.4861166Z 2025-03-04T21:08:25.4861584Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:08:25.4861732Z arange_4: "f32[76][1]cpu" = torch.arange(0.0, 1216, step = 16, dtype = torch.float32) 2025-03-04T21:08:25.4861794Z 2025-03-04T21:08:25.4862093Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:25.4862227Z shifts_x_2: "f32[76][1]cpu" = arange_4.to(device(type='cpu')); arange_4 = None 2025-03-04T21:08:25.4862295Z 2025-03-04T21:08:25.4862727Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:08:25.4862903Z arange_5: "f32[74][1]cpu" = torch.arange(0.0, 1184, step = 16, dtype = torch.float32) 2025-03-04T21:08:25.4862963Z 2025-03-04T21:08:25.4863253Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:25.4863389Z shifts_y_2: "f32[74][1]cpu" = arange_5.to(device(type='cpu')); arange_5 = None 2025-03-04T21:08:25.4863447Z 2025-03-04T21:08:25.4863814Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:08:25.4863999Z meshgrid_2 = torch.functional.meshgrid(shifts_y_2, shifts_x_2); shifts_y_2 = shifts_x_2 = None 2025-03-04T21:08:25.4864113Z shift_y_4: "f32[74, 76][1, 0]cpu" = meshgrid_2[0] 2025-03-04T21:08:25.4864228Z shift_x_4: "f32[74, 76][0, 1]cpu" = meshgrid_2[1]; meshgrid_2 = None 2025-03-04T21:08:25.4864290Z 2025-03-04T21:08:25.4864605Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:08:25.4864731Z shift_x_5: "f32[5624][1]cpu" = shift_x_4.reshape(-1); shift_x_4 = None 2025-03-04T21:08:25.4864788Z 2025-03-04T21:08:25.4865125Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:08:25.4865240Z shift_y_5: "f32[5624][1]cpu" = shift_y_4.reshape(-1); shift_y_4 = None 2025-03-04T21:08:25.4865308Z 2025-03-04T21:08:25.4865683Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:08:25.4865900Z shifts_2: "f32[5624, 4][4, 1]cpu" = torch.stack((shift_x_5, shift_y_5, shift_x_5, shift_y_5), dim = 1); shift_x_5 = shift_y_5 = None 2025-03-04T21:08:25.4865963Z 2025-03-04T21:08:25.4866372Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:08:25.4866498Z view_4: "f32[5624, 1, 4][4, 4, 1]cpu" = shifts_2.view(-1, 1, 4); shifts_2 = None 2025-03-04T21:08:25.4866915Z view_5: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = None 2025-03-04T21:08:25.4867036Z add_5: "f32[5624, 3, 4][12, 4, 1]cpu" = view_4 + view_5; view_4 = view_5 = None 2025-03-04T21:08:25.4867169Z x_118: "f32[16872, 4][4, 1]cpu" = add_5.reshape(-1, 4); add_5 = None 2025-03-04T21:08:25.4867228Z 2025-03-04T21:08:25.4867655Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:08:25.4867791Z arange_6: "f32[38][1]cpu" = torch.arange(0.0, 1216, step = 32, dtype = torch.float32) 2025-03-04T21:08:25.4867858Z 2025-03-04T21:08:25.4868143Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:25.4868283Z shifts_x_3: "f32[38][1]cpu" = arange_6.to(device(type='cpu')); arange_6 = None 2025-03-04T21:08:25.4868343Z 2025-03-04T21:08:25.4868764Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:08:25.4868915Z arange_7: "f32[37][1]cpu" = torch.arange(0.0, 1184, step = 32, dtype = torch.float32) 2025-03-04T21:08:25.4868982Z 2025-03-04T21:08:25.4869266Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:25.4869404Z shifts_y_3: "f32[37][1]cpu" = arange_7.to(device(type='cpu')); arange_7 = None 2025-03-04T21:08:25.4869470Z 2025-03-04T21:08:25.4869830Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:08:25.4870038Z meshgrid_3 = torch.functional.meshgrid(shifts_y_3, shifts_x_3); shifts_y_3 = shifts_x_3 = None 2025-03-04T21:08:25.4870133Z shift_y_6: "f32[37, 38][1, 0]cpu" = meshgrid_3[0] 2025-03-04T21:08:25.4870254Z shift_x_6: "f32[37, 38][0, 1]cpu" = meshgrid_3[1]; meshgrid_3 = None 2025-03-04T21:08:25.4870314Z 2025-03-04T21:08:25.4870637Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:08:25.4870755Z shift_x_7: "f32[1406][1]cpu" = shift_x_6.reshape(-1); shift_x_6 = None 2025-03-04T21:08:25.4870822Z 2025-03-04T21:08:25.4871154Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:08:25.4871273Z shift_y_7: "f32[1406][1]cpu" = shift_y_6.reshape(-1); shift_y_6 = None 2025-03-04T21:08:25.4871334Z 2025-03-04T21:08:25.4871712Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:08:25.4871920Z shifts_3: "f32[1406, 4][4, 1]cpu" = torch.stack((shift_x_7, shift_y_7, shift_x_7, shift_y_7), dim = 1); shift_x_7 = shift_y_7 = None 2025-03-04T21:08:25.4871988Z 2025-03-04T21:08:25.4872389Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:08:25.4872518Z view_6: "f32[1406, 1, 4][4, 4, 1]cpu" = shifts_3.view(-1, 1, 4); shifts_3 = None 2025-03-04T21:08:25.4872924Z view_7: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = None 2025-03-04T21:08:25.4873051Z add_6: "f32[1406, 3, 4][12, 4, 1]cpu" = view_6 + view_7; view_6 = view_7 = None 2025-03-04T21:08:25.4873179Z x_119: "f32[4218, 4][4, 1]cpu" = add_6.reshape(-1, 4); add_6 = None 2025-03-04T21:08:25.4873249Z 2025-03-04T21:08:25.4873674Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:08:25.4873829Z arange_8: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32) 2025-03-04T21:08:25.4873889Z 2025-03-04T21:08:25.4874182Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:25.4874311Z shifts_x_4: "f32[19][1]cpu" = arange_8.to(device(type='cpu')); arange_8 = None 2025-03-04T21:08:25.4874382Z 2025-03-04T21:08:25.4874826Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:08:25.4874973Z arange_9: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32) 2025-03-04T21:08:25.4875034Z 2025-03-04T21:08:25.4875328Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:25.4875465Z shifts_y_4: "f32[19][1]cpu" = arange_9.to(device(type='cpu')); arange_9 = None 2025-03-04T21:08:25.4875526Z 2025-03-04T21:08:25.4875901Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:08:25.4876134Z meshgrid_4 = torch.functional.meshgrid(shifts_y_4, shifts_x_4); shifts_y_4 = shifts_x_4 = None 2025-03-04T21:08:25.4876237Z shift_y_8: "f32[19, 19][1, 0]cpu" = meshgrid_4[0] 2025-03-04T21:08:25.4876415Z shift_x_8: "f32[19, 19][0, 1]cpu" = meshgrid_4[1]; meshgrid_4 = None 2025-03-04T21:08:25.4876487Z 2025-03-04T21:08:25.4876815Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:08:25.4876944Z shift_x_9: "f32[361][1]cpu" = shift_x_8.reshape(-1); shift_x_8 = None 2025-03-04T21:08:25.4877006Z 2025-03-04T21:08:25.4877353Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:08:25.4877473Z shift_y_9: "f32[361][1]cpu" = shift_y_8.reshape(-1); shift_y_8 = None 2025-03-04T21:08:25.4877544Z 2025-03-04T21:08:25.4877919Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:08:25.4878135Z shifts_4: "f32[361, 4][4, 1]cpu" = torch.stack((shift_x_9, shift_y_9, shift_x_9, shift_y_9), dim = 1); shift_x_9 = shift_y_9 = None 2025-03-04T21:08:25.4878195Z 2025-03-04T21:08:25.4878614Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:08:25.4878739Z view_8: "f32[361, 1, 4][4, 4, 1]cpu" = shifts_4.view(-1, 1, 4); shifts_4 = None 2025-03-04T21:08:25.4879172Z view_9: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_.view(1, -1, 4); l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = None 2025-03-04T21:08:25.4879311Z add_7: "f32[361, 3, 4][12, 4, 1]cpu" = view_8 + view_9; view_8 = view_9 = None 2025-03-04T21:08:25.4879430Z x_120: "f32[1083, 4][4, 1]cpu" = add_7.reshape(-1, 4); add_7 = None 2025-03-04T21:08:25.4879491Z 2025-03-04T21:08:25.4879797Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:08:25.4879924Z tensor: "f32[269952, 4][4, 1]cpu" = x_116.to(torch.float32); x_116 = None 2025-03-04T21:08:25.4880060Z tensor_1: "f32[67488, 4][4, 1]cpu" = x_117.to(torch.float32); x_117 = None 2025-03-04T21:08:25.4880182Z tensor_2: "f32[16872, 4][4, 1]cpu" = x_118.to(torch.float32); x_118 = None 2025-03-04T21:08:25.4880308Z tensor_3: "f32[4218, 4][4, 1]cpu" = x_119.to(torch.float32); x_119 = None 2025-03-04T21:08:25.4880425Z tensor_4: "f32[1083, 4][4, 1]cpu" = x_120.to(torch.float32); x_120 = None 2025-03-04T21:08:25.4880497Z 2025-03-04T21:08:25.4880778Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4881302Z x_121: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_115, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_115 = None 2025-03-04T21:08:25.4881363Z 2025-03-04T21:08:25.4881648Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:08:25.4881851Z x_122: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.relu(x_121, inplace = False); x_121 = None 2025-03-04T21:08:25.4881928Z 2025-03-04T21:08:25.4882322Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:08:25.4882872Z score: "f32[4, 3, 296, 304][269952, 89984, 304, 1]cpu" = torch.conv2d(x_122, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:08:25.4882939Z 2025-03-04T21:08:25.4883323Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:08:25.4883859Z x_131: "f32[4, 12, 296, 304][1079808, 89984, 304, 1]cpu" = torch.conv2d(x_122, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_122 = None 2025-03-04T21:08:25.4883922Z 2025-03-04T21:08:25.4884193Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4884681Z x_123: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(x_113, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_113 = None 2025-03-04T21:08:25.4884747Z 2025-03-04T21:08:25.4885018Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:08:25.4885217Z x_124: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.relu(x_123, inplace = False); x_123 = None 2025-03-04T21:08:25.4885278Z 2025-03-04T21:08:25.4885667Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:08:25.4886219Z score_1: "f32[4, 3, 148, 152][67488, 22496, 152, 1]cpu" = torch.conv2d(x_124, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:08:25.4886281Z 2025-03-04T21:08:25.4886645Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:08:25.4887170Z x_132: "f32[4, 12, 148, 152][269952, 22496, 152, 1]cpu" = torch.conv2d(x_124, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_124 = None 2025-03-04T21:08:25.4887240Z 2025-03-04T21:08:25.4887507Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4887986Z x_125: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(x_111, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_111 = None 2025-03-04T21:08:25.4888046Z 2025-03-04T21:08:25.4888331Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:08:25.4888513Z x_126: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.relu(x_125, inplace = False); x_125 = None 2025-03-04T21:08:25.4888597Z 2025-03-04T21:08:25.4888979Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:08:25.4889492Z score_2: "f32[4, 3, 74, 76][16872, 5624, 76, 1]cpu" = torch.conv2d(x_126, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:08:25.4889562Z 2025-03-04T21:08:25.4889935Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:08:25.4890445Z x_133: "f32[4, 12, 74, 76][67488, 5624, 76, 1]cpu" = torch.conv2d(x_126, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_126 = None 2025-03-04T21:08:25.4890508Z 2025-03-04T21:08:25.4890771Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4891239Z x_127: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_109, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); x_109 = None 2025-03-04T21:08:25.4891308Z 2025-03-04T21:08:25.4891579Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:08:25.4891765Z x_128: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.nn.functional.relu(x_127, inplace = False); x_127 = None 2025-03-04T21:08:25.4891829Z 2025-03-04T21:08:25.4892223Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:08:25.4892725Z score_3: "f32[4, 3, 37, 38][4218, 1406, 38, 1]cpu" = torch.conv2d(x_128, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:08:25.4892792Z 2025-03-04T21:08:25.4893151Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:08:25.4893654Z x_134: "f32[4, 12, 37, 38][16872, 1406, 38, 1]cpu" = torch.conv2d(x_128, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_128 = None 2025-03-04T21:08:25.4893724Z 2025-03-04T21:08:25.4893991Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:25.4894762Z x_129: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.conv2d(res, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); res = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T21:08:25.4894822Z 2025-03-04T21:08:25.4895093Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:08:25.4895276Z x_130: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.relu(x_129, inplace = False); x_129 = None 2025-03-04T21:08:25.4895343Z 2025-03-04T21:08:25.4895709Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:08:25.4896565Z score_4: "f32[4, 3, 19, 19][1083, 361, 19, 1]cpu" = torch.conv2d(x_130, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T21:08:25.4896635Z 2025-03-04T21:08:25.4896978Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:08:25.4897783Z x_135: "f32[4, 12, 19, 19][4332, 361, 19, 1]cpu" = torch.conv2d(x_130, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_130 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T21:08:25.4897845Z 2025-03-04T21:08:25.4898186Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T21:08:25.4898351Z permute: "f32[4, 296, 304, 3][269952, 304, 1, 89984]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T21:08:25.4898517Z logits_i: "f32[4, 269952][269952, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T21:08:25.4898688Z permute_1: "f32[4, 148, 152, 3][67488, 152, 1, 22496]cpu" = score_1.permute(0, 2, 3, 1); score_1 = None 2025-03-04T21:08:25.4898833Z logits_i_1: "f32[4, 67488][67488, 1]cpu" = permute_1.flatten(1); permute_1 = None 2025-03-04T21:08:25.4898980Z permute_2: "f32[4, 74, 76, 3][16872, 76, 1, 5624]cpu" = score_2.permute(0, 2, 3, 1); score_2 = None 2025-03-04T21:08:25.4899120Z logits_i_2: "f32[4, 16872][16872, 1]cpu" = permute_2.flatten(1); permute_2 = None 2025-03-04T21:08:25.4899262Z permute_3: "f32[4, 37, 38, 3][4218, 38, 1, 1406]cpu" = score_3.permute(0, 2, 3, 1); score_3 = None 2025-03-04T21:08:25.4899394Z logits_i_3: "f32[4, 4218][4218, 1]cpu" = permute_3.flatten(1); permute_3 = None 2025-03-04T21:08:25.4899536Z permute_4: "f32[4, 19, 19, 3][1083, 19, 1, 361]cpu" = score_4.permute(0, 2, 3, 1); score_4 = None 2025-03-04T21:08:25.4899669Z logits_i_4: "f32[4, 1083][1083, 1]cpu" = permute_4.flatten(1); permute_4 = None 2025-03-04T21:08:25.4899729Z 2025-03-04T21:08:25.4900174Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T21:08:25.4900351Z view_10: "f32[4, 3, 4, 296, 304][1079808, 359936, 89984, 304, 1]cpu" = x_131.view(4, -1, 4, 296, 304); x_131 = None 2025-03-04T21:08:25.4900529Z permute_5: "f32[4, 296, 304, 3, 4][1079808, 304, 1, 359936, 89984]cpu" = view_10.permute(0, 3, 4, 1, 2); view_10 = None 2025-03-04T21:08:25.4900709Z pred_anchor_deltas_i: "f32[4, 269952, 4][1079808, 4, 1]cpu" = permute_5.flatten(1, -2); permute_5 = None 2025-03-04T21:08:25.4900883Z view_11: "f32[4, 3, 4, 148, 152][269952, 89984, 22496, 152, 1]cpu" = x_132.view(4, -1, 4, 148, 152); x_132 = None 2025-03-04T21:08:25.4901062Z permute_6: "f32[4, 148, 152, 3, 4][269952, 152, 1, 89984, 22496]cpu" = view_11.permute(0, 3, 4, 1, 2); view_11 = None 2025-03-04T21:08:25.4901229Z pred_anchor_deltas_i_2: "f32[4, 67488, 4][269952, 4, 1]cpu" = permute_6.flatten(1, -2); permute_6 = None 2025-03-04T21:08:25.4901378Z view_12: "f32[4, 3, 4, 74, 76][67488, 22496, 5624, 76, 1]cpu" = x_133.view(4, -1, 4, 74, 76); x_133 = None 2025-03-04T21:08:25.4901537Z permute_7: "f32[4, 74, 76, 3, 4][67488, 76, 1, 22496, 5624]cpu" = view_12.permute(0, 3, 4, 1, 2); view_12 = None 2025-03-04T21:08:25.4901722Z pred_anchor_deltas_i_4: "f32[4, 16872, 4][67488, 4, 1]cpu" = permute_7.flatten(1, -2); permute_7 = None 2025-03-04T21:08:25.4901862Z view_13: "f32[4, 3, 4, 37, 38][16872, 5624, 1406, 38, 1]cpu" = x_134.view(4, -1, 4, 37, 38); x_134 = None 2025-03-04T21:08:25.4902120Z permute_8: "f32[4, 37, 38, 3, 4][16872, 38, 1, 5624, 1406]cpu" = view_13.permute(0, 3, 4, 1, 2); view_13 = None 2025-03-04T21:08:25.4902288Z pred_anchor_deltas_i_6: "f32[4, 4218, 4][16872, 4, 1]cpu" = permute_8.flatten(1, -2); permute_8 = None 2025-03-04T21:08:25.4902431Z view_14: "f32[4, 3, 4, 19, 19][4332, 1444, 361, 19, 1]cpu" = x_135.view(4, -1, 4, 19, 19); x_135 = None 2025-03-04T21:08:25.4902583Z permute_9: "f32[4, 19, 19, 3, 4][4332, 19, 1, 1444, 361]cpu" = view_14.permute(0, 3, 4, 1, 2); view_14 = None 2025-03-04T21:08:25.4902751Z pred_anchor_deltas_i_8: "f32[4, 1083, 4][4332, 4, 1]cpu" = permute_9.flatten(1, -2); permute_9 = None 2025-03-04T21:08:25.4902812Z 2025-03-04T21:08:25.4903215Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:08:25.4903412Z pred_anchor_deltas_i_1: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T21:08:25.4903512Z 2025-03-04T21:08:25.4903942Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:08:25.4904100Z unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T21:08:25.4904250Z expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T21:08:25.4904390Z anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T21:08:25.4904459Z 2025-03-04T21:08:25.4904842Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:08:25.4905015Z deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T21:08:25.4905075Z 2025-03-04T21:08:25.4905411Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:08:25.4905551Z boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T21:08:25.4905620Z 2025-03-04T21:08:25.4905927Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:08:25.4906063Z getitem_10: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:08:25.4906188Z getitem_11: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:08:25.4906339Z widths: "f32[1079808][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T21:08:25.4906419Z 2025-03-04T21:08:25.4906734Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:08:25.4906856Z getitem_12: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:08:25.4906978Z getitem_13: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:08:25.4907124Z heights: "f32[1079808][1]cpu" = getitem_12 - getitem_13; getitem_12 = getitem_13 = None 2025-03-04T21:08:25.4907191Z 2025-03-04T21:08:25.4907523Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:08:25.4907651Z getitem_14: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:08:25.4907732Z mul: "f32[1079808][1]cpu" = 0.5 * widths 2025-03-04T21:08:25.4907863Z ctr_x: "f32[1079808][1]cpu" = getitem_14 + mul; getitem_14 = mul = None 2025-03-04T21:08:25.4907924Z 2025-03-04T21:08:25.4908236Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:08:25.4908377Z getitem_15: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:08:25.4908470Z mul_1: "f32[1079808][1]cpu" = 0.5 * heights 2025-03-04T21:08:25.4908596Z ctr_y: "f32[1079808][1]cpu" = getitem_15 + mul_1; getitem_15 = mul_1 = None 2025-03-04T21:08:25.4908663Z 2025-03-04T21:08:25.4908994Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:08:25.4909152Z getitem_16: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:08:25.4909266Z dx: "f32[1079808, 1][1, 1]cpu" = getitem_16 / 1.0; getitem_16 = None 2025-03-04T21:08:25.4909349Z 2025-03-04T21:08:25.4909642Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:08:25.4909797Z getitem_17: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:08:25.4909903Z dy: "f32[1079808, 1][1, 1]cpu" = getitem_17 / 1.0; getitem_17 = None 2025-03-04T21:08:25.4909972Z 2025-03-04T21:08:25.4910261Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:08:25.4910412Z getitem_18: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:08:25.4910517Z dw: "f32[1079808, 1][1, 1]cpu" = getitem_18 / 1.0; getitem_18 = None 2025-03-04T21:08:25.4910585Z 2025-03-04T21:08:25.4910889Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:08:25.4911083Z getitem_19: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:08:25.4911195Z dh: "f32[1079808, 1][1, 1]cpu" = getitem_19 / 1.0; getitem_19 = None 2025-03-04T21:08:25.4911255Z 2025-03-04T21:08:25.4911589Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:08:25.4911729Z dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:08:25.4911796Z 2025-03-04T21:08:25.4912119Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:08:25.4912273Z dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:08:25.4912336Z 2025-03-04T21:08:25.4912687Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:08:25.4912823Z getitem_20: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:08:25.4912959Z mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_20; dx = getitem_20 = None 2025-03-04T21:08:25.4913120Z getitem_21: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:08:25.4913261Z pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_21; mul_2 = getitem_21 = None 2025-03-04T21:08:25.4913321Z 2025-03-04T21:08:25.4913675Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:08:25.4913816Z getitem_22: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:08:25.4913943Z mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_22; dy = getitem_22 = None 2025-03-04T21:08:25.4914090Z getitem_23: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:08:25.4914231Z pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_23; mul_3 = getitem_23 = None 2025-03-04T21:08:25.4914293Z 2025-03-04T21:08:25.4914632Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:08:25.4914748Z exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:08:25.4914915Z getitem_24: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:08:25.4915063Z pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_24; exp = getitem_24 = None 2025-03-04T21:08:25.4915131Z 2025-03-04T21:08:25.4915463Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:08:25.4915587Z exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:08:25.4915750Z getitem_25: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:08:25.4915891Z pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_25; exp_1 = getitem_25 = None 2025-03-04T21:08:25.4915950Z 2025-03-04T21:08:25.4916316Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:08:25.4916421Z mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T21:08:25.4916546Z x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:08:25.4916607Z 2025-03-04T21:08:25.4916940Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:08:25.4917033Z mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T21:08:25.4917152Z y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:08:25.4917214Z 2025-03-04T21:08:25.4917528Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:08:25.4917651Z mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:08:25.4917787Z x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:08:25.4917862Z 2025-03-04T21:08:25.4918168Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:08:25.4918274Z mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:08:25.4918403Z y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:08:25.4918463Z 2025-03-04T21:08:25.4918806Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:08:25.4919003Z pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:08:25.4919066Z 2025-03-04T21:08:25.4919395Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:08:25.4919554Z proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4)); pred_boxes = None 2025-03-04T21:08:25.4919620Z 2025-03-04T21:08:25.4919989Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:08:25.4920164Z proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T21:08:25.4920222Z 2025-03-04T21:08:25.4920613Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:08:25.4920814Z pred_anchor_deltas_i_3: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_2.reshape(-1, 4); pred_anchor_deltas_i_2 = None 2025-03-04T21:08:25.4920882Z 2025-03-04T21:08:25.4921324Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:08:25.4921481Z unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = tensor_1.unsqueeze(0); tensor_1 = None 2025-03-04T21:08:25.4921623Z expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1); unsqueeze_1 = None 2025-03-04T21:08:25.4921764Z anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4); expand_1 = None 2025-03-04T21:08:25.4921824Z 2025-03-04T21:08:25.4922195Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:08:25.4922359Z deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float(); pred_anchor_deltas_i_3 = None 2025-03-04T21:08:25.4922429Z 2025-03-04T21:08:25.4922747Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:08:25.4922895Z boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32); anchors_i_1 = None 2025-03-04T21:08:25.4922953Z 2025-03-04T21:08:25.4923271Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:08:25.4923398Z getitem_26: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)] 2025-03-04T21:08:25.4923528Z getitem_27: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T21:08:25.4923671Z widths_1: "f32[269952][1]cpu" = getitem_26 - getitem_27; getitem_26 = getitem_27 = None 2025-03-04T21:08:25.4923754Z 2025-03-04T21:08:25.4924061Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:08:25.4924189Z getitem_28: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)] 2025-03-04T21:08:25.4924304Z getitem_29: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)] 2025-03-04T21:08:25.4924455Z heights_1: "f32[269952][1]cpu" = getitem_28 - getitem_29; getitem_28 = getitem_29 = None 2025-03-04T21:08:25.4924514Z 2025-03-04T21:08:25.4924835Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:08:25.4924952Z getitem_30: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T21:08:25.4925046Z mul_10: "f32[269952][1]cpu" = 0.5 * widths_1 2025-03-04T21:08:25.4925174Z ctr_x_1: "f32[269952][1]cpu" = getitem_30 + mul_10; getitem_30 = mul_10 = None 2025-03-04T21:08:25.4925244Z 2025-03-04T21:08:25.4925544Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:08:25.4925694Z getitem_31: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]; boxes_1 = None 2025-03-04T21:08:25.4925788Z mul_11: "f32[269952][1]cpu" = 0.5 * heights_1 2025-03-04T21:08:25.4925914Z ctr_y_1: "f32[269952][1]cpu" = getitem_31 + mul_11; getitem_31 = mul_11 = None 2025-03-04T21:08:25.4925981Z 2025-03-04T21:08:25.4926281Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:08:25.4926440Z getitem_32: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:08:25.4926561Z dx_1: "f32[269952, 1][1, 1]cpu" = getitem_32 / 1.0; getitem_32 = None 2025-03-04T21:08:25.4926641Z 2025-03-04T21:08:25.4926934Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:08:25.4927088Z getitem_33: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:08:25.4927197Z dy_1: "f32[269952, 1][1, 1]cpu" = getitem_33 / 1.0; getitem_33 = None 2025-03-04T21:08:25.4927264Z 2025-03-04T21:08:25.4927561Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:08:25.4927717Z getitem_34: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:08:25.4927825Z dw_2: "f32[269952, 1][1, 1]cpu" = getitem_34 / 1.0; getitem_34 = None 2025-03-04T21:08:25.4927894Z 2025-03-04T21:08:25.4928195Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:08:25.4928401Z getitem_35: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))]; deltas_1 = None 2025-03-04T21:08:25.4928508Z dh_2: "f32[269952, 1][1, 1]cpu" = getitem_35 / 1.0; getitem_35 = None 2025-03-04T21:08:25.4928579Z 2025-03-04T21:08:25.4928911Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:08:25.4929055Z dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356); dw_2 = None 2025-03-04T21:08:25.4929118Z 2025-03-04T21:08:25.4929454Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:08:25.4929616Z dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356); dh_2 = None 2025-03-04T21:08:25.4929684Z 2025-03-04T21:08:25.4930028Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:08:25.4930168Z getitem_36: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)] 2025-03-04T21:08:25.4930291Z mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_36; dx_1 = getitem_36 = None 2025-03-04T21:08:25.4930467Z getitem_37: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)]; ctr_x_1 = None 2025-03-04T21:08:25.4930608Z pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_37; mul_12 = getitem_37 = None 2025-03-04T21:08:25.4930677Z 2025-03-04T21:08:25.4931024Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:08:25.4931168Z getitem_38: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)] 2025-03-04T21:08:25.4931295Z mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_38; dy_1 = getitem_38 = None 2025-03-04T21:08:25.4931444Z getitem_39: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)]; ctr_y_1 = None 2025-03-04T21:08:25.4931586Z pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_39; mul_13 = getitem_39 = None 2025-03-04T21:08:25.4931646Z 2025-03-04T21:08:25.4931979Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:08:25.4932090Z exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3); dw_3 = None 2025-03-04T21:08:25.4932257Z getitem_40: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]; widths_1 = None 2025-03-04T21:08:25.4932405Z pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_40; exp_2 = getitem_40 = None 2025-03-04T21:08:25.4932474Z 2025-03-04T21:08:25.4932803Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:08:25.4932916Z exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3); dh_3 = None 2025-03-04T21:08:25.4933079Z getitem_41: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]; heights_1 = None 2025-03-04T21:08:25.4933216Z pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_41; exp_3 = getitem_41 = None 2025-03-04T21:08:25.4933276Z 2025-03-04T21:08:25.4933592Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:08:25.4933689Z mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1 2025-03-04T21:08:25.4933837Z x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16; mul_16 = None 2025-03-04T21:08:25.4933902Z 2025-03-04T21:08:25.4934214Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:08:25.4934308Z mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1 2025-03-04T21:08:25.4934428Z y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17; mul_17 = None 2025-03-04T21:08:25.4934488Z 2025-03-04T21:08:25.4934798Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:08:25.4934912Z mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1; pred_w_1 = None 2025-03-04T21:08:25.4935067Z x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18; pred_ctr_x_1 = mul_18 = None 2025-03-04T21:08:25.4935130Z 2025-03-04T21:08:25.4935443Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:08:25.4935555Z mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1; pred_h_1 = None 2025-03-04T21:08:25.4935692Z y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19; pred_ctr_y_1 = mul_19 = None 2025-03-04T21:08:25.4935752Z 2025-03-04T21:08:25.4936124Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:08:25.4936316Z pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1); x1_1 = y1_1 = x2_1 = y2_1 = None 2025-03-04T21:08:25.4936389Z 2025-03-04T21:08:25.4936719Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:08:25.4936893Z proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4)); pred_boxes_1 = None 2025-03-04T21:08:25.4936956Z 2025-03-04T21:08:25.4937341Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:08:25.4937513Z proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4); proposals_i_1 = None 2025-03-04T21:08:25.4937581Z 2025-03-04T21:08:25.4937980Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:08:25.4938193Z pred_anchor_deltas_i_5: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_4.reshape(-1, 4); pred_anchor_deltas_i_4 = None 2025-03-04T21:08:25.4938272Z 2025-03-04T21:08:25.4938709Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:08:25.4938866Z unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = tensor_2.unsqueeze(0); tensor_2 = None 2025-03-04T21:08:25.4939015Z expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1); unsqueeze_2 = None 2025-03-04T21:08:25.4939156Z anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4); expand_2 = None 2025-03-04T21:08:25.4939219Z 2025-03-04T21:08:25.4939593Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:08:25.4939760Z deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_5.float(); pred_anchor_deltas_i_5 = None 2025-03-04T21:08:25.4939827Z 2025-03-04T21:08:25.4940151Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:08:25.4940299Z boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32); anchors_i_2 = None 2025-03-04T21:08:25.4940359Z 2025-03-04T21:08:25.4940687Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:08:25.4940809Z getitem_42: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)] 2025-03-04T21:08:25.4940934Z getitem_43: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T21:08:25.4941090Z widths_2: "f32[67488][1]cpu" = getitem_42 - getitem_43; getitem_42 = getitem_43 = None 2025-03-04T21:08:25.4941157Z 2025-03-04T21:08:25.4941468Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:08:25.4941595Z getitem_44: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)] 2025-03-04T21:08:25.4941710Z getitem_45: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)] 2025-03-04T21:08:25.4941860Z heights_2: "f32[67488][1]cpu" = getitem_44 - getitem_45; getitem_44 = getitem_45 = None 2025-03-04T21:08:25.4941919Z 2025-03-04T21:08:25.4942243Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:08:25.4942359Z getitem_46: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T21:08:25.4942453Z mul_20: "f32[67488][1]cpu" = 0.5 * widths_2 2025-03-04T21:08:25.4942580Z ctr_x_2: "f32[67488][1]cpu" = getitem_46 + mul_20; getitem_46 = mul_20 = None 2025-03-04T21:08:25.4942647Z 2025-03-04T21:08:25.4942952Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:08:25.4943100Z getitem_47: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]; boxes_2 = None 2025-03-04T21:08:25.4943190Z mul_21: "f32[67488][1]cpu" = 0.5 * heights_2 2025-03-04T21:08:25.4943322Z ctr_y_2: "f32[67488][1]cpu" = getitem_47 + mul_21; getitem_47 = mul_21 = None 2025-03-04T21:08:25.4943382Z 2025-03-04T21:08:25.4943684Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:08:25.4943832Z getitem_48: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:08:25.4943962Z dx_2: "f32[67488, 1][1, 1]cpu" = getitem_48 / 1.0; getitem_48 = None 2025-03-04T21:08:25.4944021Z 2025-03-04T21:08:25.4944324Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:08:25.4944471Z getitem_49: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:08:25.4944588Z dy_2: "f32[67488, 1][1, 1]cpu" = getitem_49 / 1.0; getitem_49 = None 2025-03-04T21:08:25.4944647Z 2025-03-04T21:08:25.4944956Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:08:25.4945104Z getitem_50: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:08:25.4945211Z dw_4: "f32[67488, 1][1, 1]cpu" = getitem_50 / 1.0; getitem_50 = None 2025-03-04T21:08:25.4945281Z 2025-03-04T21:08:25.4945586Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:08:25.4945770Z getitem_51: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))]; deltas_2 = None 2025-03-04T21:08:25.4945873Z dh_4: "f32[67488, 1][1, 1]cpu" = getitem_51 / 1.0; getitem_51 = None 2025-03-04T21:08:25.4945941Z 2025-03-04T21:08:25.4946267Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:08:25.4946403Z dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356); dw_4 = None 2025-03-04T21:08:25.4946474Z 2025-03-04T21:08:25.4946811Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:08:25.4946944Z dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356); dh_4 = None 2025-03-04T21:08:25.4947008Z 2025-03-04T21:08:25.4947339Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:08:25.4947474Z getitem_52: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)] 2025-03-04T21:08:25.4947609Z mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_52; dx_2 = getitem_52 = None 2025-03-04T21:08:25.4947765Z getitem_53: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)]; ctr_x_2 = None 2025-03-04T21:08:25.4947899Z pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_53; mul_22 = getitem_53 = None 2025-03-04T21:08:25.4947965Z 2025-03-04T21:08:25.4948300Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:08:25.4948439Z getitem_54: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)] 2025-03-04T21:08:25.4948555Z mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_54; dy_2 = getitem_54 = None 2025-03-04T21:08:25.4948706Z getitem_55: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)]; ctr_y_2 = None 2025-03-04T21:08:25.4948838Z pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_55; mul_23 = getitem_55 = None 2025-03-04T21:08:25.4948904Z 2025-03-04T21:08:25.4949225Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:08:25.4949341Z exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5); dw_5 = None 2025-03-04T21:08:25.4949511Z getitem_56: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]; widths_2 = None 2025-03-04T21:08:25.4949647Z pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_56; exp_4 = getitem_56 = None 2025-03-04T21:08:25.4949707Z 2025-03-04T21:08:25.4950037Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:08:25.4950143Z exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5); dh_5 = None 2025-03-04T21:08:25.4950309Z getitem_57: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]; heights_2 = None 2025-03-04T21:08:25.4950435Z pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_57; exp_5 = getitem_57 = None 2025-03-04T21:08:25.4950504Z 2025-03-04T21:08:25.4950805Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:08:25.4950905Z mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2 2025-03-04T21:08:25.4951035Z x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26; mul_26 = None 2025-03-04T21:08:25.4951106Z 2025-03-04T21:08:25.4951407Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:08:25.4951502Z mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2 2025-03-04T21:08:25.4951611Z y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27; mul_27 = None 2025-03-04T21:08:25.4951680Z 2025-03-04T21:08:25.4951973Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:08:25.4952109Z mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2; pred_w_2 = None 2025-03-04T21:08:25.4952245Z x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28; pred_ctr_x_2 = mul_28 = None 2025-03-04T21:08:25.4952305Z 2025-03-04T21:08:25.4952607Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:08:25.4952716Z mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2; pred_h_2 = None 2025-03-04T21:08:25.4952846Z y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29; pred_ctr_y_2 = mul_29 = None 2025-03-04T21:08:25.4952906Z 2025-03-04T21:08:25.4953261Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:08:25.4953442Z pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1); x1_2 = y1_2 = x2_2 = y2_2 = None 2025-03-04T21:08:25.4953511Z 2025-03-04T21:08:25.4953834Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:08:25.4953995Z proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4)); pred_boxes_2 = None 2025-03-04T21:08:25.4954055Z 2025-03-04T21:08:25.4954432Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:08:25.4954598Z proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4); proposals_i_2 = None 2025-03-04T21:08:25.4954665Z 2025-03-04T21:08:25.4955055Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:08:25.4955275Z pred_anchor_deltas_i_7: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_6.reshape(-1, 4); pred_anchor_deltas_i_6 = None 2025-03-04T21:08:25.4955336Z 2025-03-04T21:08:25.4955768Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:08:25.4955912Z unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = tensor_3.unsqueeze(0); tensor_3 = None 2025-03-04T21:08:25.4956064Z expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1); unsqueeze_3 = None 2025-03-04T21:08:25.4956208Z anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4); expand_3 = None 2025-03-04T21:08:25.4956331Z 2025-03-04T21:08:25.4956707Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:08:25.4956903Z deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_7.float(); pred_anchor_deltas_i_7 = None 2025-03-04T21:08:25.4956967Z 2025-03-04T21:08:25.4957291Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:08:25.4957432Z boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32); anchors_i_3 = None 2025-03-04T21:08:25.4957503Z 2025-03-04T21:08:25.4957819Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:08:25.4957954Z getitem_58: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)] 2025-03-04T21:08:25.4958092Z getitem_59: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T21:08:25.4958247Z widths_3: "f32[16872][1]cpu" = getitem_58 - getitem_59; getitem_58 = getitem_59 = None 2025-03-04T21:08:25.4958307Z 2025-03-04T21:08:25.4958635Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:08:25.4958763Z getitem_60: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)] 2025-03-04T21:08:25.4958880Z getitem_61: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)] 2025-03-04T21:08:25.4959050Z heights_3: "f32[16872][1]cpu" = getitem_60 - getitem_61; getitem_60 = getitem_61 = None 2025-03-04T21:08:25.4959113Z 2025-03-04T21:08:25.4959430Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:08:25.4959551Z getitem_62: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T21:08:25.4959647Z mul_30: "f32[16872][1]cpu" = 0.5 * widths_3 2025-03-04T21:08:25.4959778Z ctr_x_3: "f32[16872][1]cpu" = getitem_62 + mul_30; getitem_62 = mul_30 = None 2025-03-04T21:08:25.4959844Z 2025-03-04T21:08:25.4960154Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:08:25.4960304Z getitem_63: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]; boxes_3 = None 2025-03-04T21:08:25.4960395Z mul_31: "f32[16872][1]cpu" = 0.5 * heights_3 2025-03-04T21:08:25.4960530Z ctr_y_3: "f32[16872][1]cpu" = getitem_63 + mul_31; getitem_63 = mul_31 = None 2025-03-04T21:08:25.4960591Z 2025-03-04T21:08:25.4960903Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:08:25.4961055Z getitem_64: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:08:25.4961189Z dx_3: "f32[16872, 1][1, 1]cpu" = getitem_64 / 1.0; getitem_64 = None 2025-03-04T21:08:25.4961250Z 2025-03-04T21:08:25.4961553Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:08:25.4961699Z getitem_65: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:08:25.4961812Z dy_3: "f32[16872, 1][1, 1]cpu" = getitem_65 / 1.0; getitem_65 = None 2025-03-04T21:08:25.4961873Z 2025-03-04T21:08:25.4962172Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:08:25.4962318Z getitem_66: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:08:25.4962430Z dw_6: "f32[16872, 1][1, 1]cpu" = getitem_66 / 1.0; getitem_66 = None 2025-03-04T21:08:25.4962490Z 2025-03-04T21:08:25.4962809Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:08:25.4962991Z getitem_67: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))]; deltas_3 = None 2025-03-04T21:08:25.4963106Z dh_6: "f32[16872, 1][1, 1]cpu" = getitem_67 / 1.0; getitem_67 = None 2025-03-04T21:08:25.4963166Z 2025-03-04T21:08:25.4963510Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:08:25.4963646Z dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356); dw_6 = None 2025-03-04T21:08:25.4963732Z 2025-03-04T21:08:25.4964064Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:08:25.4964205Z dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356); dh_6 = None 2025-03-04T21:08:25.4964266Z 2025-03-04T21:08:25.4964615Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:08:25.4964745Z getitem_68: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)] 2025-03-04T21:08:25.4964918Z mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_68; dx_3 = getitem_68 = None 2025-03-04T21:08:25.4965075Z getitem_69: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)]; ctr_x_3 = None 2025-03-04T21:08:25.4965214Z pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_69; mul_32 = getitem_69 = None 2025-03-04T21:08:25.4965282Z 2025-03-04T21:08:25.4965628Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:08:25.4965770Z getitem_70: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)] 2025-03-04T21:08:25.4965888Z mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_70; dy_3 = getitem_70 = None 2025-03-04T21:08:25.4966042Z getitem_71: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)]; ctr_y_3 = None 2025-03-04T21:08:25.4966177Z pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_71; mul_33 = getitem_71 = None 2025-03-04T21:08:25.4966244Z 2025-03-04T21:08:25.4966573Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:08:25.4966707Z exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7); dw_7 = None 2025-03-04T21:08:25.4966863Z getitem_72: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]; widths_3 = None 2025-03-04T21:08:25.4966999Z pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_72; exp_6 = getitem_72 = None 2025-03-04T21:08:25.4967059Z 2025-03-04T21:08:25.4967402Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:08:25.4967517Z exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7); dh_7 = None 2025-03-04T21:08:25.4967685Z getitem_73: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]; heights_3 = None 2025-03-04T21:08:25.4967811Z pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_73; exp_7 = getitem_73 = None 2025-03-04T21:08:25.4967879Z 2025-03-04T21:08:25.4968186Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:08:25.4968329Z mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3 2025-03-04T21:08:25.4968443Z x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36; mul_36 = None 2025-03-04T21:08:25.4968512Z 2025-03-04T21:08:25.4968817Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:08:25.4968910Z mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3 2025-03-04T21:08:25.4969019Z y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37; mul_37 = None 2025-03-04T21:08:25.4969087Z 2025-03-04T21:08:25.4969390Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:08:25.4969528Z mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3; pred_w_3 = None 2025-03-04T21:08:25.4969660Z x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38; pred_ctr_x_3 = mul_38 = None 2025-03-04T21:08:25.4969728Z 2025-03-04T21:08:25.4970028Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:08:25.4970149Z mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3; pred_h_3 = None 2025-03-04T21:08:25.4970277Z y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39; pred_ctr_y_3 = mul_39 = None 2025-03-04T21:08:25.4970361Z 2025-03-04T21:08:25.4970714Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:08:25.4970907Z pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1); x1_3 = y1_3 = x2_3 = y2_3 = None 2025-03-04T21:08:25.4970967Z 2025-03-04T21:08:25.4971309Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:08:25.4971462Z proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4)); pred_boxes_3 = None 2025-03-04T21:08:25.4971526Z 2025-03-04T21:08:25.4971894Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:08:25.4972064Z proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4); proposals_i_3 = None 2025-03-04T21:08:25.4972122Z 2025-03-04T21:08:25.4972515Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:08:25.4972741Z pred_anchor_deltas_i_9: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_8.reshape(-1, 4); pred_anchor_deltas_i_8 = None 2025-03-04T21:08:25.4972800Z 2025-03-04T21:08:25.4973227Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:08:25.4973368Z unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = tensor_4.unsqueeze(0); tensor_4 = None 2025-03-04T21:08:25.4973516Z expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1); unsqueeze_4 = None 2025-03-04T21:08:25.4973641Z anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4); expand_4 = None 2025-03-04T21:08:25.4973708Z 2025-03-04T21:08:25.4974073Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:08:25.4974255Z deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_9.float(); pred_anchor_deltas_i_9 = None 2025-03-04T21:08:25.4974315Z 2025-03-04T21:08:25.4974622Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:08:25.4974753Z boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32); anchors_i_4 = None 2025-03-04T21:08:25.4974817Z 2025-03-04T21:08:25.4975117Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:08:25.4975244Z getitem_74: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)] 2025-03-04T21:08:25.4975378Z getitem_75: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T21:08:25.4975525Z widths_4: "f32[4332][1]cpu" = getitem_74 - getitem_75; getitem_74 = getitem_75 = None 2025-03-04T21:08:25.4975586Z 2025-03-04T21:08:25.4975900Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:08:25.4976014Z getitem_76: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)] 2025-03-04T21:08:25.4976131Z getitem_77: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)] 2025-03-04T21:08:25.4976283Z heights_4: "f32[4332][1]cpu" = getitem_76 - getitem_77; getitem_76 = getitem_77 = None 2025-03-04T21:08:25.4976352Z 2025-03-04T21:08:25.4976655Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:08:25.4976779Z getitem_78: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T21:08:25.4976863Z mul_40: "f32[4332][1]cpu" = 0.5 * widths_4 2025-03-04T21:08:25.4976994Z ctr_x_4: "f32[4332][1]cpu" = getitem_78 + mul_40; getitem_78 = mul_40 = None 2025-03-04T21:08:25.4977054Z 2025-03-04T21:08:25.4977365Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:08:25.4977502Z getitem_79: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]; boxes_4 = None 2025-03-04T21:08:25.4977597Z mul_41: "f32[4332][1]cpu" = 0.5 * heights_4 2025-03-04T21:08:25.4977718Z ctr_y_4: "f32[4332][1]cpu" = getitem_79 + mul_41; getitem_79 = mul_41 = None 2025-03-04T21:08:25.4977785Z 2025-03-04T21:08:25.4978078Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:08:25.4978254Z getitem_80: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:08:25.4978361Z dx_4: "f32[4332, 1][1, 1]cpu" = getitem_80 / 1.0; getitem_80 = None 2025-03-04T21:08:25.4978430Z 2025-03-04T21:08:25.4978719Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:08:25.4978870Z getitem_81: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:08:25.4978983Z dy_4: "f32[4332, 1][1, 1]cpu" = getitem_81 / 1.0; getitem_81 = None 2025-03-04T21:08:25.4979044Z 2025-03-04T21:08:25.4979336Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:08:25.4979478Z getitem_82: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:08:25.4979588Z dw_8: "f32[4332, 1][1, 1]cpu" = getitem_82 / 1.0; getitem_82 = None 2025-03-04T21:08:25.4979660Z 2025-03-04T21:08:25.4979960Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:08:25.4980132Z getitem_83: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))]; deltas_4 = None 2025-03-04T21:08:25.4980240Z dh_8: "f32[4332, 1][1, 1]cpu" = getitem_83 / 1.0; getitem_83 = None 2025-03-04T21:08:25.4980299Z 2025-03-04T21:08:25.4980629Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:08:25.4980773Z dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356); dw_8 = None 2025-03-04T21:08:25.4980840Z 2025-03-04T21:08:25.4981162Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:08:25.4981295Z dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356); dh_8 = None 2025-03-04T21:08:25.4981356Z 2025-03-04T21:08:25.4981696Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:08:25.4981836Z getitem_84: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)] 2025-03-04T21:08:25.4981960Z mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_84; dx_4 = getitem_84 = None 2025-03-04T21:08:25.4982104Z getitem_85: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)]; ctr_x_4 = None 2025-03-04T21:08:25.4982244Z pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_85; mul_42 = getitem_85 = None 2025-03-04T21:08:25.4982304Z 2025-03-04T21:08:25.4982646Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:08:25.4982772Z getitem_86: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)] 2025-03-04T21:08:25.4982894Z mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_86; dy_4 = getitem_86 = None 2025-03-04T21:08:25.4983037Z getitem_87: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)]; ctr_y_4 = None 2025-03-04T21:08:25.4983176Z pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_87; mul_43 = getitem_87 = None 2025-03-04T21:08:25.4983235Z 2025-03-04T21:08:25.4983565Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:08:25.4983689Z exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9); dw_9 = None 2025-03-04T21:08:25.4983850Z getitem_88: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]; widths_4 = None 2025-03-04T21:08:25.4983973Z pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_88; exp_8 = getitem_88 = None 2025-03-04T21:08:25.4984040Z 2025-03-04T21:08:25.4984356Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:08:25.4984470Z exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9); dh_9 = None 2025-03-04T21:08:25.4984628Z getitem_89: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]; heights_4 = None 2025-03-04T21:08:25.4984756Z pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_89; exp_9 = getitem_89 = None 2025-03-04T21:08:25.4984816Z 2025-03-04T21:08:25.4985138Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:08:25.4985238Z mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4 2025-03-04T21:08:25.4985346Z x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46; mul_46 = None 2025-03-04T21:08:25.4985404Z 2025-03-04T21:08:25.4985709Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:08:25.4985805Z mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4 2025-03-04T21:08:25.4985912Z y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47; mul_47 = None 2025-03-04T21:08:25.4985978Z 2025-03-04T21:08:25.4986272Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:08:25.4986402Z mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4; pred_w_4 = None 2025-03-04T21:08:25.4986528Z x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48; pred_ctr_x_4 = mul_48 = None 2025-03-04T21:08:25.4986593Z 2025-03-04T21:08:25.4986885Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:08:25.4986997Z mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4; pred_h_4 = None 2025-03-04T21:08:25.4987132Z y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49; pred_ctr_y_4 = mul_49 = None 2025-03-04T21:08:25.4987198Z 2025-03-04T21:08:25.4987530Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:08:25.4987714Z pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1); x1_4 = y1_4 = x2_4 = y2_4 = None 2025-03-04T21:08:25.4987775Z 2025-03-04T21:08:25.4988104Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:08:25.4988256Z proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4)); pred_boxes_4 = None 2025-03-04T21:08:25.4988321Z 2025-03-04T21:08:25.4988694Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:08:25.4988860Z proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4); proposals_i_4 = None 2025-03-04T21:08:25.4988920Z 2025-03-04T21:08:25.4989400Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T21:08:25.4989545Z arange_10: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T21:08:25.4989611Z 2025-03-04T21:08:25.4989899Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:25.4990040Z batch_idx: "i64[4][1]cpu" = arange_10.to(device(type='cpu')); arange_10 = None 2025-03-04T21:08:25.4990098Z 2025-03-04T21:08:25.4990528Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:08:25.4990637Z topk = logits_i.topk(1000, dim = 1); logits_i = None 2025-03-04T21:08:25.4990744Z topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0] 2025-03-04T21:08:25.4990853Z topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1]; topk = None 2025-03-04T21:08:25.4990921Z 2025-03-04T21:08:25.4991384Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:08:25.4991521Z getitem_92: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:08:25.4991745Z topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_92, topk_idx)]; proposals_i_5 = getitem_92 = topk_idx = None 2025-03-04T21:08:25.4991813Z 2025-03-04T21:08:25.4992259Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:08:25.4992447Z full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:08:25.4992516Z 2025-03-04T21:08:25.4992802Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:25.4992924Z to_21: "i64[1000][1]cpu" = full.to(device(type='cpu')); full = None 2025-03-04T21:08:25.4992985Z 2025-03-04T21:08:25.4993443Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:08:25.4993561Z topk_1 = logits_i_1.topk(1000, dim = 1); logits_i_1 = None 2025-03-04T21:08:25.4993676Z topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0] 2025-03-04T21:08:25.4993791Z topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1]; topk_1 = None 2025-03-04T21:08:25.4993860Z 2025-03-04T21:08:25.4994317Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:08:25.4994456Z getitem_96: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:08:25.4994693Z topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_96, topk_idx_1)]; proposals_i_6 = getitem_96 = topk_idx_1 = None 2025-03-04T21:08:25.4994765Z 2025-03-04T21:08:25.4995230Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:08:25.4995405Z full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:08:25.4995486Z 2025-03-04T21:08:25.4995794Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:25.4995924Z to_22: "i64[1000][1]cpu" = full_1.to(device(type='cpu')); full_1 = None 2025-03-04T21:08:25.4995993Z 2025-03-04T21:08:25.4996514Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:08:25.4996643Z topk_2 = logits_i_2.topk(1000, dim = 1); logits_i_2 = None 2025-03-04T21:08:25.4996753Z topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0] 2025-03-04T21:08:25.4996883Z topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1]; topk_2 = None 2025-03-04T21:08:25.4996948Z 2025-03-04T21:08:25.4997466Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:08:25.4997603Z getitem_100: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:08:25.4997853Z topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_100, topk_idx_2)]; proposals_i_7 = getitem_100 = topk_idx_2 = None 2025-03-04T21:08:25.4997917Z 2025-03-04T21:08:25.4998397Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:08:25.4998562Z full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:08:25.4998648Z 2025-03-04T21:08:25.4998947Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:25.4999079Z to_23: "i64[1000][1]cpu" = full_2.to(device(type='cpu')); full_2 = None 2025-03-04T21:08:25.4999147Z 2025-03-04T21:08:25.4999587Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:08:25.4999721Z topk_3 = logits_i_3.topk(1000, dim = 1); logits_i_3 = None 2025-03-04T21:08:25.4999826Z topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0] 2025-03-04T21:08:25.4999954Z topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1]; topk_3 = None 2025-03-04T21:08:25.5000018Z 2025-03-04T21:08:25.5000491Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:08:25.5000629Z getitem_104: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:08:25.5000876Z topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_104, topk_idx_3)]; proposals_i_8 = getitem_104 = topk_idx_3 = None 2025-03-04T21:08:25.5000939Z 2025-03-04T21:08:25.5001410Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:08:25.5001571Z full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:08:25.5001643Z 2025-03-04T21:08:25.5002029Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:25.5002201Z to_24: "i64[1000][1]cpu" = full_3.to(device(type='cpu')); full_3 = None 2025-03-04T21:08:25.5002267Z 2025-03-04T21:08:25.5002720Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:08:25.5002836Z topk_4 = logits_i_4.topk(1000, dim = 1); logits_i_4 = None 2025-03-04T21:08:25.5002953Z topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0] 2025-03-04T21:08:25.5003071Z topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1]; topk_4 = None 2025-03-04T21:08:25.5003148Z 2025-03-04T21:08:25.5003616Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:08:25.5003818Z getitem_108: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T21:08:25.5004054Z topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_108, topk_idx_4)]; proposals_i_9 = getitem_108 = topk_idx_4 = None 2025-03-04T21:08:25.5004125Z 2025-03-04T21:08:25.5004591Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:08:25.5004761Z full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:08:25.5004823Z 2025-03-04T21:08:25.5005145Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:25.5005272Z to_25: "i64[1000][1]cpu" = full_4.to(device(type='cpu')); full_4 = None 2025-03-04T21:08:25.5005333Z 2025-03-04T21:08:25.5005618Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:08:25.5006009Z topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1); topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None 2025-03-04T21:08:25.5006099Z 2025-03-04T21:08:25.5006386Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:08:25.5006865Z topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1); topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None 2025-03-04T21:08:25.5006932Z 2025-03-04T21:08:25.5007214Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:08:25.5007413Z level_ids: "i64[5000][1]cpu" = torch.cat([to_21, to_22, to_23, to_24, to_25], 0); to_21 = to_22 = to_23 = to_24 = to_25 = level_ids = None 2025-03-04T21:08:25.5007483Z 2025-03-04T21:08:25.5007879Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T21:08:25.5008022Z getitem_110: "f32[5000, 4][4, 1]cpu" = topk_proposals[0]; topk_proposals = None 2025-03-04T21:08:25.5008082Z 2025-03-04T21:08:25.5008374Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:08:25.5008533Z tensor_5: "f32[5000, 4][4, 1]cpu" = getitem_110.to(torch.float32); getitem_110 = None 2025-03-04T21:08:25.5008599Z 2025-03-04T21:08:25.5008964Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T21:08:25.5009100Z scores_per_img: "f32[5000][1]cpu" = topk_scores[0]; topk_scores = None 2025-03-04T21:08:25.5009160Z 2025-03-04T21:08:25.5009635Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T21:08:25.5009767Z isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor_5); tensor_5 = None 2025-03-04T21:08:25.5009892Z all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:08:25.5010055Z isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T21:08:25.5010190Z valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T21:08:25.5010249Z 2025-03-04T21:08:25.5010612Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T21:08:25.5010725Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T21:08:25.5010794Z 2025-03-04T21:08:30.9691266Z 2025-03-04T21:08:30.9692159Z class GraphModule(torch.nn.Module): 2025-03-04T21:08:30.9694956Z def forward(self, L_features_p2_: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu", L_features_p3_: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu", L_features_p4_: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu", L_features_p5_: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu", L_features_p6_: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_1_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_2_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_3_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_4_: "f32[3, 4][4, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_bias_: "f32[256][1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[3, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[3][1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[12, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[12][1]cpu"): 2025-03-04T21:08:30.9697684Z l_features_p2_ = L_features_p2_ 2025-03-04T21:08:30.9697917Z l_features_p3_ = L_features_p3_ 2025-03-04T21:08:30.9698169Z l_features_p4_ = L_features_p4_ 2025-03-04T21:08:30.9698399Z l_features_p5_ = L_features_p5_ 2025-03-04T21:08:30.9698620Z l_features_p6_ = L_features_p6_ 2025-03-04T21:08:30.9699076Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T21:08:30.9699731Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_1_ 2025-03-04T21:08:30.9700349Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_2_ 2025-03-04T21:08:30.9701029Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_3_ 2025-03-04T21:08:30.9701617Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_4_ 2025-03-04T21:08:30.9702408Z l_self_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T21:08:30.9702891Z l_self_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T21:08:30.9703414Z l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T21:08:30.9703995Z l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T21:08:30.9704569Z l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T21:08:30.9705236Z l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T21:08:30.9705592Z 2025-03-04T21:08:30.9706142Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:08:30.9706782Z arange: "f32[304][1]cpu" = torch.arange(0.0, 1216, step = 4, dtype = torch.float32) 2025-03-04T21:08:30.9707041Z 2025-03-04T21:08:30.9707415Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:30.9707922Z shifts_x: "f32[304][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T21:08:30.9708170Z 2025-03-04T21:08:30.9708683Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:08:30.9709303Z arange_1: "f32[296][1]cpu" = torch.arange(0.0, 1184, step = 4, dtype = torch.float32) 2025-03-04T21:08:30.9709569Z 2025-03-04T21:08:30.9709949Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:30.9710463Z shifts_y: "f32[296][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T21:08:30.9710735Z 2025-03-04T21:08:30.9711193Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:08:30.9711799Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T21:08:30.9712128Z shift_y: "f32[296, 304][1, 0]cpu" = meshgrid[0] 2025-03-04T21:08:30.9712436Z shift_x: "f32[296, 304][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T21:08:30.9712670Z 2025-03-04T21:08:30.9713084Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:08:30.9713605Z shift_x_1: "f32[89984][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T21:08:30.9713853Z 2025-03-04T21:08:30.9714279Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:08:30.9714804Z shift_y_1: "f32[89984][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T21:08:30.9715080Z 2025-03-04T21:08:30.9715573Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:08:30.9716298Z shifts: "f32[89984, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T21:08:30.9716626Z 2025-03-04T21:08:30.9717131Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:08:30.9717722Z view: "f32[89984, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T21:08:30.9718210Z view_1: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T21:08:30.9718688Z add: "f32[89984, 3, 4][12, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T21:08:30.9718993Z x: "f32[269952, 4][4, 1]cpu" = add.reshape(-1, 4); add = None 2025-03-04T21:08:30.9719220Z 2025-03-04T21:08:30.9719724Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:08:30.9720346Z arange_2: "f32[152][1]cpu" = torch.arange(0.0, 1216, step = 8, dtype = torch.float32) 2025-03-04T21:08:30.9720603Z 2025-03-04T21:08:30.9720973Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:30.9721472Z shifts_x_1: "f32[152][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T21:08:30.9721725Z 2025-03-04T21:08:30.9722231Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:08:30.9722850Z arange_3: "f32[148][1]cpu" = torch.arange(0.0, 1184, step = 8, dtype = torch.float32) 2025-03-04T21:08:30.9723105Z 2025-03-04T21:08:30.9723473Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:30.9723958Z shifts_y_1: "f32[148][1]cpu" = arange_3.to(device(type='cpu')); arange_3 = None 2025-03-04T21:08:30.9724207Z 2025-03-04T21:08:30.9724647Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:08:30.9725255Z meshgrid_1 = torch.functional.meshgrid(shifts_y_1, shifts_x_1); shifts_y_1 = shifts_x_1 = None 2025-03-04T21:08:30.9725619Z shift_y_2: "f32[148, 152][1, 0]cpu" = meshgrid_1[0] 2025-03-04T21:08:30.9725889Z shift_x_2: "f32[148, 152][0, 1]cpu" = meshgrid_1[1]; meshgrid_1 = None 2025-03-04T21:08:30.9726123Z 2025-03-04T21:08:30.9726517Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:08:30.9727014Z shift_x_3: "f32[22496][1]cpu" = shift_x_2.reshape(-1); shift_x_2 = None 2025-03-04T21:08:30.9727251Z 2025-03-04T21:08:30.9727646Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:08:30.9728140Z shift_y_3: "f32[22496][1]cpu" = shift_y_2.reshape(-1); shift_y_2 = None 2025-03-04T21:08:30.9728374Z 2025-03-04T21:08:30.9728847Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:08:30.9729481Z shifts_1: "f32[22496, 4][4, 1]cpu" = torch.stack((shift_x_3, shift_y_3, shift_x_3, shift_y_3), dim = 1); shift_x_3 = shift_y_3 = None 2025-03-04T21:08:30.9729797Z 2025-03-04T21:08:30.9730285Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:08:30.9730869Z view_2: "f32[22496, 1, 4][4, 4, 1]cpu" = shifts_1.view(-1, 1, 4); shifts_1 = None 2025-03-04T21:08:30.9731353Z view_3: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_ = None 2025-03-04T21:08:30.9731829Z add_1: "f32[22496, 3, 4][12, 4, 1]cpu" = view_2 + view_3; view_2 = view_3 = None 2025-03-04T21:08:30.9732133Z x_1: "f32[67488, 4][4, 1]cpu" = add_1.reshape(-1, 4); add_1 = None 2025-03-04T21:08:30.9732363Z 2025-03-04T21:08:30.9732860Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:08:30.9733484Z arange_4: "f32[76][1]cpu" = torch.arange(0.0, 1216, step = 16, dtype = torch.float32) 2025-03-04T21:08:30.9733744Z 2025-03-04T21:08:30.9734107Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:30.9734597Z shifts_x_2: "f32[76][1]cpu" = arange_4.to(device(type='cpu')); arange_4 = None 2025-03-04T21:08:30.9734848Z 2025-03-04T21:08:30.9735355Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:08:30.9735972Z arange_5: "f32[74][1]cpu" = torch.arange(0.0, 1184, step = 16, dtype = torch.float32) 2025-03-04T21:08:30.9736226Z 2025-03-04T21:08:30.9736593Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:30.9737079Z shifts_y_2: "f32[74][1]cpu" = arange_5.to(device(type='cpu')); arange_5 = None 2025-03-04T21:08:30.9737328Z 2025-03-04T21:08:30.9737772Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:08:30.9738379Z meshgrid_2 = torch.functional.meshgrid(shifts_y_2, shifts_x_2); shifts_y_2 = shifts_x_2 = None 2025-03-04T21:08:30.9738719Z shift_y_4: "f32[74, 76][1, 0]cpu" = meshgrid_2[0] 2025-03-04T21:08:30.9738984Z shift_x_4: "f32[74, 76][0, 1]cpu" = meshgrid_2[1]; meshgrid_2 = None 2025-03-04T21:08:30.9739216Z 2025-03-04T21:08:30.9739623Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:08:30.9740120Z shift_x_5: "f32[5624][1]cpu" = shift_x_4.reshape(-1); shift_x_4 = None 2025-03-04T21:08:30.9740355Z 2025-03-04T21:08:30.9740750Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:08:30.9741240Z shift_y_5: "f32[5624][1]cpu" = shift_y_4.reshape(-1); shift_y_4 = None 2025-03-04T21:08:30.9741487Z 2025-03-04T21:08:30.9741935Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:08:30.9742556Z shifts_2: "f32[5624, 4][4, 1]cpu" = torch.stack((shift_x_5, shift_y_5, shift_x_5, shift_y_5), dim = 1); shift_x_5 = shift_y_5 = None 2025-03-04T21:08:30.9742868Z 2025-03-04T21:08:30.9743347Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:08:30.9743931Z view_4: "f32[5624, 1, 4][4, 4, 1]cpu" = shifts_2.view(-1, 1, 4); shifts_2 = None 2025-03-04T21:08:30.9744429Z view_5: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_ = None 2025-03-04T21:08:30.9744901Z add_2: "f32[5624, 3, 4][12, 4, 1]cpu" = view_4 + view_5; view_4 = view_5 = None 2025-03-04T21:08:30.9745205Z x_2: "f32[16872, 4][4, 1]cpu" = add_2.reshape(-1, 4); add_2 = None 2025-03-04T21:08:30.9745436Z 2025-03-04T21:08:30.9745948Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:08:30.9746562Z arange_6: "f32[38][1]cpu" = torch.arange(0.0, 1216, step = 32, dtype = torch.float32) 2025-03-04T21:08:30.9746816Z 2025-03-04T21:08:30.9747185Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:30.9747673Z shifts_x_3: "f32[38][1]cpu" = arange_6.to(device(type='cpu')); arange_6 = None 2025-03-04T21:08:30.9747922Z 2025-03-04T21:08:30.9748427Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:08:30.9749035Z arange_7: "f32[37][1]cpu" = torch.arange(0.0, 1184, step = 32, dtype = torch.float32) 2025-03-04T21:08:30.9749286Z 2025-03-04T21:08:30.9749651Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:30.9750132Z shifts_y_3: "f32[37][1]cpu" = arange_7.to(device(type='cpu')); arange_7 = None 2025-03-04T21:08:30.9750379Z 2025-03-04T21:08:30.9750821Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:08:30.9751422Z meshgrid_3 = torch.functional.meshgrid(shifts_y_3, shifts_x_3); shifts_y_3 = shifts_x_3 = None 2025-03-04T21:08:30.9751761Z shift_y_6: "f32[37, 38][1, 0]cpu" = meshgrid_3[0] 2025-03-04T21:08:30.9752023Z shift_x_6: "f32[37, 38][0, 1]cpu" = meshgrid_3[1]; meshgrid_3 = None 2025-03-04T21:08:30.9752251Z 2025-03-04T21:08:30.9752649Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:08:30.9753143Z shift_x_7: "f32[1406][1]cpu" = shift_x_6.reshape(-1); shift_x_6 = None 2025-03-04T21:08:30.9753376Z 2025-03-04T21:08:30.9753777Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:08:30.9754275Z shift_y_7: "f32[1406][1]cpu" = shift_y_6.reshape(-1); shift_y_6 = None 2025-03-04T21:08:30.9755546Z 2025-03-04T21:08:30.9756160Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:08:30.9756829Z shifts_3: "f32[1406, 4][4, 1]cpu" = torch.stack((shift_x_7, shift_y_7, shift_x_7, shift_y_7), dim = 1); shift_x_7 = shift_y_7 = None 2025-03-04T21:08:30.9757158Z 2025-03-04T21:08:30.9757666Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:08:30.9758269Z view_6: "f32[1406, 1, 4][4, 4, 1]cpu" = shifts_3.view(-1, 1, 4); shifts_3 = None 2025-03-04T21:08:30.9758771Z view_7: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_ = None 2025-03-04T21:08:30.9759268Z add_3: "f32[1406, 3, 4][12, 4, 1]cpu" = view_6 + view_7; view_6 = view_7 = None 2025-03-04T21:08:30.9759590Z x_3: "f32[4218, 4][4, 1]cpu" = add_3.reshape(-1, 4); add_3 = None 2025-03-04T21:08:30.9759828Z 2025-03-04T21:08:30.9760417Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:08:30.9761110Z arange_8: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32) 2025-03-04T21:08:30.9761382Z 2025-03-04T21:08:30.9761774Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:30.9762283Z shifts_x_4: "f32[19][1]cpu" = arange_8.to(device(type='cpu')); arange_8 = None 2025-03-04T21:08:30.9762542Z 2025-03-04T21:08:30.9763072Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:08:30.9763715Z arange_9: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32) 2025-03-04T21:08:30.9763979Z 2025-03-04T21:08:30.9764364Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:30.9764877Z shifts_y_4: "f32[19][1]cpu" = arange_9.to(device(type='cpu')); arange_9 = None 2025-03-04T21:08:30.9765134Z 2025-03-04T21:08:30.9765599Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:08:30.9766235Z meshgrid_4 = torch.functional.meshgrid(shifts_y_4, shifts_x_4); shifts_y_4 = shifts_x_4 = None 2025-03-04T21:08:30.9766588Z shift_y_8: "f32[19, 19][1, 0]cpu" = meshgrid_4[0] 2025-03-04T21:08:30.9766989Z shift_x_8: "f32[19, 19][0, 1]cpu" = meshgrid_4[1]; meshgrid_4 = None 2025-03-04T21:08:30.9767221Z 2025-03-04T21:08:30.9767625Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:08:30.9768123Z shift_x_9: "f32[361][1]cpu" = shift_x_8.reshape(-1); shift_x_8 = None 2025-03-04T21:08:30.9768360Z 2025-03-04T21:08:30.9768759Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:08:30.9769258Z shift_y_9: "f32[361][1]cpu" = shift_y_8.reshape(-1); shift_y_8 = None 2025-03-04T21:08:30.9769516Z 2025-03-04T21:08:30.9769975Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:08:30.9770614Z shifts_4: "f32[361, 4][4, 1]cpu" = torch.stack((shift_x_9, shift_y_9, shift_x_9, shift_y_9), dim = 1); shift_x_9 = shift_y_9 = None 2025-03-04T21:08:30.9770936Z 2025-03-04T21:08:30.9771428Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:08:30.9772009Z view_8: "f32[361, 1, 4][4, 4, 1]cpu" = shifts_4.view(-1, 1, 4); shifts_4 = None 2025-03-04T21:08:30.9772493Z view_9: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_ = None 2025-03-04T21:08:30.9772965Z add_4: "f32[361, 3, 4][12, 4, 1]cpu" = view_8 + view_9; view_8 = view_9 = None 2025-03-04T21:08:30.9773263Z x_4: "f32[1083, 4][4, 1]cpu" = add_4.reshape(-1, 4); add_4 = None 2025-03-04T21:08:30.9773484Z 2025-03-04T21:08:30.9773854Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:08:30.9774308Z tensor: "f32[269952, 4][4, 1]cpu" = x.to(torch.float32); x = None 2025-03-04T21:08:30.9774602Z tensor_1: "f32[67488, 4][4, 1]cpu" = x_1.to(torch.float32); x_1 = None 2025-03-04T21:08:30.9774891Z tensor_2: "f32[16872, 4][4, 1]cpu" = x_2.to(torch.float32); x_2 = None 2025-03-04T21:08:30.9775168Z tensor_3: "f32[4218, 4][4, 1]cpu" = x_3.to(torch.float32); x_3 = None 2025-03-04T21:08:30.9775465Z tensor_4: "f32[1083, 4][4, 1]cpu" = x_4.to(torch.float32); x_4 = None 2025-03-04T21:08:30.9775695Z 2025-03-04T21:08:30.9776031Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:30.9776756Z x_5: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(l_features_p2_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_p2_ = None 2025-03-04T21:08:30.9777276Z 2025-03-04T21:08:30.9777642Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:08:30.9778149Z x_6: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.relu(x_5, inplace = False); x_5 = None 2025-03-04T21:08:30.9778446Z 2025-03-04T21:08:30.9778911Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:08:30.9779737Z score: "f32[4, 3, 296, 304][269952, 89984, 304, 1]cpu" = torch.conv2d(x_6, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:08:30.9780255Z 2025-03-04T21:08:30.9780696Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:08:30.9781534Z x_15: "f32[4, 12, 296, 304][1079808, 89984, 304, 1]cpu" = torch.conv2d(x_6, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_6 = None 2025-03-04T21:08:30.9782047Z 2025-03-04T21:08:30.9782379Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:30.9783099Z x_7: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(l_features_p3_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_p3_ = None 2025-03-04T21:08:30.9783627Z 2025-03-04T21:08:30.9783975Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:08:30.9784483Z x_8: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.relu(x_7, inplace = False); x_7 = None 2025-03-04T21:08:30.9784778Z 2025-03-04T21:08:30.9785238Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:08:30.9786093Z score_1: "f32[4, 3, 148, 152][67488, 22496, 152, 1]cpu" = torch.conv2d(x_8, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:08:30.9786623Z 2025-03-04T21:08:30.9787064Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:08:30.9787898Z x_16: "f32[4, 12, 148, 152][269952, 22496, 152, 1]cpu" = torch.conv2d(x_8, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_8 = None 2025-03-04T21:08:30.9788438Z 2025-03-04T21:08:30.9788786Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:30.9789532Z x_9: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(l_features_p4_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_p4_ = None 2025-03-04T21:08:30.9790048Z 2025-03-04T21:08:30.9790404Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:08:30.9790911Z x_10: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.relu(x_9, inplace = False); x_9 = None 2025-03-04T21:08:30.9791209Z 2025-03-04T21:08:30.9791698Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:08:30.9792550Z score_2: "f32[4, 3, 74, 76][16872, 5624, 76, 1]cpu" = torch.conv2d(x_10, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:08:30.9793078Z 2025-03-04T21:08:30.9793536Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:08:30.9794362Z x_17: "f32[4, 12, 74, 76][67488, 5624, 76, 1]cpu" = torch.conv2d(x_10, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_10 = None 2025-03-04T21:08:30.9794876Z 2025-03-04T21:08:30.9795239Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:30.9796048Z x_11: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(l_features_p5_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_p5_ = None 2025-03-04T21:08:30.9796615Z 2025-03-04T21:08:30.9796985Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:08:30.9797505Z x_12: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.nn.functional.relu(x_11, inplace = False); x_11 = None 2025-03-04T21:08:30.9797796Z 2025-03-04T21:08:30.9798258Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:08:30.9799070Z score_3: "f32[4, 3, 37, 38][4218, 1406, 38, 1]cpu" = torch.conv2d(x_12, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:08:30.9799577Z 2025-03-04T21:08:30.9800033Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:08:30.9800841Z x_18: "f32[4, 12, 37, 38][16872, 1406, 38, 1]cpu" = torch.conv2d(x_12, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_12 = None 2025-03-04T21:08:30.9801354Z 2025-03-04T21:08:30.9801695Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:30.9802798Z x_13: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.conv2d(l_features_p6_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_p6_ = l_self_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T21:08:30.9803538Z 2025-03-04T21:08:30.9803889Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:08:30.9804384Z x_14: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.relu(x_13, inplace = False); x_13 = None 2025-03-04T21:08:30.9804673Z 2025-03-04T21:08:30.9805163Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:08:30.9806225Z score_4: "f32[4, 3, 19, 19][1083, 361, 19, 1]cpu" = torch.conv2d(x_14, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T21:08:30.9806966Z 2025-03-04T21:08:30.9807405Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:08:30.9808405Z x_19: "f32[4, 12, 19, 19][4332, 361, 19, 1]cpu" = torch.conv2d(x_14, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_14 = l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T21:08:30.9809113Z 2025-03-04T21:08:30.9809533Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T21:08:30.9810090Z permute: "f32[4, 296, 304, 3][269952, 304, 1, 89984]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T21:08:30.9810473Z logits_i: "f32[4, 269952][269952, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T21:08:30.9810834Z permute_1: "f32[4, 148, 152, 3][67488, 152, 1, 22496]cpu" = score_1.permute(0, 2, 3, 1); score_1 = None 2025-03-04T21:08:30.9811204Z logits_i_1: "f32[4, 67488][67488, 1]cpu" = permute_1.flatten(1); permute_1 = None 2025-03-04T21:08:30.9811545Z permute_2: "f32[4, 74, 76, 3][16872, 76, 1, 5624]cpu" = score_2.permute(0, 2, 3, 1); score_2 = None 2025-03-04T21:08:30.9811877Z logits_i_2: "f32[4, 16872][16872, 1]cpu" = permute_2.flatten(1); permute_2 = None 2025-03-04T21:08:30.9812207Z permute_3: "f32[4, 37, 38, 3][4218, 38, 1, 1406]cpu" = score_3.permute(0, 2, 3, 1); score_3 = None 2025-03-04T21:08:30.9812535Z logits_i_3: "f32[4, 4218][4218, 1]cpu" = permute_3.flatten(1); permute_3 = None 2025-03-04T21:08:30.9812864Z permute_4: "f32[4, 19, 19, 3][1083, 19, 1, 361]cpu" = score_4.permute(0, 2, 3, 1); score_4 = None 2025-03-04T21:08:30.9813213Z logits_i_4: "f32[4, 1083][1083, 1]cpu" = permute_4.flatten(1); permute_4 = None 2025-03-04T21:08:30.9813466Z 2025-03-04T21:08:30.9813968Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T21:08:30.9814596Z view_10: "f32[4, 3, 4, 296, 304][1079808, 359936, 89984, 304, 1]cpu" = x_15.view(4, -1, 4, 296, 304); x_15 = None 2025-03-04T21:08:30.9814996Z permute_5: "f32[4, 296, 304, 3, 4][1079808, 304, 1, 359936, 89984]cpu" = view_10.permute(0, 3, 4, 1, 2); view_10 = None 2025-03-04T21:08:30.9815402Z pred_anchor_deltas_i: "f32[4, 269952, 4][1079808, 4, 1]cpu" = permute_5.flatten(1, -2); permute_5 = None 2025-03-04T21:08:30.9815800Z view_11: "f32[4, 3, 4, 148, 152][269952, 89984, 22496, 152, 1]cpu" = x_16.view(4, -1, 4, 148, 152); x_16 = None 2025-03-04T21:08:30.9816174Z permute_6: "f32[4, 148, 152, 3, 4][269952, 152, 1, 89984, 22496]cpu" = view_11.permute(0, 3, 4, 1, 2); view_11 = None 2025-03-04T21:08:30.9816559Z pred_anchor_deltas_i_2: "f32[4, 67488, 4][269952, 4, 1]cpu" = permute_6.flatten(1, -2); permute_6 = None 2025-03-04T21:08:30.9816915Z view_12: "f32[4, 3, 4, 74, 76][67488, 22496, 5624, 76, 1]cpu" = x_17.view(4, -1, 4, 74, 76); x_17 = None 2025-03-04T21:08:30.9817264Z permute_7: "f32[4, 74, 76, 3, 4][67488, 76, 1, 22496, 5624]cpu" = view_12.permute(0, 3, 4, 1, 2); view_12 = None 2025-03-04T21:08:30.9817649Z pred_anchor_deltas_i_4: "f32[4, 16872, 4][67488, 4, 1]cpu" = permute_7.flatten(1, -2); permute_7 = None 2025-03-04T21:08:30.9818003Z view_13: "f32[4, 3, 4, 37, 38][16872, 5624, 1406, 38, 1]cpu" = x_18.view(4, -1, 4, 37, 38); x_18 = None 2025-03-04T21:08:30.9818355Z permute_8: "f32[4, 37, 38, 3, 4][16872, 38, 1, 5624, 1406]cpu" = view_13.permute(0, 3, 4, 1, 2); view_13 = None 2025-03-04T21:08:30.9818735Z pred_anchor_deltas_i_6: "f32[4, 4218, 4][16872, 4, 1]cpu" = permute_8.flatten(1, -2); permute_8 = None 2025-03-04T21:08:30.9819094Z view_14: "f32[4, 3, 4, 19, 19][4332, 1444, 361, 19, 1]cpu" = x_19.view(4, -1, 4, 19, 19); x_19 = None 2025-03-04T21:08:30.9819441Z permute_9: "f32[4, 19, 19, 3, 4][4332, 19, 1, 1444, 361]cpu" = view_14.permute(0, 3, 4, 1, 2); view_14 = None 2025-03-04T21:08:30.9819812Z pred_anchor_deltas_i_8: "f32[4, 1083, 4][4332, 4, 1]cpu" = permute_9.flatten(1, -2); permute_9 = None 2025-03-04T21:08:30.9820100Z 2025-03-04T21:08:30.9820586Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:08:30.9821231Z pred_anchor_deltas_i_1: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T21:08:30.9821563Z 2025-03-04T21:08:30.9822067Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:08:30.9822689Z unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T21:08:30.9823033Z expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T21:08:30.9823360Z anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T21:08:30.9823611Z 2025-03-04T21:08:30.9824070Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:08:30.9824659Z deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T21:08:30.9824955Z 2025-03-04T21:08:30.9825359Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:08:30.9825858Z boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T21:08:30.9826110Z 2025-03-04T21:08:30.9826499Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:08:30.9826994Z getitem_10: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:08:30.9827297Z getitem_11: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:08:30.9827640Z widths: "f32[1079808][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T21:08:30.9827903Z 2025-03-04T21:08:30.9828297Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:08:30.9828785Z getitem_12: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:08:30.9829080Z getitem_13: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:08:30.9829401Z heights: "f32[1079808][1]cpu" = getitem_12 - getitem_13; getitem_12 = getitem_13 = None 2025-03-04T21:08:30.9829656Z 2025-03-04T21:08:30.9830063Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:08:30.9830538Z getitem_14: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:08:30.9830801Z mul: "f32[1079808][1]cpu" = 0.5 * widths 2025-03-04T21:08:30.9831062Z ctr_x: "f32[1079808][1]cpu" = getitem_14 + mul; getitem_14 = mul = None 2025-03-04T21:08:30.9831300Z 2025-03-04T21:08:30.9831684Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:08:30.9832178Z getitem_15: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:08:30.9832462Z mul_1: "f32[1079808][1]cpu" = 0.5 * heights 2025-03-04T21:08:30.9832749Z ctr_y: "f32[1079808][1]cpu" = getitem_15 + mul_1; getitem_15 = mul_1 = None 2025-03-04T21:08:30.9832995Z 2025-03-04T21:08:30.9833424Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:08:30.9833929Z getitem_16: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:08:30.9834252Z dx: "f32[1079808, 1][1, 1]cpu" = getitem_16 / 1.0; getitem_16 = None 2025-03-04T21:08:30.9834514Z 2025-03-04T21:08:30.9834906Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:08:30.9835422Z getitem_17: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:08:30.9835751Z dy: "f32[1079808, 1][1, 1]cpu" = getitem_17 / 1.0; getitem_17 = None 2025-03-04T21:08:30.9836040Z 2025-03-04T21:08:30.9836437Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:08:30.9836938Z getitem_18: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:08:30.9837258Z dw: "f32[1079808, 1][1, 1]cpu" = getitem_18 / 1.0; getitem_18 = None 2025-03-04T21:08:30.9837488Z 2025-03-04T21:08:30.9837899Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:08:30.9838442Z getitem_19: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:08:30.9838794Z dh: "f32[1079808, 1][1, 1]cpu" = getitem_19 / 1.0; getitem_19 = None 2025-03-04T21:08:30.9839027Z 2025-03-04T21:08:30.9839461Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:08:30.9840019Z dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:08:30.9840280Z 2025-03-04T21:08:30.9840730Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:08:30.9841296Z dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:08:30.9841558Z 2025-03-04T21:08:30.9841992Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:08:30.9842545Z getitem_20: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:08:30.9842879Z mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_20; dx = getitem_20 = None 2025-03-04T21:08:30.9843236Z getitem_21: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:08:30.9843593Z pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_21; mul_2 = getitem_21 = None 2025-03-04T21:08:30.9843856Z 2025-03-04T21:08:30.9844294Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:08:30.9844852Z getitem_22: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:08:30.9845181Z mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_22; dy = getitem_22 = None 2025-03-04T21:08:30.9845521Z getitem_23: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:08:30.9845872Z pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_23; mul_3 = getitem_23 = None 2025-03-04T21:08:30.9846136Z 2025-03-04T21:08:30.9846574Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:08:30.9847087Z exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:08:30.9847427Z getitem_24: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:08:30.9847806Z pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_24; exp = getitem_24 = None 2025-03-04T21:08:30.9848082Z 2025-03-04T21:08:30.9848489Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:08:30.9848987Z exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:08:30.9849320Z getitem_25: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:08:30.9849676Z pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_25; exp_1 = getitem_25 = None 2025-03-04T21:08:30.9849933Z 2025-03-04T21:08:30.9850328Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:08:30.9850794Z mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T21:08:30.9851060Z x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:08:30.9851417Z 2025-03-04T21:08:30.9851804Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:08:30.9852251Z mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T21:08:30.9852504Z y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:08:30.9852729Z 2025-03-04T21:08:30.9853110Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:08:30.9853578Z mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:08:30.9853902Z x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:08:30.9854148Z 2025-03-04T21:08:30.9854546Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:08:30.9855006Z mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:08:30.9855288Z y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:08:30.9855525Z 2025-03-04T21:08:30.9855960Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:08:30.9856527Z pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:08:30.9856810Z 2025-03-04T21:08:30.9857217Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:08:30.9857755Z proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4)); pred_boxes = None 2025-03-04T21:08:30.9858027Z 2025-03-04T21:08:30.9858478Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:08:30.9859072Z proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T21:08:30.9859358Z 2025-03-04T21:08:30.9859823Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:08:30.9860472Z pred_anchor_deltas_i_3: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_2.reshape(-1, 4); pred_anchor_deltas_i_2 = None 2025-03-04T21:08:30.9860806Z 2025-03-04T21:08:30.9861307Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:08:30.9861929Z unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = tensor_1.unsqueeze(0); tensor_1 = None 2025-03-04T21:08:30.9862276Z expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1); unsqueeze_1 = None 2025-03-04T21:08:30.9862616Z anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4); expand_1 = None 2025-03-04T21:08:30.9862871Z 2025-03-04T21:08:30.9863316Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:08:30.9863900Z deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float(); pred_anchor_deltas_i_3 = None 2025-03-04T21:08:30.9864190Z 2025-03-04T21:08:30.9864600Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:08:30.9865113Z boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32); anchors_i_1 = None 2025-03-04T21:08:30.9865382Z 2025-03-04T21:08:30.9865785Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:08:30.9866304Z getitem_26: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)] 2025-03-04T21:08:30.9866613Z getitem_27: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T21:08:30.9866958Z widths_1: "f32[269952][1]cpu" = getitem_26 - getitem_27; getitem_26 = getitem_27 = None 2025-03-04T21:08:30.9867224Z 2025-03-04T21:08:30.9867621Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:08:30.9868115Z getitem_28: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)] 2025-03-04T21:08:30.9868419Z getitem_29: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)] 2025-03-04T21:08:30.9868753Z heights_1: "f32[269952][1]cpu" = getitem_28 - getitem_29; getitem_28 = getitem_29 = None 2025-03-04T21:08:30.9869022Z 2025-03-04T21:08:30.9869437Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:08:30.9869935Z getitem_30: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T21:08:30.9870209Z mul_10: "f32[269952][1]cpu" = 0.5 * widths_1 2025-03-04T21:08:30.9870486Z ctr_x_1: "f32[269952][1]cpu" = getitem_30 + mul_10; getitem_30 = mul_10 = None 2025-03-04T21:08:30.9870745Z 2025-03-04T21:08:30.9871144Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:08:30.9871663Z getitem_31: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]; boxes_1 = None 2025-03-04T21:08:30.9871963Z mul_11: "f32[269952][1]cpu" = 0.5 * heights_1 2025-03-04T21:08:30.9872237Z ctr_y_1: "f32[269952][1]cpu" = getitem_31 + mul_11; getitem_31 = mul_11 = None 2025-03-04T21:08:30.9872490Z 2025-03-04T21:08:30.9872880Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:08:30.9873390Z getitem_32: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:08:30.9873733Z dx_1: "f32[269952, 1][1, 1]cpu" = getitem_32 / 1.0; getitem_32 = None 2025-03-04T21:08:30.9873963Z 2025-03-04T21:08:30.9874342Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:08:30.9874848Z getitem_33: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:08:30.9875167Z dy_1: "f32[269952, 1][1, 1]cpu" = getitem_33 / 1.0; getitem_33 = None 2025-03-04T21:08:30.9875400Z 2025-03-04T21:08:30.9875787Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:08:30.9876375Z getitem_34: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:08:30.9876697Z dw_2: "f32[269952, 1][1, 1]cpu" = getitem_34 / 1.0; getitem_34 = None 2025-03-04T21:08:30.9876933Z 2025-03-04T21:08:30.9877345Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:08:30.9877884Z getitem_35: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))]; deltas_1 = None 2025-03-04T21:08:30.9878238Z dh_2: "f32[269952, 1][1, 1]cpu" = getitem_35 / 1.0; getitem_35 = None 2025-03-04T21:08:30.9878469Z 2025-03-04T21:08:30.9878890Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:08:30.9879421Z dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356); dw_2 = None 2025-03-04T21:08:30.9879683Z 2025-03-04T21:08:30.9880117Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:08:30.9880643Z dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356); dh_2 = None 2025-03-04T21:08:30.9880897Z 2025-03-04T21:08:30.9881323Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:08:30.9881862Z getitem_36: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)] 2025-03-04T21:08:30.9882202Z mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_36; dx_1 = getitem_36 = None 2025-03-04T21:08:30.9882530Z getitem_37: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)]; ctr_x_1 = None 2025-03-04T21:08:30.9882873Z pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_37; mul_12 = getitem_37 = None 2025-03-04T21:08:30.9883125Z 2025-03-04T21:08:30.9883542Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:08:30.9884079Z getitem_38: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)] 2025-03-04T21:08:30.9884398Z mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_38; dy_1 = getitem_38 = None 2025-03-04T21:08:30.9884731Z getitem_39: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)]; ctr_y_1 = None 2025-03-04T21:08:30.9885084Z pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_39; mul_13 = getitem_39 = None 2025-03-04T21:08:30.9885337Z 2025-03-04T21:08:30.9885741Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:08:30.9886229Z exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3); dw_3 = None 2025-03-04T21:08:30.9886575Z getitem_40: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]; widths_1 = None 2025-03-04T21:08:30.9886919Z pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_40; exp_2 = getitem_40 = None 2025-03-04T21:08:30.9887156Z 2025-03-04T21:08:30.9887560Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:08:30.9888047Z exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3); dh_3 = None 2025-03-04T21:08:30.9888377Z getitem_41: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]; heights_1 = None 2025-03-04T21:08:30.9888724Z pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_41; exp_3 = getitem_41 = None 2025-03-04T21:08:30.9888970Z 2025-03-04T21:08:30.9889357Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:08:30.9889811Z mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1 2025-03-04T21:08:30.9890089Z x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16; mul_16 = None 2025-03-04T21:08:30.9890321Z 2025-03-04T21:08:30.9890705Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:08:30.9891151Z mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1 2025-03-04T21:08:30.9891406Z y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17; mul_17 = None 2025-03-04T21:08:30.9891629Z 2025-03-04T21:08:30.9892007Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:08:30.9892490Z mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1; pred_w_1 = None 2025-03-04T21:08:30.9892789Z x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18; pred_ctr_x_1 = mul_18 = None 2025-03-04T21:08:30.9893033Z 2025-03-04T21:08:30.9893413Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:08:30.9893878Z mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1; pred_h_1 = None 2025-03-04T21:08:30.9894167Z y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19; pred_ctr_y_1 = mul_19 = None 2025-03-04T21:08:30.9894414Z 2025-03-04T21:08:30.9894867Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:08:30.9895468Z pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1); x1_1 = y1_1 = x2_1 = y2_1 = None 2025-03-04T21:08:30.9895773Z 2025-03-04T21:08:30.9896190Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:08:30.9896742Z proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4)); pred_boxes_1 = None 2025-03-04T21:08:30.9897019Z 2025-03-04T21:08:30.9897484Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:08:30.9898080Z proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4); proposals_i_1 = None 2025-03-04T21:08:30.9898373Z 2025-03-04T21:08:30.9898854Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:08:30.9899536Z pred_anchor_deltas_i_5: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_4.reshape(-1, 4); pred_anchor_deltas_i_4 = None 2025-03-04T21:08:30.9899866Z 2025-03-04T21:08:30.9900384Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:08:30.9901023Z unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = tensor_2.unsqueeze(0); tensor_2 = None 2025-03-04T21:08:30.9901381Z expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1); unsqueeze_2 = None 2025-03-04T21:08:30.9901728Z anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4); expand_2 = None 2025-03-04T21:08:30.9902173Z 2025-03-04T21:08:30.9902639Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:08:30.9903306Z deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_5.float(); pred_anchor_deltas_i_5 = None 2025-03-04T21:08:30.9903611Z 2025-03-04T21:08:30.9904010Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:08:30.9904531Z boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32); anchors_i_2 = None 2025-03-04T21:08:30.9904810Z 2025-03-04T21:08:30.9905227Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:08:30.9905735Z getitem_42: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)] 2025-03-04T21:08:30.9906081Z getitem_43: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T21:08:30.9906421Z widths_2: "f32[67488][1]cpu" = getitem_42 - getitem_43; getitem_42 = getitem_43 = None 2025-03-04T21:08:30.9906697Z 2025-03-04T21:08:30.9907101Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:08:30.9907599Z getitem_44: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)] 2025-03-04T21:08:30.9907896Z getitem_45: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)] 2025-03-04T21:08:30.9908251Z heights_2: "f32[67488][1]cpu" = getitem_44 - getitem_45; getitem_44 = getitem_45 = None 2025-03-04T21:08:30.9908524Z 2025-03-04T21:08:30.9908930Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:08:30.9909425Z getitem_46: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T21:08:30.9909697Z mul_20: "f32[67488][1]cpu" = 0.5 * widths_2 2025-03-04T21:08:30.9909970Z ctr_x_2: "f32[67488][1]cpu" = getitem_46 + mul_20; getitem_46 = mul_20 = None 2025-03-04T21:08:30.9910220Z 2025-03-04T21:08:30.9910613Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:08:30.9911128Z getitem_47: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]; boxes_2 = None 2025-03-04T21:08:30.9911421Z mul_21: "f32[67488][1]cpu" = 0.5 * heights_2 2025-03-04T21:08:30.9911692Z ctr_y_2: "f32[67488][1]cpu" = getitem_47 + mul_21; getitem_47 = mul_21 = None 2025-03-04T21:08:30.9911938Z 2025-03-04T21:08:30.9912333Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:08:30.9912853Z getitem_48: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:08:30.9913211Z dx_2: "f32[67488, 1][1, 1]cpu" = getitem_48 / 1.0; getitem_48 = None 2025-03-04T21:08:30.9913449Z 2025-03-04T21:08:30.9913847Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:08:30.9914368Z getitem_49: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:08:30.9914698Z dy_2: "f32[67488, 1][1, 1]cpu" = getitem_49 / 1.0; getitem_49 = None 2025-03-04T21:08:30.9914935Z 2025-03-04T21:08:30.9915326Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:08:30.9915848Z getitem_50: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:08:30.9916235Z dw_4: "f32[67488, 1][1, 1]cpu" = getitem_50 / 1.0; getitem_50 = None 2025-03-04T21:08:30.9916469Z 2025-03-04T21:08:30.9916883Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:08:30.9917439Z getitem_51: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))]; deltas_2 = None 2025-03-04T21:08:30.9917792Z dh_4: "f32[67488, 1][1, 1]cpu" = getitem_51 / 1.0; getitem_51 = None 2025-03-04T21:08:30.9918040Z 2025-03-04T21:08:30.9918467Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:08:30.9919001Z dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356); dw_4 = None 2025-03-04T21:08:30.9919289Z 2025-03-04T21:08:30.9919719Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:08:30.9920259Z dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356); dh_4 = None 2025-03-04T21:08:30.9920519Z 2025-03-04T21:08:30.9920954Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:08:30.9921503Z getitem_52: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)] 2025-03-04T21:08:30.9921844Z mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_52; dx_2 = getitem_52 = None 2025-03-04T21:08:30.9922186Z getitem_53: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)]; ctr_x_2 = None 2025-03-04T21:08:30.9922549Z pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_53; mul_22 = getitem_53 = None 2025-03-04T21:08:30.9922817Z 2025-03-04T21:08:30.9923264Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:08:30.9923821Z getitem_54: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)] 2025-03-04T21:08:30.9924147Z mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_54; dy_2 = getitem_54 = None 2025-03-04T21:08:30.9924483Z getitem_55: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)]; ctr_y_2 = None 2025-03-04T21:08:30.9924841Z pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_55; mul_23 = getitem_55 = None 2025-03-04T21:08:30.9925107Z 2025-03-04T21:08:30.9925535Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:08:30.9926067Z exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5); dw_5 = None 2025-03-04T21:08:30.9926407Z getitem_56: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]; widths_2 = None 2025-03-04T21:08:30.9926772Z pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_56; exp_4 = getitem_56 = None 2025-03-04T21:08:30.9927034Z 2025-03-04T21:08:30.9927467Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:08:30.9927991Z exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5); dh_5 = None 2025-03-04T21:08:30.9928338Z getitem_57: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]; heights_2 = None 2025-03-04T21:08:30.9928717Z pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_57; exp_5 = getitem_57 = None 2025-03-04T21:08:30.9928976Z 2025-03-04T21:08:30.9929370Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:08:30.9929852Z mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2 2025-03-04T21:08:30.9930113Z x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26; mul_26 = None 2025-03-04T21:08:30.9930345Z 2025-03-04T21:08:30.9930723Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:08:30.9931169Z mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2 2025-03-04T21:08:30.9931420Z y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27; mul_27 = None 2025-03-04T21:08:30.9931649Z 2025-03-04T21:08:30.9932028Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:08:30.9932515Z mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2; pred_w_2 = None 2025-03-04T21:08:30.9932812Z x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28; pred_ctr_x_2 = mul_28 = None 2025-03-04T21:08:30.9933057Z 2025-03-04T21:08:30.9933437Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:08:30.9933904Z mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2; pred_h_2 = None 2025-03-04T21:08:30.9934198Z y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29; pred_ctr_y_2 = mul_29 = None 2025-03-04T21:08:30.9934459Z 2025-03-04T21:08:30.9934906Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:08:30.9935507Z pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1); x1_2 = y1_2 = x2_2 = y2_2 = None 2025-03-04T21:08:30.9935799Z 2025-03-04T21:08:30.9936204Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:08:30.9936736Z proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4)); pred_boxes_2 = None 2025-03-04T21:08:30.9937007Z 2025-03-04T21:08:30.9937463Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:08:30.9938051Z proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4); proposals_i_2 = None 2025-03-04T21:08:30.9938343Z 2025-03-04T21:08:30.9938845Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:08:30.9939536Z pred_anchor_deltas_i_7: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_6.reshape(-1, 4); pred_anchor_deltas_i_6 = None 2025-03-04T21:08:30.9939866Z 2025-03-04T21:08:30.9940393Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:08:30.9941039Z unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = tensor_3.unsqueeze(0); tensor_3 = None 2025-03-04T21:08:30.9941410Z expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1); unsqueeze_3 = None 2025-03-04T21:08:30.9941741Z anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4); expand_3 = None 2025-03-04T21:08:30.9941992Z 2025-03-04T21:08:30.9942434Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:08:30.9943034Z deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_7.float(); pred_anchor_deltas_i_7 = None 2025-03-04T21:08:30.9943323Z 2025-03-04T21:08:30.9943720Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:08:30.9944238Z boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32); anchors_i_3 = None 2025-03-04T21:08:30.9944505Z 2025-03-04T21:08:30.9944916Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:08:30.9945434Z getitem_58: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)] 2025-03-04T21:08:30.9945760Z getitem_59: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T21:08:30.9946088Z widths_3: "f32[16872][1]cpu" = getitem_58 - getitem_59; getitem_58 = getitem_59 = None 2025-03-04T21:08:30.9946353Z 2025-03-04T21:08:30.9946746Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:08:30.9947240Z getitem_60: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)] 2025-03-04T21:08:30.9947537Z getitem_61: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)] 2025-03-04T21:08:30.9947878Z heights_3: "f32[16872][1]cpu" = getitem_60 - getitem_61; getitem_60 = getitem_61 = None 2025-03-04T21:08:30.9948145Z 2025-03-04T21:08:30.9948542Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:08:30.9949034Z getitem_62: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T21:08:30.9949305Z mul_30: "f32[16872][1]cpu" = 0.5 * widths_3 2025-03-04T21:08:30.9949579Z ctr_x_3: "f32[16872][1]cpu" = getitem_62 + mul_30; getitem_62 = mul_30 = None 2025-03-04T21:08:30.9949827Z 2025-03-04T21:08:30.9950220Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:08:30.9950732Z getitem_63: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]; boxes_3 = None 2025-03-04T21:08:30.9951026Z mul_31: "f32[16872][1]cpu" = 0.5 * heights_3 2025-03-04T21:08:30.9951295Z ctr_y_3: "f32[16872][1]cpu" = getitem_63 + mul_31; getitem_63 = mul_31 = None 2025-03-04T21:08:30.9951542Z 2025-03-04T21:08:30.9951929Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:08:30.9952472Z getitem_64: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:08:30.9952793Z dx_3: "f32[16872, 1][1, 1]cpu" = getitem_64 / 1.0; getitem_64 = None 2025-03-04T21:08:30.9953029Z 2025-03-04T21:08:30.9953408Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:08:30.9953913Z getitem_65: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:08:30.9954234Z dy_3: "f32[16872, 1][1, 1]cpu" = getitem_65 / 1.0; getitem_65 = None 2025-03-04T21:08:30.9954470Z 2025-03-04T21:08:30.9954860Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:08:30.9955384Z getitem_66: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:08:30.9955704Z dw_6: "f32[16872, 1][1, 1]cpu" = getitem_66 / 1.0; getitem_66 = None 2025-03-04T21:08:30.9956010Z 2025-03-04T21:08:30.9956416Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:08:30.9956958Z getitem_67: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))]; deltas_3 = None 2025-03-04T21:08:30.9957305Z dh_6: "f32[16872, 1][1, 1]cpu" = getitem_67 / 1.0; getitem_67 = None 2025-03-04T21:08:30.9957539Z 2025-03-04T21:08:30.9957960Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:08:30.9958521Z dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356); dw_6 = None 2025-03-04T21:08:30.9958783Z 2025-03-04T21:08:30.9959201Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:08:30.9959725Z dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356); dh_6 = None 2025-03-04T21:08:30.9959990Z 2025-03-04T21:08:30.9960441Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:08:30.9961049Z getitem_68: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)] 2025-03-04T21:08:30.9961371Z mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_68; dx_3 = getitem_68 = None 2025-03-04T21:08:30.9961721Z getitem_69: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)]; ctr_x_3 = None 2025-03-04T21:08:30.9962082Z pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_69; mul_32 = getitem_69 = None 2025-03-04T21:08:30.9962346Z 2025-03-04T21:08:30.9962791Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:08:30.9963315Z getitem_70: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)] 2025-03-04T21:08:30.9963623Z mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_70; dy_3 = getitem_70 = None 2025-03-04T21:08:30.9963943Z getitem_71: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)]; ctr_y_3 = None 2025-03-04T21:08:30.9964286Z pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_71; mul_33 = getitem_71 = None 2025-03-04T21:08:30.9964540Z 2025-03-04T21:08:30.9964963Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:08:30.9965470Z exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7); dw_7 = None 2025-03-04T21:08:30.9965792Z getitem_72: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]; widths_3 = None 2025-03-04T21:08:30.9966134Z pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_72; exp_6 = getitem_72 = None 2025-03-04T21:08:30.9966376Z 2025-03-04T21:08:30.9966806Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:08:30.9967297Z exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7); dh_7 = None 2025-03-04T21:08:30.9967625Z getitem_73: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]; heights_3 = None 2025-03-04T21:08:30.9967973Z pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_73; exp_7 = getitem_73 = None 2025-03-04T21:08:30.9968223Z 2025-03-04T21:08:30.9968628Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:08:30.9969075Z mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3 2025-03-04T21:08:30.9969331Z x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36; mul_36 = None 2025-03-04T21:08:30.9969567Z 2025-03-04T21:08:30.9969958Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:08:30.9970415Z mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3 2025-03-04T21:08:30.9970678Z y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37; mul_37 = None 2025-03-04T21:08:30.9970911Z 2025-03-04T21:08:30.9971326Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:08:30.9971814Z mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3; pred_w_3 = None 2025-03-04T21:08:30.9972123Z x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38; pred_ctr_x_3 = mul_38 = None 2025-03-04T21:08:30.9972376Z 2025-03-04T21:08:30.9972767Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:08:30.9973245Z mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3; pred_h_3 = None 2025-03-04T21:08:30.9973564Z y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39; pred_ctr_y_3 = mul_39 = None 2025-03-04T21:08:30.9973814Z 2025-03-04T21:08:30.9974248Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:08:30.9974844Z pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1); x1_3 = y1_3 = x2_3 = y2_3 = None 2025-03-04T21:08:30.9975138Z 2025-03-04T21:08:30.9975546Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:08:30.9976085Z proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4)); pred_boxes_3 = None 2025-03-04T21:08:30.9976363Z 2025-03-04T21:08:30.9976826Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:08:30.9977428Z proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4); proposals_i_3 = None 2025-03-04T21:08:30.9977715Z 2025-03-04T21:08:30.9978189Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:08:30.9978865Z pred_anchor_deltas_i_9: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_8.reshape(-1, 4); pred_anchor_deltas_i_8 = None 2025-03-04T21:08:30.9979183Z 2025-03-04T21:08:30.9979694Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:08:30.9980323Z unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = tensor_4.unsqueeze(0); tensor_4 = None 2025-03-04T21:08:30.9980673Z expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1); unsqueeze_4 = None 2025-03-04T21:08:30.9981015Z anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4); expand_4 = None 2025-03-04T21:08:30.9981276Z 2025-03-04T21:08:30.9981758Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:08:30.9982360Z deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_9.float(); pred_anchor_deltas_i_9 = None 2025-03-04T21:08:30.9982645Z 2025-03-04T21:08:30.9983045Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:08:30.9983566Z boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32); anchors_i_4 = None 2025-03-04T21:08:30.9983830Z 2025-03-04T21:08:30.9984237Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:08:30.9984770Z getitem_74: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)] 2025-03-04T21:08:30.9985082Z getitem_75: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T21:08:30.9985415Z widths_4: "f32[4332][1]cpu" = getitem_74 - getitem_75; getitem_74 = getitem_75 = None 2025-03-04T21:08:30.9985679Z 2025-03-04T21:08:30.9986092Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:08:30.9986596Z getitem_76: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)] 2025-03-04T21:08:30.9986921Z getitem_77: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)] 2025-03-04T21:08:30.9987255Z heights_4: "f32[4332][1]cpu" = getitem_76 - getitem_77; getitem_76 = getitem_77 = None 2025-03-04T21:08:30.9987525Z 2025-03-04T21:08:30.9987932Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:08:30.9988434Z getitem_78: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T21:08:30.9988697Z mul_40: "f32[4332][1]cpu" = 0.5 * widths_4 2025-03-04T21:08:30.9988967Z ctr_x_4: "f32[4332][1]cpu" = getitem_78 + mul_40; getitem_78 = mul_40 = None 2025-03-04T21:08:30.9989213Z 2025-03-04T21:08:30.9989612Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:08:30.9990125Z getitem_79: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]; boxes_4 = None 2025-03-04T21:08:30.9990418Z mul_41: "f32[4332][1]cpu" = 0.5 * heights_4 2025-03-04T21:08:30.9990682Z ctr_y_4: "f32[4332][1]cpu" = getitem_79 + mul_41; getitem_79 = mul_41 = None 2025-03-04T21:08:30.9990927Z 2025-03-04T21:08:30.9991315Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:08:30.9991843Z getitem_80: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:08:30.9992157Z dx_4: "f32[4332, 1][1, 1]cpu" = getitem_80 / 1.0; getitem_80 = None 2025-03-04T21:08:30.9992386Z 2025-03-04T21:08:30.9992767Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:08:30.9993268Z getitem_81: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:08:30.9993584Z dy_4: "f32[4332, 1][1, 1]cpu" = getitem_81 / 1.0; getitem_81 = None 2025-03-04T21:08:30.9993809Z 2025-03-04T21:08:30.9994193Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:08:30.9994700Z getitem_82: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:08:30.9995044Z dw_8: "f32[4332, 1][1, 1]cpu" = getitem_82 / 1.0; getitem_82 = None 2025-03-04T21:08:30.9995275Z 2025-03-04T21:08:30.9995667Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:08:30.9996302Z getitem_83: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))]; deltas_4 = None 2025-03-04T21:08:30.9996669Z dh_8: "f32[4332, 1][1, 1]cpu" = getitem_83 / 1.0; getitem_83 = None 2025-03-04T21:08:30.9996919Z 2025-03-04T21:08:30.9997337Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:08:30.9997878Z dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356); dw_8 = None 2025-03-04T21:08:30.9998129Z 2025-03-04T21:08:30.9998538Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:08:30.9999048Z dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356); dh_8 = None 2025-03-04T21:08:30.9999295Z 2025-03-04T21:08:30.9999711Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:08:31.0000266Z getitem_84: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)] 2025-03-04T21:08:31.0000572Z mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_84; dx_4 = getitem_84 = None 2025-03-04T21:08:31.0000892Z getitem_85: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)]; ctr_x_4 = None 2025-03-04T21:08:31.0001228Z pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_85; mul_42 = getitem_85 = None 2025-03-04T21:08:31.0001475Z 2025-03-04T21:08:31.0002009Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:08:31.0002556Z getitem_86: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)] 2025-03-04T21:08:31.0002868Z mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_86; dy_4 = getitem_86 = None 2025-03-04T21:08:31.0003199Z getitem_87: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)]; ctr_y_4 = None 2025-03-04T21:08:31.0003533Z pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_87; mul_43 = getitem_87 = None 2025-03-04T21:08:31.0003786Z 2025-03-04T21:08:31.0004187Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:08:31.0004723Z exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9); dw_9 = None 2025-03-04T21:08:31.0005038Z getitem_88: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]; widths_4 = None 2025-03-04T21:08:31.0005377Z pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_88; exp_8 = getitem_88 = None 2025-03-04T21:08:31.0005620Z 2025-03-04T21:08:31.0006027Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:08:31.0006511Z exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9); dh_9 = None 2025-03-04T21:08:31.0006834Z getitem_89: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]; heights_4 = None 2025-03-04T21:08:31.0007173Z pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_89; exp_9 = getitem_89 = None 2025-03-04T21:08:31.0007416Z 2025-03-04T21:08:31.0007831Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:08:31.0008284Z mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4 2025-03-04T21:08:31.0008538Z x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46; mul_46 = None 2025-03-04T21:08:31.0008760Z 2025-03-04T21:08:31.0009144Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:08:31.0009589Z mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4 2025-03-04T21:08:31.0009837Z y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47; mul_47 = None 2025-03-04T21:08:31.0010092Z 2025-03-04T21:08:31.0010474Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:08:31.0010951Z mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4; pred_w_4 = None 2025-03-04T21:08:31.0011248Z x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48; pred_ctr_x_4 = mul_48 = None 2025-03-04T21:08:31.0011495Z 2025-03-04T21:08:31.0011887Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:08:31.0012359Z mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4; pred_h_4 = None 2025-03-04T21:08:31.0012675Z y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49; pred_ctr_y_4 = mul_49 = None 2025-03-04T21:08:31.0012923Z 2025-03-04T21:08:31.0013355Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:08:31.0013939Z pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1); x1_4 = y1_4 = x2_4 = y2_4 = None 2025-03-04T21:08:31.0014237Z 2025-03-04T21:08:31.0014649Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:08:31.0015189Z proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4)); pred_boxes_4 = None 2025-03-04T21:08:31.0015462Z 2025-03-04T21:08:31.0015927Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:08:31.0016522Z proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4); proposals_i_4 = None 2025-03-04T21:08:31.0016804Z 2025-03-04T21:08:31.0017369Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T21:08:31.0018080Z arange_10: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T21:08:31.0018329Z 2025-03-04T21:08:31.0018706Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:31.0019201Z batch_idx: "i64[4][1]cpu" = arange_10.to(device(type='cpu')); arange_10 = None 2025-03-04T21:08:31.0019460Z 2025-03-04T21:08:31.0019985Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:08:31.0020590Z topk = logits_i.topk(1000, dim = 1); logits_i = None 2025-03-04T21:08:31.0020856Z topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0] 2025-03-04T21:08:31.0021141Z topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1]; topk = None 2025-03-04T21:08:31.0021364Z 2025-03-04T21:08:31.0021913Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:08:31.0022572Z getitem_92: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:08:31.0022992Z topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_92, topk_idx)]; proposals_i_5 = getitem_92 = topk_idx = None 2025-03-04T21:08:31.0023347Z 2025-03-04T21:08:31.0023888Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:08:31.0024600Z full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:08:31.0024879Z 2025-03-04T21:08:31.0025267Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:31.0025739Z to_21: "i64[1000][1]cpu" = full.to(device(type='cpu')); full = None 2025-03-04T21:08:31.0025985Z 2025-03-04T21:08:31.0026533Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:08:31.0027156Z topk_1 = logits_i_1.topk(1000, dim = 1); logits_i_1 = None 2025-03-04T21:08:31.0027438Z topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0] 2025-03-04T21:08:31.0027726Z topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1]; topk_1 = None 2025-03-04T21:08:31.0027965Z 2025-03-04T21:08:31.0028523Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:08:31.0029183Z getitem_96: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:08:31.0029613Z topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_96, topk_idx_1)]; proposals_i_6 = getitem_96 = topk_idx_1 = None 2025-03-04T21:08:31.0029970Z 2025-03-04T21:08:31.0030525Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:08:31.0031239Z full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:08:31.0031528Z 2025-03-04T21:08:31.0031920Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:31.0032412Z to_22: "i64[1000][1]cpu" = full_1.to(device(type='cpu')); full_1 = None 2025-03-04T21:08:31.0032666Z 2025-03-04T21:08:31.0033215Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:08:31.0033848Z topk_2 = logits_i_2.topk(1000, dim = 1); logits_i_2 = None 2025-03-04T21:08:31.0034141Z topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0] 2025-03-04T21:08:31.0034422Z topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1]; topk_2 = None 2025-03-04T21:08:31.0034659Z 2025-03-04T21:08:31.0035232Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:08:31.0035899Z getitem_100: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:08:31.0036410Z topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_100, topk_idx_2)]; proposals_i_7 = getitem_100 = topk_idx_2 = None 2025-03-04T21:08:31.0036770Z 2025-03-04T21:08:31.0037327Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:08:31.0038052Z full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:08:31.0038342Z 2025-03-04T21:08:31.0038732Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:31.0039223Z to_23: "i64[1000][1]cpu" = full_2.to(device(type='cpu')); full_2 = None 2025-03-04T21:08:31.0039467Z 2025-03-04T21:08:31.0039999Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:08:31.0040631Z topk_3 = logits_i_3.topk(1000, dim = 1); logits_i_3 = None 2025-03-04T21:08:31.0040910Z topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0] 2025-03-04T21:08:31.0041195Z topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1]; topk_3 = None 2025-03-04T21:08:31.0041436Z 2025-03-04T21:08:31.0041998Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:08:31.0042662Z getitem_104: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:08:31.0043094Z topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_104, topk_idx_3)]; proposals_i_8 = getitem_104 = topk_idx_3 = None 2025-03-04T21:08:31.0043451Z 2025-03-04T21:08:31.0044010Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:08:31.0044708Z full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:08:31.0045001Z 2025-03-04T21:08:31.0045413Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:31.0045895Z to_24: "i64[1000][1]cpu" = full_3.to(device(type='cpu')); full_3 = None 2025-03-04T21:08:31.0046141Z 2025-03-04T21:08:31.0046677Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:08:31.0047305Z topk_4 = logits_i_4.topk(1000, dim = 1); logits_i_4 = None 2025-03-04T21:08:31.0047577Z topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0] 2025-03-04T21:08:31.0047851Z topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1]; topk_4 = None 2025-03-04T21:08:31.0048090Z 2025-03-04T21:08:31.0048639Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:08:31.0049346Z getitem_108: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T21:08:31.0049816Z topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_108, topk_idx_4)]; proposals_i_9 = getitem_108 = topk_idx_4 = None 2025-03-04T21:08:31.0050175Z 2025-03-04T21:08:31.0050727Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:08:31.0051411Z full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:08:31.0051723Z 2025-03-04T21:08:31.0052105Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:31.0052587Z to_25: "i64[1000][1]cpu" = full_4.to(device(type='cpu')); full_4 = None 2025-03-04T21:08:31.0052830Z 2025-03-04T21:08:31.0053197Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:08:31.0053942Z topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1); topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None 2025-03-04T21:08:31.0054442Z 2025-03-04T21:08:31.0054821Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:08:31.0055629Z topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1); topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None 2025-03-04T21:08:31.0056220Z 2025-03-04T21:08:31.0056576Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:08:31.0057115Z level_ids: "i64[5000][1]cpu" = torch.cat([to_21, to_22, to_23, to_24, to_25], 0); to_21 = to_22 = to_23 = to_24 = to_25 = level_ids = None 2025-03-04T21:08:31.0057426Z 2025-03-04T21:08:31.0057899Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T21:08:31.0058486Z getitem_110: "f32[5000, 4][4, 1]cpu" = topk_proposals[0]; topk_proposals = None 2025-03-04T21:08:31.0058754Z 2025-03-04T21:08:31.0059262Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:08:31.0059763Z tensor_5: "f32[5000, 4][4, 1]cpu" = getitem_110.to(torch.float32); getitem_110 = None 2025-03-04T21:08:31.0060031Z 2025-03-04T21:08:31.0060486Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T21:08:31.0061380Z scores_per_img: "f32[5000][1]cpu" = topk_scores[0]; topk_scores = None 2025-03-04T21:08:31.0061712Z 2025-03-04T21:08:31.0062347Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T21:08:31.0063121Z isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor_5); tensor_5 = None 2025-03-04T21:08:31.0063525Z all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:08:31.0063906Z isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T21:08:31.0064376Z valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T21:08:31.0064692Z 2025-03-04T21:08:31.0065231Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T21:08:31.0065851Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T21:08:31.0066236Z 2025-03-04T21:08:31.0066581Z 2025-03-04T21:08:31.0066721Z class GraphModule(torch.nn.Module): 2025-03-04T21:08:31.0069046Z def forward(self, L_features_p2_: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu", L_features_p3_: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu", L_features_p4_: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu", L_features_p5_: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu", L_features_p6_: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_1_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_2_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_3_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_4_: "f32[3, 4][4, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_bias_: "f32[256][1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[3, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[3][1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[12, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[12][1]cpu"): 2025-03-04T21:08:31.0071620Z l_features_p2_ = L_features_p2_ 2025-03-04T21:08:31.0071908Z l_features_p3_ = L_features_p3_ 2025-03-04T21:08:31.0072221Z l_features_p4_ = L_features_p4_ 2025-03-04T21:08:31.0072503Z l_features_p5_ = L_features_p5_ 2025-03-04T21:08:31.0072751Z l_features_p6_ = L_features_p6_ 2025-03-04T21:08:31.0073337Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ 2025-03-04T21:08:31.0073989Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_1_ 2025-03-04T21:08:31.0074691Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_2_ 2025-03-04T21:08:31.0075352Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_3_ 2025-03-04T21:08:31.0076067Z l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_4_ 2025-03-04T21:08:31.0076732Z l_self_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_rpn_head_modules_conv_parameters_weight_ 2025-03-04T21:08:31.0077320Z l_self_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_rpn_head_modules_conv_parameters_bias_ 2025-03-04T21:08:31.0077959Z l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ 2025-03-04T21:08:31.0078655Z l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ 2025-03-04T21:08:31.0079332Z l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ 2025-03-04T21:08:31.0080096Z l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ 2025-03-04T21:08:31.0080551Z 2025-03-04T21:08:31.0081164Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:08:31.0096597Z arange: "f32[304][1]cpu" = torch.arange(0.0, 1216, step = 4, dtype = torch.float32) 2025-03-04T21:08:31.0096892Z 2025-03-04T21:08:31.0097317Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:31.0097923Z shifts_x: "f32[304][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T21:08:31.0098185Z 2025-03-04T21:08:31.0098730Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:08:31.0099389Z arange_1: "f32[296][1]cpu" = torch.arange(0.0, 1184, step = 4, dtype = torch.float32) 2025-03-04T21:08:31.0099655Z 2025-03-04T21:08:31.0100060Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:31.0100581Z shifts_y: "f32[296][1]cpu" = arange_1.to(device(type='cpu')); arange_1 = None 2025-03-04T21:08:31.0100860Z 2025-03-04T21:08:31.0101325Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:08:31.0102178Z meshgrid = torch.functional.meshgrid(shifts_y, shifts_x); shifts_y = shifts_x = None 2025-03-04T21:08:31.0102520Z shift_y: "f32[296, 304][1, 0]cpu" = meshgrid[0] 2025-03-04T21:08:31.0102801Z shift_x: "f32[296, 304][0, 1]cpu" = meshgrid[1]; meshgrid = None 2025-03-04T21:08:31.0103034Z 2025-03-04T21:08:31.0103445Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:08:31.0103962Z shift_x_1: "f32[89984][1]cpu" = shift_x.reshape(-1); shift_x = None 2025-03-04T21:08:31.0104208Z 2025-03-04T21:08:31.0104622Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:08:31.0105130Z shift_y_1: "f32[89984][1]cpu" = shift_y.reshape(-1); shift_y = None 2025-03-04T21:08:31.0105450Z 2025-03-04T21:08:31.0105921Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:08:31.0106575Z shifts: "f32[89984, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1); shift_x_1 = shift_y_1 = None 2025-03-04T21:08:31.0106904Z 2025-03-04T21:08:31.0107411Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:08:31.0108009Z view: "f32[89984, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4); shifts = None 2025-03-04T21:08:31.0108517Z view_1: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None 2025-03-04T21:08:31.0109052Z add: "f32[89984, 3, 4][12, 4, 1]cpu" = view + view_1; view = view_1 = None 2025-03-04T21:08:31.0109348Z x: "f32[269952, 4][4, 1]cpu" = add.reshape(-1, 4); add = None 2025-03-04T21:08:31.0109574Z 2025-03-04T21:08:31.0110097Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:08:31.0110744Z arange_2: "f32[152][1]cpu" = torch.arange(0.0, 1216, step = 8, dtype = torch.float32) 2025-03-04T21:08:31.0111018Z 2025-03-04T21:08:31.0111401Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:31.0111913Z shifts_x_1: "f32[152][1]cpu" = arange_2.to(device(type='cpu')); arange_2 = None 2025-03-04T21:08:31.0112176Z 2025-03-04T21:08:31.0112693Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:08:31.0113319Z arange_3: "f32[148][1]cpu" = torch.arange(0.0, 1184, step = 8, dtype = torch.float32) 2025-03-04T21:08:31.0113580Z 2025-03-04T21:08:31.0113972Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:31.0114516Z shifts_y_1: "f32[148][1]cpu" = arange_3.to(device(type='cpu')); arange_3 = None 2025-03-04T21:08:31.0114794Z 2025-03-04T21:08:31.0115275Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:08:31.0115929Z meshgrid_1 = torch.functional.meshgrid(shifts_y_1, shifts_x_1); shifts_y_1 = shifts_x_1 = None 2025-03-04T21:08:31.0116367Z shift_y_2: "f32[148, 152][1, 0]cpu" = meshgrid_1[0] 2025-03-04T21:08:31.0116661Z shift_x_2: "f32[148, 152][0, 1]cpu" = meshgrid_1[1]; meshgrid_1 = None 2025-03-04T21:08:31.0116902Z 2025-03-04T21:08:31.0117331Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:08:31.0117834Z shift_x_3: "f32[22496][1]cpu" = shift_x_2.reshape(-1); shift_x_2 = None 2025-03-04T21:08:31.0118077Z 2025-03-04T21:08:31.0118480Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:08:31.0118978Z shift_y_3: "f32[22496][1]cpu" = shift_y_2.reshape(-1); shift_y_2 = None 2025-03-04T21:08:31.0119244Z 2025-03-04T21:08:31.0119709Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:08:31.0120352Z shifts_1: "f32[22496, 4][4, 1]cpu" = torch.stack((shift_x_3, shift_y_3, shift_x_3, shift_y_3), dim = 1); shift_x_3 = shift_y_3 = None 2025-03-04T21:08:31.0120683Z 2025-03-04T21:08:31.0121190Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:08:31.0121769Z view_2: "f32[22496, 1, 4][4, 4, 1]cpu" = shifts_1.view(-1, 1, 4); shifts_1 = None 2025-03-04T21:08:31.0122256Z view_3: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_ = None 2025-03-04T21:08:31.0122748Z add_1: "f32[22496, 3, 4][12, 4, 1]cpu" = view_2 + view_3; view_2 = view_3 = None 2025-03-04T21:08:31.0123049Z x_1: "f32[67488, 4][4, 1]cpu" = add_1.reshape(-1, 4); add_1 = None 2025-03-04T21:08:31.0123279Z 2025-03-04T21:08:31.0123808Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:08:31.0124442Z arange_4: "f32[76][1]cpu" = torch.arange(0.0, 1216, step = 16, dtype = torch.float32) 2025-03-04T21:08:31.0124709Z 2025-03-04T21:08:31.0125087Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:31.0125597Z shifts_x_2: "f32[76][1]cpu" = arange_4.to(device(type='cpu')); arange_4 = None 2025-03-04T21:08:31.0125851Z 2025-03-04T21:08:31.0126364Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:08:31.0126993Z arange_5: "f32[74][1]cpu" = torch.arange(0.0, 1184, step = 16, dtype = torch.float32) 2025-03-04T21:08:31.0127252Z 2025-03-04T21:08:31.0127644Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:31.0128125Z shifts_y_2: "f32[74][1]cpu" = arange_5.to(device(type='cpu')); arange_5 = None 2025-03-04T21:08:31.0128376Z 2025-03-04T21:08:31.0128833Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:08:31.0129450Z meshgrid_2 = torch.functional.meshgrid(shifts_y_2, shifts_x_2); shifts_y_2 = shifts_x_2 = None 2025-03-04T21:08:31.0129795Z shift_y_4: "f32[74, 76][1, 0]cpu" = meshgrid_2[0] 2025-03-04T21:08:31.0130057Z shift_x_4: "f32[74, 76][0, 1]cpu" = meshgrid_2[1]; meshgrid_2 = None 2025-03-04T21:08:31.0130291Z 2025-03-04T21:08:31.0130706Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:08:31.0131229Z shift_x_5: "f32[5624][1]cpu" = shift_x_4.reshape(-1); shift_x_4 = None 2025-03-04T21:08:31.0131475Z 2025-03-04T21:08:31.0131890Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:08:31.0132403Z shift_y_5: "f32[5624][1]cpu" = shift_y_4.reshape(-1); shift_y_4 = None 2025-03-04T21:08:31.0132668Z 2025-03-04T21:08:31.0133145Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:08:31.0133806Z shifts_2: "f32[5624, 4][4, 1]cpu" = torch.stack((shift_x_5, shift_y_5, shift_x_5, shift_y_5), dim = 1); shift_x_5 = shift_y_5 = None 2025-03-04T21:08:31.0134121Z 2025-03-04T21:08:31.0134624Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:08:31.0135235Z view_4: "f32[5624, 1, 4][4, 4, 1]cpu" = shifts_2.view(-1, 1, 4); shifts_2 = None 2025-03-04T21:08:31.0135747Z view_5: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_ = None 2025-03-04T21:08:31.0136276Z add_2: "f32[5624, 3, 4][12, 4, 1]cpu" = view_4 + view_5; view_4 = view_5 = None 2025-03-04T21:08:31.0136566Z x_2: "f32[16872, 4][4, 1]cpu" = add_2.reshape(-1, 4); add_2 = None 2025-03-04T21:08:31.0136793Z 2025-03-04T21:08:31.0137307Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:08:31.0137942Z arange_6: "f32[38][1]cpu" = torch.arange(0.0, 1216, step = 32, dtype = torch.float32) 2025-03-04T21:08:31.0138204Z 2025-03-04T21:08:31.0138578Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:31.0139083Z shifts_x_3: "f32[38][1]cpu" = arange_6.to(device(type='cpu')); arange_6 = None 2025-03-04T21:08:31.0139333Z 2025-03-04T21:08:31.0139842Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:08:31.0140453Z arange_7: "f32[37][1]cpu" = torch.arange(0.0, 1184, step = 32, dtype = torch.float32) 2025-03-04T21:08:31.0140707Z 2025-03-04T21:08:31.0141088Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:31.0141554Z shifts_y_3: "f32[37][1]cpu" = arange_7.to(device(type='cpu')); arange_7 = None 2025-03-04T21:08:31.0141800Z 2025-03-04T21:08:31.0142166Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:08:31.0142359Z meshgrid_3 = torch.functional.meshgrid(shifts_y_3, shifts_x_3); shifts_y_3 = shifts_x_3 = None 2025-03-04T21:08:31.0142462Z shift_y_6: "f32[37, 38][1, 0]cpu" = meshgrid_3[0] 2025-03-04T21:08:31.0142577Z shift_x_6: "f32[37, 38][0, 1]cpu" = meshgrid_3[1]; meshgrid_3 = None 2025-03-04T21:08:31.0142644Z 2025-03-04T21:08:31.0142960Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:08:31.0143086Z shift_x_7: "f32[1406][1]cpu" = shift_x_6.reshape(-1); shift_x_6 = None 2025-03-04T21:08:31.0143147Z 2025-03-04T21:08:31.0143467Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:08:31.0143583Z shift_y_7: "f32[1406][1]cpu" = shift_y_6.reshape(-1); shift_y_6 = None 2025-03-04T21:08:31.0143683Z 2025-03-04T21:08:31.0144053Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:08:31.0144265Z shifts_3: "f32[1406, 4][4, 1]cpu" = torch.stack((shift_x_7, shift_y_7, shift_x_7, shift_y_7), dim = 1); shift_x_7 = shift_y_7 = None 2025-03-04T21:08:31.0144325Z 2025-03-04T21:08:31.0144736Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:08:31.0144860Z view_6: "f32[1406, 1, 4][4, 4, 1]cpu" = shifts_3.view(-1, 1, 4); shifts_3 = None 2025-03-04T21:08:31.0145172Z view_7: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_ = None 2025-03-04T21:08:31.0145312Z add_3: "f32[1406, 3, 4][12, 4, 1]cpu" = view_6 + view_7; view_6 = view_7 = None 2025-03-04T21:08:31.0145429Z x_3: "f32[4218, 4][4, 1]cpu" = add_3.reshape(-1, 4); add_3 = None 2025-03-04T21:08:31.0145493Z 2025-03-04T21:08:31.0145925Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32), 2025-03-04T21:08:31.0146066Z arange_8: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32) 2025-03-04T21:08:31.0146136Z 2025-03-04T21:08:31.0146427Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:31.0146587Z shifts_x_4: "f32[19][1]cpu" = arange_8.to(device(type='cpu')); arange_8 = None 2025-03-04T21:08:31.0146650Z 2025-03-04T21:08:31.0147084Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32), 2025-03-04T21:08:31.0147232Z arange_9: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32) 2025-03-04T21:08:31.0147292Z 2025-03-04T21:08:31.0147607Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:31.0147738Z shifts_y_4: "f32[19][1]cpu" = arange_9.to(device(type='cpu')); arange_9 = None 2025-03-04T21:08:31.0147805Z 2025-03-04T21:08:31.0148171Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 2025-03-04T21:08:31.0148369Z meshgrid_4 = torch.functional.meshgrid(shifts_y_4, shifts_x_4); shifts_y_4 = shifts_x_4 = None 2025-03-04T21:08:31.0148466Z shift_y_8: "f32[19, 19][1, 0]cpu" = meshgrid_4[0] 2025-03-04T21:08:31.0148588Z shift_x_8: "f32[19, 19][0, 1]cpu" = meshgrid_4[1]; meshgrid_4 = None 2025-03-04T21:08:31.0148648Z 2025-03-04T21:08:31.0148989Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1) 2025-03-04T21:08:31.0149106Z shift_x_9: "f32[361][1]cpu" = shift_x_8.reshape(-1); shift_x_8 = None 2025-03-04T21:08:31.0149172Z 2025-03-04T21:08:31.0149490Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1) 2025-03-04T21:08:31.0149612Z shift_y_9: "f32[361][1]cpu" = shift_y_8.reshape(-1); shift_y_8 = None 2025-03-04T21:08:31.0149690Z 2025-03-04T21:08:31.0150073Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) 2025-03-04T21:08:31.0150282Z shifts_4: "f32[361, 4][4, 1]cpu" = torch.stack((shift_x_9, shift_y_9, shift_x_9, shift_y_9), dim = 1); shift_x_9 = shift_y_9 = None 2025-03-04T21:08:31.0150349Z 2025-03-04T21:08:31.0150761Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) 2025-03-04T21:08:31.0150898Z view_8: "f32[361, 1, 4][4, 4, 1]cpu" = shifts_4.view(-1, 1, 4); shifts_4 = None 2025-03-04T21:08:31.0151206Z view_9: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_.view(1, -1, 4); l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_ = None 2025-03-04T21:08:31.0151349Z add_4: "f32[361, 3, 4][12, 4, 1]cpu" = view_8 + view_9; view_8 = view_9 = None 2025-03-04T21:08:31.0151457Z x_4: "f32[1083, 4][4, 1]cpu" = add_4.reshape(-1, 4); add_4 = None 2025-03-04T21:08:31.0151525Z 2025-03-04T21:08:31.0151821Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:08:31.0151947Z tensor: "f32[269952, 4][4, 1]cpu" = x.to(torch.float32); x = None 2025-03-04T21:08:31.0152068Z tensor_1: "f32[67488, 4][4, 1]cpu" = x_1.to(torch.float32); x_1 = None 2025-03-04T21:08:31.0152192Z tensor_2: "f32[16872, 4][4, 1]cpu" = x_2.to(torch.float32); x_2 = None 2025-03-04T21:08:31.0152305Z tensor_3: "f32[4218, 4][4, 1]cpu" = x_3.to(torch.float32); x_3 = None 2025-03-04T21:08:31.0152440Z tensor_4: "f32[1083, 4][4, 1]cpu" = x_4.to(torch.float32); x_4 = None 2025-03-04T21:08:31.0152505Z 2025-03-04T21:08:31.0152772Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:31.0153212Z x_5: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(l_features_p2_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_p2_ = None 2025-03-04T21:08:31.0153275Z 2025-03-04T21:08:31.0153576Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:08:31.0153767Z x_6: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.relu(x_5, inplace = False); x_5 = None 2025-03-04T21:08:31.0153837Z 2025-03-04T21:08:31.0154218Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:08:31.0154636Z score: "f32[4, 3, 296, 304][269952, 89984, 304, 1]cpu" = torch.conv2d(x_6, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:08:31.0154698Z 2025-03-04T21:08:31.0155070Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:08:31.0155501Z x_15: "f32[4, 12, 296, 304][1079808, 89984, 304, 1]cpu" = torch.conv2d(x_6, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_6 = None 2025-03-04T21:08:31.0155574Z 2025-03-04T21:08:31.0155832Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:31.0156379Z x_7: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(l_features_p3_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_p3_ = None 2025-03-04T21:08:31.0156447Z 2025-03-04T21:08:31.0156745Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:08:31.0156945Z x_8: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.relu(x_7, inplace = False); x_7 = None 2025-03-04T21:08:31.0157020Z 2025-03-04T21:08:31.0157417Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:08:31.0157852Z score_1: "f32[4, 3, 148, 152][67488, 22496, 152, 1]cpu" = torch.conv2d(x_8, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:08:31.0157921Z 2025-03-04T21:08:31.0158282Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:08:31.0158694Z x_16: "f32[4, 12, 148, 152][269952, 22496, 152, 1]cpu" = torch.conv2d(x_8, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_8 = None 2025-03-04T21:08:31.0158756Z 2025-03-04T21:08:31.0159017Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:31.0159435Z x_9: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(l_features_p4_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_p4_ = None 2025-03-04T21:08:31.0159503Z 2025-03-04T21:08:31.0159775Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:08:31.0159957Z x_10: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.relu(x_9, inplace = False); x_9 = None 2025-03-04T21:08:31.0160034Z 2025-03-04T21:08:31.0160415Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:08:31.0160816Z score_2: "f32[4, 3, 74, 76][16872, 5624, 76, 1]cpu" = torch.conv2d(x_10, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:08:31.0160886Z 2025-03-04T21:08:31.0161243Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:08:31.0161644Z x_17: "f32[4, 12, 74, 76][67488, 5624, 76, 1]cpu" = torch.conv2d(x_10, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_10 = None 2025-03-04T21:08:31.0161711Z 2025-03-04T21:08:31.0161958Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:31.0162360Z x_11: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(l_features_p5_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_p5_ = None 2025-03-04T21:08:31.0162438Z 2025-03-04T21:08:31.0162712Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:08:31.0162887Z x_12: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.nn.functional.relu(x_11, inplace = False); x_11 = None 2025-03-04T21:08:31.0162953Z 2025-03-04T21:08:31.0163325Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:08:31.0163723Z score_3: "f32[4, 3, 37, 38][4218, 1406, 38, 1]cpu" = torch.conv2d(x_12, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1) 2025-03-04T21:08:31.0163787Z 2025-03-04T21:08:31.0164170Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:08:31.0164558Z x_18: "f32[4, 12, 37, 38][16872, 1406, 38, 1]cpu" = torch.conv2d(x_12, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_12 = None 2025-03-04T21:08:31.0164626Z 2025-03-04T21:08:31.0164877Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d( 2025-03-04T21:08:31.0165455Z x_13: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.conv2d(l_features_p6_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1); l_features_p6_ = l_self_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_rpn_head_modules_conv_parameters_bias_ = None 2025-03-04T21:08:31.0165551Z 2025-03-04T21:08:31.0165821Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x) 2025-03-04T21:08:31.0165993Z x_14: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.relu(x_13, inplace = False); x_13 = None 2025-03-04T21:08:31.0166054Z 2025-03-04T21:08:31.0166461Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t)) 2025-03-04T21:08:31.0167107Z score_4: "f32[4, 3, 19, 19][1083, 361, 19, 1]cpu" = torch.conv2d(x_14, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None 2025-03-04T21:08:31.0167184Z 2025-03-04T21:08:31.0167536Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t)) 2025-03-04T21:08:31.0168131Z x_19: "f32[4, 12, 19, 19][4332, 361, 19, 1]cpu" = torch.conv2d(x_14, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1); x_14 = l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None 2025-03-04T21:08:31.0168195Z 2025-03-04T21:08:31.0168544Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in , code: score.permute(0, 2, 3, 1).flatten(1) 2025-03-04T21:08:31.0168731Z permute: "f32[4, 296, 304, 3][269952, 304, 1, 89984]cpu" = score.permute(0, 2, 3, 1); score = None 2025-03-04T21:08:31.0168874Z logits_i: "f32[4, 269952][269952, 1]cpu" = permute.flatten(1); permute = None 2025-03-04T21:08:31.0169039Z permute_1: "f32[4, 148, 152, 3][67488, 152, 1, 22496]cpu" = score_1.permute(0, 2, 3, 1); score_1 = None 2025-03-04T21:08:31.0169182Z logits_i_1: "f32[4, 67488][67488, 1]cpu" = permute_1.flatten(1); permute_1 = None 2025-03-04T21:08:31.0169342Z permute_2: "f32[4, 74, 76, 3][16872, 76, 1, 5624]cpu" = score_2.permute(0, 2, 3, 1); score_2 = None 2025-03-04T21:08:31.0169478Z logits_i_2: "f32[4, 16872][16872, 1]cpu" = permute_2.flatten(1); permute_2 = None 2025-03-04T21:08:31.0169627Z permute_3: "f32[4, 37, 38, 3][4218, 38, 1, 1406]cpu" = score_3.permute(0, 2, 3, 1); score_3 = None 2025-03-04T21:08:31.0169760Z logits_i_3: "f32[4, 4218][4218, 1]cpu" = permute_3.flatten(1); permute_3 = None 2025-03-04T21:08:31.0169906Z permute_4: "f32[4, 19, 19, 3][1083, 19, 1, 361]cpu" = score_4.permute(0, 2, 3, 1); score_4 = None 2025-03-04T21:08:31.0170060Z logits_i_4: "f32[4, 1083][1083, 1]cpu" = permute_4.flatten(1); permute_4 = None 2025-03-04T21:08:31.0170129Z 2025-03-04T21:08:31.0170542Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in , code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) 2025-03-04T21:08:31.0170718Z view_10: "f32[4, 3, 4, 296, 304][1079808, 359936, 89984, 304, 1]cpu" = x_15.view(4, -1, 4, 296, 304); x_15 = None 2025-03-04T21:08:31.0170893Z permute_5: "f32[4, 296, 304, 3, 4][1079808, 304, 1, 359936, 89984]cpu" = view_10.permute(0, 3, 4, 1, 2); view_10 = None 2025-03-04T21:08:31.0171090Z pred_anchor_deltas_i: "f32[4, 269952, 4][1079808, 4, 1]cpu" = permute_5.flatten(1, -2); permute_5 = None 2025-03-04T21:08:31.0171245Z view_11: "f32[4, 3, 4, 148, 152][269952, 89984, 22496, 152, 1]cpu" = x_16.view(4, -1, 4, 148, 152); x_16 = None 2025-03-04T21:08:31.0171418Z permute_6: "f32[4, 148, 152, 3, 4][269952, 152, 1, 89984, 22496]cpu" = view_11.permute(0, 3, 4, 1, 2); view_11 = None 2025-03-04T21:08:31.0171582Z pred_anchor_deltas_i_2: "f32[4, 67488, 4][269952, 4, 1]cpu" = permute_6.flatten(1, -2); permute_6 = None 2025-03-04T21:08:31.0171730Z view_12: "f32[4, 3, 4, 74, 76][67488, 22496, 5624, 76, 1]cpu" = x_17.view(4, -1, 4, 74, 76); x_17 = None 2025-03-04T21:08:31.0171907Z permute_7: "f32[4, 74, 76, 3, 4][67488, 76, 1, 22496, 5624]cpu" = view_12.permute(0, 3, 4, 1, 2); view_12 = None 2025-03-04T21:08:31.0172084Z pred_anchor_deltas_i_4: "f32[4, 16872, 4][67488, 4, 1]cpu" = permute_7.flatten(1, -2); permute_7 = None 2025-03-04T21:08:31.0172223Z view_13: "f32[4, 3, 4, 37, 38][16872, 5624, 1406, 38, 1]cpu" = x_18.view(4, -1, 4, 37, 38); x_18 = None 2025-03-04T21:08:31.0172387Z permute_8: "f32[4, 37, 38, 3, 4][16872, 38, 1, 5624, 1406]cpu" = view_13.permute(0, 3, 4, 1, 2); view_13 = None 2025-03-04T21:08:31.0172550Z pred_anchor_deltas_i_6: "f32[4, 4218, 4][16872, 4, 1]cpu" = permute_8.flatten(1, -2); permute_8 = None 2025-03-04T21:08:31.0172695Z view_14: "f32[4, 3, 4, 19, 19][4332, 1444, 361, 19, 1]cpu" = x_19.view(4, -1, 4, 19, 19); x_19 = None 2025-03-04T21:08:31.0172857Z permute_9: "f32[4, 19, 19, 3, 4][4332, 19, 1, 1444, 361]cpu" = view_14.permute(0, 3, 4, 1, 2); view_14 = None 2025-03-04T21:08:31.0173028Z pred_anchor_deltas_i_8: "f32[4, 1083, 4][4332, 4, 1]cpu" = permute_9.flatten(1, -2); permute_9 = None 2025-03-04T21:08:31.0173095Z 2025-03-04T21:08:31.0173489Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:08:31.0173694Z pred_anchor_deltas_i_1: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4); pred_anchor_deltas_i = None 2025-03-04T21:08:31.0173778Z 2025-03-04T21:08:31.0174212Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:08:31.0174360Z unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = tensor.unsqueeze(0); tensor = None 2025-03-04T21:08:31.0174510Z expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T21:08:31.0174644Z anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T21:08:31.0174710Z 2025-03-04T21:08:31.0175077Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:08:31.0175251Z deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T21:08:31.0175327Z 2025-03-04T21:08:31.0175638Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:08:31.0175774Z boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T21:08:31.0175841Z 2025-03-04T21:08:31.0176150Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:08:31.0176283Z getitem_10: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:08:31.0176403Z getitem_11: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:08:31.0176576Z widths: "f32[1079808][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T21:08:31.0176636Z 2025-03-04T21:08:31.0176957Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:08:31.0177075Z getitem_12: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:08:31.0177199Z getitem_13: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:08:31.0177345Z heights: "f32[1079808][1]cpu" = getitem_12 - getitem_13; getitem_12 = getitem_13 = None 2025-03-04T21:08:31.0177410Z 2025-03-04T21:08:31.0177729Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:08:31.0177855Z getitem_14: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:08:31.0177939Z mul: "f32[1079808][1]cpu" = 0.5 * widths 2025-03-04T21:08:31.0178072Z ctr_x: "f32[1079808][1]cpu" = getitem_14 + mul; getitem_14 = mul = None 2025-03-04T21:08:31.0178131Z 2025-03-04T21:08:31.0178441Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:08:31.0178583Z getitem_15: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:08:31.0178673Z mul_1: "f32[1079808][1]cpu" = 0.5 * heights 2025-03-04T21:08:31.0178797Z ctr_y: "f32[1079808][1]cpu" = getitem_15 + mul_1; getitem_15 = mul_1 = None 2025-03-04T21:08:31.0178863Z 2025-03-04T21:08:31.0179184Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:08:31.0179343Z getitem_16: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:08:31.0179476Z dx: "f32[1079808, 1][1, 1]cpu" = getitem_16 / 1.0; getitem_16 = None 2025-03-04T21:08:31.0179543Z 2025-03-04T21:08:31.0179843Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:08:31.0179991Z getitem_17: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:08:31.0180106Z dy: "f32[1079808, 1][1, 1]cpu" = getitem_17 / 1.0; getitem_17 = None 2025-03-04T21:08:31.0180164Z 2025-03-04T21:08:31.0180464Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:08:31.0180609Z getitem_18: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:08:31.0180722Z dw: "f32[1079808, 1][1, 1]cpu" = getitem_18 / 1.0; getitem_18 = None 2025-03-04T21:08:31.0180783Z 2025-03-04T21:08:31.0181098Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:08:31.0181276Z getitem_19: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:08:31.0181389Z dh: "f32[1079808, 1][1, 1]cpu" = getitem_19 / 1.0; getitem_19 = None 2025-03-04T21:08:31.0181448Z 2025-03-04T21:08:31.0181785Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:08:31.0181923Z dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:08:31.0182006Z 2025-03-04T21:08:31.0182334Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:08:31.0182478Z dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:08:31.0182539Z 2025-03-04T21:08:31.0182889Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:08:31.0183026Z getitem_20: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:08:31.0183177Z mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_20; dx = getitem_20 = None 2025-03-04T21:08:31.0183328Z getitem_21: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:08:31.0183471Z pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_21; mul_2 = getitem_21 = None 2025-03-04T21:08:31.0183542Z 2025-03-04T21:08:31.0183888Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:08:31.0184020Z getitem_22: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:08:31.0184150Z mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_22; dy = getitem_22 = None 2025-03-04T21:08:31.0184296Z getitem_23: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:08:31.0184439Z pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_23; mul_3 = getitem_23 = None 2025-03-04T21:08:31.0184500Z 2025-03-04T21:08:31.0184836Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:08:31.0184951Z exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:08:31.0185132Z getitem_24: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:08:31.0185269Z pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_24; exp = getitem_24 = None 2025-03-04T21:08:31.0185332Z 2025-03-04T21:08:31.0185666Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:08:31.0185778Z exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:08:31.0185947Z getitem_25: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:08:31.0186081Z pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_25; exp_1 = getitem_25 = None 2025-03-04T21:08:31.0186148Z 2025-03-04T21:08:31.0186472Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:08:31.0186574Z mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T21:08:31.0186721Z x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:08:31.0186789Z 2025-03-04T21:08:31.0187097Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:08:31.0187192Z mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T21:08:31.0187302Z y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:08:31.0187371Z 2025-03-04T21:08:31.0187675Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:08:31.0187821Z mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:08:31.0187950Z x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:08:31.0188015Z 2025-03-04T21:08:31.0188322Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:08:31.0188436Z mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:08:31.0188561Z y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:08:31.0188629Z 2025-03-04T21:08:31.0188995Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:08:31.0189181Z pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:08:31.0189242Z 2025-03-04T21:08:31.0189582Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:08:31.0189747Z proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4)); pred_boxes = None 2025-03-04T21:08:31.0189813Z 2025-03-04T21:08:31.0190194Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:08:31.0190376Z proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T21:08:31.0190435Z 2025-03-04T21:08:31.0190840Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:08:31.0191050Z pred_anchor_deltas_i_3: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_2.reshape(-1, 4); pred_anchor_deltas_i_2 = None 2025-03-04T21:08:31.0191136Z 2025-03-04T21:08:31.0191568Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:08:31.0191726Z unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = tensor_1.unsqueeze(0); tensor_1 = None 2025-03-04T21:08:31.0191873Z expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1); unsqueeze_1 = None 2025-03-04T21:08:31.0192015Z anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4); expand_1 = None 2025-03-04T21:08:31.0192074Z 2025-03-04T21:08:31.0192446Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:08:31.0192615Z deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float(); pred_anchor_deltas_i_3 = None 2025-03-04T21:08:31.0192681Z 2025-03-04T21:08:31.0193002Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:08:31.0193155Z boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32); anchors_i_1 = None 2025-03-04T21:08:31.0193222Z 2025-03-04T21:08:31.0193532Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:08:31.0193667Z getitem_26: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)] 2025-03-04T21:08:31.0193793Z getitem_27: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T21:08:31.0193969Z widths_1: "f32[269952][1]cpu" = getitem_26 - getitem_27; getitem_26 = getitem_27 = None 2025-03-04T21:08:31.0194032Z 2025-03-04T21:08:31.0194356Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:08:31.0194480Z getitem_28: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)] 2025-03-04T21:08:31.0194605Z getitem_29: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)] 2025-03-04T21:08:31.0194757Z heights_1: "f32[269952][1]cpu" = getitem_28 - getitem_29; getitem_28 = getitem_29 = None 2025-03-04T21:08:31.0194826Z 2025-03-04T21:08:31.0195153Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:08:31.0195282Z getitem_30: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T21:08:31.0195374Z mul_10: "f32[269952][1]cpu" = 0.5 * widths_1 2025-03-04T21:08:31.0195516Z ctr_x_1: "f32[269952][1]cpu" = getitem_30 + mul_10; getitem_30 = mul_10 = None 2025-03-04T21:08:31.0195577Z 2025-03-04T21:08:31.0195895Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:08:31.0196116Z getitem_31: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]; boxes_1 = None 2025-03-04T21:08:31.0196217Z mul_11: "f32[269952][1]cpu" = 0.5 * heights_1 2025-03-04T21:08:31.0196351Z ctr_y_1: "f32[269952][1]cpu" = getitem_31 + mul_11; getitem_31 = mul_11 = None 2025-03-04T21:08:31.0196423Z 2025-03-04T21:08:31.0196735Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:08:31.0196900Z getitem_32: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:08:31.0197043Z dx_1: "f32[269952, 1][1, 1]cpu" = getitem_32 / 1.0; getitem_32 = None 2025-03-04T21:08:31.0197113Z 2025-03-04T21:08:31.0197417Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:08:31.0197576Z getitem_33: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:08:31.0197685Z dy_1: "f32[269952, 1][1, 1]cpu" = getitem_33 / 1.0; getitem_33 = None 2025-03-04T21:08:31.0197754Z 2025-03-04T21:08:31.0198057Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:08:31.0198214Z getitem_34: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:08:31.0198326Z dw_2: "f32[269952, 1][1, 1]cpu" = getitem_34 / 1.0; getitem_34 = None 2025-03-04T21:08:31.0198398Z 2025-03-04T21:08:31.0198798Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:08:31.0198990Z getitem_35: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))]; deltas_1 = None 2025-03-04T21:08:31.0199097Z dh_2: "f32[269952, 1][1, 1]cpu" = getitem_35 / 1.0; getitem_35 = None 2025-03-04T21:08:31.0199165Z 2025-03-04T21:08:31.0199503Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:08:31.0199653Z dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356); dw_2 = None 2025-03-04T21:08:31.0199741Z 2025-03-04T21:08:31.0200102Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:08:31.0200248Z dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356); dh_2 = None 2025-03-04T21:08:31.0200310Z 2025-03-04T21:08:31.0200659Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:08:31.0200794Z getitem_36: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)] 2025-03-04T21:08:31.0200939Z mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_36; dx_1 = getitem_36 = None 2025-03-04T21:08:31.0201094Z getitem_37: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)]; ctr_x_1 = None 2025-03-04T21:08:31.0201242Z pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_37; mul_12 = getitem_37 = None 2025-03-04T21:08:31.0201303Z 2025-03-04T21:08:31.0201661Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:08:31.0201805Z getitem_38: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)] 2025-03-04T21:08:31.0202117Z mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_38; dy_1 = getitem_38 = None 2025-03-04T21:08:31.0202267Z getitem_39: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)]; ctr_y_1 = None 2025-03-04T21:08:31.0202410Z pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_39; mul_13 = getitem_39 = None 2025-03-04T21:08:31.0202471Z 2025-03-04T21:08:31.0202800Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:08:31.0202912Z exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3); dw_3 = None 2025-03-04T21:08:31.0203117Z getitem_40: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]; widths_1 = None 2025-03-04T21:08:31.0203246Z pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_40; exp_2 = getitem_40 = None 2025-03-04T21:08:31.0203312Z 2025-03-04T21:08:31.0203635Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:08:31.0203750Z exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3); dh_3 = None 2025-03-04T21:08:31.0203912Z getitem_41: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]; heights_1 = None 2025-03-04T21:08:31.0204046Z pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_41; exp_3 = getitem_41 = None 2025-03-04T21:08:31.0204106Z 2025-03-04T21:08:31.0204412Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:08:31.0204527Z mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1 2025-03-04T21:08:31.0204650Z x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16; mul_16 = None 2025-03-04T21:08:31.0204711Z 2025-03-04T21:08:31.0205018Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:08:31.0205107Z mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1 2025-03-04T21:08:31.0205227Z y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17; mul_17 = None 2025-03-04T21:08:31.0205286Z 2025-03-04T21:08:31.0205589Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:08:31.0205723Z mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1; pred_w_1 = None 2025-03-04T21:08:31.0205859Z x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18; pred_ctr_x_1 = mul_18 = None 2025-03-04T21:08:31.0205918Z 2025-03-04T21:08:31.0206220Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:08:31.0206328Z mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1; pred_h_1 = None 2025-03-04T21:08:31.0206460Z y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19; pred_ctr_y_1 = mul_19 = None 2025-03-04T21:08:31.0207050Z 2025-03-04T21:08:31.0207403Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:08:31.0207593Z pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1); x1_1 = y1_1 = x2_1 = y2_1 = None 2025-03-04T21:08:31.0207659Z 2025-03-04T21:08:31.0207987Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:08:31.0208152Z proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4)); pred_boxes_1 = None 2025-03-04T21:08:31.0208219Z 2025-03-04T21:08:31.0208595Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:08:31.0208769Z proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4); proposals_i_1 = None 2025-03-04T21:08:31.0208829Z 2025-03-04T21:08:31.0209223Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:08:31.0209443Z pred_anchor_deltas_i_5: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_4.reshape(-1, 4); pred_anchor_deltas_i_4 = None 2025-03-04T21:08:31.0209509Z 2025-03-04T21:08:31.0209927Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:08:31.0210078Z unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = tensor_2.unsqueeze(0); tensor_2 = None 2025-03-04T21:08:31.0210225Z expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1); unsqueeze_2 = None 2025-03-04T21:08:31.0210360Z anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4); expand_2 = None 2025-03-04T21:08:31.0210421Z 2025-03-04T21:08:31.0210788Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:08:31.0210975Z deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_5.float(); pred_anchor_deltas_i_5 = None 2025-03-04T21:08:31.0211042Z 2025-03-04T21:08:31.0211342Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:08:31.0211488Z boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32); anchors_i_2 = None 2025-03-04T21:08:31.0211545Z 2025-03-04T21:08:31.0211859Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:08:31.0211982Z getitem_42: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)] 2025-03-04T21:08:31.0212125Z getitem_43: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T21:08:31.0212269Z widths_2: "f32[67488][1]cpu" = getitem_42 - getitem_43; getitem_42 = getitem_43 = None 2025-03-04T21:08:31.0212337Z 2025-03-04T21:08:31.0212648Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:08:31.0212772Z getitem_44: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)] 2025-03-04T21:08:31.0212884Z getitem_45: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)] 2025-03-04T21:08:31.0213051Z heights_2: "f32[67488][1]cpu" = getitem_44 - getitem_45; getitem_44 = getitem_45 = None 2025-03-04T21:08:31.0213112Z 2025-03-04T21:08:31.0213425Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:08:31.0213541Z getitem_46: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T21:08:31.0213633Z mul_20: "f32[67488][1]cpu" = 0.5 * widths_2 2025-03-04T21:08:31.0213758Z ctr_x_2: "f32[67488][1]cpu" = getitem_46 + mul_20; getitem_46 = mul_20 = None 2025-03-04T21:08:31.0213824Z 2025-03-04T21:08:31.0214128Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:08:31.0214285Z getitem_47: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]; boxes_2 = None 2025-03-04T21:08:31.0214375Z mul_21: "f32[67488][1]cpu" = 0.5 * heights_2 2025-03-04T21:08:31.0214505Z ctr_y_2: "f32[67488][1]cpu" = getitem_47 + mul_21; getitem_47 = mul_21 = None 2025-03-04T21:08:31.0214563Z 2025-03-04T21:08:31.0214862Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:08:31.0215033Z getitem_48: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:08:31.0215146Z dx_2: "f32[67488, 1][1, 1]cpu" = getitem_48 / 1.0; getitem_48 = None 2025-03-04T21:08:31.0215216Z 2025-03-04T21:08:31.0215512Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:08:31.0215676Z getitem_49: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:08:31.0215784Z dy_2: "f32[67488, 1][1, 1]cpu" = getitem_49 / 1.0; getitem_49 = None 2025-03-04T21:08:31.0215849Z 2025-03-04T21:08:31.0216136Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:08:31.0216286Z getitem_50: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:08:31.0216391Z dw_4: "f32[67488, 1][1, 1]cpu" = getitem_50 / 1.0; getitem_50 = None 2025-03-04T21:08:31.0216474Z 2025-03-04T21:08:31.0216771Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:08:31.0216958Z getitem_51: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))]; deltas_2 = None 2025-03-04T21:08:31.0217063Z dh_4: "f32[67488, 1][1, 1]cpu" = getitem_51 / 1.0; getitem_51 = None 2025-03-04T21:08:31.0217132Z 2025-03-04T21:08:31.0217473Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:08:31.0217639Z dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356); dw_4 = None 2025-03-04T21:08:31.0217702Z 2025-03-04T21:08:31.0218047Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:08:31.0218180Z dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356); dh_4 = None 2025-03-04T21:08:31.0218247Z 2025-03-04T21:08:31.0218596Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:08:31.0218762Z getitem_52: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)] 2025-03-04T21:08:31.0218886Z mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_52; dx_2 = getitem_52 = None 2025-03-04T21:08:31.0219045Z getitem_53: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)]; ctr_x_2 = None 2025-03-04T21:08:31.0219182Z pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_53; mul_22 = getitem_53 = None 2025-03-04T21:08:31.0219248Z 2025-03-04T21:08:31.0219593Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:08:31.0219732Z getitem_54: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)] 2025-03-04T21:08:31.0219849Z mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_54; dy_2 = getitem_54 = None 2025-03-04T21:08:31.0220015Z getitem_55: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)]; ctr_y_2 = None 2025-03-04T21:08:31.0220145Z pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_55; mul_23 = getitem_55 = None 2025-03-04T21:08:31.0220207Z 2025-03-04T21:08:31.0220536Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:08:31.0220660Z exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5); dw_5 = None 2025-03-04T21:08:31.0220819Z getitem_56: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]; widths_2 = None 2025-03-04T21:08:31.0220949Z pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_56; exp_4 = getitem_56 = None 2025-03-04T21:08:31.0221010Z 2025-03-04T21:08:31.0221339Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:08:31.0221453Z exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5); dh_5 = None 2025-03-04T21:08:31.0221615Z getitem_57: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]; heights_2 = None 2025-03-04T21:08:31.0221748Z pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_57; exp_5 = getitem_57 = None 2025-03-04T21:08:31.0221808Z 2025-03-04T21:08:31.0222139Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:08:31.0222234Z mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2 2025-03-04T21:08:31.0222349Z x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26; mul_26 = None 2025-03-04T21:08:31.0222408Z 2025-03-04T21:08:31.0222720Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:08:31.0222812Z mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2 2025-03-04T21:08:31.0222928Z y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27; mul_27 = None 2025-03-04T21:08:31.0222989Z 2025-03-04T21:08:31.0223316Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:08:31.0223431Z mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2; pred_w_2 = None 2025-03-04T21:08:31.0223568Z x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28; pred_ctr_x_2 = mul_28 = None 2025-03-04T21:08:31.0223627Z 2025-03-04T21:08:31.0223936Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:08:31.0224045Z mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2; pred_h_2 = None 2025-03-04T21:08:31.0224195Z y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29; pred_ctr_y_2 = mul_29 = None 2025-03-04T21:08:31.0224259Z 2025-03-04T21:08:31.0224623Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:08:31.0224818Z pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1); x1_2 = y1_2 = x2_2 = y2_2 = None 2025-03-04T21:08:31.0224891Z 2025-03-04T21:08:31.0225229Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:08:31.0225399Z proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4)); pred_boxes_2 = None 2025-03-04T21:08:31.0225461Z 2025-03-04T21:08:31.0225866Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:08:31.0226044Z proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4); proposals_i_2 = None 2025-03-04T21:08:31.0226117Z 2025-03-04T21:08:31.0226528Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:08:31.0226760Z pred_anchor_deltas_i_7: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_6.reshape(-1, 4); pred_anchor_deltas_i_6 = None 2025-03-04T21:08:31.0226822Z 2025-03-04T21:08:31.0227255Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:08:31.0227399Z unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = tensor_3.unsqueeze(0); tensor_3 = None 2025-03-04T21:08:31.0227551Z expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1); unsqueeze_3 = None 2025-03-04T21:08:31.0227692Z anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4); expand_3 = None 2025-03-04T21:08:31.0227756Z 2025-03-04T21:08:31.0228152Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:08:31.0228323Z deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_7.float(); pred_anchor_deltas_i_7 = None 2025-03-04T21:08:31.0228392Z 2025-03-04T21:08:31.0228715Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:08:31.0228862Z boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32); anchors_i_3 = None 2025-03-04T21:08:31.0228922Z 2025-03-04T21:08:31.0229239Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:08:31.0229390Z getitem_58: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)] 2025-03-04T21:08:31.0229524Z getitem_59: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T21:08:31.0229673Z widths_3: "f32[16872][1]cpu" = getitem_58 - getitem_59; getitem_58 = getitem_59 = None 2025-03-04T21:08:31.0229743Z 2025-03-04T21:08:31.0230075Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:08:31.0230207Z getitem_60: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)] 2025-03-04T21:08:31.0230346Z getitem_61: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)] 2025-03-04T21:08:31.0230507Z heights_3: "f32[16872][1]cpu" = getitem_60 - getitem_61; getitem_60 = getitem_61 = None 2025-03-04T21:08:31.0230572Z 2025-03-04T21:08:31.0230903Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:08:31.0231031Z getitem_62: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T21:08:31.0231129Z mul_30: "f32[16872][1]cpu" = 0.5 * widths_3 2025-03-04T21:08:31.0231263Z ctr_x_3: "f32[16872][1]cpu" = getitem_62 + mul_30; getitem_62 = mul_30 = None 2025-03-04T21:08:31.0231334Z 2025-03-04T21:08:31.0231660Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:08:31.0231819Z getitem_63: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]; boxes_3 = None 2025-03-04T21:08:31.0231915Z mul_31: "f32[16872][1]cpu" = 0.5 * heights_3 2025-03-04T21:08:31.0232056Z ctr_y_3: "f32[16872][1]cpu" = getitem_63 + mul_31; getitem_63 = mul_31 = None 2025-03-04T21:08:31.0232121Z 2025-03-04T21:08:31.0232444Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:08:31.0232627Z getitem_64: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:08:31.0232749Z dx_3: "f32[16872, 1][1, 1]cpu" = getitem_64 / 1.0; getitem_64 = None 2025-03-04T21:08:31.0232812Z 2025-03-04T21:08:31.0233133Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:08:31.0233291Z getitem_65: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:08:31.0233410Z dy_3: "f32[16872, 1][1, 1]cpu" = getitem_65 / 1.0; getitem_65 = None 2025-03-04T21:08:31.0233472Z 2025-03-04T21:08:31.0233794Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:08:31.0233947Z getitem_66: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:08:31.0234083Z dw_6: "f32[16872, 1][1, 1]cpu" = getitem_66 / 1.0; getitem_66 = None 2025-03-04T21:08:31.0234147Z 2025-03-04T21:08:31.0234498Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:08:31.0234702Z getitem_67: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))]; deltas_3 = None 2025-03-04T21:08:31.0234821Z dh_6: "f32[16872, 1][1, 1]cpu" = getitem_67 / 1.0; getitem_67 = None 2025-03-04T21:08:31.0234896Z 2025-03-04T21:08:31.0235266Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:08:31.0235446Z dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356); dw_6 = None 2025-03-04T21:08:31.0235512Z 2025-03-04T21:08:31.0235891Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:08:31.0236126Z dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356); dh_6 = None 2025-03-04T21:08:31.0236205Z 2025-03-04T21:08:31.0236610Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:08:31.0236776Z getitem_68: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)] 2025-03-04T21:08:31.0236903Z mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_68; dx_3 = getitem_68 = None 2025-03-04T21:08:31.0237072Z getitem_69: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)]; ctr_x_3 = None 2025-03-04T21:08:31.0237218Z pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_69; mul_32 = getitem_69 = None 2025-03-04T21:08:31.0237292Z 2025-03-04T21:08:31.0237652Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:08:31.0237794Z getitem_70: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)] 2025-03-04T21:08:31.0237923Z mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_70; dy_3 = getitem_70 = None 2025-03-04T21:08:31.0238097Z getitem_71: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)]; ctr_y_3 = None 2025-03-04T21:08:31.0238247Z pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_71; mul_33 = getitem_71 = None 2025-03-04T21:08:31.0238324Z 2025-03-04T21:08:31.0238693Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:08:31.0238844Z exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7); dw_7 = None 2025-03-04T21:08:31.0239019Z getitem_72: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]; widths_3 = None 2025-03-04T21:08:31.0239176Z pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_72; exp_6 = getitem_72 = None 2025-03-04T21:08:31.0239242Z 2025-03-04T21:08:31.0239620Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:08:31.0239739Z exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7); dh_7 = None 2025-03-04T21:08:31.0239928Z getitem_73: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]; heights_3 = None 2025-03-04T21:08:31.0240075Z pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_73; exp_7 = getitem_73 = None 2025-03-04T21:08:31.0240154Z 2025-03-04T21:08:31.0240512Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:08:31.0240627Z mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3 2025-03-04T21:08:31.0240753Z x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36; mul_36 = None 2025-03-04T21:08:31.0240829Z 2025-03-04T21:08:31.0241172Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:08:31.0241282Z mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3 2025-03-04T21:08:31.0241405Z y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37; mul_37 = None 2025-03-04T21:08:31.0241501Z 2025-03-04T21:08:31.0241838Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:08:31.0241973Z mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3; pred_w_3 = None 2025-03-04T21:08:31.0242118Z x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38; pred_ctr_x_3 = mul_38 = None 2025-03-04T21:08:31.0242194Z 2025-03-04T21:08:31.0242530Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:08:31.0242676Z mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3; pred_h_3 = None 2025-03-04T21:08:31.0242819Z y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39; pred_ctr_y_3 = mul_39 = None 2025-03-04T21:08:31.0242895Z 2025-03-04T21:08:31.0243280Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:08:31.0243499Z pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1); x1_3 = y1_3 = x2_3 = y2_3 = None 2025-03-04T21:08:31.0243573Z 2025-03-04T21:08:31.0243942Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:08:31.0244126Z proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4)); pred_boxes_3 = None 2025-03-04T21:08:31.0244194Z 2025-03-04T21:08:31.0244630Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:08:31.0244818Z proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4); proposals_i_3 = None 2025-03-04T21:08:31.0244893Z 2025-03-04T21:08:31.0245323Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:08:31.0245531Z pred_anchor_deltas_i_9: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_8.reshape(-1, 4); pred_anchor_deltas_i_8 = None 2025-03-04T21:08:31.0245592Z 2025-03-04T21:08:31.0246028Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:08:31.0246172Z unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = tensor_4.unsqueeze(0); tensor_4 = None 2025-03-04T21:08:31.0246325Z expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1); unsqueeze_4 = None 2025-03-04T21:08:31.0246458Z anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4); expand_4 = None 2025-03-04T21:08:31.0246528Z 2025-03-04T21:08:31.0246910Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:08:31.0247086Z deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_9.float(); pred_anchor_deltas_i_9 = None 2025-03-04T21:08:31.0247147Z 2025-03-04T21:08:31.0247463Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:08:31.0247603Z boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32); anchors_i_4 = None 2025-03-04T21:08:31.0247672Z 2025-03-04T21:08:31.0247983Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:08:31.0248133Z getitem_74: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)] 2025-03-04T21:08:31.0248256Z getitem_75: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T21:08:31.0248404Z widths_4: "f32[4332][1]cpu" = getitem_74 - getitem_75; getitem_74 = getitem_75 = None 2025-03-04T21:08:31.0248464Z 2025-03-04T21:08:31.0248788Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:08:31.0248908Z getitem_76: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)] 2025-03-04T21:08:31.0249045Z getitem_77: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)] 2025-03-04T21:08:31.0249188Z heights_4: "f32[4332][1]cpu" = getitem_76 - getitem_77; getitem_76 = getitem_77 = None 2025-03-04T21:08:31.0249257Z 2025-03-04T21:08:31.0249566Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:08:31.0249695Z getitem_78: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T21:08:31.0249781Z mul_40: "f32[4332][1]cpu" = 0.5 * widths_4 2025-03-04T21:08:31.0249915Z ctr_x_4: "f32[4332][1]cpu" = getitem_78 + mul_40; getitem_78 = mul_40 = None 2025-03-04T21:08:31.0249975Z 2025-03-04T21:08:31.0250295Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:08:31.0250444Z getitem_79: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]; boxes_4 = None 2025-03-04T21:08:31.0250531Z mul_41: "f32[4332][1]cpu" = 0.5 * heights_4 2025-03-04T21:08:31.0250660Z ctr_y_4: "f32[4332][1]cpu" = getitem_79 + mul_41; getitem_79 = mul_41 = None 2025-03-04T21:08:31.0250721Z 2025-03-04T21:08:31.0251031Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:08:31.0251200Z getitem_80: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:08:31.0251315Z dx_4: "f32[4332, 1][1, 1]cpu" = getitem_80 / 1.0; getitem_80 = None 2025-03-04T21:08:31.0251376Z 2025-03-04T21:08:31.0251677Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:08:31.0251825Z getitem_81: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:08:31.0251938Z dy_4: "f32[4332, 1][1, 1]cpu" = getitem_81 / 1.0; getitem_81 = None 2025-03-04T21:08:31.0251998Z 2025-03-04T21:08:31.0252301Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:08:31.0252445Z getitem_82: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:08:31.0252579Z dw_8: "f32[4332, 1][1, 1]cpu" = getitem_82 / 1.0; getitem_82 = None 2025-03-04T21:08:31.0252643Z 2025-03-04T21:08:31.0252947Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:08:31.0253125Z getitem_83: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))]; deltas_4 = None 2025-03-04T21:08:31.0253238Z dh_8: "f32[4332, 1][1, 1]cpu" = getitem_83 / 1.0; getitem_83 = None 2025-03-04T21:08:31.0253298Z 2025-03-04T21:08:31.0253636Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:08:31.0253790Z dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356); dw_8 = None 2025-03-04T21:08:31.0253856Z 2025-03-04T21:08:31.0254224Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:08:31.0254361Z dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356); dh_8 = None 2025-03-04T21:08:31.0254420Z 2025-03-04T21:08:31.0254798Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:08:31.0254932Z getitem_84: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)] 2025-03-04T21:08:31.0255060Z mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_84; dx_4 = getitem_84 = None 2025-03-04T21:08:31.0255209Z getitem_85: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)]; ctr_x_4 = None 2025-03-04T21:08:31.0255354Z pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_85; mul_42 = getitem_85 = None 2025-03-04T21:08:31.0255414Z 2025-03-04T21:08:31.0255769Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:08:31.0255898Z getitem_86: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)] 2025-03-04T21:08:31.0256026Z mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_86; dy_4 = getitem_86 = None 2025-03-04T21:08:31.0256189Z getitem_87: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)]; ctr_y_4 = None 2025-03-04T21:08:31.0256319Z pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_87; mul_43 = getitem_87 = None 2025-03-04T21:08:31.0256386Z 2025-03-04T21:08:31.0256709Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:08:31.0256839Z exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9); dw_9 = None 2025-03-04T21:08:31.0256990Z getitem_88: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]; widths_4 = None 2025-03-04T21:08:31.0257120Z pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_88; exp_8 = getitem_88 = None 2025-03-04T21:08:31.0257179Z 2025-03-04T21:08:31.0257507Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:08:31.0257612Z exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9); dh_9 = None 2025-03-04T21:08:31.0257774Z getitem_89: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]; heights_4 = None 2025-03-04T21:08:31.0257900Z pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_89; exp_9 = getitem_89 = None 2025-03-04T21:08:31.0257965Z 2025-03-04T21:08:31.0258277Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:08:31.0258375Z mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4 2025-03-04T21:08:31.0258482Z x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46; mul_46 = None 2025-03-04T21:08:31.0258547Z 2025-03-04T21:08:31.0258847Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:08:31.0258943Z mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4 2025-03-04T21:08:31.0259048Z y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47; mul_47 = None 2025-03-04T21:08:31.0259133Z 2025-03-04T21:08:31.0259426Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:08:31.0259542Z mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4; pred_w_4 = None 2025-03-04T21:08:31.0259667Z x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48; pred_ctr_x_4 = mul_48 = None 2025-03-04T21:08:31.0259734Z 2025-03-04T21:08:31.0260025Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:08:31.0260152Z mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4; pred_h_4 = None 2025-03-04T21:08:31.0260275Z y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49; pred_ctr_y_4 = mul_49 = None 2025-03-04T21:08:31.0260342Z 2025-03-04T21:08:31.0260679Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:08:31.0260870Z pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1); x1_4 = y1_4 = x2_4 = y2_4 = None 2025-03-04T21:08:31.0260930Z 2025-03-04T21:08:31.0261268Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:08:31.0261421Z proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4)); pred_boxes_4 = None 2025-03-04T21:08:31.0261489Z 2025-03-04T21:08:31.0261868Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:08:31.0262043Z proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4); proposals_i_4 = None 2025-03-04T21:08:31.0262105Z 2025-03-04T21:08:31.0262623Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T21:08:31.0262751Z arange_10: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T21:08:31.0262819Z 2025-03-04T21:08:31.0263105Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:31.0263250Z batch_idx: "i64[4][1]cpu" = arange_10.to(device(type='cpu')); arange_10 = None 2025-03-04T21:08:31.0263309Z 2025-03-04T21:08:31.0263739Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:08:31.0263857Z topk = logits_i.topk(1000, dim = 1); logits_i = None 2025-03-04T21:08:31.0263953Z topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0] 2025-03-04T21:08:31.0264085Z topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1]; topk = None 2025-03-04T21:08:31.0264145Z 2025-03-04T21:08:31.0264608Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:08:31.0264737Z getitem_92: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:08:31.0264969Z topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_92, topk_idx)]; proposals_i_5 = getitem_92 = topk_idx = None 2025-03-04T21:08:31.0265045Z 2025-03-04T21:08:31.0265507Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:08:31.0265672Z full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:08:31.0265738Z 2025-03-04T21:08:31.0266032Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:31.0266154Z to_21: "i64[1000][1]cpu" = full.to(device(type='cpu')); full = None 2025-03-04T21:08:31.0266215Z 2025-03-04T21:08:31.0266668Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:08:31.0266783Z topk_1 = logits_i_1.topk(1000, dim = 1); logits_i_1 = None 2025-03-04T21:08:31.0266894Z topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0] 2025-03-04T21:08:31.0267017Z topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1]; topk_1 = None 2025-03-04T21:08:31.0267086Z 2025-03-04T21:08:31.0267532Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:08:31.0267664Z getitem_96: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:08:31.0267892Z topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_96, topk_idx_1)]; proposals_i_6 = getitem_96 = topk_idx_1 = None 2025-03-04T21:08:31.0267959Z 2025-03-04T21:08:31.0268401Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:08:31.0268585Z full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:08:31.0268644Z 2025-03-04T21:08:31.0268939Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:31.0269057Z to_22: "i64[1000][1]cpu" = full_1.to(device(type='cpu')); full_1 = None 2025-03-04T21:08:31.0269124Z 2025-03-04T21:08:31.0269552Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:08:31.0269671Z topk_2 = logits_i_2.topk(1000, dim = 1); logits_i_2 = None 2025-03-04T21:08:31.0269775Z topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0] 2025-03-04T21:08:31.0269896Z topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1]; topk_2 = None 2025-03-04T21:08:31.0269958Z 2025-03-04T21:08:31.0270435Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:08:31.0270573Z getitem_100: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:08:31.0270810Z topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_100, topk_idx_2)]; proposals_i_7 = getitem_100 = topk_idx_2 = None 2025-03-04T21:08:31.0270878Z 2025-03-04T21:08:31.0271330Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:08:31.0271514Z full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:08:31.0271576Z 2025-03-04T21:08:31.0271874Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:31.0271994Z to_23: "i64[1000][1]cpu" = full_2.to(device(type='cpu')); full_2 = None 2025-03-04T21:08:31.0272062Z 2025-03-04T21:08:31.0272520Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:08:31.0272639Z topk_3 = logits_i_3.topk(1000, dim = 1); logits_i_3 = None 2025-03-04T21:08:31.0272742Z topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0] 2025-03-04T21:08:31.0272862Z topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1]; topk_3 = None 2025-03-04T21:08:31.0272926Z 2025-03-04T21:08:31.0273403Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:08:31.0273534Z getitem_104: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:08:31.0273784Z topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_104, topk_idx_3)]; proposals_i_8 = getitem_104 = topk_idx_3 = None 2025-03-04T21:08:31.0273847Z 2025-03-04T21:08:31.0274316Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:08:31.0274482Z full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:08:31.0274566Z 2025-03-04T21:08:31.0274865Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:31.0274996Z to_24: "i64[1000][1]cpu" = full_3.to(device(type='cpu')); full_3 = None 2025-03-04T21:08:31.0275060Z 2025-03-04T21:08:31.0275504Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:08:31.0275616Z topk_4 = logits_i_4.topk(1000, dim = 1); logits_i_4 = None 2025-03-04T21:08:31.0275725Z topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0] 2025-03-04T21:08:31.0275836Z topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1]; topk_4 = None 2025-03-04T21:08:31.0275908Z 2025-03-04T21:08:31.0276458Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:08:31.0276639Z getitem_108: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T21:08:31.0276887Z topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_108, topk_idx_4)]; proposals_i_9 = getitem_108 = topk_idx_4 = None 2025-03-04T21:08:31.0276950Z 2025-03-04T21:08:31.0277420Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:08:31.0277584Z full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:08:31.0277683Z 2025-03-04T21:08:31.0277988Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:31.0278119Z to_25: "i64[1000][1]cpu" = full_4.to(device(type='cpu')); full_4 = None 2025-03-04T21:08:31.0278182Z 2025-03-04T21:08:31.0278476Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:08:31.0278883Z topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1); topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None 2025-03-04T21:08:31.0278955Z 2025-03-04T21:08:31.0279240Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:08:31.0279725Z topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1); topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None 2025-03-04T21:08:31.0279789Z 2025-03-04T21:08:31.0280077Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:08:31.0280281Z level_ids: "i64[5000][1]cpu" = torch.cat([to_21, to_22, to_23, to_24, to_25], 0); to_21 = to_22 = to_23 = to_24 = to_25 = level_ids = None 2025-03-04T21:08:31.0280353Z 2025-03-04T21:08:31.0280746Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T21:08:31.0280896Z getitem_110: "f32[5000, 4][4, 1]cpu" = topk_proposals[0]; topk_proposals = None 2025-03-04T21:08:31.0280981Z 2025-03-04T21:08:31.0281301Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:08:31.0281451Z tensor_5: "f32[5000, 4][4, 1]cpu" = getitem_110.to(torch.float32); getitem_110 = None 2025-03-04T21:08:31.0281518Z 2025-03-04T21:08:31.0281907Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T21:08:31.0282048Z scores_per_img: "f32[5000][1]cpu" = topk_scores[0]; topk_scores = None 2025-03-04T21:08:31.0282116Z 2025-03-04T21:08:31.0282621Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T21:08:31.0282770Z isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor_5); tensor_5 = None 2025-03-04T21:08:31.0282911Z all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:08:31.0283076Z isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T21:08:31.0283207Z valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T21:08:31.0283275Z 2025-03-04T21:08:31.0283652Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T21:08:31.0283774Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T21:08:31.0283836Z 2025-03-04T21:08:31.9596042Z 2025-03-04T21:08:31.9596763Z class GraphModule(torch.nn.Module): 2025-03-04T21:08:31.9598727Z def forward(self, L_pred_anchor_deltas_0_: "f32[4, 269952, 4][1079808, 4, 1]cpu", L_anchors_0_tensor: "f32[269952, 4][4, 1]cpu", L_pred_anchor_deltas_1_: "f32[4, 67488, 4][269952, 4, 1]cpu", L_anchors_1_tensor: "f32[67488, 4][4, 1]cpu", L_pred_anchor_deltas_2_: "f32[4, 16872, 4][67488, 4, 1]cpu", L_anchors_2_tensor: "f32[16872, 4][4, 1]cpu", L_pred_anchor_deltas_3_: "f32[4, 4218, 4][16872, 4, 1]cpu", L_anchors_3_tensor: "f32[4218, 4][4, 1]cpu", L_pred_anchor_deltas_4_: "f32[4, 1083, 4][4332, 4, 1]cpu", L_anchors_4_tensor: "f32[1083, 4][4, 1]cpu", L_pred_objectness_logits_0_: "f32[4, 269952][269952, 1]cpu", L_pred_objectness_logits_1_: "f32[4, 67488][67488, 1]cpu", L_pred_objectness_logits_2_: "f32[4, 16872][16872, 1]cpu", L_pred_objectness_logits_3_: "f32[4, 4218][4218, 1]cpu", L_pred_objectness_logits_4_: "f32[4, 1083][1083, 1]cpu"): 2025-03-04T21:08:31.9600327Z l_pred_anchor_deltas_0_ = L_pred_anchor_deltas_0_ 2025-03-04T21:08:31.9609205Z l_anchors_0_tensor = L_anchors_0_tensor 2025-03-04T21:08:31.9609665Z l_pred_anchor_deltas_1_ = L_pred_anchor_deltas_1_ 2025-03-04T21:08:31.9610027Z l_anchors_1_tensor = L_anchors_1_tensor 2025-03-04T21:08:31.9610381Z l_pred_anchor_deltas_2_ = L_pred_anchor_deltas_2_ 2025-03-04T21:08:31.9610723Z l_anchors_2_tensor = L_anchors_2_tensor 2025-03-04T21:08:31.9611714Z l_pred_anchor_deltas_3_ = L_pred_anchor_deltas_3_ 2025-03-04T21:08:31.9612127Z l_anchors_3_tensor = L_anchors_3_tensor 2025-03-04T21:08:31.9612445Z l_pred_anchor_deltas_4_ = L_pred_anchor_deltas_4_ 2025-03-04T21:08:31.9612749Z l_anchors_4_tensor = L_anchors_4_tensor 2025-03-04T21:08:31.9613035Z l_pred_objectness_logits_0_ = L_pred_objectness_logits_0_ 2025-03-04T21:08:31.9613324Z l_pred_objectness_logits_1_ = L_pred_objectness_logits_1_ 2025-03-04T21:08:31.9613604Z l_pred_objectness_logits_2_ = L_pred_objectness_logits_2_ 2025-03-04T21:08:31.9613888Z l_pred_objectness_logits_3_ = L_pred_objectness_logits_3_ 2025-03-04T21:08:31.9614386Z l_pred_objectness_logits_4_ = L_pred_objectness_logits_4_ 2025-03-04T21:08:31.9614665Z 2025-03-04T21:08:31.9615296Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:08:31.9615993Z pred_anchor_deltas_i: "f32[1079808, 4][4, 1]cpu" = l_pred_anchor_deltas_0_.reshape(-1, 4); l_pred_anchor_deltas_0_ = None 2025-03-04T21:08:31.9616332Z 2025-03-04T21:08:31.9616872Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:08:31.9617571Z unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = l_anchors_0_tensor.unsqueeze(0); l_anchors_0_tensor = None 2025-03-04T21:08:31.9617969Z expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1); unsqueeze = None 2025-03-04T21:08:31.9618421Z anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4); expand = None 2025-03-04T21:08:31.9618737Z 2025-03-04T21:08:31.9619214Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:08:31.9619807Z deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.float(); pred_anchor_deltas_i = None 2025-03-04T21:08:31.9620091Z 2025-03-04T21:08:31.9620496Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:08:31.9621037Z boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32); anchors_i = None 2025-03-04T21:08:31.9621380Z 2025-03-04T21:08:31.9621816Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:08:31.9622371Z getitem: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:08:31.9622697Z getitem_1: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:08:31.9623040Z widths: "f32[1079808][1]cpu" = getitem - getitem_1; getitem = getitem_1 = None 2025-03-04T21:08:31.9623324Z 2025-03-04T21:08:31.9623816Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:08:31.9624385Z getitem_2: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:08:31.9624704Z getitem_3: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:08:31.9625047Z heights: "f32[1079808][1]cpu" = getitem_2 - getitem_3; getitem_2 = getitem_3 = None 2025-03-04T21:08:31.9625329Z 2025-03-04T21:08:31.9625756Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:08:31.9626296Z getitem_4: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:08:31.9626584Z mul: "f32[1079808][1]cpu" = 0.5 * widths 2025-03-04T21:08:31.9626859Z ctr_x: "f32[1079808][1]cpu" = getitem_4 + mul; getitem_4 = mul = None 2025-03-04T21:08:31.9627111Z 2025-03-04T21:08:31.9627539Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:08:31.9628118Z getitem_5: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:08:31.9628426Z mul_1: "f32[1079808][1]cpu" = 0.5 * heights 2025-03-04T21:08:31.9628713Z ctr_y: "f32[1079808][1]cpu" = getitem_5 + mul_1; getitem_5 = mul_1 = None 2025-03-04T21:08:31.9629006Z 2025-03-04T21:08:31.9629476Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:08:31.9630017Z getitem_6: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:08:31.9630361Z dx: "f32[1079808, 1][1, 1]cpu" = getitem_6 / 1.0; getitem_6 = None 2025-03-04T21:08:31.9630603Z 2025-03-04T21:08:31.9631023Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:08:31.9631555Z getitem_7: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:08:31.9631896Z dy: "f32[1079808, 1][1, 1]cpu" = getitem_7 / 1.0; getitem_7 = None 2025-03-04T21:08:31.9632146Z 2025-03-04T21:08:31.9632552Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:08:31.9633084Z getitem_8: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:08:31.9633404Z dw: "f32[1079808, 1][1, 1]cpu" = getitem_8 / 1.0; getitem_8 = None 2025-03-04T21:08:31.9633628Z 2025-03-04T21:08:31.9634011Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:08:31.9634548Z getitem_9: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:08:31.9634893Z dh: "f32[1079808, 1][1, 1]cpu" = getitem_9 / 1.0; getitem_9 = None 2025-03-04T21:08:31.9635143Z 2025-03-04T21:08:31.9635694Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:08:31.9636235Z dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:08:31.9636494Z 2025-03-04T21:08:31.9636911Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:08:31.9637439Z dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:08:31.9637695Z 2025-03-04T21:08:31.9638148Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:08:31.9638695Z getitem_10: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:08:31.9639015Z mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_10; dx = getitem_10 = None 2025-03-04T21:08:31.9639349Z getitem_11: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:08:31.9639697Z pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_11; mul_2 = getitem_11 = None 2025-03-04T21:08:31.9639946Z 2025-03-04T21:08:31.9640375Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:08:31.9640910Z getitem_12: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:08:31.9641225Z mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_12; dy = getitem_12 = None 2025-03-04T21:08:31.9641556Z getitem_13: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:08:31.9641896Z pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_13; mul_3 = getitem_13 = None 2025-03-04T21:08:31.9642170Z 2025-03-04T21:08:31.9642582Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:08:31.9643079Z exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:08:31.9643406Z getitem_14: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:08:31.9643748Z pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_14; exp = getitem_14 = None 2025-03-04T21:08:31.9644000Z 2025-03-04T21:08:31.9644421Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:08:31.9644926Z exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:08:31.9645260Z getitem_15: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:08:31.9645632Z pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_15; exp_1 = getitem_15 = None 2025-03-04T21:08:31.9645887Z 2025-03-04T21:08:31.9646293Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:08:31.9646740Z mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w 2025-03-04T21:08:31.9646996Z x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:08:31.9647223Z 2025-03-04T21:08:31.9647606Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:08:31.9648052Z mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h 2025-03-04T21:08:31.9648333Z y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:08:31.9648562Z 2025-03-04T21:08:31.9648977Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:08:31.9649458Z mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:08:31.9649747Z x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:08:31.9649988Z 2025-03-04T21:08:31.9650388Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:08:31.9650849Z mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:08:31.9651140Z y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:08:31.9651386Z 2025-03-04T21:08:31.9651825Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:08:31.9652415Z pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:08:31.9652710Z 2025-03-04T21:08:31.9653135Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:08:31.9653672Z proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4)); pred_boxes = None 2025-03-04T21:08:31.9653947Z 2025-03-04T21:08:31.9654406Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:08:31.9655009Z proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4); proposals_i = None 2025-03-04T21:08:31.9655332Z 2025-03-04T21:08:31.9655801Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:08:31.9656451Z pred_anchor_deltas_i_1: "f32[269952, 4][4, 1]cpu" = l_pred_anchor_deltas_1_.reshape(-1, 4); l_pred_anchor_deltas_1_ = None 2025-03-04T21:08:31.9656770Z 2025-03-04T21:08:31.9657272Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:08:31.9657928Z unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = l_anchors_1_tensor.unsqueeze(0); l_anchors_1_tensor = None 2025-03-04T21:08:31.9658313Z expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1); unsqueeze_1 = None 2025-03-04T21:08:31.9658650Z anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4); expand_1 = None 2025-03-04T21:08:31.9658902Z 2025-03-04T21:08:31.9659370Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:08:31.9659949Z deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float(); pred_anchor_deltas_i_1 = None 2025-03-04T21:08:31.9660228Z 2025-03-04T21:08:31.9660614Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:08:31.9661109Z boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32); anchors_i_1 = None 2025-03-04T21:08:31.9661384Z 2025-03-04T21:08:31.9661770Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:08:31.9662259Z getitem_16: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)] 2025-03-04T21:08:31.9662562Z getitem_17: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T21:08:31.9662886Z widths_1: "f32[269952][1]cpu" = getitem_16 - getitem_17; getitem_16 = getitem_17 = None 2025-03-04T21:08:31.9663151Z 2025-03-04T21:08:31.9663562Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:08:31.9664052Z getitem_18: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)] 2025-03-04T21:08:31.9664348Z getitem_19: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)] 2025-03-04T21:08:31.9664675Z heights_1: "f32[269952][1]cpu" = getitem_18 - getitem_19; getitem_18 = getitem_19 = None 2025-03-04T21:08:31.9664944Z 2025-03-04T21:08:31.9665333Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:08:31.9665815Z getitem_20: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)] 2025-03-04T21:08:31.9666079Z mul_10: "f32[269952][1]cpu" = 0.5 * widths_1 2025-03-04T21:08:31.9666350Z ctr_x_1: "f32[269952][1]cpu" = getitem_20 + mul_10; getitem_20 = mul_10 = None 2025-03-04T21:08:31.9666593Z 2025-03-04T21:08:31.9666975Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:08:31.9667487Z getitem_21: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]; boxes_1 = None 2025-03-04T21:08:31.9667776Z mul_11: "f32[269952][1]cpu" = 0.5 * heights_1 2025-03-04T21:08:31.9668044Z ctr_y_1: "f32[269952][1]cpu" = getitem_21 + mul_11; getitem_21 = mul_11 = None 2025-03-04T21:08:31.9668307Z 2025-03-04T21:08:31.9668701Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:08:31.9669201Z getitem_22: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:08:31.9669515Z dx_1: "f32[269952, 1][1, 1]cpu" = getitem_22 / 1.0; getitem_22 = None 2025-03-04T21:08:31.9669748Z 2025-03-04T21:08:31.9670124Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:08:31.9670618Z getitem_23: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:08:31.9670933Z dy_1: "f32[269952, 1][1, 1]cpu" = getitem_23 / 1.0; getitem_23 = None 2025-03-04T21:08:31.9671162Z 2025-03-04T21:08:31.9671559Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:08:31.9672069Z getitem_24: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:08:31.9672386Z dw_2: "f32[269952, 1][1, 1]cpu" = getitem_24 / 1.0; getitem_24 = None 2025-03-04T21:08:31.9672619Z 2025-03-04T21:08:31.9673010Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:08:31.9673552Z getitem_25: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))]; deltas_1 = None 2025-03-04T21:08:31.9673896Z dh_2: "f32[269952, 1][1, 1]cpu" = getitem_25 / 1.0; getitem_25 = None 2025-03-04T21:08:31.9674146Z 2025-03-04T21:08:31.9674567Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:08:31.9675100Z dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356); dw_2 = None 2025-03-04T21:08:31.9675437Z 2025-03-04T21:08:31.9675863Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:08:31.9676392Z dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356); dh_2 = None 2025-03-04T21:08:31.9676676Z 2025-03-04T21:08:31.9677100Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:08:31.9677636Z getitem_26: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)] 2025-03-04T21:08:31.9677954Z mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_26; dx_1 = getitem_26 = None 2025-03-04T21:08:31.9678292Z getitem_27: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)]; ctr_x_1 = None 2025-03-04T21:08:31.9678641Z pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_27; mul_12 = getitem_27 = None 2025-03-04T21:08:31.9678899Z 2025-03-04T21:08:31.9679324Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:08:31.9679857Z getitem_28: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)] 2025-03-04T21:08:31.9680171Z mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_28; dy_1 = getitem_28 = None 2025-03-04T21:08:31.9680504Z getitem_29: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)]; ctr_y_1 = None 2025-03-04T21:08:31.9680864Z pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_29; mul_13 = getitem_29 = None 2025-03-04T21:08:31.9681120Z 2025-03-04T21:08:31.9681528Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:08:31.9682018Z exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3); dw_3 = None 2025-03-04T21:08:31.9682343Z getitem_30: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]; widths_1 = None 2025-03-04T21:08:31.9682688Z pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_30; exp_2 = getitem_30 = None 2025-03-04T21:08:31.9682934Z 2025-03-04T21:08:31.9683341Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:08:31.9683829Z exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3); dh_3 = None 2025-03-04T21:08:31.9684158Z getitem_31: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]; heights_1 = None 2025-03-04T21:08:31.9684521Z pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_31; exp_3 = getitem_31 = None 2025-03-04T21:08:31.9684767Z 2025-03-04T21:08:31.9685154Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:08:31.9685602Z mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1 2025-03-04T21:08:31.9685862Z x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16; mul_16 = None 2025-03-04T21:08:31.9686093Z 2025-03-04T21:08:31.9686470Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:08:31.9686942Z mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1 2025-03-04T21:08:31.9687191Z y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17; mul_17 = None 2025-03-04T21:08:31.9687423Z 2025-03-04T21:08:31.9687802Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:08:31.9688271Z mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1; pred_w_1 = None 2025-03-04T21:08:31.9688569Z x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18; pred_ctr_x_1 = mul_18 = None 2025-03-04T21:08:31.9688813Z 2025-03-04T21:08:31.9689211Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:08:31.9689677Z mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1; pred_h_1 = None 2025-03-04T21:08:31.9689973Z y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19; pred_ctr_y_1 = mul_19 = None 2025-03-04T21:08:31.9690214Z 2025-03-04T21:08:31.9690639Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:08:31.9691212Z pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1); x1_1 = y1_1 = x2_1 = y2_1 = None 2025-03-04T21:08:31.9691503Z 2025-03-04T21:08:31.9691905Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:08:31.9692436Z proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4)); pred_boxes_1 = None 2025-03-04T21:08:31.9692709Z 2025-03-04T21:08:31.9693160Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:08:31.9693783Z proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4); proposals_i_1 = None 2025-03-04T21:08:31.9694067Z 2025-03-04T21:08:31.9694537Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:08:31.9695185Z pred_anchor_deltas_i_2: "f32[67488, 4][4, 1]cpu" = l_pred_anchor_deltas_2_.reshape(-1, 4); l_pred_anchor_deltas_2_ = None 2025-03-04T21:08:31.9695501Z 2025-03-04T21:08:31.9696007Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:08:31.9696658Z unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = l_anchors_2_tensor.unsqueeze(0); l_anchors_2_tensor = None 2025-03-04T21:08:31.9697044Z expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1); unsqueeze_2 = None 2025-03-04T21:08:31.9697396Z anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4); expand_2 = None 2025-03-04T21:08:31.9697647Z 2025-03-04T21:08:31.9698091Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:08:31.9698669Z deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_2.float(); pred_anchor_deltas_i_2 = None 2025-03-04T21:08:31.9698947Z 2025-03-04T21:08:31.9699329Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:08:31.9699843Z boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32); anchors_i_2 = None 2025-03-04T21:08:31.9700099Z 2025-03-04T21:08:31.9700489Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:08:31.9700977Z getitem_32: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)] 2025-03-04T21:08:31.9701282Z getitem_33: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T21:08:31.9701603Z widths_2: "f32[67488][1]cpu" = getitem_32 - getitem_33; getitem_32 = getitem_33 = None 2025-03-04T21:08:31.9701865Z 2025-03-04T21:08:31.9702637Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:08:31.9703134Z getitem_34: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)] 2025-03-04T21:08:31.9703431Z getitem_35: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)] 2025-03-04T21:08:31.9703752Z heights_2: "f32[67488][1]cpu" = getitem_34 - getitem_35; getitem_34 = getitem_35 = None 2025-03-04T21:08:31.9704018Z 2025-03-04T21:08:31.9704407Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:08:31.9704888Z getitem_36: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)] 2025-03-04T21:08:31.9705153Z mul_20: "f32[67488][1]cpu" = 0.5 * widths_2 2025-03-04T21:08:31.9705422Z ctr_x_2: "f32[67488][1]cpu" = getitem_36 + mul_20; getitem_36 = mul_20 = None 2025-03-04T21:08:31.9705674Z 2025-03-04T21:08:31.9706065Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:08:31.9706569Z getitem_37: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]; boxes_2 = None 2025-03-04T21:08:31.9706897Z mul_21: "f32[67488][1]cpu" = 0.5 * heights_2 2025-03-04T21:08:31.9707156Z ctr_y_2: "f32[67488][1]cpu" = getitem_37 + mul_21; getitem_37 = mul_21 = None 2025-03-04T21:08:31.9707400Z 2025-03-04T21:08:31.9707784Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:08:31.9708280Z getitem_38: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:08:31.9708595Z dx_2: "f32[67488, 1][1, 1]cpu" = getitem_38 / 1.0; getitem_38 = None 2025-03-04T21:08:31.9708822Z 2025-03-04T21:08:31.9709197Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:08:31.9709690Z getitem_39: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:08:31.9710004Z dy_2: "f32[67488, 1][1, 1]cpu" = getitem_39 / 1.0; getitem_39 = None 2025-03-04T21:08:31.9710226Z 2025-03-04T21:08:31.9710631Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:08:31.9711122Z getitem_40: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:08:31.9711439Z dw_4: "f32[67488, 1][1, 1]cpu" = getitem_40 / 1.0; getitem_40 = None 2025-03-04T21:08:31.9711668Z 2025-03-04T21:08:31.9712061Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:08:31.9712641Z getitem_41: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))]; deltas_2 = None 2025-03-04T21:08:31.9713013Z dh_4: "f32[67488, 1][1, 1]cpu" = getitem_41 / 1.0; getitem_41 = None 2025-03-04T21:08:31.9713240Z 2025-03-04T21:08:31.9713656Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:08:31.9714183Z dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356); dw_4 = None 2025-03-04T21:08:31.9714437Z 2025-03-04T21:08:31.9714862Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:08:31.9715467Z dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356); dh_4 = None 2025-03-04T21:08:31.9715736Z 2025-03-04T21:08:31.9716170Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:08:31.9716713Z getitem_42: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)] 2025-03-04T21:08:31.9717038Z mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_42; dx_2 = getitem_42 = None 2025-03-04T21:08:31.9717377Z getitem_43: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)]; ctr_x_2 = None 2025-03-04T21:08:31.9717733Z pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_43; mul_22 = getitem_43 = None 2025-03-04T21:08:31.9717994Z 2025-03-04T21:08:31.9718435Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:08:31.9718979Z getitem_44: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)] 2025-03-04T21:08:31.9719302Z mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_44; dy_2 = getitem_44 = None 2025-03-04T21:08:31.9719634Z getitem_45: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)]; ctr_y_2 = None 2025-03-04T21:08:31.9720766Z pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_45; mul_23 = getitem_45 = None 2025-03-04T21:08:31.9721030Z 2025-03-04T21:08:31.9721454Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:08:31.9721961Z exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5); dw_5 = None 2025-03-04T21:08:31.9722304Z getitem_46: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]; widths_2 = None 2025-03-04T21:08:31.9722656Z pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_46; exp_4 = getitem_46 = None 2025-03-04T21:08:31.9722911Z 2025-03-04T21:08:31.9723336Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:08:31.9723844Z exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5); dh_5 = None 2025-03-04T21:08:31.9724191Z getitem_47: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]; heights_2 = None 2025-03-04T21:08:31.9724548Z pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_47; exp_5 = getitem_47 = None 2025-03-04T21:08:31.9724803Z 2025-03-04T21:08:31.9725198Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:08:31.9725665Z mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2 2025-03-04T21:08:31.9725928Z x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26; mul_26 = None 2025-03-04T21:08:31.9726175Z 2025-03-04T21:08:31.9726566Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:08:31.9727016Z mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2 2025-03-04T21:08:31.9727260Z y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27; mul_27 = None 2025-03-04T21:08:31.9727486Z 2025-03-04T21:08:31.9727867Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:08:31.9728335Z mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2; pred_w_2 = None 2025-03-04T21:08:31.9728647Z x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28; pred_ctr_x_2 = mul_28 = None 2025-03-04T21:08:31.9728896Z 2025-03-04T21:08:31.9729274Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:08:31.9729739Z mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2; pred_h_2 = None 2025-03-04T21:08:31.9730031Z y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29; pred_ctr_y_2 = mul_29 = None 2025-03-04T21:08:31.9730275Z 2025-03-04T21:08:31.9730698Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:08:31.9731269Z pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1); x1_2 = y1_2 = x2_2 = y2_2 = None 2025-03-04T21:08:31.9731559Z 2025-03-04T21:08:31.9731961Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:08:31.9732489Z proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4)); pred_boxes_2 = None 2025-03-04T21:08:31.9732763Z 2025-03-04T21:08:31.9733220Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:08:31.9733846Z proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4); proposals_i_2 = None 2025-03-04T21:08:31.9734135Z 2025-03-04T21:08:31.9734617Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:08:31.9735280Z pred_anchor_deltas_i_3: "f32[16872, 4][4, 1]cpu" = l_pred_anchor_deltas_3_.reshape(-1, 4); l_pred_anchor_deltas_3_ = None 2025-03-04T21:08:31.9735603Z 2025-03-04T21:08:31.9736118Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:08:31.9736786Z unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = l_anchors_3_tensor.unsqueeze(0); l_anchors_3_tensor = None 2025-03-04T21:08:31.9737186Z expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1); unsqueeze_3 = None 2025-03-04T21:08:31.9737528Z anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4); expand_3 = None 2025-03-04T21:08:31.9737783Z 2025-03-04T21:08:31.9738235Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:08:31.9738829Z deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float(); pred_anchor_deltas_i_3 = None 2025-03-04T21:08:31.9739110Z 2025-03-04T21:08:31.9739501Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:08:31.9740031Z boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32); anchors_i_3 = None 2025-03-04T21:08:31.9740294Z 2025-03-04T21:08:31.9740693Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:08:31.9741186Z getitem_48: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)] 2025-03-04T21:08:31.9741494Z getitem_49: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T21:08:31.9741847Z widths_3: "f32[16872][1]cpu" = getitem_48 - getitem_49; getitem_48 = getitem_49 = None 2025-03-04T21:08:31.9742109Z 2025-03-04T21:08:31.9742500Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:08:31.9742977Z getitem_50: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)] 2025-03-04T21:08:31.9743275Z getitem_51: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)] 2025-03-04T21:08:31.9743596Z heights_3: "f32[16872][1]cpu" = getitem_50 - getitem_51; getitem_50 = getitem_51 = None 2025-03-04T21:08:31.9743856Z 2025-03-04T21:08:31.9744242Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:08:31.9744718Z getitem_52: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)] 2025-03-04T21:08:31.9744980Z mul_30: "f32[16872][1]cpu" = 0.5 * widths_3 2025-03-04T21:08:31.9745247Z ctr_x_3: "f32[16872][1]cpu" = getitem_52 + mul_30; getitem_52 = mul_30 = None 2025-03-04T21:08:31.9745493Z 2025-03-04T21:08:31.9745881Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:08:31.9746402Z getitem_53: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]; boxes_3 = None 2025-03-04T21:08:31.9746688Z mul_31: "f32[16872][1]cpu" = 0.5 * heights_3 2025-03-04T21:08:31.9746955Z ctr_y_3: "f32[16872][1]cpu" = getitem_53 + mul_31; getitem_53 = mul_31 = None 2025-03-04T21:08:31.9747198Z 2025-03-04T21:08:31.9747587Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:08:31.9748083Z getitem_54: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:08:31.9748408Z dx_3: "f32[16872, 1][1, 1]cpu" = getitem_54 / 1.0; getitem_54 = None 2025-03-04T21:08:31.9748635Z 2025-03-04T21:08:31.9749007Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:08:31.9749499Z getitem_55: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:08:31.9749831Z dy_3: "f32[16872, 1][1, 1]cpu" = getitem_55 / 1.0; getitem_55 = None 2025-03-04T21:08:31.9750061Z 2025-03-04T21:08:31.9750431Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:08:31.9750935Z getitem_56: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:08:31.9751252Z dw_6: "f32[16872, 1][1, 1]cpu" = getitem_56 / 1.0; getitem_56 = None 2025-03-04T21:08:31.9751484Z 2025-03-04T21:08:31.9751878Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:08:31.9752489Z getitem_57: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))]; deltas_3 = None 2025-03-04T21:08:31.9752846Z dh_6: "f32[16872, 1][1, 1]cpu" = getitem_57 / 1.0; getitem_57 = None 2025-03-04T21:08:31.9753080Z 2025-03-04T21:08:31.9753515Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:08:31.9754065Z dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356); dw_6 = None 2025-03-04T21:08:31.9754322Z 2025-03-04T21:08:31.9754757Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:08:31.9755281Z dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356); dh_6 = None 2025-03-04T21:08:31.9755612Z 2025-03-04T21:08:31.9756051Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:08:31.9756599Z getitem_58: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)] 2025-03-04T21:08:31.9756909Z mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_58; dx_3 = getitem_58 = None 2025-03-04T21:08:31.9757239Z getitem_59: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)]; ctr_x_3 = None 2025-03-04T21:08:31.9757581Z pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_59; mul_32 = getitem_59 = None 2025-03-04T21:08:31.9757835Z 2025-03-04T21:08:31.9758259Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:08:31.9758784Z getitem_60: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)] 2025-03-04T21:08:31.9759094Z mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_60; dy_3 = getitem_60 = None 2025-03-04T21:08:31.9759438Z getitem_61: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)]; ctr_y_3 = None 2025-03-04T21:08:31.9759774Z pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_61; mul_33 = getitem_61 = None 2025-03-04T21:08:31.9760024Z 2025-03-04T21:08:31.9760430Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:08:31.9760913Z exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7); dw_7 = None 2025-03-04T21:08:31.9761232Z getitem_62: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]; widths_3 = None 2025-03-04T21:08:31.9761568Z pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_62; exp_6 = getitem_62 = None 2025-03-04T21:08:31.9761815Z 2025-03-04T21:08:31.9762220Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:08:31.9762721Z exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7); dh_7 = None 2025-03-04T21:08:31.9763046Z getitem_63: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]; heights_3 = None 2025-03-04T21:08:31.9763389Z pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_63; exp_7 = getitem_63 = None 2025-03-04T21:08:31.9763633Z 2025-03-04T21:08:31.9764018Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:08:31.9764464Z mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3 2025-03-04T21:08:31.9764716Z x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36; mul_36 = None 2025-03-04T21:08:31.9764963Z 2025-03-04T21:08:31.9765333Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:08:31.9765783Z mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3 2025-03-04T21:08:31.9766036Z y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37; mul_37 = None 2025-03-04T21:08:31.9766262Z 2025-03-04T21:08:31.9766639Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:08:31.9767109Z mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3; pred_w_3 = None 2025-03-04T21:08:31.9767419Z x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38; pred_ctr_x_3 = mul_38 = None 2025-03-04T21:08:31.9767665Z 2025-03-04T21:08:31.9768045Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:08:31.9768515Z mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3; pred_h_3 = None 2025-03-04T21:08:31.9768808Z y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39; pred_ctr_y_3 = mul_39 = None 2025-03-04T21:08:31.9769051Z 2025-03-04T21:08:31.9769473Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:08:31.9770044Z pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1); x1_3 = y1_3 = x2_3 = y2_3 = None 2025-03-04T21:08:31.9770337Z 2025-03-04T21:08:31.9770742Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:08:31.9771277Z proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4)); pred_boxes_3 = None 2025-03-04T21:08:31.9771571Z 2025-03-04T21:08:31.9772027Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:08:31.9772608Z proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4); proposals_i_3 = None 2025-03-04T21:08:31.9772884Z 2025-03-04T21:08:31.9773346Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) 2025-03-04T21:08:31.9773991Z pred_anchor_deltas_i_4: "f32[4332, 4][4, 1]cpu" = l_pred_anchor_deltas_4_.reshape(-1, 4); l_pred_anchor_deltas_4_ = None 2025-03-04T21:08:31.9774302Z 2025-03-04T21:08:31.9774802Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) 2025-03-04T21:08:31.9775468Z unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = l_anchors_4_tensor.unsqueeze(0); l_anchors_4_tensor = None 2025-03-04T21:08:31.9775838Z expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1); unsqueeze_4 = None 2025-03-04T21:08:31.9776167Z anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4); expand_4 = None 2025-03-04T21:08:31.9776417Z 2025-03-04T21:08:31.9776858Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:08:31.9777435Z deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_4.float(); pred_anchor_deltas_i_4 = None 2025-03-04T21:08:31.9777729Z 2025-03-04T21:08:31.9778113Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:08:31.9778606Z boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32); anchors_i_4 = None 2025-03-04T21:08:31.9778855Z 2025-03-04T21:08:31.9779240Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:08:31.9779722Z getitem_64: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)] 2025-03-04T21:08:31.9780028Z getitem_65: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T21:08:31.9780359Z widths_4: "f32[4332][1]cpu" = getitem_64 - getitem_65; getitem_64 = getitem_65 = None 2025-03-04T21:08:31.9780617Z 2025-03-04T21:08:31.9781003Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:08:31.9781483Z getitem_66: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)] 2025-03-04T21:08:31.9781768Z getitem_67: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)] 2025-03-04T21:08:31.9782077Z heights_4: "f32[4332][1]cpu" = getitem_66 - getitem_67; getitem_66 = getitem_67 = None 2025-03-04T21:08:31.9782335Z 2025-03-04T21:08:31.9782720Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:08:31.9783188Z getitem_68: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)] 2025-03-04T21:08:31.9783445Z mul_40: "f32[4332][1]cpu" = 0.5 * widths_4 2025-03-04T21:08:31.9783705Z ctr_x_4: "f32[4332][1]cpu" = getitem_68 + mul_40; getitem_68 = mul_40 = None 2025-03-04T21:08:31.9783946Z 2025-03-04T21:08:31.9784328Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:08:31.9784844Z getitem_69: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]; boxes_4 = None 2025-03-04T21:08:31.9785127Z mul_41: "f32[4332][1]cpu" = 0.5 * heights_4 2025-03-04T21:08:31.9785391Z ctr_y_4: "f32[4332][1]cpu" = getitem_69 + mul_41; getitem_69 = mul_41 = None 2025-03-04T21:08:31.9785631Z 2025-03-04T21:08:31.9786013Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:08:31.9786502Z getitem_70: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:08:31.9786813Z dx_4: "f32[4332, 1][1, 1]cpu" = getitem_70 / 1.0; getitem_70 = None 2025-03-04T21:08:31.9787037Z 2025-03-04T21:08:31.9787408Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:08:31.9787912Z getitem_71: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:08:31.9788223Z dy_4: "f32[4332, 1][1, 1]cpu" = getitem_71 / 1.0; getitem_71 = None 2025-03-04T21:08:31.9788442Z 2025-03-04T21:08:31.9788812Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:08:31.9789290Z getitem_72: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:08:31.9789591Z dw_8: "f32[4332, 1][1, 1]cpu" = getitem_72 / 1.0; getitem_72 = None 2025-03-04T21:08:31.9789809Z 2025-03-04T21:08:31.9790184Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:08:31.9790719Z getitem_73: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))]; deltas_4 = None 2025-03-04T21:08:31.9791052Z dh_8: "f32[4332, 1][1, 1]cpu" = getitem_73 / 1.0; getitem_73 = None 2025-03-04T21:08:31.9791272Z 2025-03-04T21:08:31.9791680Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:08:31.9792190Z dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356); dw_8 = None 2025-03-04T21:08:31.9792435Z 2025-03-04T21:08:31.9792852Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:08:31.9793363Z dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356); dh_8 = None 2025-03-04T21:08:31.9793609Z 2025-03-04T21:08:31.9794029Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:08:31.9794554Z getitem_74: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)] 2025-03-04T21:08:31.9794860Z mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_74; dx_4 = getitem_74 = None 2025-03-04T21:08:31.9795181Z getitem_75: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)]; ctr_x_4 = None 2025-03-04T21:08:31.9795659Z pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_75; mul_42 = getitem_75 = None 2025-03-04T21:08:31.9795930Z 2025-03-04T21:08:31.9796382Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:08:31.9796926Z getitem_76: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)] 2025-03-04T21:08:31.9797260Z mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_76; dy_4 = getitem_76 = None 2025-03-04T21:08:31.9797581Z getitem_77: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)]; ctr_y_4 = None 2025-03-04T21:08:31.9797916Z pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_77; mul_43 = getitem_77 = None 2025-03-04T21:08:31.9798168Z 2025-03-04T21:08:31.9798577Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:08:31.9799066Z exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9); dw_9 = None 2025-03-04T21:08:31.9799384Z getitem_78: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]; widths_4 = None 2025-03-04T21:08:31.9799721Z pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_78; exp_8 = getitem_78 = None 2025-03-04T21:08:31.9799968Z 2025-03-04T21:08:31.9800409Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:08:31.9800898Z exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9); dh_9 = None 2025-03-04T21:08:31.9801216Z getitem_79: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]; heights_4 = None 2025-03-04T21:08:31.9801555Z pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_79; exp_9 = getitem_79 = None 2025-03-04T21:08:31.9801798Z 2025-03-04T21:08:31.9802281Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:08:31.9802737Z mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4 2025-03-04T21:08:31.9803037Z x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46; mul_46 = None 2025-03-04T21:08:31.9803258Z 2025-03-04T21:08:31.9803645Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:08:31.9804094Z mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4 2025-03-04T21:08:31.9804344Z y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47; mul_47 = None 2025-03-04T21:08:31.9804574Z 2025-03-04T21:08:31.9804961Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:08:31.9805452Z mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4; pred_w_4 = None 2025-03-04T21:08:31.9805745Z x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48; pred_ctr_x_4 = mul_48 = None 2025-03-04T21:08:31.9805984Z 2025-03-04T21:08:31.9806364Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:08:31.9806830Z mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4; pred_h_4 = None 2025-03-04T21:08:31.9807122Z y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49; pred_ctr_y_4 = mul_49 = None 2025-03-04T21:08:31.9807361Z 2025-03-04T21:08:31.9807779Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:08:31.9808350Z pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1); x1_4 = y1_4 = x2_4 = y2_4 = None 2025-03-04T21:08:31.9808644Z 2025-03-04T21:08:31.9809047Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:08:31.9809577Z proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4)); pred_boxes_4 = None 2025-03-04T21:08:31.9809878Z 2025-03-04T21:08:31.9810342Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B)) 2025-03-04T21:08:31.9810941Z proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4); proposals_i_4 = None 2025-03-04T21:08:31.9811227Z 2025-03-04T21:08:31.9811784Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0]) 2025-03-04T21:08:31.9812541Z arange: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu')) 2025-03-04T21:08:31.9812790Z 2025-03-04T21:08:31.9813174Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:31.9813658Z batch_idx: "i64[4][1]cpu" = arange.to(device(type='cpu')); arange = None 2025-03-04T21:08:31.9813931Z 2025-03-04T21:08:31.9814451Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:08:31.9815102Z topk = l_pred_objectness_logits_0_.topk(1000, dim = 1); l_pred_objectness_logits_0_ = None 2025-03-04T21:08:31.9815428Z topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0] 2025-03-04T21:08:31.9815694Z topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1]; topk = None 2025-03-04T21:08:31.9815919Z 2025-03-04T21:08:31.9816465Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:08:31.9817124Z getitem_82: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:08:31.9817535Z topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_82, topk_idx)]; proposals_i_5 = getitem_82 = topk_idx = None 2025-03-04T21:08:31.9817871Z 2025-03-04T21:08:31.9818400Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:08:31.9819078Z full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:08:31.9819350Z 2025-03-04T21:08:31.9819717Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:31.9820175Z to_6: "i64[1000][1]cpu" = full.to(device(type='cpu')); full = None 2025-03-04T21:08:31.9820401Z 2025-03-04T21:08:31.9820905Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:08:31.9821541Z topk_1 = l_pred_objectness_logits_1_.topk(1000, dim = 1); l_pred_objectness_logits_1_ = None 2025-03-04T21:08:31.9821868Z topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0] 2025-03-04T21:08:31.9822142Z topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1]; topk_1 = None 2025-03-04T21:08:31.9822368Z 2025-03-04T21:08:31.9822897Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:08:31.9823533Z getitem_86: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:08:31.9823948Z topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_86, topk_idx_1)]; proposals_i_6 = getitem_86 = topk_idx_1 = None 2025-03-04T21:08:31.9824292Z 2025-03-04T21:08:31.9824818Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:08:31.9825478Z full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:08:31.9825756Z 2025-03-04T21:08:31.9826131Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:31.9826600Z to_7: "i64[1000][1]cpu" = full_1.to(device(type='cpu')); full_1 = None 2025-03-04T21:08:31.9826839Z 2025-03-04T21:08:31.9827363Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:08:31.9828004Z topk_2 = l_pred_objectness_logits_2_.topk(1000, dim = 1); l_pred_objectness_logits_2_ = None 2025-03-04T21:08:31.9828330Z topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0] 2025-03-04T21:08:31.9828600Z topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1]; topk_2 = None 2025-03-04T21:08:31.9828830Z 2025-03-04T21:08:31.9829359Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:08:31.9830002Z getitem_90: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:08:31.9830413Z topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_90, topk_idx_2)]; proposals_i_7 = getitem_90 = topk_idx_2 = None 2025-03-04T21:08:31.9830751Z 2025-03-04T21:08:31.9831274Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:08:31.9831924Z full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:08:31.9832212Z 2025-03-04T21:08:31.9832579Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:31.9833039Z to_8: "i64[1000][1]cpu" = full_2.to(device(type='cpu')); full_2 = None 2025-03-04T21:08:31.9833273Z 2025-03-04T21:08:31.9833780Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:08:31.9834420Z topk_3 = l_pred_objectness_logits_3_.topk(1000, dim = 1); l_pred_objectness_logits_3_ = None 2025-03-04T21:08:31.9834745Z topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0] 2025-03-04T21:08:31.9835012Z topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1]; topk_3 = None 2025-03-04T21:08:31.9835230Z 2025-03-04T21:08:31.9835828Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:08:31.9836466Z getitem_94: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)] 2025-03-04T21:08:31.9836895Z topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_94, topk_idx_3)]; proposals_i_8 = getitem_94 = topk_idx_3 = None 2025-03-04T21:08:31.9837240Z 2025-03-04T21:08:31.9837770Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:08:31.9838423Z full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:08:31.9838696Z 2025-03-04T21:08:31.9839070Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:31.9839530Z to_9: "i64[1000][1]cpu" = full_3.to(device(type='cpu')); full_3 = None 2025-03-04T21:08:31.9839761Z 2025-03-04T21:08:31.9840279Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) 2025-03-04T21:08:31.9840916Z topk_4 = l_pred_objectness_logits_4_.topk(1000, dim = 1); l_pred_objectness_logits_4_ = None 2025-03-04T21:08:31.9841240Z topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0] 2025-03-04T21:08:31.9841507Z topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1]; topk_4 = None 2025-03-04T21:08:31.9841730Z 2025-03-04T21:08:31.9842258Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 2025-03-04T21:08:31.9842912Z getitem_98: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]; batch_idx = None 2025-03-04T21:08:31.9843364Z topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_98, topk_idx_4)]; proposals_i_9 = getitem_98 = topk_idx_4 = None 2025-03-04T21:08:31.9843703Z 2025-03-04T21:08:31.9844231Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device), 2025-03-04T21:08:31.9844884Z full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu')) 2025-03-04T21:08:31.9845156Z 2025-03-04T21:08:31.9845538Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device) 2025-03-04T21:08:31.9846001Z to_10: "i64[1000][1]cpu" = full_4.to(device(type='cpu')); full_4 = None 2025-03-04T21:08:31.9846241Z 2025-03-04T21:08:31.9846596Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:08:31.9847294Z topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1); topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None 2025-03-04T21:08:31.9847768Z 2025-03-04T21:08:31.9848132Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:08:31.9848911Z topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1); topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None 2025-03-04T21:08:31.9849480Z 2025-03-04T21:08:31.9849828Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:08:31.9850371Z level_ids: "i64[5000][1]cpu" = torch.cat([to_6, to_7, to_8, to_9, to_10], 0); to_6 = to_7 = to_8 = to_9 = to_10 = level_ids = None 2025-03-04T21:08:31.9850669Z 2025-03-04T21:08:31.9851134Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n]) 2025-03-04T21:08:31.9851705Z getitem_100: "f32[5000, 4][4, 1]cpu" = topk_proposals[0]; topk_proposals = None 2025-03-04T21:08:31.9851961Z 2025-03-04T21:08:31.9852346Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32) 2025-03-04T21:08:31.9852854Z tensor: "f32[5000, 4][4, 1]cpu" = getitem_100.to(torch.float32); getitem_100 = None 2025-03-04T21:08:31.9853117Z 2025-03-04T21:08:31.9853591Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n] 2025-03-04T21:08:31.9854152Z scores_per_img: "f32[5000][1]cpu" = topk_scores[0]; topk_scores = None 2025-03-04T21:08:31.9854408Z 2025-03-04T21:08:31.9854984Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) 2025-03-04T21:08:31.9855672Z isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor); tensor = None 2025-03-04T21:08:31.9855986Z all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:08:31.9856322Z isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img); scores_per_img = None 2025-03-04T21:08:31.9856690Z valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1; all_1 = isfinite_1 = None 2025-03-04T21:08:31.9856949Z 2025-03-04T21:08:31.9857415Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all(): 2025-03-04T21:08:31.9857969Z all_2: "b8[][]cpu" = valid_mask.all(); valid_mask = all_2 = None 2025-03-04T21:08:31.9858210Z 2025-03-04T21:08:38.3599622Z 2025-03-04T21:08:38.3600950Z class GraphModule(torch.nn.Module): 2025-03-04T21:08:38.3604360Z def forward(self, L_stack0_: "f32[4000, 256, 7, 7][12544, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 12544][12544, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"): 2025-03-04T21:08:38.3607900Z l_stack0_ = L_stack0_ 2025-03-04T21:08:38.3609127Z l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_ 2025-03-04T21:08:38.3610630Z l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_ 2025-03-04T21:08:38.3611993Z l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_ 2025-03-04T21:08:38.3613245Z l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_ 2025-03-04T21:08:38.3614588Z l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_ 2025-03-04T21:08:38.3616016Z l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_ 2025-03-04T21:08:38.3617588Z l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ 2025-03-04T21:08:38.3619098Z l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ 2025-03-04T21:08:38.3620470Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:08:38.3621818Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:08:38.3623183Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:08:38.3627062Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:08:38.3627773Z 2025-03-04T21:08:38.3628400Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x) 2025-03-04T21:08:38.3628992Z x: "f32[4000, 12544][12544, 1]cpu" = l_stack0_.flatten(1, -1); l_stack0_ = None 2025-03-04T21:08:38.3629765Z x_1: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_); x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None 2025-03-04T21:08:38.3630703Z x_2: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False); x_1 = None 2025-03-04T21:08:38.3631481Z x_3: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_); x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None 2025-03-04T21:08:38.3632303Z x_4: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False); x_3 = None 2025-03-04T21:08:38.3632661Z 2025-03-04T21:08:38.3633141Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x) 2025-03-04T21:08:38.3634255Z scores: "f32[4000, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_); l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None 2025-03-04T21:08:38.3635164Z 2025-03-04T21:08:38.3635652Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x) 2025-03-04T21:08:38.3636800Z proposal_deltas: "f32[4000, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_); x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None 2025-03-04T21:08:38.3637654Z 2025-03-04T21:08:38.3638073Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:08:38.3638655Z size = l_proposals_0_fields_proposal_boxes_tensor.size() 2025-03-04T21:08:38.3638940Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:08:38.3639197Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:08:38.3639500Z size_1 = l_proposals_1_fields_proposal_boxes_tensor.size() 2025-03-04T21:08:38.3639777Z getitem_2: "Sym(s1)" = size_1[0] 2025-03-04T21:08:38.3640038Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:08:38.3640336Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:08:38.3640608Z getitem_4: "Sym(s2)" = size_2[0] 2025-03-04T21:08:38.3640858Z getitem_5 = size_2[1]; size_2 = getitem_5 = None 2025-03-04T21:08:38.3641149Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:08:38.3641422Z getitem_6: "Sym(s3)" = size_3[0] 2025-03-04T21:08:38.3641671Z getitem_7 = size_3[1]; size_3 = getitem_7 = None 2025-03-04T21:08:38.3641909Z 2025-03-04T21:08:38.3642344Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:08:38.3643122Z proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0) 2025-03-04T21:08:38.3643664Z 2025-03-04T21:08:38.3644127Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:08:38.3644706Z deltas: "f32[4000, 320][320, 1]cpu" = proposal_deltas.float(); proposal_deltas = None 2025-03-04T21:08:38.3644998Z 2025-03-04T21:08:38.3645395Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:08:38.3645929Z boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:08:38.3646215Z 2025-03-04T21:08:38.3646626Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:08:38.3647150Z getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:08:38.3647496Z getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:08:38.3647835Z widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9; getitem_8 = getitem_9 = None 2025-03-04T21:08:38.3648102Z 2025-03-04T21:08:38.3648520Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:08:38.3649037Z getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:08:38.3649362Z getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:08:38.3649693Z heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T21:08:38.3649963Z 2025-03-04T21:08:38.3650363Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:08:38.3650855Z getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:08:38.3651133Z mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths 2025-03-04T21:08:38.3651400Z ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul; getitem_12 = mul = None 2025-03-04T21:08:38.3651653Z 2025-03-04T21:08:38.3652053Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:08:38.3652571Z getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:08:38.3652869Z mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights 2025-03-04T21:08:38.3653145Z ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1; getitem_13 = mul_1 = None 2025-03-04T21:08:38.3653390Z 2025-03-04T21:08:38.3653828Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:08:38.3654353Z getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:08:38.3654682Z dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0; getitem_14 = None 2025-03-04T21:08:38.3654919Z 2025-03-04T21:08:38.3655346Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:08:38.3655851Z getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:08:38.3656169Z dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0; getitem_15 = None 2025-03-04T21:08:38.3656400Z 2025-03-04T21:08:38.3656786Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:08:38.3657290Z getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:08:38.3657610Z dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0; getitem_16 = None 2025-03-04T21:08:38.3657856Z 2025-03-04T21:08:38.3658244Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:08:38.3658790Z getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:08:38.3659138Z dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0; getitem_17 = None 2025-03-04T21:08:38.3659369Z 2025-03-04T21:08:38.3659804Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:08:38.3660380Z dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:08:38.3660631Z 2025-03-04T21:08:38.3661055Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:08:38.3661584Z dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:08:38.3661832Z 2025-03-04T21:08:38.3662270Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:08:38.3662819Z getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:08:38.3663138Z mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18; dx = getitem_18 = None 2025-03-04T21:08:38.3663472Z getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:08:38.3663819Z pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19; mul_2 = getitem_19 = None 2025-03-04T21:08:38.3664077Z 2025-03-04T21:08:38.3664523Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:08:38.3665093Z getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:08:38.3665412Z mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20; dy = getitem_20 = None 2025-03-04T21:08:38.3665737Z getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:08:38.3666079Z pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21; mul_3 = getitem_21 = None 2025-03-04T21:08:38.3666331Z 2025-03-04T21:08:38.3666758Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:08:38.3667261Z exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:08:38.3667593Z getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:08:38.3667935Z pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22; exp = getitem_22 = None 2025-03-04T21:08:38.3668198Z 2025-03-04T21:08:38.3668613Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:08:38.3669116Z exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:08:38.3669447Z getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:08:38.3669789Z pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23; exp_1 = getitem_23 = None 2025-03-04T21:08:38.3670037Z 2025-03-04T21:08:38.3670437Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:08:38.3670962Z mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:08:38.3671228Z x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:08:38.3671470Z 2025-03-04T21:08:38.3671872Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:08:38.3672349Z mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:08:38.3672616Z y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:08:38.3672856Z 2025-03-04T21:08:38.3673279Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:08:38.3673788Z mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:08:38.3674092Z x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:08:38.3674348Z 2025-03-04T21:08:38.3674761Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:08:38.3675373Z mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:08:38.3675669Z y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:08:38.3675930Z 2025-03-04T21:08:38.3676381Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:08:38.3676981Z pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:08:38.3677274Z 2025-03-04T21:08:38.3677708Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:08:38.3678334Z predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320)); pred_boxes = None 2025-03-04T21:08:38.3678624Z 2025-03-04T21:08:38.3679083Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:08:38.3679843Z split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]); predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None 2025-03-04T21:08:38.3680287Z boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0] 2025-03-04T21:08:38.3680706Z getitem_25: "f32[s1, 320][320, 1]cpu" = split[1]; getitem_25 = None 2025-03-04T21:08:38.3681106Z getitem_26: "f32[s2, 320][320, 1]cpu" = split[2]; getitem_26 = None 2025-03-04T21:08:38.3681435Z getitem_27: "f32[s3, 320][320, 1]cpu" = split[3]; split = getitem_27 = None 2025-03-04T21:08:38.3681688Z 2025-03-04T21:08:38.3682108Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:08:38.3682678Z size_4 = l_proposals_0_fields_proposal_boxes_tensor.size(); l_proposals_0_fields_proposal_boxes_tensor = None 2025-03-04T21:08:38.3683036Z getitem_28: "Sym(s0)" = size_4[0] 2025-03-04T21:08:38.3683279Z getitem_29 = size_4[1]; size_4 = getitem_29 = None 2025-03-04T21:08:38.3683653Z size_5 = l_proposals_1_fields_proposal_boxes_tensor.size(); l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:08:38.3684003Z getitem_30: "Sym(s1)" = size_5[0] 2025-03-04T21:08:38.3684242Z getitem_31 = size_5[1]; size_5 = getitem_31 = None 2025-03-04T21:08:38.3684609Z size_6 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:08:38.3684966Z getitem_32: "Sym(s2)" = size_6[0] 2025-03-04T21:08:38.3685203Z getitem_33 = size_6[1]; size_6 = getitem_33 = None 2025-03-04T21:08:38.3685577Z size_7 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:08:38.3685926Z getitem_34: "Sym(s3)" = size_7[0] 2025-03-04T21:08:38.3686169Z getitem_35 = size_7[1]; size_7 = getitem_35 = None 2025-03-04T21:08:38.3686397Z 2025-03-04T21:08:38.3686877Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:08:38.3687444Z probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1); scores = None 2025-03-04T21:08:38.3687733Z 2025-03-04T21:08:38.3688177Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:08:38.3688861Z split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0); probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None 2025-03-04T21:08:38.3689277Z scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:08:38.3689561Z getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1]; getitem_37 = None 2025-03-04T21:08:38.3689854Z getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2]; getitem_38 = None 2025-03-04T21:08:38.3690155Z getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_39 = None 2025-03-04T21:08:38.3690407Z 2025-03-04T21:08:38.3690964Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:08:38.3691663Z isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:08:38.3692026Z all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:08:38.3692362Z isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:08:38.3692701Z all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:08:38.3692989Z valid_mask: "b8[s0][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:08:38.3693224Z 2025-03-04T21:08:38.3693674Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:08:38.3694207Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:08:38.3694446Z 2025-03-04T21:08:38.3694583Z 2025-03-04T21:08:38.3694674Z class GraphModule(torch.nn.Module): 2025-03-04T21:08:38.3696640Z def forward(self, L_stack0_: "f32[4000, 256, 7, 7][12544, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 12544][12544, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"): 2025-03-04T21:08:38.3698700Z l_stack0_ = L_stack0_ 2025-03-04T21:08:38.3699075Z l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_ 2025-03-04T21:08:38.3699600Z l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_ 2025-03-04T21:08:38.3700112Z l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_ 2025-03-04T21:08:38.3700621Z l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_ 2025-03-04T21:08:38.3701152Z l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_ 2025-03-04T21:08:38.3701737Z l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_ 2025-03-04T21:08:38.3702535Z l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ 2025-03-04T21:08:38.3703112Z l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ 2025-03-04T21:08:38.3703610Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:08:38.3704030Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:08:38.3704452Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:08:38.3706213Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:08:38.3706597Z 2025-03-04T21:08:38.3707015Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x) 2025-03-04T21:08:38.3708156Z x: "f32[4000, 12544][12544, 1]cpu" = l_stack0_.flatten(1, -1); l_stack0_ = None 2025-03-04T21:08:38.3708927Z x_1: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_); x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None 2025-03-04T21:08:38.3709701Z x_2: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False); x_1 = None 2025-03-04T21:08:38.3710464Z x_3: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_); x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None 2025-03-04T21:08:38.3711252Z x_4: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False); x_3 = None 2025-03-04T21:08:38.3711551Z 2025-03-04T21:08:38.3712017Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x) 2025-03-04T21:08:38.3713115Z scores: "f32[4000, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_); l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None 2025-03-04T21:08:38.3713926Z 2025-03-04T21:08:38.3714385Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x) 2025-03-04T21:08:38.3715639Z proposal_deltas: "f32[4000, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_); x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None 2025-03-04T21:08:38.3716466Z 2025-03-04T21:08:38.3716863Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:08:38.3717350Z size = l_proposals_0_fields_proposal_boxes_tensor.size() 2025-03-04T21:08:38.3717615Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:08:38.3717891Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:08:38.3718182Z size_1 = l_proposals_1_fields_proposal_boxes_tensor.size() 2025-03-04T21:08:38.3718442Z getitem_2: "Sym(s1)" = size_1[0] 2025-03-04T21:08:38.3718689Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:08:38.3718967Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:08:38.3719227Z getitem_4: "Sym(s2)" = size_2[0] 2025-03-04T21:08:38.3719469Z getitem_5 = size_2[1]; size_2 = getitem_5 = None 2025-03-04T21:08:38.3719740Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:08:38.3719991Z getitem_6: "Sym(s3)" = size_3[0] 2025-03-04T21:08:38.3720223Z getitem_7 = size_3[1]; size_3 = getitem_7 = None 2025-03-04T21:08:38.3720441Z 2025-03-04T21:08:38.3720828Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:08:38.3721639Z proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0) 2025-03-04T21:08:38.3722215Z 2025-03-04T21:08:38.3722726Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:08:38.3723338Z deltas: "f32[4000, 320][320, 1]cpu" = proposal_deltas.float(); proposal_deltas = None 2025-03-04T21:08:38.3723621Z 2025-03-04T21:08:38.3724040Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:08:38.3724596Z boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:08:38.3724891Z 2025-03-04T21:08:38.3725313Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:08:38.3725851Z getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:08:38.3726170Z getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:08:38.3726522Z widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9; getitem_8 = getitem_9 = None 2025-03-04T21:08:38.3726780Z 2025-03-04T21:08:38.3727179Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:08:38.3727692Z getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:08:38.3728017Z getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:08:38.3728367Z heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T21:08:38.3729194Z 2025-03-04T21:08:38.3729592Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:08:38.3730092Z getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:08:38.3730369Z mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths 2025-03-04T21:08:38.3730645Z ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul; getitem_12 = mul = None 2025-03-04T21:08:38.3730889Z 2025-03-04T21:08:38.3731285Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:08:38.3731832Z getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:08:38.3732135Z mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights 2025-03-04T21:08:38.3732425Z ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1; getitem_13 = mul_1 = None 2025-03-04T21:08:38.3732676Z 2025-03-04T21:08:38.3733094Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:08:38.3733608Z getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:08:38.3733934Z dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0; getitem_14 = None 2025-03-04T21:08:38.3734170Z 2025-03-04T21:08:38.3734560Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:08:38.3735070Z getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:08:38.3735386Z dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0; getitem_15 = None 2025-03-04T21:08:38.3735623Z 2025-03-04T21:08:38.3736009Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:08:38.3736528Z getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:08:38.3736853Z dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0; getitem_16 = None 2025-03-04T21:08:38.3737090Z 2025-03-04T21:08:38.3737494Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:08:38.3738051Z getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:08:38.3738386Z dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0; getitem_17 = None 2025-03-04T21:08:38.3738610Z 2025-03-04T21:08:38.3739028Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:08:38.3739554Z dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:08:38.3739821Z 2025-03-04T21:08:38.3740236Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:08:38.3740755Z dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:08:38.3741003Z 2025-03-04T21:08:38.3741437Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:08:38.3742002Z getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:08:38.3742358Z mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18; dx = getitem_18 = None 2025-03-04T21:08:38.3742709Z getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:08:38.3743073Z pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19; mul_2 = getitem_19 = None 2025-03-04T21:08:38.3743349Z 2025-03-04T21:08:38.3743782Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:08:38.3744326Z getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:08:38.3744666Z mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20; dy = getitem_20 = None 2025-03-04T21:08:38.3744990Z getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:08:38.3745328Z pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21; mul_3 = getitem_21 = None 2025-03-04T21:08:38.3745600Z 2025-03-04T21:08:38.3746015Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:08:38.3746522Z exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:08:38.3746856Z getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:08:38.3747210Z pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22; exp = getitem_22 = None 2025-03-04T21:08:38.3747463Z 2025-03-04T21:08:38.3747903Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:08:38.3748436Z exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:08:38.3748799Z getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:08:38.3749210Z pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23; exp_1 = getitem_23 = None 2025-03-04T21:08:38.3749487Z 2025-03-04T21:08:38.3749934Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:08:38.3750446Z mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:08:38.3750737Z x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:08:38.3750994Z 2025-03-04T21:08:38.3751437Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:08:38.3751967Z mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:08:38.3752263Z y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:08:38.3752523Z 2025-03-04T21:08:38.3752998Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:08:38.3753540Z mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:08:38.3753863Z x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:08:38.3754134Z 2025-03-04T21:08:38.3754574Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:08:38.3755203Z mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:08:38.3755537Z y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:08:38.3755841Z 2025-03-04T21:08:38.3756312Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:08:38.3756927Z pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:08:38.3757230Z 2025-03-04T21:08:38.3757886Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:08:38.3758685Z predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320)); pred_boxes = None 2025-03-04T21:08:38.3759021Z 2025-03-04T21:08:38.3759495Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:08:38.3760214Z split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]); predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None 2025-03-04T21:08:38.3760648Z boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0] 2025-03-04T21:08:38.3760936Z getitem_25: "f32[s1, 320][320, 1]cpu" = split[1]; getitem_25 = None 2025-03-04T21:08:38.3761237Z getitem_26: "f32[s2, 320][320, 1]cpu" = split[2]; getitem_26 = None 2025-03-04T21:08:38.3761543Z getitem_27: "f32[s3, 320][320, 1]cpu" = split[3]; split = getitem_27 = None 2025-03-04T21:08:38.3761797Z 2025-03-04T21:08:38.3762181Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:08:38.3762748Z size_4 = l_proposals_0_fields_proposal_boxes_tensor.size(); l_proposals_0_fields_proposal_boxes_tensor = None 2025-03-04T21:08:38.3763101Z getitem_28: "Sym(s0)" = size_4[0] 2025-03-04T21:08:38.3763342Z getitem_29 = size_4[1]; size_4 = getitem_29 = None 2025-03-04T21:08:38.3763744Z size_5 = l_proposals_1_fields_proposal_boxes_tensor.size(); l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:08:38.3764096Z getitem_30: "Sym(s1)" = size_5[0] 2025-03-04T21:08:38.3764322Z getitem_31 = size_5[1]; size_5 = getitem_31 = None 2025-03-04T21:08:38.3764678Z size_6 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:08:38.3765013Z getitem_32: "Sym(s2)" = size_6[0] 2025-03-04T21:08:38.3765238Z getitem_33 = size_6[1]; size_6 = getitem_33 = None 2025-03-04T21:08:38.3765598Z size_7 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:08:38.3765934Z getitem_34: "Sym(s3)" = size_7[0] 2025-03-04T21:08:38.3766163Z getitem_35 = size_7[1]; size_7 = getitem_35 = None 2025-03-04T21:08:38.3766380Z 2025-03-04T21:08:38.3766800Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:08:38.3767392Z probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1); scores = None 2025-03-04T21:08:38.3767683Z 2025-03-04T21:08:38.3768157Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:08:38.3768845Z split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0); probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None 2025-03-04T21:08:38.3769256Z scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:08:38.3769539Z getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1]; getitem_37 = None 2025-03-04T21:08:38.3769843Z getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2]; getitem_38 = None 2025-03-04T21:08:38.3770142Z getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_39 = None 2025-03-04T21:08:38.3770388Z 2025-03-04T21:08:38.3770936Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:08:38.3771621Z isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:08:38.3771950Z all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:08:38.3772289Z isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:08:38.3772617Z all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:08:38.3772899Z valid_mask: "b8[s0][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:08:38.3773132Z 2025-03-04T21:08:38.3773576Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:08:38.3774091Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:08:38.3774322Z 2025-03-04T21:08:38.3774460Z 2025-03-04T21:08:38.3774550Z class GraphModule(torch.nn.Module): 2025-03-04T21:08:38.3776447Z def forward(self, L_stack0_: "f32[4000, 256, 7, 7][12544, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 12544][12544, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"): 2025-03-04T21:08:38.3778481Z l_stack0_ = L_stack0_ 2025-03-04T21:08:38.3778831Z l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_ 2025-03-04T21:08:38.3779312Z l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_ 2025-03-04T21:08:38.3779789Z l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_ 2025-03-04T21:08:38.3780269Z l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_ 2025-03-04T21:08:38.3780804Z l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_ 2025-03-04T21:08:38.3781369Z l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_ 2025-03-04T21:08:38.3781933Z l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ 2025-03-04T21:08:38.3782496Z l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ 2025-03-04T21:08:38.3782984Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:08:38.3783441Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:08:38.3783857Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:08:38.3784274Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:08:38.3784587Z 2025-03-04T21:08:38.3784972Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x) 2025-03-04T21:08:38.3785462Z x: "f32[4000, 12544][12544, 1]cpu" = l_stack0_.flatten(1, -1); l_stack0_ = None 2025-03-04T21:08:38.3786227Z x_1: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_); x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None 2025-03-04T21:08:38.3786983Z x_2: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False); x_1 = None 2025-03-04T21:08:38.3787746Z x_3: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_); x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None 2025-03-04T21:08:38.3788488Z x_4: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False); x_3 = None 2025-03-04T21:08:38.3788784Z 2025-03-04T21:08:38.3789216Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x) 2025-03-04T21:08:38.3790256Z scores: "f32[4000, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_); l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None 2025-03-04T21:08:38.3791071Z 2025-03-04T21:08:38.3791537Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x) 2025-03-04T21:08:38.3792676Z proposal_deltas: "f32[4000, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_); x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None 2025-03-04T21:08:38.3793524Z 2025-03-04T21:08:38.3793952Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:08:38.3794488Z size = l_proposals_0_fields_proposal_boxes_tensor.size() 2025-03-04T21:08:38.3794784Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:08:38.3795166Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:08:38.3795523Z size_1 = l_proposals_1_fields_proposal_boxes_tensor.size() 2025-03-04T21:08:38.3795820Z getitem_2: "Sym(s1)" = size_1[0] 2025-03-04T21:08:38.3796065Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:08:38.3796347Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:08:38.3796601Z getitem_4: "Sym(s2)" = size_2[0] 2025-03-04T21:08:38.3796841Z getitem_5 = size_2[1]; size_2 = getitem_5 = None 2025-03-04T21:08:38.3797102Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:08:38.3797344Z getitem_6: "Sym(s3)" = size_3[0] 2025-03-04T21:08:38.3797567Z getitem_7 = size_3[1]; size_3 = getitem_7 = None 2025-03-04T21:08:38.3797807Z 2025-03-04T21:08:38.3798203Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:08:38.3799024Z proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0) 2025-03-04T21:08:38.3799608Z 2025-03-04T21:08:38.3800092Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:08:38.3800719Z deltas: "f32[4000, 320][320, 1]cpu" = proposal_deltas.float(); proposal_deltas = None 2025-03-04T21:08:38.3801004Z 2025-03-04T21:08:38.3801425Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:08:38.3802169Z boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:08:38.3802470Z 2025-03-04T21:08:38.3802887Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:08:38.3803427Z getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:08:38.3803747Z getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:08:38.3804095Z widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9; getitem_8 = getitem_9 = None 2025-03-04T21:08:38.3804378Z 2025-03-04T21:08:38.3804805Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:08:38.3805337Z getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:08:38.3805711Z getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:08:38.3806065Z heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T21:08:38.3806350Z 2025-03-04T21:08:38.3806773Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:08:38.3807298Z getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:08:38.3807592Z mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths 2025-03-04T21:08:38.3807879Z ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul; getitem_12 = mul = None 2025-03-04T21:08:38.3808158Z 2025-03-04T21:08:38.3808580Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:08:38.3809153Z getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:08:38.3809476Z mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights 2025-03-04T21:08:38.3809751Z ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1; getitem_13 = mul_1 = None 2025-03-04T21:08:38.3809997Z 2025-03-04T21:08:38.3810394Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:08:38.3810907Z getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:08:38.3811228Z dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0; getitem_14 = None 2025-03-04T21:08:38.3811589Z 2025-03-04T21:08:38.3811982Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:08:38.3812521Z getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:08:38.3812855Z dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0; getitem_15 = None 2025-03-04T21:08:38.3813096Z 2025-03-04T21:08:38.3813494Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:08:38.3814018Z getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:08:38.3814336Z dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0; getitem_16 = None 2025-03-04T21:08:38.3814567Z 2025-03-04T21:08:38.3814956Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:08:38.3815496Z getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:08:38.3815835Z dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0; getitem_17 = None 2025-03-04T21:08:38.3816061Z 2025-03-04T21:08:38.3816506Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:08:38.3817057Z dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:08:38.3817305Z 2025-03-04T21:08:38.3817722Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:08:38.3818238Z dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:08:38.3818487Z 2025-03-04T21:08:38.3818949Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:08:38.3819491Z getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:08:38.3819809Z mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18; dx = getitem_18 = None 2025-03-04T21:08:38.3820138Z getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:08:38.3820481Z pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19; mul_2 = getitem_19 = None 2025-03-04T21:08:38.3820734Z 2025-03-04T21:08:38.3821183Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:08:38.3821757Z getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:08:38.3822092Z mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20; dy = getitem_20 = None 2025-03-04T21:08:38.3822456Z getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:08:38.3822812Z pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21; mul_3 = getitem_21 = None 2025-03-04T21:08:38.3823076Z 2025-03-04T21:08:38.3823526Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:08:38.3824063Z exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:08:38.3824388Z getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:08:38.3824747Z pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22; exp = getitem_22 = None 2025-03-04T21:08:38.3824996Z 2025-03-04T21:08:38.3825424Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:08:38.3825928Z exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:08:38.3826265Z getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:08:38.3826617Z pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23; exp_1 = getitem_23 = None 2025-03-04T21:08:38.3826869Z 2025-03-04T21:08:38.3827289Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:08:38.3827750Z mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:08:38.3828016Z x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:08:38.3828254Z 2025-03-04T21:08:38.3828676Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:08:38.3829148Z mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:08:38.3829412Z y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:08:38.3829648Z 2025-03-04T21:08:38.3830062Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:08:38.3830550Z mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:08:38.3830848Z x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:08:38.3831099Z 2025-03-04T21:08:38.3831512Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:08:38.3832020Z mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:08:38.3832311Z y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:08:38.3832562Z 2025-03-04T21:08:38.3833017Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:08:38.3833645Z pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:08:38.3833948Z 2025-03-04T21:08:38.3834409Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:08:38.3835146Z predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320)); pred_boxes = None 2025-03-04T21:08:38.3835481Z 2025-03-04T21:08:38.3836017Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:08:38.3836730Z split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]); predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None 2025-03-04T21:08:38.3837165Z boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0] 2025-03-04T21:08:38.3837456Z getitem_25: "f32[s1, 320][320, 1]cpu" = split[1]; getitem_25 = None 2025-03-04T21:08:38.3837757Z getitem_26: "f32[s2, 320][320, 1]cpu" = split[2]; getitem_26 = None 2025-03-04T21:08:38.3838064Z getitem_27: "f32[s3, 320][320, 1]cpu" = split[3]; split = getitem_27 = None 2025-03-04T21:08:38.3838333Z 2025-03-04T21:08:38.3838718Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:08:38.3839294Z size_4 = l_proposals_0_fields_proposal_boxes_tensor.size(); l_proposals_0_fields_proposal_boxes_tensor = None 2025-03-04T21:08:38.3839650Z getitem_28: "Sym(s0)" = size_4[0] 2025-03-04T21:08:38.3839896Z getitem_29 = size_4[1]; size_4 = getitem_29 = None 2025-03-04T21:08:38.3840262Z size_5 = l_proposals_1_fields_proposal_boxes_tensor.size(); l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:08:38.3840751Z getitem_30: "Sym(s1)" = size_5[0] 2025-03-04T21:08:38.3841144Z getitem_31 = size_5[1]; size_5 = getitem_31 = None 2025-03-04T21:08:38.3841566Z size_6 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:08:38.3841914Z getitem_32: "Sym(s2)" = size_6[0] 2025-03-04T21:08:38.3842149Z getitem_33 = size_6[1]; size_6 = getitem_33 = None 2025-03-04T21:08:38.3842516Z size_7 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:08:38.3842858Z getitem_34: "Sym(s3)" = size_7[0] 2025-03-04T21:08:38.3843092Z getitem_35 = size_7[1]; size_7 = getitem_35 = None 2025-03-04T21:08:38.3843308Z 2025-03-04T21:08:38.3843736Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:08:38.3844309Z probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1); scores = None 2025-03-04T21:08:38.3844596Z 2025-03-04T21:08:38.3845051Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:08:38.3845720Z split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0); probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None 2025-03-04T21:08:38.3846170Z scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:08:38.3846460Z getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1]; getitem_37 = None 2025-03-04T21:08:38.3846758Z getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2]; getitem_38 = None 2025-03-04T21:08:38.3847065Z getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_39 = None 2025-03-04T21:08:38.3847316Z 2025-03-04T21:08:38.3847884Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:08:38.3848590Z isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:08:38.3848926Z all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:08:38.3849258Z isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:08:38.3849624Z all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:08:38.3849907Z valid_mask: "b8[s0][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:08:38.3850137Z 2025-03-04T21:08:38.3850572Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:08:38.3851094Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:08:38.3851323Z 2025-03-04T21:08:38.3851457Z 2025-03-04T21:08:38.3851545Z class GraphModule(torch.nn.Module): 2025-03-04T21:08:38.3853459Z def forward(self, L_stack0_: "f32[4000, 256, 7, 7][12544, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 12544][12544, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"): 2025-03-04T21:08:38.3855497Z l_stack0_ = L_stack0_ 2025-03-04T21:08:38.3855843Z l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_ 2025-03-04T21:08:38.3856331Z l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_ 2025-03-04T21:08:38.3856810Z l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_ 2025-03-04T21:08:38.3857284Z l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_ 2025-03-04T21:08:38.3857815Z l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_ 2025-03-04T21:08:38.3858393Z l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_ 2025-03-04T21:08:38.3858971Z l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ 2025-03-04T21:08:38.3859568Z l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ 2025-03-04T21:08:38.3860060Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:08:38.3860476Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:08:38.3860903Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:08:38.3862283Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:08:38.3862588Z 2025-03-04T21:08:38.3862967Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x) 2025-03-04T21:08:38.3863442Z x: "f32[4000, 12544][12544, 1]cpu" = l_stack0_.flatten(1, -1); l_stack0_ = None 2025-03-04T21:08:38.3864182Z x_1: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_); x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None 2025-03-04T21:08:38.3864906Z x_2: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False); x_1 = None 2025-03-04T21:08:38.3865629Z x_3: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_); x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None 2025-03-04T21:08:38.3866346Z x_4: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False); x_3 = None 2025-03-04T21:08:38.3866661Z 2025-03-04T21:08:38.3867082Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x) 2025-03-04T21:08:38.3868089Z scores: "f32[4000, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_); l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None 2025-03-04T21:08:38.3868801Z 2025-03-04T21:08:38.3869227Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x) 2025-03-04T21:08:38.3870277Z proposal_deltas: "f32[4000, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_); x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None 2025-03-04T21:08:38.3871079Z 2025-03-04T21:08:38.3871493Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:08:38.3872003Z size = l_proposals_0_fields_proposal_boxes_tensor.size() 2025-03-04T21:08:38.3872280Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:08:38.3872535Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:08:38.3872835Z size_1 = l_proposals_1_fields_proposal_boxes_tensor.size() 2025-03-04T21:08:38.3873111Z getitem_2: "Sym(s1)" = size_1[0] 2025-03-04T21:08:38.3873370Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:08:38.3873668Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:08:38.3873941Z getitem_4: "Sym(s2)" = size_2[0] 2025-03-04T21:08:38.3874243Z getitem_5 = size_2[1]; size_2 = getitem_5 = None 2025-03-04T21:08:38.3874537Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:08:38.3874812Z getitem_6: "Sym(s3)" = size_3[0] 2025-03-04T21:08:38.3875161Z getitem_7 = size_3[1]; size_3 = getitem_7 = None 2025-03-04T21:08:38.3875463Z 2025-03-04T21:08:38.3875897Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:08:38.3876721Z proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0) 2025-03-04T21:08:38.3877268Z 2025-03-04T21:08:38.3877795Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:08:38.3878485Z deltas: "f32[4000, 320][320, 1]cpu" = proposal_deltas.float(); proposal_deltas = None 2025-03-04T21:08:38.3878794Z 2025-03-04T21:08:38.3879248Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:08:38.3879852Z boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:08:38.3880160Z 2025-03-04T21:08:38.3880617Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:08:38.3881190Z getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:08:38.3881574Z getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:08:38.3881955Z widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9; getitem_8 = getitem_9 = None 2025-03-04T21:08:38.3882260Z 2025-03-04T21:08:38.3882733Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:08:38.3883313Z getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:08:38.3883674Z getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:08:38.3884073Z heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T21:08:38.3884381Z 2025-03-04T21:08:38.3884834Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:08:38.3885370Z getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:08:38.3885662Z mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths 2025-03-04T21:08:38.3885946Z ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul; getitem_12 = mul = None 2025-03-04T21:08:38.3886201Z 2025-03-04T21:08:38.3886622Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:08:38.3887166Z getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:08:38.3887486Z mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights 2025-03-04T21:08:38.3887779Z ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1; getitem_13 = mul_1 = None 2025-03-04T21:08:38.3888036Z 2025-03-04T21:08:38.3888462Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:08:38.3889037Z getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:08:38.3889379Z dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0; getitem_14 = None 2025-03-04T21:08:38.3889624Z 2025-03-04T21:08:38.3890030Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:08:38.3890567Z getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:08:38.3890908Z dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0; getitem_15 = None 2025-03-04T21:08:38.3891153Z 2025-03-04T21:08:38.3891553Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:08:38.3892085Z getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:08:38.3892441Z dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0; getitem_16 = None 2025-03-04T21:08:38.3892696Z 2025-03-04T21:08:38.3893104Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:08:38.3893672Z getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:08:38.3894041Z dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0; getitem_17 = None 2025-03-04T21:08:38.3894269Z 2025-03-04T21:08:38.3894691Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:08:38.3895242Z dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:08:38.3895493Z 2025-03-04T21:08:38.3895908Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:08:38.3896421Z dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:08:38.3896677Z 2025-03-04T21:08:38.3897125Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:08:38.3897709Z getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:08:38.3898040Z mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18; dx = getitem_18 = None 2025-03-04T21:08:38.3898386Z getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:08:38.3898748Z pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19; mul_2 = getitem_19 = None 2025-03-04T21:08:38.3899014Z 2025-03-04T21:08:38.3899470Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:08:38.3900038Z getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:08:38.3900370Z mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20; dy = getitem_20 = None 2025-03-04T21:08:38.3900713Z getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:08:38.3901073Z pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21; mul_3 = getitem_21 = None 2025-03-04T21:08:38.3901332Z 2025-03-04T21:08:38.3901774Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:08:38.3902481Z exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:08:38.3902847Z getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:08:38.3903233Z pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22; exp = getitem_22 = None 2025-03-04T21:08:38.3903509Z 2025-03-04T21:08:38.3903975Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:08:38.3904504Z exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:08:38.3904857Z getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:08:38.3905230Z pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23; exp_1 = getitem_23 = None 2025-03-04T21:08:38.3905497Z 2025-03-04T21:08:38.3905995Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:08:38.3906507Z mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:08:38.3906780Z x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:08:38.3907030Z 2025-03-04T21:08:38.3907449Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:08:38.3907959Z mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:08:38.3908242Z y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:08:38.3908521Z 2025-03-04T21:08:38.3908955Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:08:38.3909494Z mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:08:38.3909825Z x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:08:38.3910099Z 2025-03-04T21:08:38.3910547Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:08:38.3911095Z mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:08:38.3911452Z y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:08:38.3911729Z 2025-03-04T21:08:38.3912229Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:08:38.3912905Z pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:08:38.3913236Z 2025-03-04T21:08:38.3913719Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:08:38.3914361Z predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320)); pred_boxes = None 2025-03-04T21:08:38.3914686Z 2025-03-04T21:08:38.3915299Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:08:38.3916099Z split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]); predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None 2025-03-04T21:08:38.3916568Z boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0] 2025-03-04T21:08:38.3916920Z getitem_25: "f32[s1, 320][320, 1]cpu" = split[1]; getitem_25 = None 2025-03-04T21:08:38.3917245Z getitem_26: "f32[s2, 320][320, 1]cpu" = split[2]; getitem_26 = None 2025-03-04T21:08:38.3917575Z getitem_27: "f32[s3, 320][320, 1]cpu" = split[3]; split = getitem_27 = None 2025-03-04T21:08:38.3917848Z 2025-03-04T21:08:38.3918265Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:08:38.3918879Z size_4 = l_proposals_0_fields_proposal_boxes_tensor.size(); l_proposals_0_fields_proposal_boxes_tensor = None 2025-03-04T21:08:38.3919266Z getitem_28: "Sym(s0)" = size_4[0] 2025-03-04T21:08:38.3919524Z getitem_29 = size_4[1]; size_4 = getitem_29 = None 2025-03-04T21:08:38.3919924Z size_5 = l_proposals_1_fields_proposal_boxes_tensor.size(); l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:08:38.3920303Z getitem_30: "Sym(s1)" = size_5[0] 2025-03-04T21:08:38.3920560Z getitem_31 = size_5[1]; size_5 = getitem_31 = None 2025-03-04T21:08:38.3920980Z size_6 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:08:38.3921337Z getitem_32: "Sym(s2)" = size_6[0] 2025-03-04T21:08:38.3921584Z getitem_33 = size_6[1]; size_6 = getitem_33 = None 2025-03-04T21:08:38.3921961Z size_7 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:08:38.3922316Z getitem_34: "Sym(s3)" = size_7[0] 2025-03-04T21:08:38.3922557Z getitem_35 = size_7[1]; size_7 = getitem_35 = None 2025-03-04T21:08:38.3922780Z 2025-03-04T21:08:38.3923225Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:08:38.3923835Z probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1); scores = None 2025-03-04T21:08:38.3924134Z 2025-03-04T21:08:38.3924601Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:08:38.3925297Z split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0); probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None 2025-03-04T21:08:38.3925729Z scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:08:38.3926047Z getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1]; getitem_37 = None 2025-03-04T21:08:38.3926354Z getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2]; getitem_38 = None 2025-03-04T21:08:38.3926671Z getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_39 = None 2025-03-04T21:08:38.3926945Z 2025-03-04T21:08:38.3927537Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:08:38.3928274Z isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:08:38.3928632Z all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:08:38.3928986Z isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:08:38.3929345Z all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:08:38.3929649Z valid_mask: "b8[s0][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:08:38.3929902Z 2025-03-04T21:08:38.3930374Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:08:38.3930950Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:08:38.3931192Z 2025-03-04T21:08:38.9473193Z 2025-03-04T21:08:38.9473987Z class GraphModule(torch.nn.Module): 2025-03-04T21:08:38.9475170Z def forward(self, L_predictions_0_: "f32[4000, 81][81, 1]cpu", L_predictions_1_: "f32[4000, 320][320, 1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"): 2025-03-04T21:08:38.9476088Z l_predictions_0_ = L_predictions_0_ 2025-03-04T21:08:38.9476332Z l_predictions_1_ = L_predictions_1_ 2025-03-04T21:08:38.9476678Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:08:38.9477111Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:08:38.9477875Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:08:38.9478300Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:08:38.9478613Z 2025-03-04T21:08:38.9479116Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:08:38.9479621Z size = l_proposals_0_fields_proposal_boxes_tensor.size() 2025-03-04T21:08:38.9479882Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:08:38.9480129Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:08:38.9480418Z size_1 = l_proposals_1_fields_proposal_boxes_tensor.size() 2025-03-04T21:08:38.9480741Z getitem_2: "Sym(s1)" = size_1[0] 2025-03-04T21:08:38.9480987Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:08:38.9481270Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:08:38.9481527Z getitem_4: "Sym(s2)" = size_2[0] 2025-03-04T21:08:38.9481767Z getitem_5 = size_2[1]; size_2 = getitem_5 = None 2025-03-04T21:08:38.9482042Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:08:38.9482300Z getitem_6: "Sym(s3)" = size_3[0] 2025-03-04T21:08:38.9482536Z getitem_7 = size_3[1]; size_3 = getitem_7 = None 2025-03-04T21:08:38.9482760Z 2025-03-04T21:08:38.9483200Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:08:38.9484035Z proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0) 2025-03-04T21:08:38.9484656Z 2025-03-04T21:08:38.9485156Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:08:38.9485736Z deltas: "f32[4000, 320][320, 1]cpu" = l_predictions_1_.float(); l_predictions_1_ = None 2025-03-04T21:08:38.9486003Z 2025-03-04T21:08:38.9486401Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:08:38.9486934Z boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:08:38.9487217Z 2025-03-04T21:08:38.9487619Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:08:38.9488162Z getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:08:38.9488477Z getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:08:38.9488805Z widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9; getitem_8 = getitem_9 = None 2025-03-04T21:08:38.9489068Z 2025-03-04T21:08:38.9489474Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:08:38.9489986Z getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:08:38.9490298Z getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:08:38.9490635Z heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T21:08:38.9490903Z 2025-03-04T21:08:38.9491321Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:08:38.9491817Z getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:08:38.9492095Z mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths 2025-03-04T21:08:38.9492367Z ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul; getitem_12 = mul = None 2025-03-04T21:08:38.9492608Z 2025-03-04T21:08:38.9493002Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:08:38.9493522Z getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:08:38.9493841Z mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights 2025-03-04T21:08:38.9494120Z ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1; getitem_13 = mul_1 = None 2025-03-04T21:08:38.9494365Z 2025-03-04T21:08:38.9494797Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:08:38.9495314Z getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:08:38.9495637Z dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0; getitem_14 = None 2025-03-04T21:08:38.9495870Z 2025-03-04T21:08:38.9496313Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:08:38.9496818Z getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:08:38.9497139Z dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0; getitem_15 = None 2025-03-04T21:08:38.9497371Z 2025-03-04T21:08:38.9497814Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:08:38.9498340Z getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:08:38.9498658Z dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0; getitem_16 = None 2025-03-04T21:08:38.9498888Z 2025-03-04T21:08:38.9499276Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:08:38.9499815Z getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:08:38.9500158Z dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0; getitem_17 = None 2025-03-04T21:08:38.9500388Z 2025-03-04T21:08:38.9500808Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:08:38.9501356Z dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:08:38.9501614Z 2025-03-04T21:08:38.9502273Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:08:38.9502802Z dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:08:38.9503051Z 2025-03-04T21:08:38.9503484Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:08:38.9504031Z getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:08:38.9504352Z mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18; dx = getitem_18 = None 2025-03-04T21:08:38.9504724Z getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:08:38.9505067Z pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19; mul_2 = getitem_19 = None 2025-03-04T21:08:38.9505317Z 2025-03-04T21:08:38.9505744Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:08:38.9506285Z getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:08:38.9506598Z mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20; dy = getitem_20 = None 2025-03-04T21:08:38.9506923Z getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:08:38.9507321Z pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21; mul_3 = getitem_21 = None 2025-03-04T21:08:38.9507581Z 2025-03-04T21:08:38.9508007Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:08:38.9508509Z exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:08:38.9508842Z getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:08:38.9509215Z pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22; exp = getitem_22 = None 2025-03-04T21:08:38.9509463Z 2025-03-04T21:08:38.9509883Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:08:38.9510382Z exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:08:38.9510716Z getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:08:38.9511064Z pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23; exp_1 = getitem_23 = None 2025-03-04T21:08:38.9511313Z 2025-03-04T21:08:38.9511708Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:08:38.9512162Z mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:08:38.9512421Z x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:08:38.9512654Z 2025-03-04T21:08:38.9513046Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:08:38.9513496Z mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:08:38.9513769Z y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:08:38.9513997Z 2025-03-04T21:08:38.9514386Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:08:38.9514856Z mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:08:38.9515214Z x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:08:38.9515459Z 2025-03-04T21:08:38.9515848Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:08:38.9516318Z mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:08:38.9516605Z y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:08:38.9516848Z 2025-03-04T21:08:38.9517305Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:08:38.9517892Z pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:08:38.9518185Z 2025-03-04T21:08:38.9518613Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:08:38.9519160Z predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320)); pred_boxes = None 2025-03-04T21:08:38.9519435Z 2025-03-04T21:08:38.9519873Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:08:38.9520564Z split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]); predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None 2025-03-04T21:08:38.9520986Z boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0] 2025-03-04T21:08:38.9521267Z getitem_25: "f32[s1, 320][320, 1]cpu" = split[1]; getitem_25 = None 2025-03-04T21:08:38.9521554Z getitem_26: "f32[s2, 320][320, 1]cpu" = split[2]; getitem_26 = None 2025-03-04T21:08:38.9521857Z getitem_27: "f32[s3, 320][320, 1]cpu" = split[3]; split = getitem_27 = None 2025-03-04T21:08:38.9522094Z 2025-03-04T21:08:38.9522476Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:08:38.9523029Z size_4 = l_proposals_0_fields_proposal_boxes_tensor.size(); l_proposals_0_fields_proposal_boxes_tensor = None 2025-03-04T21:08:38.9523369Z getitem_28: "Sym(s0)" = size_4[0] 2025-03-04T21:08:38.9523602Z getitem_29 = size_4[1]; size_4 = getitem_29 = None 2025-03-04T21:08:38.9523958Z size_5 = l_proposals_1_fields_proposal_boxes_tensor.size(); l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:08:38.9524285Z getitem_30: "Sym(s1)" = size_5[0] 2025-03-04T21:08:38.9524510Z getitem_31 = size_5[1]; size_5 = getitem_31 = None 2025-03-04T21:08:38.9524877Z size_6 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:08:38.9525228Z getitem_32: "Sym(s2)" = size_6[0] 2025-03-04T21:08:38.9525457Z getitem_33 = size_6[1]; size_6 = getitem_33 = None 2025-03-04T21:08:38.9525810Z size_7 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:08:38.9526149Z getitem_34: "Sym(s3)" = size_7[0] 2025-03-04T21:08:38.9526381Z getitem_35 = size_7[1]; size_7 = getitem_35 = None 2025-03-04T21:08:38.9526637Z 2025-03-04T21:08:38.9527059Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:08:38.9527673Z probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1); l_predictions_0_ = None 2025-03-04T21:08:38.9528005Z 2025-03-04T21:08:38.9528462Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:08:38.9529145Z split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0); probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None 2025-03-04T21:08:38.9529567Z scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:08:38.9529857Z getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1]; getitem_37 = None 2025-03-04T21:08:38.9530155Z getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2]; getitem_38 = None 2025-03-04T21:08:38.9530478Z getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_39 = None 2025-03-04T21:08:38.9530731Z 2025-03-04T21:08:38.9531295Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:08:38.9532002Z isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:08:38.9532343Z all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:08:38.9532680Z isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:08:38.9533035Z all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:08:38.9535595Z valid_mask: "b8[s0][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:08:38.9535840Z 2025-03-04T21:08:38.9536305Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:08:38.9536837Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:08:38.9537074Z 2025-03-04T21:08:38.9553081Z 2025-03-04T21:08:38.9553369Z class GraphModule(torch.nn.Module): 2025-03-04T21:08:38.9554413Z def forward(self, L_predictions_0_: "f32[4000, 81][81, 1]cpu", L_predictions_1_: "f32[4000, 320][320, 1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"): 2025-03-04T21:08:38.9555506Z l_predictions_0_ = L_predictions_0_ 2025-03-04T21:08:38.9555769Z l_predictions_1_ = L_predictions_1_ 2025-03-04T21:08:38.9556123Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:08:38.9556540Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:08:38.9556944Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:08:38.9557347Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:08:38.9557659Z 2025-03-04T21:08:38.9558095Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:08:38.9558595Z size = l_proposals_0_fields_proposal_boxes_tensor.size() 2025-03-04T21:08:38.9558894Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:08:38.9559199Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:08:38.9559477Z size_1 = l_proposals_1_fields_proposal_boxes_tensor.size() 2025-03-04T21:08:38.9559735Z getitem_2: "Sym(s1)" = size_1[0] 2025-03-04T21:08:38.9559977Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:08:38.9560252Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:08:38.9560507Z getitem_4: "Sym(s2)" = size_2[0] 2025-03-04T21:08:38.9560743Z getitem_5 = size_2[1]; size_2 = getitem_5 = None 2025-03-04T21:08:38.9561017Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:08:38.9561271Z getitem_6: "Sym(s3)" = size_3[0] 2025-03-04T21:08:38.9561499Z getitem_7 = size_3[1]; size_3 = getitem_7 = None 2025-03-04T21:08:38.9561705Z 2025-03-04T21:08:38.9562080Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:08:38.9562888Z proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0) 2025-03-04T21:08:38.9563451Z 2025-03-04T21:08:38.9563905Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:08:38.9564491Z deltas: "f32[4000, 320][320, 1]cpu" = l_predictions_1_.float(); l_predictions_1_ = None 2025-03-04T21:08:38.9564754Z 2025-03-04T21:08:38.9565151Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:08:38.9565682Z boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:08:38.9566042Z 2025-03-04T21:08:38.9566450Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:08:38.9566957Z getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:08:38.9567273Z getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:08:38.9567602Z widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9; getitem_8 = getitem_9 = None 2025-03-04T21:08:38.9567892Z 2025-03-04T21:08:38.9568301Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:08:38.9568821Z getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:08:38.9569146Z getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:08:38.9569497Z heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T21:08:38.9569779Z 2025-03-04T21:08:38.9570184Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:08:38.9570691Z getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:08:38.9570974Z mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths 2025-03-04T21:08:38.9571247Z ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul; getitem_12 = mul = None 2025-03-04T21:08:38.9571492Z 2025-03-04T21:08:38.9571899Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:08:38.9572448Z getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:08:38.9572759Z mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights 2025-03-04T21:08:38.9573044Z ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1; getitem_13 = mul_1 = None 2025-03-04T21:08:38.9573296Z 2025-03-04T21:08:38.9573725Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:08:38.9574250Z getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:08:38.9574582Z dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0; getitem_14 = None 2025-03-04T21:08:38.9574819Z 2025-03-04T21:08:38.9575214Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:08:38.9575736Z getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:08:38.9576074Z dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0; getitem_15 = None 2025-03-04T21:08:38.9576314Z 2025-03-04T21:08:38.9576711Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:08:38.9577232Z getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:08:38.9577563Z dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0; getitem_16 = None 2025-03-04T21:08:38.9577800Z 2025-03-04T21:08:38.9578197Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:08:38.9578778Z getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:08:38.9579176Z dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0; getitem_17 = None 2025-03-04T21:08:38.9579411Z 2025-03-04T21:08:38.9579844Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:08:38.9580384Z dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:08:38.9580648Z 2025-03-04T21:08:38.9581092Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:08:38.9581620Z dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:08:38.9581876Z 2025-03-04T21:08:38.9582318Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:08:38.9582877Z getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:08:38.9583206Z mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18; dx = getitem_18 = None 2025-03-04T21:08:38.9583545Z getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:08:38.9583906Z pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19; mul_2 = getitem_19 = None 2025-03-04T21:08:38.9584160Z 2025-03-04T21:08:38.9584585Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:08:38.9585134Z getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:08:38.9585463Z mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20; dy = getitem_20 = None 2025-03-04T21:08:38.9585789Z getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:08:38.9586130Z pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21; mul_3 = getitem_21 = None 2025-03-04T21:08:38.9586382Z 2025-03-04T21:08:38.9586797Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:08:38.9587295Z exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:08:38.9587617Z getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:08:38.9587960Z pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22; exp = getitem_22 = None 2025-03-04T21:08:38.9588203Z 2025-03-04T21:08:38.9588622Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:08:38.9589132Z exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:08:38.9589471Z getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:08:38.9589826Z pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23; exp_1 = getitem_23 = None 2025-03-04T21:08:38.9590078Z 2025-03-04T21:08:38.9590490Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:08:38.9590961Z mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:08:38.9591225Z x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:08:38.9591461Z 2025-03-04T21:08:38.9591905Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:08:38.9592381Z mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:08:38.9592654Z y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:08:38.9592886Z 2025-03-04T21:08:38.9593281Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:08:38.9593775Z mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:08:38.9594071Z x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:08:38.9594319Z 2025-03-04T21:08:38.9594714Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:08:38.9595283Z mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:08:38.9595586Z y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:08:38.9595846Z 2025-03-04T21:08:38.9596313Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:08:38.9596922Z pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:08:38.9597215Z 2025-03-04T21:08:38.9597636Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:08:38.9598191Z predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320)); pred_boxes = None 2025-03-04T21:08:38.9598497Z 2025-03-04T21:08:38.9598941Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:08:38.9599618Z split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]); predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None 2025-03-04T21:08:38.9600037Z boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0] 2025-03-04T21:08:38.9600318Z getitem_25: "f32[s1, 320][320, 1]cpu" = split[1]; getitem_25 = None 2025-03-04T21:08:38.9600612Z getitem_26: "f32[s2, 320][320, 1]cpu" = split[2]; getitem_26 = None 2025-03-04T21:08:38.9600912Z getitem_27: "f32[s3, 320][320, 1]cpu" = split[3]; split = getitem_27 = None 2025-03-04T21:08:38.9601158Z 2025-03-04T21:08:38.9601525Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:08:38.9602361Z size_4 = l_proposals_0_fields_proposal_boxes_tensor.size(); l_proposals_0_fields_proposal_boxes_tensor = None 2025-03-04T21:08:38.9616002Z getitem_28: "Sym(s0)" = size_4[0] 2025-03-04T21:08:38.9616284Z getitem_29 = size_4[1]; size_4 = getitem_29 = None 2025-03-04T21:08:38.9616675Z size_5 = l_proposals_1_fields_proposal_boxes_tensor.size(); l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:08:38.9617032Z getitem_30: "Sym(s1)" = size_5[0] 2025-03-04T21:08:38.9617271Z getitem_31 = size_5[1]; size_5 = getitem_31 = None 2025-03-04T21:08:38.9617638Z size_6 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:08:38.9617981Z getitem_32: "Sym(s2)" = size_6[0] 2025-03-04T21:08:38.9618215Z getitem_33 = size_6[1]; size_6 = getitem_33 = None 2025-03-04T21:08:38.9618608Z size_7 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:08:38.9619004Z getitem_34: "Sym(s3)" = size_7[0] 2025-03-04T21:08:38.9619226Z getitem_35 = size_7[1]; size_7 = getitem_35 = None 2025-03-04T21:08:38.9619449Z 2025-03-04T21:08:38.9619906Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:08:38.9620533Z probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1); l_predictions_0_ = None 2025-03-04T21:08:38.9620859Z 2025-03-04T21:08:38.9621423Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:08:38.9622112Z split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0); probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None 2025-03-04T21:08:38.9622539Z scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:08:38.9622832Z getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1]; getitem_37 = None 2025-03-04T21:08:38.9623133Z getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2]; getitem_38 = None 2025-03-04T21:08:38.9623438Z getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_39 = None 2025-03-04T21:08:38.9623692Z 2025-03-04T21:08:38.9624262Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:08:38.9625095Z isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:08:38.9625567Z all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:08:38.9625935Z isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:08:38.9626269Z all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:08:38.9626554Z valid_mask: "b8[s0][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:08:38.9626789Z 2025-03-04T21:08:38.9627245Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:08:38.9627771Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:08:38.9628005Z 2025-03-04T21:08:38.9629389Z 2025-03-04T21:08:38.9629647Z class GraphModule(torch.nn.Module): 2025-03-04T21:08:38.9630638Z def forward(self, L_predictions_0_: "f32[4000, 81][81, 1]cpu", L_predictions_1_: "f32[4000, 320][320, 1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"): 2025-03-04T21:08:38.9631555Z l_predictions_0_ = L_predictions_0_ 2025-03-04T21:08:38.9631811Z l_predictions_1_ = L_predictions_1_ 2025-03-04T21:08:38.9632161Z l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor 2025-03-04T21:08:38.9632599Z l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor 2025-03-04T21:08:38.9633019Z l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor 2025-03-04T21:08:38.9633409Z l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor 2025-03-04T21:08:38.9633696Z 2025-03-04T21:08:38.9634085Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:08:38.9634585Z size = l_proposals_0_fields_proposal_boxes_tensor.size() 2025-03-04T21:08:38.9634839Z getitem: "Sym(s0)" = size[0] 2025-03-04T21:08:38.9635150Z getitem_1 = size[1]; size = getitem_1 = None 2025-03-04T21:08:38.9635418Z size_1 = l_proposals_1_fields_proposal_boxes_tensor.size() 2025-03-04T21:08:38.9635670Z getitem_2: "Sym(s1)" = size_1[0] 2025-03-04T21:08:38.9635910Z getitem_3 = size_1[1]; size_1 = getitem_3 = None 2025-03-04T21:08:38.9636203Z size_2 = l_proposals_2_fields_proposal_boxes_tensor.size() 2025-03-04T21:08:38.9636453Z getitem_4: "Sym(s2)" = size_2[0] 2025-03-04T21:08:38.9636684Z getitem_5 = size_2[1]; size_2 = getitem_5 = None 2025-03-04T21:08:38.9636951Z size_3 = l_proposals_3_fields_proposal_boxes_tensor.size() 2025-03-04T21:08:38.9637192Z getitem_6: "Sym(s3)" = size_3[0] 2025-03-04T21:08:38.9637422Z getitem_7 = size_3[1]; size_3 = getitem_7 = None 2025-03-04T21:08:38.9637633Z 2025-03-04T21:08:38.9638011Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim) 2025-03-04T21:08:38.9638784Z proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0) 2025-03-04T21:08:38.9639335Z 2025-03-04T21:08:38.9639798Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float() # ensure fp32 for decoding precision 2025-03-04T21:08:38.9640389Z deltas: "f32[4000, 320][320, 1]cpu" = l_predictions_1_.float(); l_predictions_1_ = None 2025-03-04T21:08:38.9640649Z 2025-03-04T21:08:38.9641036Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype) 2025-03-04T21:08:38.9641569Z boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32); proposal_boxes = None 2025-03-04T21:08:38.9641841Z 2025-03-04T21:08:38.9642232Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0] 2025-03-04T21:08:38.9642717Z getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)] 2025-03-04T21:08:38.9643025Z getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:08:38.9643342Z widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9; getitem_8 = getitem_9 = None 2025-03-04T21:08:38.9643598Z 2025-03-04T21:08:38.9643988Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1] 2025-03-04T21:08:38.9644496Z getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)] 2025-03-04T21:08:38.9644800Z getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)] 2025-03-04T21:08:38.9645129Z heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11; getitem_10 = getitem_11 = None 2025-03-04T21:08:38.9645397Z 2025-03-04T21:08:38.9645785Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths 2025-03-04T21:08:38.9646277Z getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)] 2025-03-04T21:08:38.9646556Z mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths 2025-03-04T21:08:38.9646819Z ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul; getitem_12 = mul = None 2025-03-04T21:08:38.9647093Z 2025-03-04T21:08:38.9647481Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights 2025-03-04T21:08:38.9647984Z getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]; boxes = None 2025-03-04T21:08:38.9648277Z mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights 2025-03-04T21:08:38.9648552Z ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1; getitem_13 = mul_1 = None 2025-03-04T21:08:38.9648795Z 2025-03-04T21:08:38.9649232Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx 2025-03-04T21:08:38.9649757Z getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))] 2025-03-04T21:08:38.9650081Z dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0; getitem_14 = None 2025-03-04T21:08:38.9650319Z 2025-03-04T21:08:38.9650721Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy 2025-03-04T21:08:38.9651211Z getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))] 2025-03-04T21:08:38.9651518Z dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0; getitem_15 = None 2025-03-04T21:08:38.9651739Z 2025-03-04T21:08:38.9652122Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww 2025-03-04T21:08:38.9652623Z getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))] 2025-03-04T21:08:38.9652936Z dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0; getitem_16 = None 2025-03-04T21:08:38.9653163Z 2025-03-04T21:08:38.9653567Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh 2025-03-04T21:08:38.9654101Z getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))]; deltas = None 2025-03-04T21:08:38.9654434Z dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0; getitem_17 = None 2025-03-04T21:08:38.9654652Z 2025-03-04T21:08:38.9655059Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp) 2025-03-04T21:08:38.9655575Z dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356); dw = None 2025-03-04T21:08:38.9655818Z 2025-03-04T21:08:38.9656219Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp) 2025-03-04T21:08:38.9656722Z dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356); dh = None 2025-03-04T21:08:38.9656962Z 2025-03-04T21:08:38.9657392Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] 2025-03-04T21:08:38.9657921Z getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)] 2025-03-04T21:08:38.9658239Z mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18; dx = getitem_18 = None 2025-03-04T21:08:38.9658568Z getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)]; ctr_x = None 2025-03-04T21:08:38.9658911Z pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19; mul_2 = getitem_19 = None 2025-03-04T21:08:38.9659165Z 2025-03-04T21:08:38.9659596Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] 2025-03-04T21:08:38.9660156Z getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)] 2025-03-04T21:08:38.9660475Z mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20; dy = getitem_20 = None 2025-03-04T21:08:38.9660787Z getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)]; ctr_y = None 2025-03-04T21:08:38.9661121Z pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21; mul_3 = getitem_21 = None 2025-03-04T21:08:38.9661370Z 2025-03-04T21:08:38.9661818Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None] 2025-03-04T21:08:38.9662321Z exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1); dw_1 = None 2025-03-04T21:08:38.9662647Z getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]; widths = None 2025-03-04T21:08:38.9662993Z pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22; exp = getitem_22 = None 2025-03-04T21:08:38.9663238Z 2025-03-04T21:08:38.9663654Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None] 2025-03-04T21:08:38.9664151Z exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1); dh_1 = None 2025-03-04T21:08:38.9664485Z getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]; heights = None 2025-03-04T21:08:38.9664832Z pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23; exp_1 = getitem_23 = None 2025-03-04T21:08:38.9665086Z 2025-03-04T21:08:38.9665483Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w 2025-03-04T21:08:38.9665958Z mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w 2025-03-04T21:08:38.9666211Z x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6; mul_6 = None 2025-03-04T21:08:38.9666440Z 2025-03-04T21:08:38.9666833Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h 2025-03-04T21:08:38.9667287Z mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h 2025-03-04T21:08:38.9667539Z y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7; mul_7 = None 2025-03-04T21:08:38.9667768Z 2025-03-04T21:08:38.9668170Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w 2025-03-04T21:08:38.9668646Z mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w; pred_w = None 2025-03-04T21:08:38.9668929Z x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8; pred_ctr_x = mul_8 = None 2025-03-04T21:08:38.9669175Z 2025-03-04T21:08:38.9669582Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h 2025-03-04T21:08:38.9670054Z mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h; pred_h = None 2025-03-04T21:08:38.9670342Z y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9; pred_ctr_y = mul_9 = None 2025-03-04T21:08:38.9670588Z 2025-03-04T21:08:38.9671027Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) 2025-03-04T21:08:38.9671619Z pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1); x1 = y1 = x2 = y2 = None 2025-03-04T21:08:38.9671913Z 2025-03-04T21:08:38.9672337Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape) 2025-03-04T21:08:38.9672912Z predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320)); pred_boxes = None 2025-03-04T21:08:38.9673195Z 2025-03-04T21:08:38.9673631Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image) 2025-03-04T21:08:38.9674316Z split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]); predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None 2025-03-04T21:08:38.9674733Z boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0] 2025-03-04T21:08:38.9675097Z getitem_25: "f32[s1, 320][320, 1]cpu" = split[1]; getitem_25 = None 2025-03-04T21:08:38.9675403Z getitem_26: "f32[s2, 320][320, 1]cpu" = split[2]; getitem_26 = None 2025-03-04T21:08:38.9675706Z getitem_27: "f32[s3, 320][320, 1]cpu" = split[3]; split = getitem_27 = None 2025-03-04T21:08:38.9675968Z 2025-03-04T21:08:38.9676349Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0] 2025-03-04T21:08:38.9676901Z size_4 = l_proposals_0_fields_proposal_boxes_tensor.size(); l_proposals_0_fields_proposal_boxes_tensor = None 2025-03-04T21:08:38.9677250Z getitem_28: "Sym(s0)" = size_4[0] 2025-03-04T21:08:38.9677492Z getitem_29 = size_4[1]; size_4 = getitem_29 = None 2025-03-04T21:08:38.9677856Z size_5 = l_proposals_1_fields_proposal_boxes_tensor.size(); l_proposals_1_fields_proposal_boxes_tensor = None 2025-03-04T21:08:38.9678196Z getitem_30: "Sym(s1)" = size_5[0] 2025-03-04T21:08:38.9678426Z getitem_31 = size_5[1]; size_5 = getitem_31 = None 2025-03-04T21:08:38.9678781Z size_6 = l_proposals_2_fields_proposal_boxes_tensor.size(); l_proposals_2_fields_proposal_boxes_tensor = None 2025-03-04T21:08:38.9679144Z getitem_32: "Sym(s2)" = size_6[0] 2025-03-04T21:08:38.9679380Z getitem_33 = size_6[1]; size_6 = getitem_33 = None 2025-03-04T21:08:38.9679742Z size_7 = l_proposals_3_fields_proposal_boxes_tensor.size(); l_proposals_3_fields_proposal_boxes_tensor = None 2025-03-04T21:08:38.9680090Z getitem_34: "Sym(s3)" = size_7[0] 2025-03-04T21:08:38.9680315Z getitem_35 = size_7[1]; size_7 = getitem_35 = None 2025-03-04T21:08:38.9680530Z 2025-03-04T21:08:38.9680940Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1) 2025-03-04T21:08:38.9681537Z probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1); l_predictions_0_ = None 2025-03-04T21:08:38.9681852Z 2025-03-04T21:08:38.9682293Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0) 2025-03-04T21:08:38.9682963Z split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0); probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None 2025-03-04T21:08:38.9683362Z scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0] 2025-03-04T21:08:38.9683634Z getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1]; getitem_37 = None 2025-03-04T21:08:38.9683914Z getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2]; getitem_38 = None 2025-03-04T21:08:38.9684204Z getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3]; split_1 = getitem_39 = None 2025-03-04T21:08:38.9684440Z 2025-03-04T21:08:38.9684974Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:08:38.9685660Z isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image); boxes_per_image = None 2025-03-04T21:08:38.9685986Z all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:08:38.9686306Z isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image); scores_per_image = None 2025-03-04T21:08:38.9686628Z all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:08:38.9686900Z valid_mask: "b8[s0][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:08:38.9687127Z 2025-03-04T21:08:38.9687576Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:08:38.9688103Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:08:38.9688328Z 2025-03-04T21:08:39.3639433Z 2025-03-04T21:08:39.3641314Z class GraphModule(torch.nn.Module): 2025-03-04T21:08:39.3642109Z def forward(self, s0: "Sym(s0)", L_scores_0_: "f32[s0, 81][81, 1]cpu", s1: "Sym(s0)", L_boxes_0_: "f32[s0, 320][320, 1]cpu"): 2025-03-04T21:08:39.3642831Z l_scores_0_ = L_scores_0_ 2025-03-04T21:08:39.3648362Z l_boxes_0_ = L_boxes_0_ 2025-03-04T21:08:39.3650790Z 2025-03-04T21:08:39.3651433Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:08:39.3652158Z isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(l_boxes_0_); l_boxes_0_ = None 2025-03-04T21:08:39.3652482Z all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:08:39.3652820Z isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(l_scores_0_); l_scores_0_ = None 2025-03-04T21:08:39.3653139Z all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:08:39.3653685Z valid_mask: "b8[s0][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:08:39.3653944Z 2025-03-04T21:08:39.3654393Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:08:39.3654922Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:08:39.3655160Z 2025-03-04T21:08:39.3655248Z 2025-03-04T21:08:39.3655346Z class GraphModule(torch.nn.Module): 2025-03-04T21:08:39.3655681Z def forward(self, s0: "Sym(s0)", L_scores_0_: "f32[s0, 81][81, 1]cpu", s1: "Sym(s0)", L_boxes_0_: "f32[s0, 320][320, 1]cpu"): 2025-03-04T21:08:39.3655992Z l_scores_0_ = L_scores_0_ 2025-03-04T21:08:39.3656194Z l_boxes_0_ = L_boxes_0_ 2025-03-04T21:08:39.3656374Z 2025-03-04T21:08:39.3656979Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) 2025-03-04T21:08:39.3657644Z isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(l_boxes_0_); l_boxes_0_ = None 2025-03-04T21:08:39.3657956Z all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1); isfinite = None 2025-03-04T21:08:39.3658260Z isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(l_scores_0_); l_scores_0_ = None 2025-03-04T21:08:39.3658562Z all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1); isfinite_1 = None 2025-03-04T21:08:39.3658844Z valid_mask: "b8[s0][1]cpu" = all_1 & all_2; all_1 = all_2 = None 2025-03-04T21:08:39.3659076Z 2025-03-04T21:08:39.3659517Z # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all(): 2025-03-04T21:08:39.3660107Z all_3: "b8[][]cpu" = valid_mask.all(); valid_mask = all_3 = None 2025-03-04T21:08:39.3660334Z 2025-03-04T21:09:32.0170516Z Compilation time (from dynamo_timed): 68.622829376 2025-03-04T21:09:32.0175273Z pass 2025-03-04T21:09:32.0183924Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T21:09:32.0186271Z TIMING: entire_frame_compile:68.62283 gc:0.04502 _recursive_pre_grad_passes:0.04008 async_compile.wait:5.73355 backend_compile:16.64765 _recursive_joint_graph_passes:0.15268 inductor_compile:6.61388 _recursive_post_grad_passes:0.02158 code_gen:6.24642 total_wall_time:68.62283 2025-03-04T21:09:32.0191113Z STATS: call_* op count: 1138 | FakeTensorMode.__torch_dispatch__:16550 | FakeTensor.__torch_dispatch__:437 | ProxyTorchDispatchMode.__torch_dispatch__:1365 | attempt fast:182 | slow no contiguity match:40 | fast is_contiguous:138 | slow both tensors nontrivially broadcast:4 2025-03-04T21:09:32.0191925Z Dynamo produced 78 graphs covering 1138 ops with 62 graph breaks (8 unique) 2025-03-04T21:09:38.6138081Z 2025-03-04T21:09:44.3569995Z loading model: 0it [00:00, ?it/s] 2025-03-04T21:09:44.3570351Z loading model: 0it [00:05, ?it/s] 2025-03-04T21:09:44.3572387Z cpu eval dlrm 2025-03-04T21:09:44.9099240Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T21:09:45.0866482Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T21:09:45.2920109Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T21:09:54.8923311Z Compilation time (from dynamo_timed): 8.388731448 2025-03-04T21:09:54.8925712Z pass 2025-03-04T21:09:54.8926062Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu] 2025-03-04T21:09:54.8926889Z TIMING: _recursive_pre_grad_passes:0.00357 _recursive_joint_graph_passes:0.09862 _recursive_post_grad_passes:0.00932 async_compile.wait:1.24939 code_gen:7.2048 inductor_compile:7.32677 backend_compile:8.05371 entire_frame_compile:8.38873 gc:0.00091 total_wall_time:8.38873 2025-03-04T21:09:54.8928315Z STATS: call_* op count: 36 | FakeTensorMode.__torch_dispatch__:1711 | ProxyTorchDispatchMode.__torch_dispatch__:500 | FakeTensor.__torch_dispatch__:81 2025-03-04T21:09:54.8929688Z Dynamo produced 1 graphs covering 36 ops with 0 graph breaks (0 unique) 2025-03-04T21:09:58.6628367Z 2025-03-04T21:09:59.5985307Z loading model: 0it [00:00, ?it/s]Downloading https://doctr-static.mindee.com/models?id=v0.7.0/db_resnet50-79bd7d70.pt&src=0 to /var/lib/jenkins/.cache/doctr/models/db_resnet50-79bd7d70.pt 2025-03-04T21:10:00.0400671Z 2025-03-04T21:10:00.0404516Z 2025-03-04T21:10:00.1402740Z 0% 0/102021912 [00:00=1.35.33 2025-03-04T21:23:02.5669548Z Downloading botocore-1.35.99-py3-none-any.whl (13.3 MB) 2025-03-04T21:23:02.6732575Z Requirement already satisfied: jmespath<2.0.0,>=0.7.1 in /usr/lib/python3.9/site-packages (from boto3==1.35.33) (0.10.0) 2025-03-04T21:23:02.6994315Z Collecting s3transfer<0.11.0,>=0.10.0 2025-03-04T21:23:02.7034872Z Downloading s3transfer-0.10.4-py3-none-any.whl (83 kB) 2025-03-04T21:23:02.7127072Z Requirement already satisfied: python-dateutil<3.0.0,>=2.1 in /usr/lib/python3.9/site-packages (from botocore<1.36.0,>=1.35.33->boto3==1.35.33) (2.8.1) 2025-03-04T21:23:02.7134011Z Requirement already satisfied: urllib3<1.27,>=1.25.4 in /usr/lib/python3.9/site-packages (from botocore<1.36.0,>=1.35.33->boto3==1.35.33) (1.25.10) 2025-03-04T21:23:02.8067788Z Requirement already satisfied: six>=1.5 in /usr/lib/python3.9/site-packages (from python-dateutil<3.0.0,>=2.1->botocore<1.36.0,>=1.35.33->boto3==1.35.33) (1.15.0) 2025-03-04T21:23:02.8680019Z Installing collected packages: botocore, s3transfer, boto3 2025-03-04T21:23:03.2372008Z Successfully installed boto3-1.35.33 botocore-1.35.99 s3transfer-0.10.4 2025-03-04T21:23:03.3406607Z ##[group]Run set -eux 2025-03-04T21:23:03.3406834Z set -eux 2025-03-04T21:23:03.3406998Z  2025-03-04T21:23:03.3407175Z if [[ -z "${GITHUB_TOKEN}" ]]; then 2025-03-04T21:23:03.3407410Z  echo "Missing github-token input" 2025-03-04T21:23:03.3407616Z  exit 1 2025-03-04T21:23:03.3407774Z fi 2025-03-04T21:23:03.3412388Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T21:23:03.3412643Z env: 2025-03-04T21:23:03.3412811Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:23:03.3413115Z DOCKER_CONTAINER_ID: ad526e66cea466c172d0f0e6ea6665dd89838a93b43196189ae102ffb4d51ddd 2025-03-04T21:23:03.3413668Z GITHUB_TOKEN: *** 2025-03-04T21:23:03.3413845Z ##[endgroup] 2025-03-04T21:23:03.3433305Z + [[ -z *** ]] 2025-03-04T21:23:03.3473674Z ##[group]Run pytorch/test-infra/.github/actions/get-workflow-job-id@main 2025-03-04T21:23:03.3474002Z with: 2025-03-04T21:23:03.3474315Z github-token: *** 2025-03-04T21:23:03.3474499Z env: 2025-03-04T21:23:03.3474676Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:23:03.3475176Z DOCKER_CONTAINER_ID: ad526e66cea466c172d0f0e6ea6665dd89838a93b43196189ae102ffb4d51ddd 2025-03-04T21:23:03.3475530Z ##[endgroup] 2025-03-04T21:23:03.3492732Z ##[group]Run set -eux 2025-03-04T21:23:03.3492945Z set -eux 2025-03-04T21:23:03.3493121Z  2025-03-04T21:23:03.3493582Z python3 "${GITHUB_ACTION_PATH}/../../scripts/get_workflow_job_id.py" "${GITHUB_RUN_ID}" "${RUNNER_NAME}" 2025-03-04T21:23:03.3497669Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T21:23:03.3497907Z env: 2025-03-04T21:23:03.3498062Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:23:03.3498384Z DOCKER_CONTAINER_ID: ad526e66cea466c172d0f0e6ea6665dd89838a93b43196189ae102ffb4d51ddd 2025-03-04T21:23:03.3498972Z GITHUB_TOKEN: *** 2025-03-04T21:23:03.3499146Z ##[endgroup] 2025-03-04T21:23:03.3524119Z + python3 /home/ec2-user/actions-runner/_work/_actions/pytorch/test-infra/main/.github/actions/get-workflow-job-id/../../scripts/get_workflow_job_id.py 13661696663 i-034d656ab88aab9e9 2025-03-04T21:23:04.3884117Z setting job-id=38195233363 2025-03-04T21:23:04.3888848Z setting job-name=linux-jammy-cpu-py3.9-gcc11-inductor / test (cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx) 2025-03-04T21:23:04.3988899Z ##[group]Run set -eux 2025-03-04T21:23:04.3989163Z set -eux 2025-03-04T21:23:04.3989327Z  2025-03-04T21:23:04.3989620Z python3 "${GITHUB_ACTION_PATH}/../../scripts/benchmarks/gather_metadata.py" \ 2025-03-04T21:23:04.3989940Z  --schema-version "${SCHEMA_VERSION}" \ 2025-03-04T21:23:04.3990168Z  --repo "${REPO}" \ 2025-03-04T21:23:04.3990374Z  --head-branch "${HEAD_BRANCH}" \ 2025-03-04T21:23:04.3990591Z  --head-sha "${HEAD_SHA}" \ 2025-03-04T21:23:04.3990809Z  --workflow-id "${WORKFLOW_RUN_ID}" \ 2025-03-04T21:23:04.3991045Z  --run-attempt "${RUN_ATTEMPT}" \ 2025-03-04T21:23:04.3991265Z  --job-id "${JOB_ID}" \ 2025-03-04T21:23:04.3991468Z  --job-name "${JOB_NAME}" 2025-03-04T21:23:04.3996255Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T21:23:04.3996509Z env: 2025-03-04T21:23:04.3996681Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:23:04.3996992Z DOCKER_CONTAINER_ID: ad526e66cea466c172d0f0e6ea6665dd89838a93b43196189ae102ffb4d51ddd 2025-03-04T21:23:04.3997314Z SCHEMA_VERSION: v3 2025-03-04T21:23:04.3997652Z REPO: pytorch/pytorch 2025-03-04T21:23:04.3997866Z HEAD_BRANCH: refs/tags/ciflow/inductor/148205 2025-03-04T21:23:04.3998122Z HEAD_SHA: 1b7498080987913ecb3aff6253c5e88f3540d911 2025-03-04T21:23:04.3998358Z WORKFLOW_RUN_ID: 13661696663 2025-03-04T21:23:04.3998555Z RUN_ATTEMPT: 1 2025-03-04T21:23:04.3998734Z JOB_ID: 38195233363 2025-03-04T21:23:04.3999065Z JOB_NAME: linux-jammy-cpu-py3.9-gcc11-inductor / test (cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx) 2025-03-04T21:23:04.3999538Z ##[endgroup] 2025-03-04T21:23:04.4022070Z + python3 /home/ec2-user/actions-runner/_work/_actions/pytorch/test-infra/main/.github/actions/upload-benchmark-results/../../scripts/benchmarks/gather_metadata.py --schema-version v3 --repo pytorch/pytorch --head-branch refs/tags/ciflow/inductor/148205 --head-sha 1b7498080987913ecb3aff6253c5e88f3540d911 --workflow-id 13661696663 --run-attempt 1 --job-id 38195233363 --job-name 'linux-jammy-cpu-py3.9-gcc11-inductor / test (cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx)' 2025-03-04T21:23:04.4284502Z ##[group]Run set -eux 2025-03-04T21:23:04.4284717Z set -eux 2025-03-04T21:23:04.4284879Z  2025-03-04T21:23:04.4285064Z # TODO (huydhn): Implement this part 2025-03-04T21:23:04.4285306Z echo "runners=[]" >> "${GITHUB_OUTPUT}" 2025-03-04T21:23:04.4289194Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T21:23:04.4289434Z env: 2025-03-04T21:23:04.4289599Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:23:04.4289911Z DOCKER_CONTAINER_ID: ad526e66cea466c172d0f0e6ea6665dd89838a93b43196189ae102ffb4d51ddd 2025-03-04T21:23:04.4290216Z ##[endgroup] 2025-03-04T21:23:04.4308921Z + echo 'runners=[]' 2025-03-04T21:23:04.4333410Z ##[group]Run set -eux 2025-03-04T21:23:04.4333613Z set -eux 2025-03-04T21:23:04.4333777Z  2025-03-04T21:23:04.4333960Z # TODO (huydhn): Implement this part 2025-03-04T21:23:04.4334214Z echo "dependencies={}" >> "${GITHUB_OUTPUT}" 2025-03-04T21:23:04.4337798Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T21:23:04.4338043Z env: 2025-03-04T21:23:04.4338204Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:23:04.4338496Z DOCKER_CONTAINER_ID: ad526e66cea466c172d0f0e6ea6665dd89838a93b43196189ae102ffb4d51ddd 2025-03-04T21:23:04.4338802Z ##[endgroup] 2025-03-04T21:23:04.4355900Z + echo 'dependencies={}' 2025-03-04T21:23:04.4383272Z ##[group]Run set -eux 2025-03-04T21:23:04.4383477Z set -eux 2025-03-04T21:23:04.4383714Z  2025-03-04T21:23:04.4383898Z if [[ ! -d "${BENCHMARK_RESULTS_DIR}" ]]; then 2025-03-04T21:23:04.4384174Z  echo "${BENCHMARK_RESULTS_DIR} does not exist, skipping" 2025-03-04T21:23:04.4384474Z  # We don't want the job to fail if the directory doesn't exist 2025-03-04T21:23:04.4384717Z  exit 0 2025-03-04T21:23:04.4384875Z fi 2025-03-04T21:23:04.4385031Z  2025-03-04T21:23:04.4385198Z if [[ "${DRY_RUN}" == "true" ]]; then 2025-03-04T21:23:04.4385503Z  python3 "${GITHUB_ACTION_PATH}/../../scripts/upload_benchmark_results.py" \ 2025-03-04T21:23:04.4385840Z  --benchmark-results-dir "${BENCHMARK_RESULTS_DIR}" \ 2025-03-04T21:23:04.4386105Z  --metadata "${BENCHMARK_METADATA}" \ 2025-03-04T21:23:04.4386330Z  --runners "${RUNNER_INFO}" \ 2025-03-04T21:23:04.4386556Z  --dependencies "${DEPENDENCIES}" \ 2025-03-04T21:23:04.4386767Z  --dry-run 2025-03-04T21:23:04.4386943Z else 2025-03-04T21:23:04.4387173Z  python3 "${GITHUB_ACTION_PATH}/../../scripts/upload_benchmark_results.py" \ 2025-03-04T21:23:04.4387718Z  --benchmark-results-dir "${BENCHMARK_RESULTS_DIR}" \ 2025-03-04T21:23:04.4387979Z  --metadata "${BENCHMARK_METADATA}" \ 2025-03-04T21:23:04.4388203Z  --runners "${RUNNER_INFO}" \ 2025-03-04T21:23:04.4388422Z  --dependencies "${DEPENDENCIES}" 2025-03-04T21:23:04.4388630Z fi 2025-03-04T21:23:04.4391838Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T21:23:04.4392077Z env: 2025-03-04T21:23:04.4392244Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:23:04.4392543Z DOCKER_CONTAINER_ID: ad526e66cea466c172d0f0e6ea6665dd89838a93b43196189ae102ffb4d51ddd 2025-03-04T21:23:04.4392876Z BENCHMARK_RESULTS_DIR: test/test-reports 2025-03-04T21:23:04.4393091Z DRY_RUN: false 2025-03-04T21:23:04.4394099Z BENCHMARK_METADATA: {"timestamp": 1741123384, "schema_version": "v3", "name": "linux-jammy-cpu-py3.9-gcc11-inductor / test (cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx)", "repo": "pytorch/pytorch", "head_branch": "refs/tags/ciflow/inductor/148205", "head_sha": "1b7498080987913ecb3aff6253c5e88f3540d911", "workflow_id": 13661696663, "run_attempt": 1, "job_id": 38195233363} 2025-03-04T21:23:04.4395018Z RUNNER_INFO: [] 2025-03-04T21:23:04.4395193Z DEPENDENCIES: {} 2025-03-04T21:23:04.4395370Z ##[endgroup] 2025-03-04T21:23:04.4418074Z + [[ ! -d test/test-reports ]] 2025-03-04T21:23:04.4420691Z + [[ false == \t\r\u\e ]] 2025-03-04T21:23:04.4422279Z + python3 /home/ec2-user/actions-runner/_work/_actions/pytorch/test-infra/main/.github/actions/upload-benchmark-results/../../scripts/upload_benchmark_results.py --benchmark-results-dir test/test-reports --metadata '{"timestamp": 1741123384, "schema_version": "v3", "name": "linux-jammy-cpu-py3.9-gcc11-inductor / test (cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx)", "repo": "pytorch/pytorch", "head_branch": "refs/tags/ciflow/inductor/148205", "head_sha": "1b7498080987913ecb3aff6253c5e88f3540d911", "workflow_id": 13661696663, "run_attempt": 1, "job_id": 38195233363}' --runners '[]' --dependencies '{}' 2025-03-04T21:23:04.5571548Z INFO:root:Upload test/test-reports/inference_torchbench.json to s3://ossci-benchmarks/v3/pytorch/pytorch/13661696663/38195233363/inference_torchbench.json 2025-03-04T21:23:04.5837575Z INFO:botocore.credentials:Found credentials from IAM Role: gh-ci-github-action-runners-runner-role 2025-03-04T21:23:04.8405528Z INFO:root:Upload test/test-reports/inference_torchbench_graph_breaks.json to s3://ossci-benchmarks/v3/pytorch/pytorch/13661696663/38195233363/inference_torchbench_graph_breaks.json 2025-03-04T21:23:05.0872084Z ##[group]Run cat test/**/*_toprint.log || true 2025-03-04T21:23:05.0872366Z cat test/**/*_toprint.log || true 2025-03-04T21:23:05.0876804Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T21:23:05.0877064Z env: 2025-03-04T21:23:05.0877310Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:23:05.0877780Z DOCKER_CONTAINER_ID: ad526e66cea466c172d0f0e6ea6665dd89838a93b43196189ae102ffb4d51ddd 2025-03-04T21:23:05.0878108Z ##[endgroup] 2025-03-04T21:23:05.0937604Z cat: 'test/**/*_toprint.log': No such file or directory 2025-03-04T21:23:05.0989741Z ##[group]Run kill "$MONITOR_SCRIPT_PID" 2025-03-04T21:23:05.0989979Z kill "$MONITOR_SCRIPT_PID" 2025-03-04T21:23:05.0993778Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T21:23:05.0994030Z env: 2025-03-04T21:23:05.0994196Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:23:05.0994489Z DOCKER_CONTAINER_ID: ad526e66cea466c172d0f0e6ea6665dd89838a93b43196189ae102ffb4d51ddd 2025-03-04T21:23:05.0994810Z MONITOR_SCRIPT_PID: 45115 2025-03-04T21:23:05.0994999Z ##[endgroup] 2025-03-04T21:23:05.1119232Z Prepare all required actions 2025-03-04T21:23:05.1119585Z Getting action download info 2025-03-04T21:23:05.2737510Z Download action repository 'actions/upload-artifact@v4' (SHA:4cec3d8aa04e39d1a68397de0c4cd6fb9dce8ec1) 2025-03-04T21:23:05.6682965Z ##[group]Run ./.github/actions/upload-test-artifacts 2025-03-04T21:23:05.6683207Z with: 2025-03-04T21:23:05.6683469Z file-suffix: test-cpu_inductor_torchbench-1-2-linux.8xlarge.amx_38195233363 2025-03-04T21:23:05.6683767Z s3-bucket: gha-artifacts 2025-03-04T21:23:05.6683953Z env: 2025-03-04T21:23:05.6684112Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:23:05.6684427Z DOCKER_CONTAINER_ID: ad526e66cea466c172d0f0e6ea6665dd89838a93b43196189ae102ffb4d51ddd 2025-03-04T21:23:05.6684730Z ##[endgroup] 2025-03-04T21:23:05.6712726Z ##[group]Run # Remove any previous test jsons if they exist 2025-03-04T21:23:05.6713049Z # Remove any previous test jsons if they exist 2025-03-04T21:23:05.6713301Z rm -f test-jsons-*.zip 2025-03-04T21:23:05.6713625Z zip -r "test-jsons-${FILE_SUFFIX}.zip" test/test-reports -i '*.json' 2025-03-04T21:23:05.6718165Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T21:23:05.6718424Z env: 2025-03-04T21:23:05.6718667Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:23:05.6718964Z DOCKER_CONTAINER_ID: ad526e66cea466c172d0f0e6ea6665dd89838a93b43196189ae102ffb4d51ddd 2025-03-04T21:23:05.6719372Z FILE_SUFFIX: test-cpu_inductor_torchbench-1-2-linux.8xlarge.amx_38195233363 2025-03-04T21:23:05.6719654Z ##[endgroup] 2025-03-04T21:23:05.6981117Z adding: test/test-reports/inference_torchbench.json (deflated 99%) 2025-03-04T21:23:05.7188713Z adding: test/test-reports/inference_torchbench_graph_breaks.json (deflated 99%) 2025-03-04T21:23:05.7222909Z ##[group]Run # Remove any previous test reports if they exist 2025-03-04T21:23:05.7223231Z # Remove any previous test reports if they exist 2025-03-04T21:23:05.7223485Z rm -f test-reports-*.zip 2025-03-04T21:23:05.7223807Z zip -r "test-reports-${FILE_SUFFIX}.zip" test/test-reports -i '*.xml' -i '*.csv' 2025-03-04T21:23:05.7227936Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T21:23:05.7228179Z env: 2025-03-04T21:23:05.7228348Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:23:05.7228644Z DOCKER_CONTAINER_ID: ad526e66cea466c172d0f0e6ea6665dd89838a93b43196189ae102ffb4d51ddd 2025-03-04T21:23:05.7229035Z FILE_SUFFIX: test-cpu_inductor_torchbench-1-2-linux.8xlarge.amx_38195233363 2025-03-04T21:23:05.7229308Z ##[endgroup] 2025-03-04T21:23:05.7286212Z adding: test/test-reports/inference_torchbench.csv (deflated 64%) 2025-03-04T21:23:05.7314044Z adding: test/test-reports/inference_torchbench_graph_breaks.csv (deflated 97%) 2025-03-04T21:23:05.7314494Z adding: test/test-reports/inference_torchbench_graph_break_deduped.csv (deflated 80%) 2025-03-04T21:23:05.7346380Z ##[group]Run # Remove any previous usage logs if they exist 2025-03-04T21:23:05.7346699Z # Remove any previous usage logs if they exist 2025-03-04T21:23:05.7346944Z rm -f logs-*.zip 2025-03-04T21:23:05.7347235Z # this workflow is also run in bazel build test, but we dont generate usage reports for it 2025-03-04T21:23:05.7347624Z # so check to see if the file exists first 2025-03-04T21:23:05.7347856Z if [ -f 'usage_log.txt' ]; then 2025-03-04T21:23:05.7348098Z  zip "logs-${FILE_SUFFIX}.zip" 'usage_log.txt' 2025-03-04T21:23:05.7348322Z fi 2025-03-04T21:23:05.7348565Z if find "test/test-reports" -name "*.log" 2>/dev/null | grep -q .; then 2025-03-04T21:23:05.7348896Z  zip -r "logs-${FILE_SUFFIX}.zip" test/test-reports -i '*.log' 2025-03-04T21:23:05.7349141Z fi 2025-03-04T21:23:05.7352679Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T21:23:05.7352920Z env: 2025-03-04T21:23:05.7353083Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:23:05.7353480Z DOCKER_CONTAINER_ID: ad526e66cea466c172d0f0e6ea6665dd89838a93b43196189ae102ffb4d51ddd 2025-03-04T21:23:05.7353877Z FILE_SUFFIX: test-cpu_inductor_torchbench-1-2-linux.8xlarge.amx_38195233363 2025-03-04T21:23:05.7354148Z ##[endgroup] 2025-03-04T21:23:05.7414234Z adding: usage_log.txt (deflated 96%) 2025-03-04T21:23:05.7521015Z ##[group]Run # Remove any previous debugging artifacts if they exist 2025-03-04T21:23:05.7521373Z # Remove any previous debugging artifacts if they exist 2025-03-04T21:23:05.7521643Z rm -f debug-*.zip 2025-03-04T21:23:05.7521848Z if [ -d 'test/debug' ]; then 2025-03-04T21:23:05.7522105Z  zip -r "debug-${FILE_SUFFIX}.zip" test/debug 2025-03-04T21:23:05.7523178Z fi 2025-03-04T21:23:05.7529731Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T21:23:05.7529981Z env: 2025-03-04T21:23:05.7530147Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:23:05.7530443Z DOCKER_CONTAINER_ID: ad526e66cea466c172d0f0e6ea6665dd89838a93b43196189ae102ffb4d51ddd 2025-03-04T21:23:05.7530843Z FILE_SUFFIX: test-cpu_inductor_torchbench-1-2-linux.8xlarge.amx_38195233363 2025-03-04T21:23:05.7531117Z ##[endgroup] 2025-03-04T21:23:05.7602469Z ##[group]Run seemethere/upload-artifact-s3@v5 2025-03-04T21:23:05.7602721Z with: 2025-03-04T21:23:05.7602952Z s3-bucket: gha-artifacts 2025-03-04T21:23:05.7603184Z s3-prefix: pytorch/pytorch/13661696663/1/artifact 2025-03-04T21:23:05.7603428Z retention-days: 14 2025-03-04T21:23:05.7603620Z if-no-files-found: warn 2025-03-04T21:23:05.7603823Z path: test-jsons-*.zip 2025-03-04T21:23:05.7604013Z name: artifact 2025-03-04T21:23:05.7604179Z region: us-east-1 2025-03-04T21:23:05.7604353Z env: 2025-03-04T21:23:05.7604519Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:23:05.7604832Z DOCKER_CONTAINER_ID: ad526e66cea466c172d0f0e6ea6665dd89838a93b43196189ae102ffb4d51ddd 2025-03-04T21:23:05.7605155Z ##[endgroup] 2025-03-04T21:23:06.0451473Z NOTE: s3-prefix specified, ignoring name parameter 2025-03-04T21:23:06.0451986Z With the provided path, there will be 1 file uploaded 2025-03-04T21:23:06.0452379Z Uploading to s3 prefix: pytorch/pytorch/13661696663/1/artifact 2025-03-04T21:23:06.0475822Z Starting upload of test-jsons-test-cpu_inductor_torchbench-1-2-linux.8xlarge.amx_38195233363.zip 2025-03-04T21:23:06.2082070Z Finished upload of test-jsons-test-cpu_inductor_torchbench-1-2-linux.8xlarge.amx_38195233363.zip 2025-03-04T21:23:06.2249439Z ##[group]Run seemethere/upload-artifact-s3@v5 2025-03-04T21:23:06.2249674Z with: 2025-03-04T21:23:06.2249849Z s3-bucket: gha-artifacts 2025-03-04T21:23:06.2250070Z s3-prefix: pytorch/pytorch/13661696663/1/artifact 2025-03-04T21:23:06.2250298Z retention-days: 14 2025-03-04T21:23:06.2250473Z if-no-files-found: error 2025-03-04T21:23:06.2250659Z path: test-reports-*.zip 2025-03-04T21:23:06.2250835Z name: artifact 2025-03-04T21:23:06.2250991Z region: us-east-1 2025-03-04T21:23:06.2251150Z env: 2025-03-04T21:23:06.2251305Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:23:06.2251604Z DOCKER_CONTAINER_ID: ad526e66cea466c172d0f0e6ea6665dd89838a93b43196189ae102ffb4d51ddd 2025-03-04T21:23:06.2251909Z ##[endgroup] 2025-03-04T21:23:06.4607964Z NOTE: s3-prefix specified, ignoring name parameter 2025-03-04T21:23:06.4613115Z With the provided path, there will be 1 file uploaded 2025-03-04T21:23:06.4615874Z Uploading to s3 prefix: pytorch/pytorch/13661696663/1/artifact 2025-03-04T21:23:06.4629435Z Starting upload of test-reports-test-cpu_inductor_torchbench-1-2-linux.8xlarge.amx_38195233363.zip 2025-03-04T21:23:06.6588413Z Finished upload of test-reports-test-cpu_inductor_torchbench-1-2-linux.8xlarge.amx_38195233363.zip 2025-03-04T21:23:06.6756697Z ##[group]Run seemethere/upload-artifact-s3@v5 2025-03-04T21:23:06.6756961Z with: 2025-03-04T21:23:06.6757179Z s3-bucket: gha-artifacts 2025-03-04T21:23:06.6757598Z s3-prefix: pytorch/pytorch/13661696663/1/artifact 2025-03-04T21:23:06.6757870Z retention-days: 14 2025-03-04T21:23:06.6758077Z if-no-files-found: ignore 2025-03-04T21:23:06.6758305Z path: logs-*.zip 2025-03-04T21:23:06.6758666Z name: artifact 2025-03-04T21:23:06.6758864Z region: us-east-1 2025-03-04T21:23:06.6759059Z env: 2025-03-04T21:23:06.6759244Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:23:06.6759589Z DOCKER_CONTAINER_ID: ad526e66cea466c172d0f0e6ea6665dd89838a93b43196189ae102ffb4d51ddd 2025-03-04T21:23:06.6759944Z ##[endgroup] 2025-03-04T21:23:06.9130331Z NOTE: s3-prefix specified, ignoring name parameter 2025-03-04T21:23:06.9132434Z With the provided path, there will be 1 file uploaded 2025-03-04T21:23:06.9139267Z Uploading to s3 prefix: pytorch/pytorch/13661696663/1/artifact 2025-03-04T21:23:06.9151540Z Starting upload of logs-test-cpu_inductor_torchbench-1-2-linux.8xlarge.amx_38195233363.zip 2025-03-04T21:23:07.0721047Z Finished upload of logs-test-cpu_inductor_torchbench-1-2-linux.8xlarge.amx_38195233363.zip 2025-03-04T21:23:07.0892367Z ##[group]Run seemethere/upload-artifact-s3@v5 2025-03-04T21:23:07.0892598Z with: 2025-03-04T21:23:07.0892767Z s3-bucket: gha-artifacts 2025-03-04T21:23:07.0893005Z s3-prefix: pytorch/pytorch/13661696663/1/artifact 2025-03-04T21:23:07.0893230Z retention-days: 14 2025-03-04T21:23:07.0893406Z if-no-files-found: ignore 2025-03-04T21:23:07.0893593Z path: debug-*.zip 2025-03-04T21:23:07.0893765Z name: artifact 2025-03-04T21:23:07.0893996Z region: us-east-1 2025-03-04T21:23:07.0894157Z env: 2025-03-04T21:23:07.0894313Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:23:07.0894602Z DOCKER_CONTAINER_ID: ad526e66cea466c172d0f0e6ea6665dd89838a93b43196189ae102ffb4d51ddd 2025-03-04T21:23:07.0894903Z ##[endgroup] 2025-03-04T21:23:07.3246231Z No files were found with the provided path: debug-*.zip. No artifacts will be uploaded. 2025-03-04T21:23:07.3443062Z ##[group]Run # shellcheck disable=SC2156 2025-03-04T21:23:07.3443334Z # shellcheck disable=SC2156 2025-03-04T21:23:07.3443707Z find . -iname "core.[1-9]*" -exec docker exec "${DOCKER_CONTAINER_ID}" sh -c "gdb python {} -ex 'bt' -ex 'q'" \; 2025-03-04T21:23:07.3449652Z shell: /usr/bin/bash -e {0} 2025-03-04T21:23:07.3449861Z env: 2025-03-04T21:23:07.3450023Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:23:07.3450329Z DOCKER_CONTAINER_ID: ad526e66cea466c172d0f0e6ea6665dd89838a93b43196189ae102ffb4d51ddd 2025-03-04T21:23:07.3450643Z ##[endgroup] 2025-03-04T21:23:07.4723797Z Prepare all required actions 2025-03-04T21:23:07.4724122Z Getting action download info 2025-03-04T21:23:07.5777621Z ##[group]Run ./.github/actions/upload-utilization-stats 2025-03-04T21:23:07.5777872Z with: 2025-03-04T21:23:07.5778038Z job_id: 38195233363 2025-03-04T21:23:07.5778372Z job_name: linux-jammy-cpu-py3.9-gcc11-inductor / test (cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx) 2025-03-04T21:23:07.5778721Z workflow_name: inductor 2025-03-04T21:23:07.5778908Z workflow_run_id: 13661696663 2025-03-04T21:23:07.5779092Z workflow_attempt: 1 2025-03-04T21:23:07.5779259Z env: 2025-03-04T21:23:07.5779413Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:23:07.5779704Z DOCKER_CONTAINER_ID: ad526e66cea466c172d0f0e6ea6665dd89838a93b43196189ae102ffb4d51ddd 2025-03-04T21:23:07.5780003Z ##[endgroup] 2025-03-04T21:23:07.5800443Z ##[group]Run echo "workflow_id: 13661696663" 2025-03-04T21:23:07.5800710Z echo "workflow_id: 13661696663" 2025-03-04T21:23:07.5800967Z echo "workflow_attempt: 1" 2025-03-04T21:23:07.5801259Z echo "workflow_Name: inductor" 2025-03-04T21:23:07.5801471Z echo "job_id: 38195233363" 2025-03-04T21:23:07.5801851Z echo "job_name: linux-jammy-cpu-py3.9-gcc11-inductor / test (cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx)" 2025-03-04T21:23:07.5806612Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T21:23:07.5806859Z env: 2025-03-04T21:23:07.5807026Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:23:07.5807330Z DOCKER_CONTAINER_ID: ad526e66cea466c172d0f0e6ea6665dd89838a93b43196189ae102ffb4d51ddd 2025-03-04T21:23:07.5807649Z ##[endgroup] 2025-03-04T21:23:07.5827644Z workflow_id: 13661696663 2025-03-04T21:23:07.5827967Z workflow_attempt: 1 2025-03-04T21:23:07.5828239Z workflow_Name: inductor 2025-03-04T21:23:07.5828493Z job_id: 38195233363 2025-03-04T21:23:07.5828904Z job_name: linux-jammy-cpu-py3.9-gcc11-inductor / test (cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx) 2025-03-04T21:23:07.5861766Z ##[group]Run nick-fields/retry@v3.0.0 2025-03-04T21:23:07.5861983Z with: 2025-03-04T21:23:07.5862136Z shell: bash 2025-03-04T21:23:07.5862293Z timeout_minutes: 5 2025-03-04T21:23:07.5862463Z max_attempts: 5 2025-03-04T21:23:07.5862623Z retry_wait_seconds: 30 2025-03-04T21:23:07.5862918Z command: set -eu python3 -m pip install python-dateutil==2.8.2 boto3==1.35.42 pandas==2.1.3 2025-03-04T21:23:07.5863227Z polling_interval_seconds: 1 2025-03-04T21:23:07.5863415Z warning_on_retry: true 2025-03-04T21:23:07.5863594Z continue_on_error: false 2025-03-04T21:23:07.5863768Z env: 2025-03-04T21:23:07.5863921Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:23:07.5864210Z DOCKER_CONTAINER_ID: ad526e66cea466c172d0f0e6ea6665dd89838a93b43196189ae102ffb4d51ddd 2025-03-04T21:23:07.5864513Z ##[endgroup] 2025-03-04T21:23:07.8295382Z Defaulting to user installation because normal site-packages is not writeable 2025-03-04T21:23:07.8931047Z Collecting python-dateutil==2.8.2 2025-03-04T21:23:07.9124507Z Downloading python_dateutil-2.8.2-py2.py3-none-any.whl (247 kB) 2025-03-04T21:23:08.5233823Z Collecting boto3==1.35.42 2025-03-04T21:23:08.5264295Z Downloading boto3-1.35.42-py3-none-any.whl (139 kB) 2025-03-04T21:23:08.8606826Z Collecting pandas==2.1.3 2025-03-04T21:23:08.8668026Z Downloading pandas-2.1.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (12.3 MB) 2025-03-04T21:23:08.9908153Z Requirement already satisfied: six>=1.5 in /usr/lib/python3.9/site-packages (from python-dateutil==2.8.2) (1.15.0) 2025-03-04T21:23:08.9941018Z Requirement already satisfied: jmespath<2.0.0,>=0.7.1 in /usr/lib/python3.9/site-packages (from boto3==1.35.42) (0.10.0) 2025-03-04T21:23:08.9945636Z Requirement already satisfied: botocore<1.36.0,>=1.35.42 in /home/ec2-user/.local/lib/python3.9/site-packages (from boto3==1.35.42) (1.35.99) 2025-03-04T21:23:08.9947493Z Requirement already satisfied: s3transfer<0.11.0,>=0.10.0 in /home/ec2-user/.local/lib/python3.9/site-packages (from boto3==1.35.42) (0.10.4) 2025-03-04T21:23:09.5593384Z Collecting numpy<2,>=1.22.4 2025-03-04T21:23:09.5638063Z Downloading numpy-1.26.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (18.2 MB) 2025-03-04T21:23:09.7183673Z Collecting tzdata>=2022.1 2025-03-04T21:23:09.7214182Z Downloading tzdata-2025.1-py2.py3-none-any.whl (346 kB) 2025-03-04T21:23:09.7294548Z Requirement already satisfied: pytz>=2020.1 in /usr/lib/python3.9/site-packages (from pandas==2.1.3) (2022.7.1) 2025-03-04T21:23:09.7353664Z Requirement already satisfied: urllib3<1.27,>=1.25.4 in /usr/lib/python3.9/site-packages (from botocore<1.36.0,>=1.35.42->boto3==1.35.42) (1.25.10) 2025-03-04T21:23:09.8479428Z Installing collected packages: python-dateutil, tzdata, numpy, pandas, boto3 2025-03-04T21:23:13.4806145Z Attempting uninstall: boto3 2025-03-04T21:23:13.4806644Z Found existing installation: boto3 1.35.33 2025-03-04T21:23:13.4869402Z Uninstalling boto3-1.35.33: 2025-03-04T21:23:13.4875661Z Successfully uninstalled boto3-1.35.33 2025-03-04T21:23:13.5293093Z Successfully installed boto3-1.35.42 numpy-1.26.4 pandas-2.1.3 python-dateutil-2.8.2 tzdata-2025.1 2025-03-04T21:23:13.6530200Z Command completed after 1 attempt(s). 2025-03-04T21:23:13.6590642Z ##[group]Run python3 -m tools.stats.upload_utilization_stats.upload_utilization_stats \ 2025-03-04T21:23:13.6591064Z python3 -m tools.stats.upload_utilization_stats.upload_utilization_stats \ 2025-03-04T21:23:13.6591377Z  --workflow-run-id "13661696663" \ 2025-03-04T21:23:13.6591602Z  --workflow-name "inductor" \ 2025-03-04T21:23:13.6591819Z  --workflow-run-attempt "1" \ 2025-03-04T21:23:13.6592024Z  --job-id "38195233363" \ 2025-03-04T21:23:13.6592383Z  --job-name "linux-jammy-cpu-py3.9-gcc11-inductor / test (cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx)" 2025-03-04T21:23:13.6596560Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T21:23:13.6596805Z env: 2025-03-04T21:23:13.6596970Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:23:13.6597410Z DOCKER_CONTAINER_ID: ad526e66cea466c172d0f0e6ea6665dd89838a93b43196189ae102ffb4d51ddd 2025-03-04T21:23:13.6597727Z ##[endgroup] 2025-03-04T21:23:14.6501869Z repo: pytorch/pytorch 2025-03-04T21:23:14.6502912Z Downloading logs-test-cpu_inductor_torchbench-1-2-linux.8xlarge.amx_38195233363.zip 2025-03-04T21:23:14.6503274Z Converted Log Model: UtilizationMetadata: 2025-03-04T21:23:14.6504123Z UtilizationMetadata(level='metadata', workflow_id='13661696663', job_id='38195233363', workflow_name='inductor', job_name='linux-jammy-cpu-py3.9-gcc11-inductor / test (cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx)', usage_collect_interval=1.0, data_model_version=1.0, start_at=1741119413, gpu_count=0, cpu_count=32, gpu_type='', error=None) 2025-03-04T21:23:14.6504998Z [Db Segments] detected pytest cmd: 14, generated segments: 14 2025-03-04T21:23:14.6505267Z [db model] Peek db timeseries 2025-03-04T21:23:14.6505458Z :{ 2025-03-04T21:23:14.6505618Z "created_at": 1741123394, 2025-03-04T21:23:14.6505855Z "type": "utilization", 2025-03-04T21:23:14.6506351Z "tags": [ 2025-03-04T21:23:14.6506511Z "record" 2025-03-04T21:23:14.6506672Z ], 2025-03-04T21:23:14.6506826Z "time_stamp": 1741119413, 2025-03-04T21:23:14.6507030Z "repo": "pytorch/pytorch", 2025-03-04T21:23:14.6507234Z "workflow_id": 13661696663, 2025-03-04T21:23:14.6507431Z "run_attempt": 1, 2025-03-04T21:23:14.6507621Z "job_id": 38195233363, 2025-03-04T21:23:14.6507807Z "workflow_name": "inductor", 2025-03-04T21:23:14.6508169Z "job_name": "linux-jammy-cpu-py3.9-gcc11-inductor / test (cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx)", 2025-03-04T21:23:14.6508498Z "json_data": "{}" 2025-03-04T21:23:14.6508658Z } 2025-03-04T21:23:14.6508968Z Writing 1 documents to S3 ossci-utilization/util_metadata/v_1.0/pytorch/pytorch/13661696663/1/38195233363/metadata 2025-03-04T21:23:14.6509638Z Done! Finish writing document to S3 ossci-utilization/util_metadata/v_1.0/pytorch/pytorch/13661696663/1/38195233363/metadata 2025-03-04T21:23:14.6510186Z Writing 785 documents to S3 ossci-utilization/util_timeseries/v_1.0/pytorch/pytorch/13661696663/1/38195233363/time_series 2025-03-04T21:23:14.6510730Z Done! Finish writing document to S3 ossci-utilization/util_timeseries/v_1.0/pytorch/pytorch/13661696663/1/38195233363/time_series 2025-03-04T21:23:14.7452279Z ##[group]Run pytorch/test-infra/.github/actions/teardown-linux@main 2025-03-04T21:23:14.7452565Z with: 2025-03-04T21:23:14.7452724Z env: 2025-03-04T21:23:14.7452885Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:23:14.7453184Z DOCKER_CONTAINER_ID: ad526e66cea466c172d0f0e6ea6665dd89838a93b43196189ae102ffb4d51ddd 2025-03-04T21:23:14.7453492Z ##[endgroup] 2025-03-04T21:23:14.7473725Z ##[group]Run set -eou pipefail 2025-03-04T21:23:14.7473985Z set -eou pipefail 2025-03-04T21:23:14.7474183Z  2025-03-04T21:23:14.7474446Z echo "Holding runner for 2 hours until all ssh sessions have logged out" 2025-03-04T21:23:14.7474761Z for _ in $(seq 1440); do 2025-03-04T21:23:14.7475090Z  # Break if no ssh session exists anymore 2025-03-04T21:23:14.7475339Z  if [ "$(who)" = "" ]; then 2025-03-04T21:23:14.7475555Z  break 2025-03-04T21:23:14.7475742Z  fi 2025-03-04T21:23:14.7475950Z  echo "." 2025-03-04T21:23:14.7476128Z  sleep 5 2025-03-04T21:23:14.7476298Z done 2025-03-04T21:23:14.7480780Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T21:23:14.7481037Z env: 2025-03-04T21:23:14.7481214Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:23:14.7481524Z DOCKER_CONTAINER_ID: ad526e66cea466c172d0f0e6ea6665dd89838a93b43196189ae102ffb4d51ddd 2025-03-04T21:23:14.7481843Z ##[endgroup] 2025-03-04T21:23:14.7499654Z Holding runner for 2 hours until all ssh sessions have logged out 2025-03-04T21:23:14.7588654Z ##[group]Run # ignore expansion of "docker ps -q" since it could be empty 2025-03-04T21:23:14.7589029Z # ignore expansion of "docker ps -q" since it could be empty 2025-03-04T21:23:14.7589321Z # shellcheck disable=SC2046 2025-03-04T21:23:14.7589552Z docker stop $(docker ps -q) || true 2025-03-04T21:23:14.7589784Z # Prune all of the docker images 2025-03-04T21:23:14.7590005Z docker system prune -af 2025-03-04T21:23:14.7594116Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T21:23:14.7594368Z env: 2025-03-04T21:23:14.7594537Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:23:14.7594844Z DOCKER_CONTAINER_ID: ad526e66cea466c172d0f0e6ea6665dd89838a93b43196189ae102ffb4d51ddd 2025-03-04T21:23:14.7595169Z ##[endgroup] 2025-03-04T21:23:26.1191342Z ad526e66cea4 2025-03-04T21:23:27.2790461Z Deleted Containers: 2025-03-04T21:23:27.2790986Z ad526e66cea466c172d0f0e6ea6665dd89838a93b43196189ae102ffb4d51ddd 2025-03-04T21:23:27.2791588Z 2025-03-04T21:23:30.5703415Z Deleted Images: 2025-03-04T21:23:30.5708112Z untagged: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e 2025-03-04T21:23:30.5709848Z untagged: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks@sha256:bd007eb2fe9e7d1c860264514799356825b802ed452803de01a654c76280cd51 2025-03-04T21:23:30.5710634Z 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2025-03-04T21:23:31.0786222Z Entering 'third_party/tensorpipe/third_party/libuv' 2025-03-04T21:23:31.0834270Z Entering 'third_party/tensorpipe/third_party/pybind11' 2025-03-04T21:23:31.0879787Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2025-03-04T21:23:31.0953173Z [command]/usr/bin/git config --local --name-only --get-regexp http\.https\:\/\/github\.com\/\.extraheader 2025-03-04T21:23:31.0974600Z http.https://github.com/.extraheader 2025-03-04T21:23:31.0983428Z [command]/usr/bin/git config --local --unset-all http.https://github.com/.extraheader 2025-03-04T21:23:31.1014913Z [command]/usr/bin/git submodule foreach --recursive sh -c "git config --local --name-only --get-regexp 'http\.https\:\/\/github\.com\/\.extraheader' && git config --local --unset-all 'http.https://github.com/.extraheader' || :" 2025-03-04T21:23:31.1310833Z Entering 'android/libs/fbjni' 2025-03-04T21:23:31.1340961Z http.https://github.com/.extraheader 2025-03-04T21:23:31.1371990Z Entering 'third_party/FP16' 2025-03-04T21:23:31.1399233Z http.https://github.com/.extraheader 2025-03-04T21:23:31.1434477Z Entering 'third_party/FXdiv' 2025-03-04T21:23:31.1464852Z http.https://github.com/.extraheader 2025-03-04T21:23:31.1500235Z Entering 'third_party/NNPACK' 2025-03-04T21:23:31.1532958Z http.https://github.com/.extraheader 2025-03-04T21:23:31.1567883Z Entering 'third_party/NVTX' 2025-03-04T21:23:31.1596658Z http.https://github.com/.extraheader 2025-03-04T21:23:31.1633209Z Entering 'third_party/VulkanMemoryAllocator' 2025-03-04T21:23:31.1664111Z http.https://github.com/.extraheader 2025-03-04T21:23:31.1691796Z Entering 'third_party/XNNPACK' 2025-03-04T21:23:31.1722292Z http.https://github.com/.extraheader 2025-03-04T21:23:31.1779102Z Entering 'third_party/benchmark' 2025-03-04T21:23:31.1809241Z http.https://github.com/.extraheader 2025-03-04T21:23:31.1846883Z Entering 'third_party/composable_kernel' 2025-03-04T21:23:31.1877223Z http.https://github.com/.extraheader 2025-03-04T21:23:31.1913998Z Entering 'third_party/cpp-httplib' 2025-03-04T21:23:31.1946022Z http.https://github.com/.extraheader 2025-03-04T21:23:31.1976510Z Entering 'third_party/cpuinfo' 2025-03-04T21:23:31.2007437Z http.https://github.com/.extraheader 2025-03-04T21:23:31.2042691Z Entering 'third_party/cudnn_frontend' 2025-03-04T21:23:31.2071814Z http.https://github.com/.extraheader 2025-03-04T21:23:31.2100940Z Entering 'third_party/cutlass' 2025-03-04T21:23:31.2137684Z http.https://github.com/.extraheader 2025-03-04T21:23:31.2177721Z Entering 'third_party/eigen' 2025-03-04T21:23:31.2207460Z http.https://github.com/.extraheader 2025-03-04T21:23:31.2236849Z Entering 'third_party/fbgemm' 2025-03-04T21:23:31.2266567Z http.https://github.com/.extraheader 2025-03-04T21:23:31.2295925Z Entering 'third_party/fbgemm/third_party/asmjit' 2025-03-04T21:23:31.2328579Z http.https://github.com/.extraheader 2025-03-04T21:23:31.2358944Z Entering 'third_party/fbgemm/third_party/cpuinfo' 2025-03-04T21:23:31.2392898Z http.https://github.com/.extraheader 2025-03-04T21:23:31.2418249Z Entering 'third_party/fbgemm/third_party/cutlass' 2025-03-04T21:23:31.2448392Z http.https://github.com/.extraheader 2025-03-04T21:23:31.2486350Z Entering 'third_party/fbgemm/third_party/googletest' 2025-03-04T21:23:31.2515376Z http.https://github.com/.extraheader 2025-03-04T21:23:31.2551999Z Entering 'third_party/fbgemm/third_party/hipify_torch' 2025-03-04T21:23:31.2582599Z http.https://github.com/.extraheader 2025-03-04T21:23:31.2612914Z Entering 'third_party/flash-attention' 2025-03-04T21:23:31.2645655Z http.https://github.com/.extraheader 2025-03-04T21:23:31.2684133Z Entering 'third_party/flash-attention/csrc/composable_kernel' 2025-03-04T21:23:31.2714542Z http.https://github.com/.extraheader 2025-03-04T21:23:31.2756411Z Entering 'third_party/flash-attention/csrc/cutlass' 2025-03-04T21:23:31.2791180Z http.https://github.com/.extraheader 2025-03-04T21:23:31.2828068Z Entering 'third_party/flatbuffers' 2025-03-04T21:23:31.2858001Z http.https://github.com/.extraheader 2025-03-04T21:23:31.2896549Z Entering 'third_party/fmt' 2025-03-04T21:23:31.2927121Z http.https://github.com/.extraheader 2025-03-04T21:23:31.2962187Z Entering 'third_party/gemmlowp/gemmlowp' 2025-03-04T21:23:31.2991023Z http.https://github.com/.extraheader 2025-03-04T21:23:31.3022073Z Entering 'third_party/gloo' 2025-03-04T21:23:31.3051408Z http.https://github.com/.extraheader 2025-03-04T21:23:31.3107525Z Entering 'third_party/googletest' 2025-03-04T21:23:31.3138036Z http.https://github.com/.extraheader 2025-03-04T21:23:31.3172473Z Entering 'third_party/ideep' 2025-03-04T21:23:31.3198617Z http.https://github.com/.extraheader 2025-03-04T21:23:31.3233126Z Entering 'third_party/ideep/mkl-dnn' 2025-03-04T21:23:31.3260628Z http.https://github.com/.extraheader 2025-03-04T21:23:31.3307732Z Entering 'third_party/ittapi' 2025-03-04T21:23:31.3335571Z http.https://github.com/.extraheader 2025-03-04T21:23:31.3372118Z Entering 'third_party/kineto' 2025-03-04T21:23:31.3401438Z http.https://github.com/.extraheader 2025-03-04T21:23:31.3434205Z Entering 'third_party/kineto/libkineto/third_party/dynolog' 2025-03-04T21:23:31.3464287Z http.https://github.com/.extraheader 2025-03-04T21:23:31.3500540Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2025-03-04T21:23:31.3531176Z http.https://github.com/.extraheader 2025-03-04T21:23:31.3563819Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2025-03-04T21:23:31.3594116Z http.https://github.com/.extraheader 2025-03-04T21:23:31.3626631Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2025-03-04T21:23:31.3655985Z http.https://github.com/.extraheader 2025-03-04T21:23:31.3687467Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2025-03-04T21:23:31.3716044Z http.https://github.com/.extraheader 2025-03-04T21:23:31.3745840Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2025-03-04T21:23:31.3775872Z http.https://github.com/.extraheader 2025-03-04T21:23:31.3815079Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2025-03-04T21:23:31.3842358Z http.https://github.com/.extraheader 2025-03-04T21:23:31.3874416Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2025-03-04T21:23:31.3907409Z http.https://github.com/.extraheader 2025-03-04T21:23:31.3940132Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2025-03-04T21:23:31.3966783Z http.https://github.com/.extraheader 2025-03-04T21:23:31.3997113Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2025-03-04T21:23:31.4028536Z http.https://github.com/.extraheader 2025-03-04T21:23:31.4061603Z Entering 'third_party/kineto/libkineto/third_party/fmt' 2025-03-04T21:23:31.4092069Z http.https://github.com/.extraheader 2025-03-04T21:23:31.4130561Z Entering 'third_party/kineto/libkineto/third_party/googletest' 2025-03-04T21:23:31.4156406Z http.https://github.com/.extraheader 2025-03-04T21:23:31.4192158Z Entering 'third_party/kleidiai' 2025-03-04T21:23:31.4222079Z http.https://github.com/.extraheader 2025-03-04T21:23:31.4253685Z Entering 'third_party/mimalloc' 2025-03-04T21:23:31.4282613Z http.https://github.com/.extraheader 2025-03-04T21:23:31.4314346Z Entering 'third_party/nlohmann' 2025-03-04T21:23:31.4349068Z http.https://github.com/.extraheader 2025-03-04T21:23:31.4385808Z Entering 'third_party/onnx' 2025-03-04T21:23:31.4417975Z http.https://github.com/.extraheader 2025-03-04T21:23:31.4463031Z Entering 'third_party/onnx/third_party/pybind11' 2025-03-04T21:23:31.4499004Z http.https://github.com/.extraheader 2025-03-04T21:23:31.4531725Z Entering 'third_party/opentelemetry-cpp' 2025-03-04T21:23:31.4563184Z http.https://github.com/.extraheader 2025-03-04T21:23:31.4591537Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark' 2025-03-04T21:23:31.4622208Z http.https://github.com/.extraheader 2025-03-04T21:23:31.4656391Z Entering 'third_party/opentelemetry-cpp/third_party/googletest' 2025-03-04T21:23:31.4682895Z http.https://github.com/.extraheader 2025-03-04T21:23:31.4714491Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2025-03-04T21:23:31.4744652Z http.https://github.com/.extraheader 2025-03-04T21:23:31.4775334Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2025-03-04T21:23:31.4807299Z http.https://github.com/.extraheader 2025-03-04T21:23:31.4835200Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2025-03-04T21:23:31.4865063Z http.https://github.com/.extraheader 2025-03-04T21:23:31.4889940Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2025-03-04T21:23:31.4919629Z http.https://github.com/.extraheader 2025-03-04T21:23:31.4963214Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2025-03-04T21:23:31.4989768Z http.https://github.com/.extraheader 2025-03-04T21:23:31.5021314Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2025-03-04T21:23:31.5050269Z http.https://github.com/.extraheader 2025-03-04T21:23:31.5079921Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2025-03-04T21:23:31.5107385Z http.https://github.com/.extraheader 2025-03-04T21:23:31.5146356Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2025-03-04T21:23:31.5175715Z http.https://github.com/.extraheader 2025-03-04T21:23:31.5221393Z Entering 'third_party/pocketfft' 2025-03-04T21:23:31.5251539Z http.https://github.com/.extraheader 2025-03-04T21:23:31.5278710Z Entering 'third_party/protobuf' 2025-03-04T21:23:31.5313020Z http.https://github.com/.extraheader 2025-03-04T21:23:31.5365753Z Entering 'third_party/protobuf/third_party/benchmark' 2025-03-04T21:23:31.5394651Z http.https://github.com/.extraheader 2025-03-04T21:23:31.5426374Z Entering 'third_party/protobuf/third_party/googletest' 2025-03-04T21:23:31.5455818Z http.https://github.com/.extraheader 2025-03-04T21:23:31.5490947Z Entering 'third_party/psimd' 2025-03-04T21:23:31.5522816Z http.https://github.com/.extraheader 2025-03-04T21:23:31.5551841Z Entering 'third_party/pthreadpool' 2025-03-04T21:23:31.5585397Z http.https://github.com/.extraheader 2025-03-04T21:23:31.5615872Z Entering 'third_party/pybind11' 2025-03-04T21:23:31.5645981Z http.https://github.com/.extraheader 2025-03-04T21:23:31.5677650Z Entering 'third_party/python-peachpy' 2025-03-04T21:23:31.5707560Z http.https://github.com/.extraheader 2025-03-04T21:23:31.5740409Z Entering 'third_party/sleef' 2025-03-04T21:23:31.5768442Z http.https://github.com/.extraheader 2025-03-04T21:23:31.5797445Z Entering 'third_party/tensorpipe' 2025-03-04T21:23:31.5829824Z http.https://github.com/.extraheader 2025-03-04T21:23:31.5862092Z Entering 'third_party/tensorpipe/third_party/googletest' 2025-03-04T21:23:31.5889324Z http.https://github.com/.extraheader 2025-03-04T21:23:31.5926094Z Entering 'third_party/tensorpipe/third_party/libnop' 2025-03-04T21:23:31.5955197Z http.https://github.com/.extraheader 2025-03-04T21:23:31.5986898Z Entering 'third_party/tensorpipe/third_party/libuv' 2025-03-04T21:23:31.6015696Z http.https://github.com/.extraheader 2025-03-04T21:23:31.6046394Z Entering 'third_party/tensorpipe/third_party/pybind11' 2025-03-04T21:23:31.6076547Z http.https://github.com/.extraheader 2025-03-04T21:23:31.6106174Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2025-03-04T21:23:31.6134267Z http.https://github.com/.extraheader 2025-03-04T21:23:31.6268344Z A job completed hook has been configured by the self-hosted runner administrator 2025-03-04T21:23:31.6290568Z ##[group]Run '/home/ec2-user/runner-scripts/after_job.sh' 2025-03-04T21:23:31.6293701Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T21:23:31.6293953Z ##[endgroup] 2025-03-04T21:23:31.6366519Z [!ALERT!] Swap in detected! [!ALERT!] 2025-03-04T21:23:39.8294257Z [!ALERT!] Swap out detected [!ALERT!] 2025-03-04T21:23:52.7229000Z Cleaning up orphan processes